Y Combinator

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description: American startup accelerator

168 results

pages: 332 words: 97,325

The Launch Pad: Inside Y Combinator, Silicon Valley's Most Exclusive School for Startups
by Randall Stross
Published 4 Sep 2013

Thank You Everyone Who Helped,” HT blog, March 27, 2008, http://blog.harjtaggar.com/auctomatic-is-acquired-thank-you-everyone-who. 23. HT answering Quora question: “What Does Harjeet Taggar’s Role at Y Combinator Entail, and How Did He Become Partner at 25?” Quora, September 25, 2011, www.quora.com/What-does-Harjeet-Taggars-role-at-Y-Combinator-entail-and-how-did-he-become-partner-at-25. 24. “Y Combinator Announces Two New Partners, Paul Buchheit and Harj Taggar,” YC Posterous, November 12, 2010, http://ycombinator.posterous.com/y-combinator-announces-two-new-partners-paul. 25. HT, “I’m a Partner at Y Combinator.” 26. “Welcome Sam, Garry, Emmett, and Justin,” YC Posterous, June 18, 2011, http://ycombinator.posterous.com/welcome-sam-garry-emmett-and-justin. 27.

It would have been more difficult to convince YC applicants of the desirability of moving to where YC was had it stayed where it started, in Cambridge. But after the first YC batch had finished in August 2005, Graham decided it would be a good idea to plant the flag in Silicon Valley. He expected that other seed funds would pop up and copy the Y Combinator model, and he didn’t want to leave open an opportunity for someone to come along and say, “We’re the Y Combinator of Silicon Valley.” He wanted Y Combinator to be the Y Combinator of Silicon Valley. He and Livingston planned to move out to the Bay Area for the next batch, in winter 2006, and then alternate, running a summer batch in Cambridge and a winter one in the Valley. They would need one room large enough to seat everyone in the batch for the weekly dinner, but only for a few months every year.

HT, “What I Expected from YC and What I Got,” Meal Ticket blog, April 15, 2007, http://mealticket.wordpress.com/2007/04/15/what-i-expected-from-yc-and-what-i-got/. 7. HT, “The Lessons I’ve Learnt During Y Combinator,” Meal Ticket blog, March 11, 2007, http://mealticket.wordpress.com/2007/03/11/the-lessons-ive-learnt-during-y-combinator/. 8. Andrew Warner interview of Jessica Livingston, “How the Author of Founders at Work Helps Y Combinator Discover and Mentor Startups—with Jessica Livingston,” Mixergy, April 19, 2010, http://mixergy.com/y-combinator-jessica-livingston-interview/. 9. HT, “First Week in ‘Frisco,’” Meal Ticket blog, January 15, 2007, http://mealticket.wordpress.com/2007/01/15/first-week-in-frisco/. 10.

pages: 216 words: 61,061

Without Their Permission: How the 21st Century Will Be Made, Not Managed
by Alexis Ohanian
Published 30 Sep 2013

Traction starts with a product people want; as word spreads, you’ll start seeing the week-over-week and month-over-month growth that gets investors pulling out their checkbooks and briefcases full of money.4 Investment Summer Camp for Startups There’s an unassuming, slightly bizarre-looking building located at 135 Garden Street in Cambridge, Massachusetts. It’s the original home of Y Combinator. When Paul Graham, Jessica Livingston, Dr. Robert Morris, and Trevor Blackwell decided to start a new kind of seed-stage venture capital firm, not many people understood it, let alone expected it to revolutionize tech investment as it has. Today there are scores of Y Combinator clones all over the world, such as TechStars, 500 Startups, and Seedcamp. And I was lucky enough to have been in that first class of founders who showed up for what was then called the Summer Founders Program. That first class at Y Combinator may have been a special sample of fortunate founders, but the group itself had a range of personalities.

He’s my personal lawyer to this day.6 All of us were there that first summer to learn as much as we could, both from the experts who visited every week for special off-the-record talks and Q&A and from each other. Over time, that network of Y Combinator guests and alums has become one of its strongest assets. At the time, however, not even Paul and the other founders of Y Combinator were aware of the value in the network they were creating. As more founders went through the program, the previous participants felt honor-bound to assist them, a tradition that continues to this day. Encounter a problem you’ve never experienced before? There’s probably someone in the network who has—just ask. It’s been referred to as the YC mafia. But it’s not exclusive to Y Combinator. The most healthy startup communities have a network of founders who are genuinely interested in helping one another.

There’s Nothing Fun About Funding Unless you get incredibly lucky (remember, there are already many factors going against you), you’ll need to have at least built something people want before you can get your first round of funding. The application process varies, but most accelerators follow Y Combinator’s lead and start with a written application (submitted online, of course) followed by offers for in-person interviews. I’m biased, but not only did Y Combinator create the blueprint, they also set the standard. So at least for as long as they’re doing that, let’s use them as a benchmark. If you get in to Y Combinator, you’ll trade some equity (typically between 2 percent and 10 percent, but usually between 6 percent and 7 percent) for somewhere around $18,000 (on average) in funding and their three-month program.

We Are the Nerds: The Birth and Tumultuous Life of Reddit, the Internet's Culture Laboratory
by Christine Lagorio-Chafkin
Published 1 Oct 2018

Graham snapped a photo of Livingston, who was just beaming, to immortalize the moment. The investment vehicle that would fund the Summer Founders Program soon earned another name: Y Combinator. The name came from an obscure concept in lambda calculus (which uses fixed-point combinators) that allows mathematical equation-writing to achieve something called Curry’s paradox. Put into plain English, a Y combinator might help form a sentence such as, “If this sentence is true, mayonnaise is made from peat moss.” The same way that sentence defeats itself, a Y combinator can show that lambda calculus is an unsound system, by finding inconsistency in mathematical logic. The concept is referenced in certain computer programming styles, and had become something of a hacker inside joke.

The concept is referenced in certain computer programming styles, and had become something of a hacker inside joke. Learning what the heck a “Y combinator” is would be a little Easter egg to every applicant who Googled it—so much so that for a while YC’s blog included a tagline only a nerd could adore: “Y Combinator: Not your standard fixed-point combinator.” Y Combinator would be not your standard startup incubator. It would start tiny, intended to help a handful of little companies get their legal framework set up, get a product established, and then introduce the founders to bigger, real investors.

Rejected by girls: Alexis Ohanian, Without Their Permission: How the 21st Century Will Be Made, Not Managed (New York: Business Plus, 2013), 21–22. How to Start a Startup an eighty-page thesis: “Our Y Combinator Summer 05 Application,” posted by Alexis Ohanian on November 29, 2010, AlexisOhanian.com. Swartz was pondering: Aaron Swartz, “The Case Against Lawrence Summers,” aaronsw.com, March 9, 2005. “If you want to do it”: Paul Graham, “How to Start a Startup,” essay delivered before the Harvard Computer Society, posted online March 2005. Not Your Standard Fixed-Point Combinator Graham snapped a photo: Paul Graham, “How Y Combinator Started,” blog post on Y Combinator’s former website, March 15, 2012. “How do we even tell people”: Jessica Livingston, Founders at Work: Stories of Startups’ Early Days (New York: Apress, 2007), 448.

pages: 226 words: 65,516

Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street
by Jeff John Roberts
Published 15 Dec 2020

And a garage in Palo Alto, known as the birthplace of Silicon Valley and now an official California state landmark, did not belong to a lone inventor but to two men: Bill Hewlett and Dave Packard, who founded HP. Experience had taught Y Combinator’s overseers that a good cofounder is as important as a good business plan. “If you look at the history of successful companies, they’ve been founded by partners,” says Y Combinator’s Altman. “In our experience, it’s very, very hard to be a single founder. The ups and downs of a startup are so intense that you need to cheer each other up when someone is struggling.” And right up until the start of the Y Combinator program, Brian had a cofounder. His name was Ben Reeves. A shy, young British kid, Ben was a programming wizard who believed in bitcoin with the same passion as Brian.

The divorce with Ben came at the prodding of a senior executive at Y Combinator, and Brian believes it was necessary. But at the time, it was also a major problem. As a result of his last-minute breakup with Ben, Brian became the rare entrepreneur to go through Y Combinator as a single founder. In doing so, he had reaped the accelerator’s coaching experience and could tap into its fantastic Rolodex of mentors and investors. But he had no one to cheer him up or encourage him when things got hard. And they were about to get very hard. While Y Combinator offered prestige and publicity because of the small number of companies it accepted into its fold, acceptance was not the same as success.

PART ONE * * * From Open Secret to Civil War 1 Brian Has a Secret Brian Armstrong stepped out of his car, felt soft California sunshine on his bald head, and smelled eucalyptus. He gazed at the façade of Y Combinator: the one-story building, just five miles from Google’s Mountain View campus, looked more like a sleepy suburban office park than a famous startup school that had educated the founders of Stripe, Dropbox, and other billion-dollar companies. Brian didn’t care about the place’s humdrum appearance. He knew who had gone there before him. The founders of Airbnb, a company he’d just left, had come out of Y Combinator, and so had the CEOs of other Silicon Valley stars like Doordash, Twitch, and Reddit. Brian, pale and shy-looking at first glance, exuded a quiet confidence from his trim frame and wasn’t bothered that he’d broken up with his would-be cofounder just days before, making him the rare entrepreneur to do the program alone.

pages: 290 words: 87,549

The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions...and Created Plenty of Controversy
by Leigh Gallagher
Published 14 Feb 2017

“It’ll be a better story someday.”) One night in November 2008, Chesky and Gebbia were having dinner with Seibel, who suggested that they consider applying to Y Combinator. Chesky took umbrage at the suggestion. Y Combinator was for prelaunch companies. AirBed & Breakfast had already launched—they had customers! They had been written up on TechCrunch! But Seibel delivered the truth that, deep down, they all knew: “Look at you,” he said. “You guys are dying. Do Y Combinator.” The application deadline had passed, but Seibel sent a message to Graham, who said he’d consider them if they got their application in by midnight.

But back then he was twenty-five, had only recently become a first-time CEO, and didn’t have much experience. “I wasn’t someone people pitched,” he says. Chesky and Gebbia were the first founders who had ever asked him for advice. But he had just gone through Y Combinator, the prestigious start-up accelerator program cofounded by the entrepreneur and venture capitalist Paul Graham (Seibel is now CEO of the Y Combinator program). Seibel told them he’d help give them counsel, and as they began to devise something more tangible, he could maybe introduce them to some angels. Chesky had no idea what he was talking about (“I’m, like, ‘Oh my god, this guy believes in angels.

It was a full-on start-up school, as well known for the access it provided—through dinners, speakers, and the high degree of hand-holding provided by its leaders—as for its specific way of doing things. Its motto, “Make something people want,” originally attributed to Paul Buchheit, the creator of Gmail and now a Y Combinator partner, is one of many YC principles that often run counter to conventional MBA wisdom. Chesky would later say that although he went to RISD, he graduated from the school of Y Combinator. Graham himself has become a Silicon Valley folk hero, a prolific thinker and writer on entrepreneurialism known as much for his wisdom as for his tough-love approach. These days, YC takes on more than a hundred companies each season, but in January 2009, AirBed & Breakfast was one of just sixteen start-ups participating.

pages: 223 words: 71,414

Abolish Silicon Valley: How to Liberate Technology From Capitalism
by Wendy Liu
Published 22 Mar 2020

The list of attendees included executives from Microsoft, Facebook, Google, Apple, Amazon, IBM, and Tesla. 3 See, for example, “Google Gets a Seat on the Trump Transition Team” by David Dayen for The Intercept, published November 15, 2016, at https://theintercept.com/2016/11/15/google-gets-a-seat-on-the-trump-transition-team/. 4 See “Y Combinator boss Sam Altman says he’s not going to cut ties with Peter Thiel for supporting Donald Trump” by Peter Kafka (probably no relation) on October 16, 2016, for Recode, at https://www.vox.com/2016/10/16/13302120/y-combinator-sam-altman-peter-thiel-donald-trump. Thiel’s affiliation with Y Combinator ended soon after that under unclear circumstances, as reported by Ryan Mac for Buzzfeed News on November 17, 2017: “Y Combinator Cuts Ties With Peter Thiel After Ending Part-Time Partner Program”, at https://www.buzzfeednews.com/article/ryanmac/y-combinator-cuts-ties-with-peter-thiel-ends-part-time. 5 For a brief overview of incidents in this vein, see “The Ugly Unethical Underside of Silicon Valley” by Erin Griffith for Fortune, published December 28, 2016, at https://fortune.com/longform/silicon-valley-startups-fraud-venture-capital/. 6 See, for example, “Uber Drivers Speak Out: We’re Making A Lot Less Money Than Uber Is Telling People” by Maya Kosoff for Business Insider, published October 29, 2014, at https://www.businessinsider.com/uber-drivers-say-theyre-making-less-than-minimum-wage-2014-10. 7 See, for example, “Inside an Amazon Warehouse, the Relentless Need to ‘“Make Rate’”” by Hamilton Nolan for Gawker, published June 6, 2016, at https://gawker.com/inside-an-amazon-warehouse-the-relentless-need-to-mak-1780800336. 8 See, for example, “Foxconn Working Conditions Slammed bBy Workers Rights Group” by Steven Musil for CNET, published May 30, 2012, at https://www.cnet.com/news/foxconn-working-conditions-slammed-by-workers-rights-group/. 9 Several tech billionaires have signed “The Giving Pledge”, a movement led by Bill Gates and Warren Buffet.

And as the person who had worked on it for three years, I would be the perfect CTO. He thought we might even get into Y Combinator, the accelerator founded by my startup idol Paul Graham. I had technically already committed to working at Google, but I was intrigued nonetheless. Even though I had devoured copious blog posts about startups, and even though I read stories about startups on Hacker News almost daily, it hadn’t occurred to me that a startup was something I could just do. But it sounded like a viable alternative to the dread I associated with Google, so I looked over the application for Y Combinator and tried to come up with a clever response to the question of when I last “hacked some (non-computer) system”.

This time, the excuse was not getting an interview with Y Combinator — we’d applied again, even getting alumni feedback on our answers, to no avail. But we decided to go to California anyway, giving the trip the moniker of Y Not. We were outwardly blasé about the rejection but really it hurt; we needed guidance, because it felt like we were spinning aimlessly. Nick had arranged a bunch of meetings for us on the day after we arrived. The first meeting was in Palo Alto, and in the morning we took the Caltrain down just in time for lunch on a rooftop terrace under the soothing California sun. The meeting was with a founder of a Y Combinator-backed startup building tools to optimise social media marketing.

pages: 559 words: 155,372

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley
by Antonio Garcia Martinez
Published 27 Jun 2016

Getting a first look at a potential Uber or Airbnb is what distinguished a first-class VC from an also-ran. Given Y Combinator’s immense success in drawing the best entrepreneurs, it had a quasi-stranglehold on the best early-stage deal flow in the Valley. And since early-stage deal flow today translated into later-stage deal flow tomorrow, via the follow-on investing phenomena described, Y Combinator was the gatekeeper to the best present and future deals in the Valley. Like control of the water supply in some arid agricultural region, whoever had the most upstream control of the water sluice controlled everything else—which is what Y Combinator’s Demo Day represented. Thus, powerful and haughty VCs who wanted to attend Y Combinator’s showcase pitch event had to kneel and kowtow to a sandal-wearing bear of a man with a distaste for bullshit and a flair for the written word.

§ Venture capitalists (VCs for short) are the bettors at the startup casino, funding startups from the earliest stages, at which investments are the price of a new car or less, to the latest, in which funding rounds can be in the hundreds of millions of dollars. * The bar-code-reading problem circa 2010 was still relatively unsolved. A company named RedLaser would soon do so, and it was almost instantly acquired by eBay. * Little-known fact: Y Combinator alums are the first readers of all Y Combinator applications, and are essentially the first filter. This is the one question I always make it a point to read when reviewing Y Combinator applications. If the answer is left blank, my cursor is already halfway to the No button. If it reads something like “I’d never hack a system or do anything illegal” I hit that No button faster than a Jeopardy contestant buzzes in

PG, as he’s known to the cognoscenti, founded an online store builder called Viaweb in the early days of the Web, which got bought in the $40 million range in 1997, and eventually became Yahoo Shopping. In his postacquisition freedom, he created one of the more incredible institutions in Silicon Valley: Y Combinator.* Twice a year, every year, Y Combinator accepts a few dozen startup hopefuls into what can only be described as a startup boot camp.† They are given a tiny amount of money and the goal of shipping a product by the end of three months. Some come in with nothing but a few hacked-up lines of code and an idea; some have entire going concerns that have already raised money.‡ Three months later, they all pitch at Demo Day, a major event on the Bay Area’s venture capital calendar.§ PG is the leading apostle, to not say messiah, of the startup gospel, and other than maybe Marc Andreeson, possesses the only prose style among techies that doesn’t trigger a literary gag reflex.

pages: 935 words: 197,338

The Power Law: Venture Capital and the Making of the New Future
by Sebastian Mallaby
Published 1 Feb 2022

They would also receive practical and emotional help. Y Combinator, as the Graham-Livingston operation was called, would incorporate the participants’ startups, open company bank accounts, and advise about patents. Graham and a few of his smart friends would provide feedback on the young hackers’ projects, and there would be a dinner once a week so that the summer schoolers got to know each other. In return, Y Combinator would take equity—usually 6 percent of the shares—in each micro-company that it incorporated.[68] At first Graham thought of the summer program as a temporary expedient. Y Combinator would invest in several teams at once so it could learn what worked and what didn’t.

Yet some pitches actually seemed quite likely to work. There was a poised nineteen-year-old from Stanford who appeared wise beyond his years; this was Sam Altman, who went on to succeed Graham as Y Combinator’s guiding spirit. And there were Huffman and Ohanian, the pair from Virginia, who later ditched their restaurant-booking scheme in favor of a news site called Reddit, which provided YC with its first profitable exit. Altogether, eight teams made the cut. Y Combinator’s acceptance rate was 3.5 percent, comparable to that of Harvard Medical School. With enough money for rent and pizza and not much else, the chosen worked maniacally, replicating the round-the-clock programming lifestyle that Graham had embraced when building Viaweb.

One visitor presented a slide with a discussion question for the group: “VCs: soulless agents of Satan, or just clumsy rapists?”[73] A couple of years later, when Y Combinator had established itself in Palo Alto, Graham invited none other than Mark Zuckerberg to speak at an event at Stanford. The veteran of the Wirehog presentation stood up and voiced the shared conviction of the rising generation: “Young people are just smarter.”[74] Coming on the heels of Masayoshi Son’s growth checks, the spread of Bechtolsheim-type angels, and Peter Thiel’s hands-off investing, Y Combinator represented yet another challenge to traditional venture capital. Having diagnosed the shortcomings of the venture incumbents, Graham was offering micro-investments on the theory that large checks were toxic for fledgling software startups.

pages: 468 words: 233,091

Founders at Work: Stories of Startups' Early Days
by Jessica Livingston
Published 14 Aug 2008

The Alliant management team in 1985: (from left to right) Rich McAndrew, Craig Mundie, Ronald Gruner, John Clary, and David Micciche C H A P T E R 33 Jessica Livingston Cofounder, Y Combinator Jessica Livingston founded Y Combinator in 2005 with Paul Graham, Robert Morris, and Trevor Blackwell. Y Combinator developed a new approach to venture funding: to fund startups in batches, giving them just enough money to get started, working closely with them to refine their ideas, and then introducing them to later stage investors for further funding. In three years they have funded more than 100 startups. When did you start Y Combinator? Livingston: We started Y Combinator in March 2005. Around that same time, I had gotten a book deal for Founders at Work, so I had planned to quit my job doing marketing at an investment bank and work full-time for a little while on the book.

Around that same time, I had gotten a book deal for Founders at Work, so I had planned to quit my job doing marketing at an investment bank and work full-time for a little while on the book. But we started Y Combinator simultaneously, so I didn’t really get to spend much time on the book. What was the process when Y Combinator got started? Livingston: That would assume that we had a process. There was no process. Remember, Y Combinator started off as an experiment. Paul had wanted to do angel investing. He wanted to help people start companies. But he didn’t really want all the requirements that come with being an angel investor, so he thought he should start an organization that could handle all of this for him.

I give the founders a lot of credit, because this was a brand new concept and Y Combinator had no track record. The deal was: move to Cambridge for the summer and get $12,000 or $18,000, depending on whether you were two or three founders. We based the amount of money on the MIT graduate student stipend, which was a couple grand a month. We said, “Come to Cambridge and we’ll work with you, and we’ll get together for dinner and hear from guest speakers every week.” (Unfortunately for Paul, we hijacked his personal office to use for Y Combinator.) So seven of them said yes, and I went into work on Monday thinking “Y Combinator is real now”—even though we didn’t even have Y Combinator legally set up at this point.

pages: 251 words: 80,831

Super Founders: What Data Reveals About Billion-Dollar Startups
by Ali Tamaseb
Published 14 Sep 2021

But three months in, we didn’t really want to study anymore. We wanted to start another company. We were tired of payments and fintech, so we tried to do something on the bleeding edge. We got into Y Combinator with an idea for a virtual reality company, but after a few months we realized we had no clue what we were doing. We decided to pivot back to payments because that was what we knew a lot about. At Y Combinator, there were a bunch of startups that couldn’t get credit cards, and it gave us the idea of building a corporate credit card for startups. Building a credit card with no personal guarantee and a bunch of features was actually very hard.

The data, however, tells another story: 85 percent of billion-dollar startups did not go through any accelerator program. The ones that did—including Stripe, Airbnb, Coinbase, and Instacart—mostly graduated from Y Combinator. That’s not to knock the value of these programs. The attention, resources, and networks provided by accelerators and incubators can certainly give startups a leg up. Compared with the random group, companies that had gone through Y Combinator, for example, were indeed more likely to achieve billion-dollar valuations. The point, however, is that going through such programs should not be the big end goal, and that most billion-dollar companies did not.

The value of a Super Founder is their ability to scale a company to a certain size and outcome, and to do it more than once. The Super Founders of today will create the billion-dollar companies of tomorrow. Take the Collison brothers, Patrick and John. As teenagers, the two founded Auctomatic, an auction-management system for power sellers on eBay. They brought the idea to Y Combinator in the winter of 2007 and raised a small seed round from investors including Chris Sacca and Paul Buchheit. Ten months after incorporating, Auctomatic was acquired by a Canadian public company for around $5 million, turning the Collison brothers into millionaires before their twenty-first birthdays.

System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich , Mehran Sahami and Jeremy M. Weinstein
Published 6 Sep 2021

“most investments fail”: Dave McClure, “99 VC Problems but a Batch Ain’t 1: Why Portfolio Size Matters for Returns,” Medium, August 31, 2015, https://500hats.com/99-vc-problems-but-a-batch-ain-t-one-why-portfolio-size-matters-for-returns-16cf556d4af0. “If unicorns happen only”: Ibid. “Each batch of YC companies”: “Investors,” Y Combinator (website), June 2019, https://www.ycombinator.com/investors/. “Since 2005, Y Combinator has funded”: Y Combinator (website), https://www.ycombinator.com/. Andreessen Horowitz created a separate fund: Meghan Kelly, “Andreessen-Horowitz to Give $50K to All Y Combinator Startups through Start Fund,” VentureBeat, October 15, 2011, https://venturebeat.com/2011/10/14/andreessen-horowitz-to-give-50k-to-all-y-combinator-startups-through-start-fund/. “Facebook and a variety”: Megan Geuss, “Illinois Senator’s Plan to Weaken Biometric Privacy Law Put on Hold,” Ars Technica, May 27, 2016, https://arstechnica.com/tech-policy/2016/05/illinois-senators-plan-to-weaken-biometric-privacy-law-put-on-hold/.

Even the RSS debates were better than this.” Swartz spent much of his time coding on his own. During his freshman year, he applied to join Y Combinator, a newly created tech incubator, to start a company called Infogami that would help manage content on websites. He was selected for the very first cohort of Y Combinator’s Summer Founders Program. By the end of the summer, he decided to continue working on the company, which would soon merge with another Y Combinator start-up, Reddit. Two years later Reddit was sold to Condé Nast, reportedly for between $10 million and $20 million, and Swartz became a young millionaire.

One of the better-known venture firms to realize the potential of vastly increasing the number of start-up investments is Y Combinator, founded in 2005. The name of the firm comes from the theory of computation and refers to a function that generates other functions. Indeed, Y Combinator’s entire goal is to create other companies. Given the techie name, it’s perhaps not surprising that three of the firm’s four founders hold PhDs in computer science. They made their initial fortune through founding and selling a prior company, Viaweb, to Yahoo! in 1998 for $50 million. Y Combinator—YC for short—is often referred to as a start-up “accelerator” as it not only invests in very young companies but helps to bring together small groups of entrepreneurs in “batches” to create such ventures and mentors them through the process of securing additional investment.

pages: 414 words: 109,622

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz
Published 15 Mar 2021

The company was called Loopt, and it eventually raised $30 million in venture capital, including one of the first investments made by Y Combinator and its founder Paul Graham. Seven years later, Loopt’s social networking service was shut down after being sold at a loss for its investors. But this was a successful exit for Altman, a trim, compact man with sharp green eyes and a particular talent for raising money. Graham soon announced that he was stepping down as president of Y Combinator, and he named Altman as his replacement, an appointment that surprised many across the family of Y Combinator companies. This made Altman an advisor to an endless stream of start-ups. In exchange for advice and capital, Y Combinator received a stake in each company, and Altman personally invested in some companies, too, becoming very wealthy, very quickly.

Sergey Brin was among those who walked the red carpet, wearing what seemed to be a Native American rug draped over his shoulders like a shawl. Many of the other guests were the millionaire founders of start-ups that had recently emerged from Y Combinator, the start-up accelerator overseen by Sam Altman. Some were among the founders who’d responded to a mysterious invitation five years earlier, filed into a conference room inside the Y Combinator offices in San Francisco, and looked on in surprise as a robot rolled into the room with an iPad where its head should have been, a live closeup of Yuri Milner appeared on the iPad, and Milner suddenly announced he was investing $150,000 in each and every one of their brand-new companies.

Brockman vowed to build the new lab they all seemed to want: Cade Metz, “Inside OpenAI, Elon Musk’s Wild Plan to Set Artificial Intelligence Free,” Wired, April 27, 2016, https://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/. nearly $2 million for the first year: OpenAI, form 990, 2016. Musk and Altman painted OpenAI as a counterweight: Steven Levy, “How Elon Musk and Y Combinator Plan to Stop Computers from Taking Over,” “Backchannel,” Wired, December 11, 2015, https://www.wired.com/2015/12/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over/. backed by over a billion dollars in funding: Ibid. AI would be available to everyone: Ibid. if they open-sourced all their research, the bad actors: Ibid. But they argued that the threat of malicious AI: Ibid.

pages: 282 words: 63,385

Attention Factory: The Story of TikTok and China's ByteDance
by Matthew Brennan
Published 9 Oct 2020

今日头条融资故事:得到的和错过的 - 2018-10-24 https://www.huxiu.com/article/268415.html SIG Asia Official Website http://www.sig-china.com/ 快公司之三: ”技术控 ”今日头条的媒体式烦恼 2015-09-04 https://finance.qq.com/cross/20150901/78V57DPP.html 对话今日头条创始人: 1亿美元融资背后的故事 2014-06-05 http://tech.sina.com.cn/i/2014-06-05/04399418360.shtml “酷讯系 ”的新产品 2013 February edition of Cyzone Magazine http://magazine.cyzone.cn/article/199140.html 【张一鸣专栏】南开时光三件事:耐心,知识,伙伴 2015-11-17 https://www.pingwest.com/a/61954 酷讯创业帮 2016-09-03 http://www.startup-partner.com/3654.html Steve Jobs: Technology & Liberal Arts 2011-10-06 https://www.youtube.com/watch?v=KlI1MR-qNt8 盈都大厦官方网站 http://yingdudasha.cn/ Zhen Fund Official Website http://en.zhenfund.com/About 90% of Y Combinator Startups Have Already Accepted The $150k Start Fund Offer 2011-01-30 https://techcrunch.com/2011/01/29/90-of-y-combinator-startups-have-already-accepted-the-150k-start-fund-offer/ What is it like to get funded by Y Combinator? https://www.quora.com/What-is-it-like-to-get-funded-by-Y-Combinator 张一鸣年会演讲显露今日头条锋芒: 2016要决战”国内第一”!凭什么? 2016-03-12 https://m.huxiu.com/article/141687.html 从 5亿美金到 750亿,今日头条如何在 BAT围剿中建成 ”流量帝国 ”? 2019-07-20 https://dy.163.com/article/EKI5CPM50511D84J.html; Tencent, Xiaomi Invested in TikTok’s Parent, ByteDance 2020-08-20 https://www.theinformation.com/articles/tencent-xiaomi-invested-in-tiktoks-parent-bytedance Chapter 3: Recommendation, From YouTube to TikTok 算法狂飙,张一鸣且行且珍惜 2018.07.05 https://finance.sina.cn/2018-07-05/detail-ihexfcvi8061268.d.html?

This was the mysterious message sent out to all 43 startup teams taking part in the 2011 Y Combinator startup accelerator, Silicon Valley’s most prestigious program for new startups. The cryptic announcement from the program partners drove wild speculation amongst the founders as to what would happen. They weren’t told why they were supposed to be there, just that something important was happening. Some guessed a high-profile celebrity such as Steve Jobs 58 might be speaking. Excitement grew as the time came close. When it came round to Friday night, the room was full. All the entrepreneurs had gathered at the Y Combinator headquarters in Mountain View, row after row of young founders.

Despite his deteriorating health, he was still appearing at events up until June. 59 A common early-stage startup financing method that avoids setting a valuation. Notes can be converted later into a specified number of shares of common stock or cash of equal value. 60 https://www.quora.com/What-is-it-like-to-get-funded-by-Y-Combinator 61 https://techcrunch.com/2011/01/29/90-of-y-combinator-startups-have-already-accepted-the-150k-start-fund-offer/ 62 The investment was made through a fund called Apoletto, the investment vehicle of a foundation that supports Milner’s philanthropic endeavors. ByteDance’s valuation on the private secondary markets is reportedly in the $100 billion range at the time of writing. 63 https://www.ixigua.com/pseries/6805466361402229262_6805715182820524556/?

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The Upstarts: How Uber, Airbnb, and the Killer Companies of the New Silicon Valley Are Changing the World
by Brad Stone
Published 30 Jan 2017

By spending time at Justin.tv, the Airbnb founders got to see what a real tech startup looked like, one with real offices, real employees, and actual venture capital in the bank. (Justin.tv later spun off a video-game service, Twitch.tv, which was acquired by Amazon in 2014 for $970 million.) Continuing this education, they attended a one-day event called Startup School, organized by the startup incubator Y Combinator and hosted by Stanford University. The speakers that year included Amazon CEO Jeff Bezos and the investor Marc Andreessen, an inventor of the web browser. But the speech the founders remembered best was by Greg McAdoo, a venture capitalist at the top-tier VC firm Sequoia Capital, a man whom they would soon get to know well.

It did not propel the company to immediate success or generate any significant wealth; in fact, they were still barely making ends meet and began subsisting on the surplus Cap’n McCains. But it did demonstrate an extreme level of commitment and an ability to think creatively that, ultimately, would lead to their long-awaited break. A few weeks later, Chesky decided that the founders of the struggling company should apply to the prestigious Y Combinator startup school, which invested seventeen thousand dollars in each startup, took a 7 percent ownership stake, and surrounded founders with mentors and technology luminaries during an intense three-month program. It was a last-ditch effort and Chesky actually missed the application deadline by a day.

“The business was just not working.” Before they left for the interview, Gebbia went to grab boxes of the cereal. Blecharczyk snapped at him. “No, no, no,” he said. “Keep the cereal at home.” Gebbia pretended to acquiesce, then surreptitiously slipped two boxes into his bag anyway. The interview at Y Combinator’s offices in Mountain View was practically hostile. “People are actually doing this?” asked Paul Graham, the program’s legendary co-founder, when the three men described the home-sharing concept. “Why? What’s wrong with them?” Graham, then forty-four, later admitted that he didn’t get it. “I wouldn’t want to stay on anyone else’s sofa and I didn’t want anyone to stay on mine,” he says.

pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World
by Christopher Steiner
Published 29 Aug 2012

The idea eventually became Aisle50, a Web site that offers deals on grocery items for consumers to purchase before they head to the store. While building Aisle50, we applied and were accepted to be part of Silicon Valley’s Y Combinator, which helps startups get off the ground with funding, mentorship, and connections to investors. A good percentage of my cohort at Y Combinator picked up programming by the time they were fourteen years old. When they reached college, often an elite university, they could already string together thousands of lines of code, create the guts of a stable application, or bolt up an original Web site design in a matter of hours.

Countless Web communities and chat rooms are dedicated solely to the writing of code and the construction of algorithms. One company from my Y Combinator group, Codecademy, hit upon an idea so popular—a well-designed site for learning how to program online—that it drew more than 200,000 users in its first two weeks after launching. Just six months after launch, Codecademy landed a partnership with the White House to promote computer programming. This is the new reality we live in: a twenty-one-year-old with the tools to conceive complex algorithms can form a partnership with the president of the United States. Y Combinator is just a microcosm of a movement, often pushed forward by youth, that is putting algorithms inside everything we do.

It was quite a change for me but also one that I embraced. There have been many helping hands along the way, some of the most formidable ones coming from our investors and advisers at Y Combinator, Paul Graham and Jessica Livingston. They have built something special in Silicon Valley, and, for the curious, there happens to be a book being released at exactly the same time as this one, by the same publisher, that is the best chronicle ever put together on Y Combinator: Randy Stross’s The Launch Pad. Read it. As for Aisle50, I have high hopes thanks to our crack sales and engineering teams, who have proven to be up to every challenge we faced so far, and there have been many.

pages: 294 words: 82,438

Simple Rules: How to Thrive in a Complex World
by Donald Sull and Kathleen M. Eisenhardt
Published 20 Apr 2015

. [>] Because of the success: Ibid. [>] Y Combinator is a “seed accelerator”: Benjamin L. Hallen, Christopher B. Bingham, and Susan L. Cohen, “Do Accelerators Accelerate? A Study of Venture Accelerators as a Path to Success” (working paper, University of Washington, Seattle, 2013). [>] At this point: Paul Graham, October 2013, “What Happens at Y Combinator,” http://ycombinator.com/atyc.html, accessed April 28, 2014; and Freedman, 2013, “YC Without Being in YC,” http://blog.42floors.com, accessed April 28, 2014. Firsthand account of how former Y Combinator entrepreneurs mimicked the Y Combinator experience by pretending that they had just been accepted again. [>] Another way of learning: Derek Thompson, “Airbnb CEO Brian Chesky on Building a Company and Starting a Sharing Revolution,” Atlantic, August 13, 2013, http://www.theatlantic.com/business/archive/2013/08/airbnb-ceo-brian-chesky-on-building-a-company-and-starting-a- sharing-revolution/278635/. [>] As Brian recalled: Ibid. [>] Like clockwork: Tame, “From Toilet Seats to $1 Billion.” [>] The founders coupled these: Jessie Hempel, “More Than a Place to Crash,” Fortune, May 3, 2012, http://fortune.com/2012/05/03/airbnb-more-than-a-place-to-crash/. [>] The founders also had: Vella and Bradley, “Airbnb CEO—‘Grow Fast but not Too Fast.’” [>] Airbnb ended up with: Tomio Geron, “Airbnb Hires Joie de Vivre’s Chip Conley as Head of Hospitality,” Forbes, September 17, 2013, http://www.Forbes.com/sites/tomiogeron/2013/09/17. [>] In fact, Airbnb: Salter, “Airbnb: The Story Behind the $1.3bn Room-Letting Website.” [>] Airbnb has become: Thompson, “Airbnb CEO Brian Chesky on Building a Company and Starting a Sharing Revolution.” 8.

The big picture was, however, that Airbnb was floundering, with a few initial rules that cried out for improvement. A much-needed turning point came when Airbnb joined Y Combinator. Y Combinator is a “seed accelerator” providing financing, advice, and connections to cohorts of early-stage ventures, but its headliner mission is helping entrepreneurs improve very fast. At this point, Airbnb’s entrepreneurs began multitasking different ways to learn. One way was from weekly Tuesday-night dinners at Y Combinator. Each week, a famous founder or other luminary delivered an off-the-record speech full of stories and advise about building companies.

This was another opportunity to learn—this time through presenting Airbnb’s story and getting feedback and insights from peers. These dinners created a relentless weekly rhythm of stepping back to reflect, getting feedback and ideas, and heading back to work. Another way of learning was through tailored expert advice. The Airbnb founders gained two pivotal insights from Y Combinator cofounder Paul Graham that critically reframed their conception of what to do. One piece of advice was counterintuitive—forget about growing Airbnb, and instead focus on creating the perfect Airbnb experience. Graham’s argument was, “It’s better to have a hundred people love you than to have a million people like you.”

pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time
by George Berkowski
Published 3 Sep 2014

, Quora.com, www.quora.com/What-does-it-cost-to-do-press-releases-and-what-services-Marketwire-PRWeb-BusinessWire-etc-are-best. Chapter 11: Is Your App Ready for Investment? 1 According to a post from a partner at Y Combinator on ‘How Many People/Teams Get Rejected by Y Combinator During Each Application Period?’, Quora.com, www.quora.com/How-many-people-teams-get-rejected-by-Y-Combinator-during-each-application-period. 2 This information comes from an interview with Alice Bentinck, cofounder of Entrepreneur First conducted on 24 February 2014. Chapter 12: How Much is Your App Worth and How Much Money Should You Raise?

It’s a great alternative to just going and chasing seed investors via AngelList (or good old-fashioned hitting the pavement). I have a few friends who have graduated from two accelerators – Y Combinator and Techstars – and the feedback has been mixed. Americans love having seals of approval from prestigious institutions, so the programme seems to yield great results for them, but the Europeans seem a bit more tepid about the format. Personally, I think you get back what you put in, and having access to their network of alumni and their inside knowledge is a precious resource. Let’s have a look the three top programmes. Y Combinator is probably the best seed accelerator. It was started in March 2005 and has funded more than 500 companies – including Dropbox, Airbnb and Stripe.

He knew that he had a lot to learn and relied on any resources he could lay his hands on. He knew he had to adapt quickly if he was going to make his company a success. Y Combinator – the accelerator programme – helped the young CEO along the way. ‘It’s a mix of a variety of different things,’ he says, ‘like mentors – people who are experienced and have been through the process many times – and peers. Some of my best friends are Y Combinator founders, and we all went through the same kind of thing at the same time.’ Dropbox’s early board of directors also helped him recognise patterns and gave him an idea of what he should be thinking about.

pages: 94 words: 26,453

The End of Nice: How to Be Human in a World Run by Robots (Kindle Single)
by Richard Newton
Published 11 Apr 2015

Far from being the passive recipient of great work, the truth is that creativity is work – a great deal of it. Grit The magic lies, brace yourself, in determination. When he spoke about the number one quality he looks for in founders, Paul Graham of Y Combinator said: “Determination. This has turned out to be the most important quality in start-up founders. We thought when we started Y Combinator that the most important quality would be intelligence. That’s the myth in the Valley. And certainly you don’t want founders to be stupid. But as long as you’re over a certain threshold of intelligence, what matters most is determination.

Morally, they care about getting the big questions right, but not about observing proprieties. That’s why I’d use the word naughty rather than evil. They delight in breaking rules, but not rules that matter. This quality may be redundant though; it may be implied by imagination.” – Paul Graham, founder of Y Combinator In his autobiography, Mark Twain tells of his childhood friend, Tom Blankenship, who was the inspiration for Huckleberry Finn, a character who was poorly educated and stood outside society but as a result appraised the world and society around him with a clear and critical eye: “In Huckleberry Finn I have drawn Tom Blankenship exactly as he was.

Cramped around shared desks and a battery of 24/7 coffee machines eleven start-up companies from seven different countries are plotting to disrupt established big businesses. The program is run by a company called TechStars which is one of the three big names in “start-up accelerators” alongside Y-Combinator and 500 Start-Ups. Accelerators are entrepreneurial bootcamps which provide embryonic companies with a workspace, business expertise and access to money, press, partners, know-how and a giant corps d’esprit. After a typical three-month program, the start-up companies are released into the commercial world to disrupt, innovate and clean up!

pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

The investor isn’t looking for stability: They want rapid growth that leads to a bigger return on their investment. The Y Combinator accelerator—which takes in several dozen tech firms each year, to try and help them into the big leagues—ends each cohort’s program with a Demo Day, where the young companies show off their products for a room of handpicked venture capitalists. The start-ups are inevitably desperate to include in their presentation a hockey-stick chart—the one that shows their user base suddenly blasting off into the sky. One evening, I visited the hackerhouse of People.ai, a company that just days earlier had done their Y Combinator demo. They pecked at keyboards and exhaustedly described how they’d spent the three months in Y Combinator frantically registering new clients for their service, in an attempt to produce that hockey stick.

for sex by investors: Reed Albergotti, “Silicon Valley Women Tell of VC’s Unwanted Advances,” The Information, June 22, 2017, https://www.theinformation.com/articles/silicon-valley-women-tell-of-vcs-unwanted-advances; Sara O’Brien, “Sexual Harassment in Tech: Women Tell Their Stories,” CNN Tech, https://money.cnn.com/technology/sexual-harassment-tech/; Katie Benner, “Women in Tech Speak Frankly on Culture of Harassment,” New York Times, June 30, 2017, https://www.nytimes.com/2017/06/30/technology/women-entrepreneurs-speak-out-sexual-harassment.html; all accessed August 19, 2018.. Pao later wrote: Ellen Pao, Reset: My Fight for Inclusion and Lasting Change (New York: Random House, 2017), 78. gender for Y Combinator: Cadran Cowansage, “Ask a Female Engineer: Thoughts on the Google Memo,” Y Combinator (blog), August 15, 2017, accessed August 19, 2018, https://blog.ycombinator.com/ask-a-female-engineer-thoughts-on-the-google-memo/. as a lawsuit alleged: Jordan Pearson, “How the Magic Leap Lawsuit Illuminates Tech’s Gendered Design Bias,” Motherboard, February 15, 2017, accessed August 19, 2018, https://motherboard.vice.com/en_us/article/aeygje/how-the-magic-leap-lawsuit-illuminates-techs-gendered-design-bias.

He was right, as I discovered as I slugged back the drink. But I finished the entire meal in five minutes, which is the point: Soylent is the ultimately optimized meal. Soylent was invented in 2013 by a 25-year-old Rob Rhinehart, a programmer whose start-up—founded with two young collaborators—had gotten $170,000 from Y Combinator to create inexpensive, newfangled cell phone towers. Alas, their tech was a bust. But Rhinehart had, in his spare time, been pondering a new problem of everyday life: food. Food, Rhinehart decided, was an enormous waste of time. He figured he spent about two hours a day on meals. “Typically I would cook eggs for breakfast, eat out for lunch, and cook a quesadilla, pasta, or a burger for dinner,” he calculated on his blog.

pages: 361 words: 107,461

How I Built This: The Unexpected Paths to Success From the World's Most Inspiring Entrepreneurs
by Guy Raz
Published 14 Sep 2020

“At $40 a box times 500 boxes, we made $20,000 in breakfast cereal,” Joe explained, “which was just enough to pay off our credit cards.” Obama O’s and Cap’n McCain’s didn’t fuel the Airbnb rocket ship, but it kept them going long enough for them to get accepted into Y Combinator’s winter class that year. In fact, it was the ingenuity they displayed in making the cereal, not actually their Airbnb idea, that convinced Y Combinator co-founder Paul Graham to find a place for them in the program. “Through the breakfast cereal, we had proven to him that we had hustle, we had grit,” Joe said. “If we could figure out how to sell breakfast cereal for $40 a box, we could figure out how to make our website work.”

Passion for the idea may have gotten Bruce out of bed every morning, and it surely helped him persevere whenever he felt like he might quit, but it was never going to sell his product, nor would it make the Sharks any money, because customers don’t pay for passion. They pay for things they can use. The same month the episode of Shark Tank with Bruce Gaither aired, Paul Graham, co-founder of the startup accelerator Y Combinator and a kind of entrepreneurial Confucius, wrote a long essay titled “How to Get Startup Ideas” for his blog. It opens with a discussion of problems and reads like Graham had just watched Bruce on Shark Tank and was talking directly to him. “The way to get startup ideas is not to try to think of startup ideas,” Graham wrote.

“Two thousand eight was the worst year of my life.” In theory, this should have been the end of Airbedandbreakfast.com. And in a way, it was. Or at least it was the beginning of the end. That’s because within eight months, the name would be shortened to Airbnb; the trio of co-founders would be accepted to, and then graduate from, the Y Combinator startup incubator; and their fledg­ling website would have 10,000 users and 2,500 listings. This all happened, ironically, all because of two boxes of cereal and a binder full of credit cards. “You know those binders where you keep baseball cards?” Joe asked me when I was curious how they managed to keep going after striking out with investors.

pages: 406 words: 105,602

The Startup Way: Making Entrepreneurship a Fundamental Discipline of Every Enterprise
by Eric Ries
Published 15 Mar 2017

At the start of 2017, the government selected roughly two thousand unemployed workers from fields ranging from technology to construction and enrolled them in a pilot UBI program to see what will happen.21 Y Combinator is also running an experiment with basic income, having selected one hundred families in Oakland, California, who will receive $1,000 to $2,000 a month as part of a five-year program designed to look at how ready money affects people’s “happiness, well-being, financial health, as well as how people spend their time.” The data and research methods will be shared at the project’s end so others can learn from and build on the experiment, which is testing the idea, as Y Combinator president Sam Altman says, that a basic income could “give people the freedom to pursue further education or training, find or create a better job, and plan for the future.”22 In France, an experiment that allowed people to keep their unemployment benefits while starting a new business saw an increase of 25 percent per month in the creation of new companies.23 And the Dutch and Canadians aren’t far behind—both countries also launched experiments in 2017.24 REGULATORY RELIEF FOR STARTUPS “Sliding Scale” Regulations Regulation can destroy startups without even meaning to.

Even if you wanted to design a program that was only for entrepreneurs, it would be impossible to do so. What makes someone an entrepreneur is not that she or he got assigned that role by someone at corporate HQ. Good ideas come from unexpected places. In fact, one of the lessons of the rise of startup accelerators like Y Combinator (YC) and Techstars is that they achieved their disproportionate impact on the world, in part, by bringing new people into the entrepreneurial ecosystem. This is one of the most striking things about reading the early YC applications in particular. Many of the founders of multibillion-dollar startups weren’t sure they were cut out for entrepreneurship at all.

Let’s design our experiments to prove that.4 In addition to the learning benefits I mentioned above, this approach offers another major bonus: Sometimes the team really is right! COACHING STRUCTURE In the startup world, coaching has been a long-standing part of our practice. Investors have always maintained networks of mentors and advisors to help teams develop and grow. More recent accelerator programs, such as Y Combinator and Techstars, and more modern VCs, such as Andreessen Horowitz, have formalized this approach into a more structured program of services and support. Advice and mentorship are available to startups in the portfolio, but they are never—ever—substitutes for leadership. Nobody is forced to talk to any specific mentor or do what that mentor says.

pages: 287 words: 69,655

Don't Trust Your Gut: Using Data to Get What You Really Want in LIfe
by Seth Stephens-Davidowitz
Published 9 May 2022

Venture capitalists and investors have bought into the media-driven narrative that younger people are more likely to build great companies. Vinod Khosla, a cofounder of Sun Microsystems and venture capitalist, said, “People under 35 are the people who make change happen . . . people over 45 basically die in terms of new ideas.” Paul Graham, the founder of Y Combinator, the famous start-up accelerator, said that, when a founder is over the age of thirty-two, investors “start to be a little skeptical.” Zuckerberg himself famously said, with his characteristic absence of tact, “Young people are just smarter.” But, it turns out, when it comes to age, the entrepreneurs we learn about in the media are not representative.

She was also, by just about any conventional metric, a failure. Recall that she had declared bankruptcy and had numerous business ventures go bust. She was outside the margins of the successful. Could Batiz’s lack of success actually, as strange as it may seem, have been an advantage? Paul Graham, the brilliant essayist and founder of Y Combinator, a start-up accelerator, wrote a fascinating and provocative essay arguing that people who have failed a lot can actually have an edge in entrepreneurship. In the essay, called “The Power of the Marginal,” Graham notes that “great new things often come from the margins.” Graham points to the examples of the founders of Apple, Steve Jobs and Steve Wozniak.

The two young men, however, did design and sell cereal based on the presidential candidates—Obama O’s (“The breakfast of change”) and Cap’n McCain’s (“A maverick in every bite”). Remarkably, they sold enough to pay back their debt. In any case, their business was basically dead when, one evening, Chesky and Gebbia met up with Seibel, the man who had liked them in Austin. Seibel, still impressed with the two young men, suggested that they apply for Y Combinator, a start-up accelerator that was run by his friend Paul Graham in Silicon Valley. The deadline had passed, but Seibel had enough pull with Graham to get their application a look. This was their first big break. Graham didn’t like Chesky’s and Gebbia’s business idea, but, when told of their cereal story, he was impressed with their moxie.

pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It)
by Jamie Bartlett
Published 4 Apr 2018

A growing number of people are proposing a bold new idea to deal with this. In 2017 I interviewed Sam Altman, the president of Y Combinator, the most important fund in Silicon Valley for tech start-ups. Thousands of businesses apply every year to access Y Combinator’s funding and guidance, in exchange for a small slice of their company. Sam is a Princeton dropout and frequently wears a hoodie, yet when I met him, he was only 31 years old and already a multi-millionaire. He is often described as ‘the man who invents the future’. The companies Y Combinator have funded include Airbnb and Starsky Robotics, and are now altogether valued at $80 billion.

The companies Y Combinator have funded include Airbnb and Starsky Robotics, and are now altogether valued at $80 billion. Aware of the potential turbulence that AI might unleash, Y Combinator recently started to fund a pilot in universal basic income. UBI, as it is commonly referred to, is an increasingly popular idea to deal with the possible rise of joblessness and tech-fuelled inequality. The basic concept is that governments should give everyone enough money to live on, with no strings attached. Several pilot schemes, including Oakland, California and Finland, are examining the idea (although it’s too early to say how well they are working yet), and a number of serious thinkers and writers believe it is worth further investigation.

For some on the left, including a handful of radicals in the UK circling Labour leader Jeremy Corbyn, it represents a way to redistribute wealth more fairly. And for the utopians, it would allow people to do more meaningful things with their lives than monotonous labour.* Sam doesn’t think anyone is ready for AI. ‘We are going to need to have new distribution, new social safety nets,’ he told me in his Y Combinator office. ‘What happens if you just give people money to live on? . . . Say, “Here’s enough money to have a house and eat and have fun”.’ It’s an interesting idea. There are an awful lot of jobs that people don’t really want to do. If the things that people gain from work – economic means, structure, purpose – can be achieved in other ways, that’s worth exploring.

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Facebook: The Inside Story
by Steven Levy
Published 25 Feb 2020

Chris Hughes: In addition to personal interview, Hughes tells his own story in Fair Shot: Rethinking Inequality and How We Learn (St. Martin’s Press, 2018). “People would just spend hours”: Interview with Sam Altman, Y Combinator, “Mark Zuckerberg: How to Build the Future,” August 16, Zuckerberg Transcripts, 171. steam coming from the suite’s bathroom: Interview with Y Combinator, “Mark Zuckerberg at Startup School 2013,” October 25, 2013, Zuckerberg Transcripts, 160. his first notice: S. F. Brickman, “Not So Artificial Intelligence,” Harvard Crimson, October 23, 2003. “a bitch”: The online journal cited here, and first published by Luke O’Brien in the online Harvard alumni journal 02138 in “Poking Facebook,” would become notorious in the movie The Social Network.

“unfazed”: Matt Welsh blogged, “How I Almost Killed Facebook,” February 20, 2009. Harry Lewis: Alexis C. Madrigal, “Before It Conquered the World, Facebook Conquered Harvard,” The Atlantic, February 4, 2019. “There was nothing like that”: Interview with Y Combinator, “Mark Zuckerberg at Startup School 2013,” October 25, 2013, Zuckerberg Transcripts, 160. come from Microsoft: Interview with Y Combinator, “Mark Zuckerberg at Startup School 2012,” October 20, 2012, Zuckerberg Transcripts, 161. Saverin kicked in: Information about Eduardo Saverin is drawn from Kirkpatrick, The Facebook Effect; Mezrich, The Accidental Billionaires (Saverin cooperated with the book); and Nicholas Carlson, “How Mark Zuckerberg Booted His Co-Founder Out of the Company,” Business Insider, May 15, 2012.

Google’s elders were professors who wrote the textbooks that its leaders learned from; Facebook hired Mark Zuckerberg’s Harvard TA. True, even in 2005 there was a smattering of thirtysomethings on staff—a few of them married, with kids. But while Zuckerberg understood the value of veterans like Jeff Rothschild, at his core he believed that younger people were . . . smarter. He said exactly that in a Y Combinator start-up school in 2007, telling 650 would-be founders to hire people who were young and technical. “Why are most chess masters under thirty?” he asked. His later apology for that remark (which, if it truly reflected Facebook’s hiring policy, would put the company in violation of federal labor laws) didn’t cover up the fact that his original statement seemed totally in sync with his worldview.

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WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly
Published 9 Oct 2017

Bryce came up with a creative solution, which he called indie.vc. Indie.vc is modeled on Y Combinator, the classic Silicon Valley accelerator, which takes a small but meaningful stake in very-early-stage companies in exchange for a very small amount of cash, plus a lot of help in business planning, networking with other entrepreneurs, and eventually, showcasing the company to VCs. Y Combinator has been phenomenally successful, helping to birth companies such as Airbnb and Dropbox. But the focus of Y Combinator’s program specifically, and VC-funded companies generally, is on raising the next round of funding. In the case of Y Combinator, months of work and preparation are put into nailing the perfect pitch for a performance in fundraising called demo day.

The generous redistribution of oil profits and a strong social safety net funded by the wealth that is understood to belong to all makes Norway one of the happiest and wealthiest countries in the world. For a technology perspective, I turned to Paul Buchheit, creator of Gmail and now a partner at Y Combinator, and Sam Altman, the head of Y Combinator. In a 2016 conversation, Paul said to me: “There may need to be two kinds of money: machine money, and human money. Machine money is what you use to buy things that are produced by machines. These things are always getting cheaper. Human money is what you use to buy things that only humans can produce.”

The experiment was so successful that they decided to build out a short-term room, apartment, and home rental service for the upcoming SXSW technology conference in Austin, Texas, because they knew that every hotel room in the city would be sold out. They followed that up by doing the same thing for the 2008 Democratic National Convention, held in Denver, Colorado. In 2009, they were accepted into Y Combinator, the prestigious Silicon Valley startup incubator, and then received funding from one of Silicon Valley’s top venture firms, Sequoia Capital. But despite a promising start, they were still struggling with acquiring users fast enough. The breakthrough came when they realized that hosts were taking lousy photographs of their properties, leading to lower trust and thus lower interest by possible renters.

pages: 56 words: 16,788

The New Kingmakers
by Stephen O'Grady
Published 14 Mar 2013

Even when venture capitalists took an interest, the deals they offered often were not favorable for entrepreneurs—they frequently provided more money than was required in order to obtain the largest possible share of the company. Then in 2008, Paul Graham’s Y Combinator launched. Recognizing that the technology landscape had dramatically lowered the cost of starting a business, Y Combinator offered substantially less money—typically less than $20,000—in return for a commensurately smaller share of the company. Its average equity stake was around 6%. The falling costs of business creation led to a decoupling of the average deal size with the average deal volume.

The falling costs of business creation led to a decoupling of the average deal size with the average deal volume. Because the changing technology landscape had dramatically lowered the cost of starting a technology business, its small investments were sufficient to get these young companies off the ground. With the amount of money each company needed in decline, more businesses were given less money, and Y Combinator and other programs like TechStars have played a critical role in this. Seed-stage investment funds democratized access to capital much as the cloud lowered the friction associated with hardware acquisition and open source erased the barriers between developers and software. The result? Businesses like Dropbox, which turned down a nine-digit offer from Steve Jobs and subsequently raised money at a four-billion-dollar valuation.

pages: 309 words: 96,168

Masters of Scale: Surprising Truths From the World's Most Successful Entrepreneurs
by Reid Hoffman , June Cohen and Deron Triff
Published 14 Oct 2021

Most MBA students will tell you, “That doesn’t scale.” But ignoring those details won’t work—not in the long run, says Y Combinator’s president from 2014 to 2019, Sam Altman. An acolyte of Paul Graham’s, Sam adhered to the core Y Combinator dictum: It’s better to have one hundred users who love you than a million users who just kind of like you. It’s counterintuitive. You may be thinking If a million people “kind of like” my product enough to buy it, isn’t that better for business than a hundred obsessive oddballs? To which Sam would say…definitely not. Y Combinator has incubated more than fifty companies that have reached $100 million in value or more—so they have a fairly good sense of what does and doesn’t scale.

“The unhelpful no” If you’re the kind of person who might get easily discouraged or talked out of your idea, you need to keep your idea away from people whose opinions you have an emotional investment in. 2 Do Things That Don’t Scale The meeting definitely did not go as he expected. In 2009, Brian Chesky, a young entrepreneur with a big idea, was meeting with Paul Graham, co-founder of Y Combinator, the renowned Silicon Valley startup accelerator. Brian’s company, Airbnb, was partway through the Y Combinator program, and he was ready to wow Paul with his vision of a bright future for an unconventional new business that enabled people to rent out their spare rooms or sofa beds to total strangers. Airbnb was already up and running, but at this early stage not many people seemed to know about it.

Entrepreneurs were saying, “I’m just going to make another photo-sharing app.” Y Combinator instead became interested in startups that were trying something more ambitious, what Sam calls “bits-to-atoms companies, where you had software, but you also had to do this very complex thing in the real world.” Because these companies were trying to do something hard, and potentially game-changing, they didn’t have as much competition as all the copycat startups. One such company was Airbnb. * * * — When Brian Chesky and his partner, Joe Gebbia, got to New York, at the urging of Y Combinator’s Paul Graham, they had a clear mandate: Go to your users.

Systematic Trading: A Unique New Method for Designing Trading and Investing Systems
by Robert Carver
Published 13 Sep 2015

Each subsystem tries to predict the price of an individual instrument, and calculate the appropriate position required. These subsystems are then combined into a portfolio, which forms the final trading system. TABLE 15: EXAMPLE OF COMPONENTS IN A TRADING SYSTEM Instruments Trading rule A, variation 2 A2 forecast for instrument Y Trading rule B, variation 1 B1 forecast for instrument Y Combined forecast Y weights A1 forecast, instrument Y Subsystem position in Y Instrument Trading rule A, variation 1 sizing B1 forecast for instrument X Position Trading rule B, variation 1 Portfolio weighted position in X Subsystem position in X Combined forecast X targeting A2 forecast for instrument X Volatility Trading rule A, variation 2 weights A1 forecast for instrument X Forecast Trading rule A, variation 1 Portfolio weighted position in Y Customising for speed and size This trading system has two trading rules A and B; three rule variations A1, A2 and B1; and two instruments X and Y.

Given that information, how should you then tailor your system? I’ll address both of these issues in detail in the final chapter of part three. Chapter Six. Instruments Instruments Trading rule A, variation 2 A2 forecast for instrument Y Trading rule B, variation 1 B1 forecast for instrument Y Combined forecast Y weights A1 forecast, instrument Y Subsystem position in Y Instrument Trading rule A, variation 1 sizing B1 forecast for instrument X Position Trading rule B, variation 1 Portfolio weighted position in X Subsystem position in X Combined forecast X targeting A2 forecast for instrument X Volatility Trading rule A, variation 2 weights A1 forecast for instrument X Forecast Trading rule A, variation 1 Portfolio weighted position in Y Customising for speed and size B EFORE YOU THINK ABOUT HOW YOU TRADE YOU NEED TO consider what you’re going to trade – the actual instruments to buy or sell.

But if you’re using multiple rules or variations then you’ll need to read chapter eight to discover how to combine forecasts from different rules. 123 Chapter Eight. Combined Forecasts Instruments Trading rule A, variation 2 A2 forecast for instrument Y Trading rule B, variation 1 B1 forecast for instrument Y Combined forecast Y weights A1 forecast, instrument Y Subsystem position in Y Instrument Trading rule A, variation 1 sizing B1 forecast for instrument X Portfolio weighted position in X Subsystem position in X Position Trading rule B, variation 1 Combined forecast X targeting A2 forecast for instrument X Volatility Trading rule A, variation 2 weights A1 forecast for instrument X Forecast Trading rule A, variation 1 Portfolio weighted position in Y Customising for speed and size Staunch Systems Trader This chapter is about combining forecasts from different trading rules, including variations of the same rule, so it’s required reading for most staunch systems traders.

pages: 179 words: 42,006

Startup Weekend: How to Take a Company From Concept to Creation in 54 Hours
by Marc Nager , Clint Nelsen and Franck Nouyrigat
Published 8 Nov 2011

New groups of VCs called super angels, which are generally smaller than the traditional multihundred-million-dollar VC fund, can make the small investments necessary to help launch a consumer Internet startup. These angels make lots of early bets and double-down when early results appear. And the results do appear years earlier than they would in a traditional startup. In addition to super angels, incubators like Y Combinator, TechStars, and the 100-plus others like them worldwide have begun to formalize seed-investing. They pay expenses in a formal three-month program, while a startup builds something impressive enough to raise money on a larger scale. However, the penultimate events in this area are Startup Weekends: 54-hour conferences that allow developers, designers, marketers, product managers, and startup enthusiasts to come together to share ideas, form teams, build products, and launch startups.

Lovell also warns entrepreneurs against concentrating too much on their ideas, or thinking of Startup Weekend merely as a place to go to find people to do some free weekend work on their idea. “When people are married to an idea, it can go horribly wrong,” she says. One of our facilitators, who has worked with other startup mentorship programs like Y-Combinator, says that these types of programs pick companies to support based on the people who comprise the teams, and expect that the ideas will change along the way. “As a facilitator, I look for attendees on the sidelines Friday night who are struggling to figure out what team to join or [who] feel discouraged because their idea wasn't picked.

There are several programs out there that do mentoring incubation; that is, they will help to support entrepreneurs both financially and educationally for a few weeks or months to see if their ideas take off. These programs are not easy to get into; however, once you're in, you have the freedom and the mentor expertise at your disposal to really pursue your project full-time and wholeheartedly. Y-Combinator, Tech Stars, and Startup Labs are all great entry points into the world of Startup Funding. This is the stage at which somebody—an outsider— really starts to believe in your dream. That's when you can move closer to jumping off the entrepreneurial cliff. This brings us to the next step in the entrepreneurial ladder—the scaling leap.

pages: 307 words: 82,680

A Pelican Introduction: Basic Income
by Guy Standing
Published 3 May 2017

Segal has recommended a monthly income guarantee of C$1,320 per person, about 75 per cent of the provincial poverty line, with an extra $500 for disability, to be paid for a minimum of three years. Y Combinator, California In 2016, the start-up ‘accelerator’ Y Combinator announced a plan to conduct a small-scale basic income pilot in Oakland, California, for which $20 million has been put aside, probably to be supplemented by other donors. A ‘pre-pilot’ was launched in September 2016 to test logistics and study design, and a three- or four-year pilot was set to start in mid-2017.14 Sam Altman, the young venture capitalist who is president of Y Combinator, has said he wanted to fund a study on basic income because of the potential of artificial intelligence to eliminate traditional jobs and widen inequalities.

Supporters in this fourth wave include: Nobel Prize winners James Buchanan, Herbert Simon, Angus Deaton, Christopher Pissarides and Joseph Stiglitz; academics Tony Atkinson, Robert Skidelsky and Robert Reich, former Secretary of Labour under Bill Clinton; prominent economic journalists Sam Brittan and Martin Wolf; and leading figures in the BIEN movement, such as German sociologist Claus Offe and the Belgian philosopher Philippe van Parijs. Latterly, the idea has been taken up by Silicon Valley luminaries and venture capitalists, some putting up money for the cause, as we shall see. They include Robin Chase, co-founder of Zipcar, Sam Altman, head of the start-up incubator Y Combinator, Albert Wenger, a prominent venture capitalist, Chris Hughes, co-founder of Facebook, Elon Musk, founder of SolarCity, Tesla and SpaceX, Marc Benioff, CEO of Salesforce, Pierre Omidyar, founder of eBay, and Eric Schmidt, Executive Chairman of Alphabet, Google’s parent. Some people have rejected basic income on the rather crude reasoning that support from this quarter means it must be wrong!

And we can be reasonably sure that these changes will continue to worsen inequalities and be seriously disruptive, in often unpredictable ways that will hit many people through absolutely no fault of their own. In these circumstances, introducing a basic income system now would be sensibly precautionary and an equitable way to respond to the already visible disruption and inequality. Sam Altman, president of the American start-up incubator Y Combinator, has justified his allocation of funds to a basic income pilot (discussed in Chapter 11) on the grounds that we need to know how people would respond if the jobless future were to be realized and a basic income introduced. He told Bloomberg, ‘I’m fairly confident that at some point in the future, as technology continues to eliminate traditional jobs and massive new wealth gets created, we’re going to see some version of this [basic income] at a national scale.’21 In another interview, he put that point at ‘no fewer than 10 years’ and ‘no more than 100’.22 However, the immediate problem is one of income distribution rather than a sudden disappearance of work for humans to do.

pages: 286 words: 87,401

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies
by Reid Hoffman and Chris Yeh
Published 14 Apr 2018

In the beginning, every investor the founders approached had turned them down or, worse, ignored them. The company was on the upswing now, but the painful early days were still fresh in their minds, and they weren’t looking for another battle. * * * When the Airbnb founders first met, Paul Graham, the highly regarded founder of the start-up accelerator Y Combinator (YC), told them flat out that their idea was terrible. “People are actually doing this?!” he incredulously asked. When Brian told him yes, people were, in fact, renting out their living spaces for a night, Graham’s response was “What’s wrong with them?” Still, Graham had accepted the Airbnb guys into the three-month-long YC program.

Competing against them would be one hell of an uphill battle. Tired of the fund-raising grind, especially its emotional toll, Brian wondered whether he had it in him to take on this new and likely bruising fight. But he and his team had spent eighteen seemingly fruitless months working on Airbnb before entering Y Combinator, racking up tens of thousands of dollars in credit card debt. After all the blood, sweat, and tears, were they really willing to give up a quarter of their company? Ultimately, Brian decided not to buy Wimdu, swayed in part by the arguments of his key advisers. Facebook founder Mark Zuckerberg counseled him to fight.

It simply isn’t large enough to help them achieve their goal of returning more than three times their investors’ money. When Brian Chesky was pitching venture capitalists to invest in Airbnb, one of the people he consulted was the entrepreneur and investor Sam Altman, who later became the president of the Y Combinator start-up accelerator. Altman saw Chesky’s pitch deck and told him it was perfect, except that he needed to change the market-size slide from a modest $30 million to $30 billion. “Investors want B’s, baby,” Altman told Chesky. Of course, Altman wasn’t telling Chesky to lie; rather, he argued that if the Airbnb team truly believed in their own assumptions, $30 million was a gross underestimate, and they should use a number that was true to their convictions.

pages: 282 words: 81,873

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley
by Corey Pein
Published 23 Apr 2018

But they soon became a habit, and in a misguided effort to become more “productive,” I devoured page after page of the self-help and motivational material these websites featured, most of it directed at startup wannabes like me. Lying awake in bed, arm stiff from holding my smartphone aloft, I sought solace in the sanguine stream of updates on Hacker News, a techie discussion forum run by a venture capital fund and startup “incubator” called Y Combinator. This outfit seemed vaguely prestigious, the commenters knowledgeable. The titles of the inspirational homilies on Hacker News reassured me that I was not alone: “Fail Fast, Fail Often, and Fail by Design,” “Failing Fast Means … Failing a Lot,” and, most succinctly, “Success Through Failure.” I took it all to heart.

“If you’re super good at sounding like you know what you’re talking about, you can fool investors for at least one and perhaps even two rounds of funding.” Did I need more than one or two rounds of funding? Not really. This was encouraging. * * * I felt even better about my prospects after learning that I didn’t really need an idea. In 2012, Y Combinator, the investment fund behind Hacker News, announced in an online post that it would begin accepting funding applications from teams who didn’t even have ideas for their startups. “So if the only thing holding you back from starting a startup is not having an idea for one,” the investors wrote, “now nothing is holding you back.”

Like private stockbrokers for startups, VCs managed large pools of funds. Which is to say, the investors had investors. Often these were big institutions like foundations and universities—Stanford, naturally, was a major player—as well as pension funds. Because such people expected results, the VC industry couldn’t appear completely irresponsible. Therefore, Y Combinator presented a flimsy rationale for its lax investment criteria in the same online post: A lot of the startups we accept change their ideas completely, and some of those do really well … The other reason we’re doing it is that our experience suggests that smart people who think they can’t come up with a good startup idea are generally mistaken.

pages: 282 words: 85,658

Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century
by Jeff Lawson
Published 12 Jan 2021

Call for Startups Paul Graham cofounded Y Combinator, one of the most successful Silicon Valley incubators and early-stage investors. Since its founding in 2005, Y Combinator has funded more than two thousand startups, including Airbnb, Stripe, DoorDash, and Dropbox. The combined value of the startups funded by YC exceeded $150 billion as of October 2019. Paul is himself a developer, entrepreneur, and computer scientist. He’s a logical thinker who relies on principles to teach budding entrepreneurs the ropes and then set them free. One way Y Combinator finds or even helps spawn new startups is by posting a list of problems that need to be solved.

One way Y Combinator finds or even helps spawn new startups is by posting a list of problems that need to be solved. Y Combinator calls this its “Request for Startups,” describing it like this: “Many of the best ideas we’ve funded were ones that surprised us, not ones we were waiting for. There are, however, some startups that we’re very interested in seeing founders apply with. Below is an updated Request for Startups (RFS), which outlines some of those ideas in general terms.” The list doesn’t specify exactly how to solve the problems. That’s a job for the entrepreneurs, usually technical, to tackle. It’s not uncommon for Y Combinator to fund multiple companies all tackling the same problem, albeit in different ways.

The problem is that many developers absolutely hate meetings—not because engineers are antisocial, but because meetings occupy valuable time that could be better spent writing code. And like any meeting, daily stand-ups can be well run and efficient, or a boundless and unfocused waste of time. We as executives are pretty accustomed to days full of meetings, and we often expect that everybody in the company has the same schedule. This is what Paul Graham, cofounder of Y Combinator, calls a “manager schedule,” which works really well for people whose primary job is interfacing with other people. Cutting a day into sixty-minute blocks is how you can coordinate a lot of people. Just add it to my calendar. But making something out of nothing usually isn’t accomplished in one-hour blocks—it requires focus and what Graham calls a “maker schedule.”

pages: 387 words: 106,753

Why Startups Fail: A New Roadmap for Entrepreneurial Success
by Tom Eisenmann
Published 29 Mar 2021

In any case, given our penchant for attribution errors—that is, blaming our own failures on uncontrollable circumstances and others’ failures on their personal faults—we should interpret founders’ explanations for startup failure with care. While most investors blame bad jockeys for startup failure, some see slow horses as the main problem. For example, billionaire entrepreneur and investor Peter Thiel says that “all failed companies are the same: they failed to escape competition.” Paul Graham, founder of the elite accelerator Y Combinator, likewise holds that having a compelling solution to a customer’s problem—a strong horse—is the key to success: “There’s just one mistake that kills startups: not making something users want. If you make something users want, you’ll probably be fine, whatever else you do or don’t do. And if you don’t make something users want, then you’re dead, whatever else you do or don’t do.”

During its product development process, founder Drew Houston explored the needs of early adopters—software developers and other sophisticated computer users—as well as mainstream consumers, and decided to omit advanced features that would appeal mainly to early adopters. Houston designed an easy-to-use product that, according to his successful application to the elite Y Combinator accelerator, takes “concepts that are proven winners from the dev community (version control, changelogs/trac, rsync, etc.) and puts them in a package that my little sister can figure out.” Houston knew that Dropbox was superior to existing file management solutions and correctly bet that early adopters would embrace it, even without the advanced features.

As with cohort performance, a goal should be to identify early indicators that are strongly correlated with the risk of more serious problems down the road. Willing? It may seem odd to ask if entrepreneurs are willing to scale their venture rapidly. Isn’t the drive for scale a hallmark of entrepreneurship? As Paul Graham, the founder of Y Combinator, asserts, “A startup is a company designed to grow fast.” Graham explains that an entrepreneur at the helm of a rapidly growing startup will feel strong pressure to raise more money in order to fuel more growth: Money to grow faster is always at the command of the most successful startups, because the VCs need them more than they need the VCs.

pages: 393 words: 91,257

The Coming of Neo-Feudalism: A Warning to the Global Middle Class
by Joel Kotkin
Published 11 May 2020

“The rise to power of net-based monopolies coincides with a new sort of religion based on becoming immortal,” writes Jaron Lanier.30 Potentially the most radical and far-reaching of the emerging creeds, transhumanism is a distinctly secular approach to achieving the long-cherished religious goal of immortality.31 The new tech religion treats mortality not as something to be transcended through moral actions, but as a “bug” to be corrected by technology.32 Although it sounds a bit like a wacky cult, transhumanism has long exercised a strong fascination for the elites of Silicon Valley. Devotees range from Sergei Brin, Larry Page, and Ray Kurzweil (of Google) to Peter Thiel and Sam Altman (Y Combinator). Kurzweil celebrates new technologies that allow for close monitoring of brain activity.33 Y Combinator is developing a technology for uploading one’s brain and preserving it digitally.34 The aim is to “develop and promote the realization of a Godhead based on Artificial Intelligence.”35 In some ways, transhumanism seems natural for those who hold technology above all other values.

Everyone else will come to subsist on some combination of part-time entrepreneurial ‘gig work’ and government aid.”11 Ferenstein says that many tech titans, in contrast to business leaders of the past, favor a radically expanded welfare state.12 Mark Zuckerberg, Elon Musk, Travis Kalanick (former head of Uber), and Sam Altman (founder of Y Combinator) all favor a guaranteed annual income, in part to allay fears of insurrection by a vulnerable and struggling workforce. Yet unlike the “Penthouse Bolsheviks” of the 1930s, they have no intention of allowing their own fortunes to be squeezed. Instead, the middle class would likely foot much of the bill for guaranteed wages, health care, free college, and housing assistance, along with subsidies for gig workers, who do not receive benefits from their employers.13 This model could best be described as oligarchical socialism.

CHAPTER 18 The Totalitarian Urban Future The new urban paradigm elevates efficiency and central control above privacy local autonomy class diversity and broad-based property ownership. The same oligarchs who dominate our commercial culture, seek to profit from manipulating our moods, and influence the behavior of our children want to structure our living environment as well.1 Major tech firms—Y Combinator, Lyft, Cisco, Google, Facebook—are aiming to build what they call the “smart city.” Promoted as a way to improve efficiency in urban services, these plans will also provide more opportunity for oligarchs to monitor our lives, as well as sell more advertising. The “smart city” would replace organic urban growth with a regime running on algorithms designed to rationalize our activities and control our way of life.2 This urban vision appeals to tech oligarchs’ belief that their mission is to “change the world,” not simply make money by meeting customers’ needs and desires.

pages: 211 words: 77

The Little Schemer
by Daniel P. Friedman , Matthias Felleisen and Duane Bibby
Published 1 Jan 1974

Which cond-line is chosen for (apply-primitive name vals) where name is cons and vals is (6 (a b e)) The third: (( eq? name (quote cons)) (cons (first vals) (second vals))). Are we finished now? Yes, we are exhausted. But what about (define ... ) It isn't needed because recursion can be obtained from the Y combinator. Is (define ... ) really not needed? Yes, but see The Seasoned Schemer. Does that mean we can run the interpreter on the interpreter if we do the transformation with the Y combinator? Yes, but don't bother. What makes value unusual? It sees representations of expressions. Should will-stop? see representations of expressions? That may help a lot. Does it help? No, don't bother-we can play the same game again.

Let's separate the function that makes length from the function that looks like length That's easy. .--------------------, (lambda (Ie) ((lambda (mk-length) (mk-length mk-length)) (lambda (mk-length) (Ie (lambda (x) ( (mk-length mk-length) x)))))) Does this function have a name? Yes, it is called the applicative-order Y combinator. (define Y (lambda (Ie) ((lambda (f) (f f)) (lambda (f) (Ie (lambda (x) ((f f) x))))))) Does (define ... ) work again? Sure, now that we know what recursion is. Do you now know why Y works? Read this chapter just one more time and you will. 172 Chapter 9 What is (Y Y) Who knows, but it works very hard.

pages: 308 words: 85,880

How to Fix the Future: Staying Human in the Digital Age
by Andrew Keen
Published 1 Mar 2018

The self-policing strategy of the DeepMind coalition sounds similar to the goals of another idealistic Elon Musk start-up—OpenAI, a Silicon Valley–based nonprofit research company focused on the promotion of an open-source platform for artificial intelligence technology. Musk cofounded OpenAI with Sam Altman, the thirty-one-year-old CEO of Y Combinator, Silicon Valley’s most successful seed investment fund. Launched in 2015 with a billion dollars raised by Silicon Valley royalty including the multi billionaires Reid Hoffman and Peter Thiel, the Silicon Valley–based OpenAI is run by a former Google expert on machine learning and staffed with an all-star team of computer scientists cherry-picked from top Big Tech firms.

We need to level up humans, because our descendants will either conquer the galaxy or extinguish consciousness in the universe forever,” he says, presenting this superintelligence threat as if it’s the plotline of a Star Trek episode.5 I ask Price what these young entrepreneurs, fabulously wealthy and gifted technologists like Deep Mind’s Demis Hassabis, or Y Combinator’s Sam Altman, need to incorporate into their self-prescribed moral code. What, I wonder, should the new men of the twenty-first century be thinking about to ensure that his Australian granddaughter will actually get to see the dawn of the twenty-second century? From where are these “moral criteria” going to come?

In spite of being an investor in OpenAI, Reid Hoffman is skeptical of hubristic Silicon Valley companies that believe they can stand outside history and fix the entire world. Rather than being courageous, he suggests, that’s just myopic. Even juvenile. “It’s great they are being ambitious,” Hoffman told the New Yorker about Sam Altman and some of his Y Combinator projects. “But classically in the Valley, when people try to reinvent an area, it ends badly.”7 Hoffman’s ambivalence about the grandiose promises of OpenAI may be one reason why he is also one of the major investors in the “Fund for Artificial Intelligence and Society” launched by the nonprofit Knight Foundation in early 2017.

pages: 353 words: 104,146

European Founders at Work
by Pedro Gairifo Santos
Published 7 Nov 2011

Santos: You think that's changed? Sohoni: We hear that from the guys we've backed. Now that they've become role models in their own local geographies, it's amazing to see them inspire future entrepreneurs and I hear those stories over and over again. Santos: Going back a bit, when you started, Y Combinator and TechStars already existed, and in the blog post when as you mentioned, Saul was thinking of the idea, he actually looked into them. In the beginning, in those talks that you were defining the model for Seedcamp, did you think to follow, more or less, the same model? Why did you choose different values, a different model, the Mini Seedcamps?

So having a much more laid back and almost a university atmosphere for the events was really crucial, and, again, putting entrepreneurs at the center of gravity really was a big shift in culture here. So I think that was crucial to do as well. One of the biggest things we've done is we invest per company three times as much as any other, Y Combinator or TechStars, do. And we take roughly the same amount of equity that they do. That was the other thing. Companies here need a longer runway to raise follow-on funding. I think that's probably changed in the last four years, but at the time we started, they needed a longer runway and you couldn't just invest $18,000.

Santos: Now going a bit more into the details behind Seedcamp. Seedcamp is also a company, so it has to pay its own investments. If we look at it, you're taking the same equity, you're offering more money, and you're even touring with the teams that go through the program in the US. Is that competitive in the long run against programs like Y Combinator, TechStars, or other things in Europe, or is it still an unproven model? Sohoni: Granted, yes, it's more funding per business, but we're not in here to build very small businesses. We have a global ambition and it's building globally relevant and globally-sized businesses. And in that sense you're coming in quite early, right?

pages: 385 words: 101,761

Creative Intelligence: Harnessing the Power to Create, Connect, and Inspire
by Bruce Nussbaum
Published 5 Mar 2013

id=309165&page=1#.UEu57q60J8E. 121 “We both went to Montessori School”: ABC News, “A Fascinating Group.” 121 Montessori alums include: Peter Sims, “The Montessori Mafia,” Wall Street Journal, April 5, 2011, accessed September 13, 2012, http://blogs.wsj.com/ideas-market/2011/04/05/the-montessori-mafia/. 121 Other entrepreneurs with educational backgrounds: “Google Logo, Founders Spell Success: Montessori,” http://HispanicBusiness.com, August 31, 2012, accessed September 13, 2012, http://www.hispanicbusiness.com/2012/8/31/ google_logo_founders_spell_success_montessori.htm. 121 Paul Graham, the founder of Y Combinator: Randall Stross, The Launch Pad: Inside Y Combinator, Silicon Valley’s Most Exclusive School for Startups (New York: Portfolio, 2012); http://paulgra ham.com/bio.html. 121 Biz Stone, cofounder of Twitter: http://CMO.com, “Twitter Creator Biz Stone Chats with Adobe CMO Lewnes at Digital Summit 2012, http://m.cmo.com/leadership/ twitter-creator-biz-stone-chats-adobe-cmo-lewnes-digital-summit-2012, accessed September 13, 2012. 121 “Being playful, less structured”: Melissa Korn and Amir Efrati, “Master of ’Biz’ Returns to School,” Wall Street Journal, September 1, 2011, accessed September 13, 2012, http://online.wsj.com/article/SB1000142405311190 4009304576533010574207444.html?

Other entrepreneurs with educational backgrounds in art, design, and music where play is intrinsic to learning have founded a whole slew of new companies, including Kickstarter, Tumblr, YouTube, Flickr, Instagram, Vimeo, Android, and, of course, Apple. And the list goes on and on: Paul Graham, the founder of Y Combinator, one of the top incubators for new start-ups in Silicon Valley, studied painting at Rhode Island School of Design and the Accademia di Belle Arti in Florence, in addition to getting his PhD in computer science from Harvard. Biz Stone, cofounder of Twitter and Xanga, says he learned a valuable lesson studying graphic design.

They didn’t limit themselves to one platform, looking instead for natural partners in other industries who would “get” them. No matter your field, your pivot network is key to getting your idea into the world. One incubator that nurtures young entrepreneurs and helps them develop their ideas, Y Combinator, was founded on the premise that an entrepreneur’s original idea may not even matter when it comes to building a new company. The important thing is the circle of people who surround the founders, providing the scaling skills and capital necessary for pivoting. So how do you build your own pivot network?

pages: 425 words: 112,220

The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture
by Scott Belsky
Published 1 Oct 2018

“I spent three weeks”: Jennifer Wang, “How 5 Successful Entrepreneurs Bounced Back After Failure,” Entrepreneur, January 23, 2013, www.entrepreneur.com/article/225204. “I was blindsided”: Ibid. “It was painful”: Ibid. The Muse was accepted: Kathryn Minshew, “The Muse’s Successful Application to Y Combinator (W12),” The Muse, accessed March 22, 2018, www.themuse.com/advice/the-muses-successful-application-to-y-combinator-w12. “My heroes, in real life”: Wang, “How 5 Successful Entrepreneurs Bounced Back.” Anthropologie’s “Woman of Character”: “Women of Character: Kathryn Minshew,” Anthropologie, September 30, 2015, www.youtube.com/watch?v=M32tPGYzCXs.

The site drew more visitors in its first month than PYP did at its peak. “It was painful, but being forced to start over was a unique sort of gift, because having been through a lot together, the team comes out of it with the confidence that nothing is going to stop us,” Minshew told Entrepreneur. In November 2011, The Muse was accepted into the Y Combinator accelerator program. Today, The Muse is among the most trusted career platforms for millennials, listing jobs and corporate profiles from hundreds of companies like Goldman Sachs, Wells Fargo, Gap, HBO, Condé Nast, and Bloomberg. “My heroes, in real life, tend to be people who either broke through barriers or overcame tremendous obstacles—not only individually, but people who opened up pathways,” Minshew said in Anthropologie’s “Women of Character” feature.

Nothing disrupts resourcefulness more than a sudden infusion of resources. The common debate in the early-stage investor community is how much money is too much money at each stage of a business. This is because there are some serious hidden costs that come along with raising capital. Jessica Livingston, cofounder of Y Combinator, one of the world’s most well-known incubators and start-up investors, talked about the perils of raising too much money too quickly during a talk at one of their annual summits: I’ve seen many startups shift from doing more with less to doing less with more once they’ve raised funding. It’s easy to think money can buy your way out of problems.

pages: 353 words: 91,520

Most Likely to Succeed: Preparing Our Kids for the Innovation Era
by Tony Wagner and Ted Dintersmith
Published 17 Aug 2015

But today innovative programs, such as Paul Graham’s Y Combinator, are giving young entrepreneurs powerful learning experiences and a “brand” as powerful as an elite MBA. Y Combinator is every bit as selective as a top business school, but with admissions criteria focused more on a person’s ideas than his or her undergraduate GPA. A young entrepreneur going to business school will study topics, start a business (now mandatory at many programs, including Harvard Business School), develop a network of contacts, and . . . pay substantially more than $100,000 in tuition. At Y Combinator, the student will learn similar topics, start a serious business, develop a powerful network, and get funding for a start-up while paying no tuition.

At Y Combinator, the student will learn similar topics, start a serious business, develop a powerful network, and get funding for a start-up while paying no tuition. Not surprisingly, Y Combinator and other similar programs are becoming the new “MBA” in the Internet economy, with an emerging sense that spending more than $100,000 for a business credential in 2015 is a bit of a sucker’s play. The field of law provides another example of an apprenticeship-based “surrogate” advanced degree. In some states (California, Virginia, Vermont, and Washington), you can take the bar without attending a single law school class. Instead, aspiring lawyers learn through a multiyear apprenticeship at a law firm.

See employment World Is Flat, The (Friedman), 61 Wright, Frank Lloyd, 24 writing courses, 105–11 importance of, 103–04, 105 mechanics of writing taught in, 110, 111 practice needed in, 105, 110–11 standardized tests and, 106–10 20th-century model of, 102–03 “writers’ workshop” approach to teaching, 105 See also English language courses Xamarin, 62 Yale University, 173, 186, 187 Y Combinator, 243 Yen, Hope, 166 Yerkes, Robert, 206 YouTube, 192, 237, 245 Zhao, Yong, 54, 55–56 Zuckerberg, Mark, 187, 250 Zverev, Nikolai, 24 SCRIBNER An Imprint of Simon & Schuster, Inc. 1230 Avenue of the Americas New York, NY 10020 www.SimonandSchuster.com Copyright © 2015 by Tony Wagner and Ted Dintersmith All rights reserved, including the right to reproduce this book or portions thereof in any form whatsoever.

pages: 179 words: 43,441

The Fourth Industrial Revolution
by Klaus Schwab
Published 11 Jan 2016

http://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence-but-are-we-taking-9313474.html 61 Greg Brockman, Ilya Sutskever & the OpenAI team, “Introducing OpenAI”, 11 December 2015 https://openai.com/blog/introducing-openai/ 62 Steven Levy, “How Elon Musk and Y Combinator Plan to Stop Computers From Taking Over”, 11 December 2015 https://medium.com/backchannel/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over-17e0e27dd02a#.qjj55npcj 63 Sara Konrath, Edward O’Brien, and Courtney Hsing. “Changes in dispositional empathy in American college students over time: A meta-analysis.” Personality and Social Psychology Review (2010). 64 Quoted in: Simon Kuper, “Log out, switch off, join in”, FT Magazine, 2 October 2015. http://www.ft.com/intl/cms/s/0/fc76fce2-67b3-11e5-97d0-1456a776a4f5.html 65 Sherry Turkle, Reclaiming Conversation: The Power of Talk in a Digital Age, Penguin, 2015. 66 Nicholas Carr, The Shallows: How the Internet is changing the way we think, read and remember, Atlantic Books, 2010. 67 Pico Iyer, The Art of Stillness: Adventures in Going Nowhere, Simon and Schuster, 2014. 68 Quoted in: Elizabeth Segran, “The Ethical Quandaries You Should Think About the Next Time You Look at Your Phone”, Fast Company, 5 October 2015.

As theoretical physicist and author Stephen Hawking and fellow scientists Stuart Russell, Max Tegmark and Frank Wilczek wrote in the newspaper The Independent when considering the implications of artificial intelligence: “Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all…All of us should ask ourselves what we can do now to improve the chances of reaping the benefits and avoiding the risks”.60 One interesting development in this area is OpenAI, a non-profit AI research company announced in December 2015 with the goal to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return”.61 The initiative – chaired by Sam Altman, President of Y Combinator, and Elon Musk, CEO of Tesla Motors - has secured $1 billion in committed funding. This initiative underscores a key point made earlier – namely, that one of the biggest impacts of the fourth industrial revolution is the empowering potential catalyzed by a fusion of new technologies. Here, as Sam Altman stated, “the best way AI can develop is if it’s about individual empowerment and making humans better, and made freely available to everyone.”62 The human impact of some particular technologies such as the internet or smart phones is relatively well understood and widely debated among experts and academics.

Falter: Has the Human Game Begun to Play Itself Out?
by Bill McKibben
Published 15 Apr 2019

Amortized across the Earth’s entire population, Merkle estimates a “surprisingly competitive” price of $24 to $32 per person.12 Currently, at least a thousand people are waiting for their chance, and they include a large selection of Silicon Valley pioneers. This being the tech industry, though, a newer iteration of the idea is already available. Nectome is one of the handful of start-ups chosen to be part of Y Combinator, the most important of California’s tech incubators. (They’re the people who first championed Dropbox, Airbnb, and Reddit.) In fact, Y Combinator head Sam Altman has already plunked down his $10,000 for Nectome’s service, which involves embalming your brain when you’re near death so that it can later be digitized and encoded. “The idea is that someday in the future scientists will scan your bricked brain and turn it into a computer simulation,” writes Antonio Regalado in MIT Technology Review.13 In fact, this notion that we will one day be meshed with computers and thus live forever has gained currency perhaps because, while bizarre, it seems somehow less absurd than the idea of Ted Williams lumbering around again in the real world.

It’s the ultimate in what the business gurus happily call disruption, and it’s been a siren song for entrepreneurs with ambitions higher than the next Snapchat plug-in. Helgesen, for instance. Tall, with long lank hair, he could be the bassist in an indie band, but the Y Combinator T-shirt he’s wearing gives the game away. He didn’t actually do a stint at Silicon Valley’s most famous incubator (his wife did), but that’s his lineage, the same one that produced Airbnb and also the company that wants to embalm your brain so you can be digitally scanned and reimplanted in an android. The Y Combinator T-shirt reads, “Make Something People Want,” which pretty much defines cheap solar power. Africans are desperate for electricity. * * * “This is how the solar revolution happens,” Kim Schreiber, Off-Grid’s communications director, whispers to me.

Supreme Court Unity Biotechnology Urban, Tim USA Today Utah utilities Vanity Fair vapor pressure deficit Vassar, Michael Venezuela Venice Verity, William Vermont Vietnam Vinci, Leonardo da Virgin Galactic Virtue of Selfishness, The (Rand) Vodafone volcanoes voting rights Walker, Scott Wall Street Journal Walton family Washington Post water Watson, James Wealth of Nations (Smith) welfare West, Michael wet-bulb temperature wheat Whippman, Ruth White, Curtis Whitehead, Emily white supremacy wilderness, protected wildlife Wilkinson, Richard Williams, Jerry Williams, Ted wind power Wired Wisconsin Wohlforth, Charles Wojcicki, Anne Wojcicki, Stanley Wojcicki, Susan women’s rights Woods, Darren World Bank World Happiness Report World Meteorological Organization World Petroleum Congress (Beijing, 1997) World War II Worster, Donald Wozniak, Steve Y Combinator Yellowstone Yosemite Valley Youtube Yudkowsky, Eliezer Zhang, Feng Zinke, Ryan Zuckerberg, Mark ALSO BY BILL McKIBBEN Radio Free Vermont Oil and Honey The Global Warming Reader Eaarth American Earth The Bill McKibben Reader Fight Global Warming Now Deep Economy The Comforting Whirlwind Wandering Home Enough Long Distance Hundred Dollar Holiday Maybe One Hope, Human and Wild The Age of Missing Information The End of Nature ABOUT THE AUTHOR BILL MCKIBBEN is a founder of the environmental organization 350.org and was among the early advocates for action on global warming.

pages: 567 words: 122,311

Lean Analytics: Use Data to Build a Better Startup Faster
by Alistair Croll and Benjamin Yoskovitz
Published 1 Mar 2013

* * * [69] http://larsleckie.blogspot.ca/2008/03/magic-number-for-saas-companies.html [70] The Parse.ly team has written a detailed explanation of these changes at http://blog.parse.ly/post/16388310218/hello-publishers-meet-dash. [71] Mike is quick to point out that this is changing, with an increased emphasis on revenue generation. See http://go.bloomberg.com/tech-deals/2012-08-22-y-combinators-young-startups-tout-revenue-over-users/. [72] http://www.slideshare.net/sixteenventures/the-reality-of-freemium-in-saas Chapter 19. Stage Five: Scale You have a product that’s sticky. You’ve got virality that’s multiplying the effectiveness of your marketing efforts. And you have revenues coming in to fuel those user and customer acquisition efforts.

Startups, Paul says, go through three distinct growth phases: slow, where the organization is searching for a product and market to tackle; fast, where it has figured out how to make and sell it at scale; and slow again, as it becomes a big company and encounters internal constraints or market saturation, and tries to overcome Porter’s “hole in the middle.” At Paul’s startup accelerator, Y Combinator, teams track growth rate weekly because of the short timeframe. “A good growth rate during YC is 5–7% a week,” he says. “If you can hit 10% a week you’re doing exceptionally well. If you can only manage 1%, it’s a sign you haven’t yet figured out what you’re doing.” If the company is at the Revenue stage, then growth is measured in revenue; if it’s not charging money yet, growth is measured in active users.

When this happens, growth eventually falls off a cliff.”[79] He goes on to say, “Sustainable growth programs are built on a core understanding of the value of your solution in the minds of your most passionate customers.” As we saw in Chapter 5, Sean’s Startup Growth Pyramid illustrates that scaling your business comes only after you’ve found product/market fit and your unfair advantage. In other words: stickiness comes before virality, and virality comes before scale. Most Y Combinator startups (and most startups, for that matter) focus on growth before they hit product/market fit. In some cases this is a necessity, particularly if the value of the startup depends on a network effect—after all, Skype’s no good if nobody else is using it. But while rapid growth can accelerate the discovery of product/market fit, it can just as easily destroy the startup if the timing isn’t right.

pages: 524 words: 130,909

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power
by Max Chafkin
Published 14 Sep 2021

He had a PhD from the University of Melbourne and a law degree from Oxford, but to the Thiel Fellows he seemed to have no obvious experience that would qualify him as a mentor to startup founders—except, of course, that he was a good-looking man Thiel seemed to like. The fellowship was often compared to Y Combinator, a startup incubator that invested $100,000 in very early-stage companies and was famously hands off. But Y Combinator forced companies to show up every two weeks for “office hours,” which created a sense of discipline for the founders and helped them make friends and contacts. The Thiel Fellowship offered neither of these things. “I was like, ‘Here I am in Silicon Valley,’ ” one said.

It was one of his favorites and had recommended that elites seek out foreign citizenship and free themselves from the undue burdens of nationhood. The work’s influence had also shown up in a talk by Thiel’s friend Balaji Srinivasan, a libertarian venture capitalist who’d invested alongside him in Curtis Yarvin’s company. At a Y Combinator event for young entrepreneurs in 2013, Srinivasan had drawn a distinction between democratic engagement—“voice,” he called it—and secession or emigration, or “exit.” He’d advocated for his audience to consider the latter, freeing themselves from the bonds of traditional citizenship by starting companies that could successfully thwart regulations or even by absenting themselves from their country entirely.

Lewis noted that he hoped to be proven wrong but added that, “from all I’ve seen thus far, a world in which President Trump makes any sense at all is not the world I want my grandchildren to inherit.” He later edited the post to remove the references critical of Thiel, as well as the line about his grandchildren. In 2015, Thiel had accepted a part-time position at Y Combinator, the early-stage incubator that was sometimes compared to the Thiel Fellowship. YC—as it was known—had since expanded to include venture capital investing and had displaced Founders Fund as the hot firm of the moment in Silicon Valley. The firm was, like Founders Fund, committed to entrepreneurs and entrepreneurial control—and fanatical about ambitious companies.

pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

Algorithms: Identify data streams that can be automated and help with product development. Implement cloud-based and open source machine and deep learning to increase insights. Leveraged Assets: Do NOT acquire assets. Use cloud computing, TechShop for product development. Use incubators like Y Combinator and Techstars for office, funding, mentoring and peer input. Starbucks as office. Engagement: Design product with engagement in mind. Gather all user interactions. Gamify where possible. Create a digital reputational system of users and suppliers to build trust and community. Use incentive prizes to engage crowd and create buzz.

After that extraordinary initial success, the open source movement settled into a stable, stratified environment over much the last decade, with the community producing little in the way of new innovation. Everything changed in 2008, however, when Chris Wanstrath, P.J. Hyett and Tom Preston-Werner (all out of Paul Graham’s Y Combinator entrepreneurial incubator program) founded a company called GitHub. An open source coding and collaboration tool and platform, GitHub has utterly transformed the open source environment. It is a social network for programmers in which people and their collaborations are central, rather than just the code itself.

Recommendation: Start an internal accelerating technologies lab, leveraging core competencies and aiming for moonshot innovations at a budget price. Partner with Accelerators, Incubators and Hackerspaces The last decade has seen an explosion of new business incubators and accelerators, ranging from Y Combinator (which created disruptive consumer Internet startups Dropbox and Uber) to the membership-based TechShop. Looking at large companies from an ExO perspective, let’s consider four examples: TechShop We first examined TechShop’s fascinating model in Chapter Three. Here we’ll explore the chain’s impact in further detail, focusing on how TechShop is helping large organizations, including Ford and Lowe’s, two companies for which it has built individual facilities.

pages: 313 words: 91,098

The Knowledge Illusion
by Steven Sloman
Published 10 Feb 2017

Because we assign intelligence to individuals, we give the heroes all the credit by attributing their ideas to them alone. But that’s not how it works, according to some of the venture capitalists who fund new start-ups. As one of them, Avin Rabheru, puts it: “Venture capitalists back teams, not ideas.” Consider the view of Y Combinator, one of the leading incubators of early-stage technology start-ups. Their strategy is based on the belief that successful start-ups rarely, if ever, capitalize on their initial idea. Ideas transform. So it’s not ideas that matter the most. Far more important than the quality of an idea is the quality of the team.

Far more important than the quality of an idea is the quality of the team. A good team can make a start-up successful because it can discover a good idea by learning how a market works and then do the work to implement the idea. A good team will divide and distribute the labor in a way that takes advantage of individual skills. Y Combinator avoids investing in start-ups that have a single founder not only because a single founder means there’s no team to divide up the labor. They avoid single founders for a reason that isn’t obvious and yet is fundamental to teamwork: Single founders lack the esprit de corps that prevents individuals from letting their friends down.

See also artificial intelligence (AI); Internet ability to self-update and solve its own problems, 135–36 adaptation of the body to new tools, 134 automation paradox, 141–45 block chain, 150 Bodmer Report, 156–59 crowdsourcing, 146–49 effects on information and commerce, 131–32 genetic engineering, 154–55, 165–67 GPS (Global Positioning System) software, 139–40, 143 as a living organism, 134–35 Luddites’ protests against, 153–54 machines’ lack of collaborative ability, 139–42 predictions about the future, 150–52 relationship between brain size and technological change, 133–34 superintelligence, 132–33, 146 venture capital funding, 211–12 Y Combinator funding, 211–12 testing intelligence. See also intelligence Binet, Alfred, 203 c factor, 209–11 collective intelligence hypothesis, 209–10 computer checkers example, 210–11 correlation of performance results, 204, 209–10 factor analysis, 204–05 g score, 204–07 Simon, Theodore, 203 Spearman, Charles, 204 of a team, 209–14 Woolley, Anita, 209–10 Thaler, Richard, 247, 250–51 thermostat mechanics example of causal reasoning, 72–73 thinking.

pages: 307 words: 88,180

AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee
Published 14 Sep 2018

But following Nixon’s unsuccessful push, discussion of a UBI or GMI largely dropped out of public discourse. That is, until Silicon Valley got excited about it. Recently, the idea has captured the imagination of the Silicon Valley elite, with giants of the industry like the prestigious Silicon Valley startup accelerator Y Combinator president Sam Altman and Facebook cofounder Chris Hughes sponsoring research and funding basic income pilot programs. Whereas GMI was initially crafted as a cure for poverty in normal economic times, Silicon Valley’s surging interest in the programs sees them as solutions for widespread technological unemployment due to AI.

To these proponents, massive redistribution schemes are potentially all that stand between an AI-driven economy and widespread joblessness and destitution. Job retraining and clever scheduling are hopeless in the face of widespread automation, they argue. Only a guaranteed income will let us avert disaster during the jobs crisis that looms ahead. How exactly a UBI would be implemented remains to be seen. A research organization associated with Y Combinator is currently running one pilot program in Oakland, California, that gives a thousand families a stipend of a thousand dollars each month for three to five years. The research group will track the well-being and activities of those families through regular questionnaires, comparing them with a control group that receives just fifty dollars per month.

A BLUEPRINT FOR HUMAN COEXISTENCE WITH AI move to a four-day work week: Seth Fiegerman, “Google Founders Talk About Ending the 40-Hour Work Week,” Mashable, July 7, 2014, https://mashable.com/2014/07/07/google-founders-interview-khosla/#tXe9XU.mr5qU. creative approaches to work-sharing: Steven Greenhouse, “Work-Sharing May Help Companies Avoid Layoffs,” New York Times, June 15, 2009, http://www.nytimes.com/2009/06/16/business/economy/16workshare.html. Y Combinator president Sam Altman: Kathleen Pender, “Oakland Group Plans to Launch Nation’s Biggest Basic-Income Research Project,” San Francisco Chronicle, September 21, 2017, https://www.sfchronicle.com/business/networth/article/Oakland-group-plans-to-launch-nation-s-biggest-12219073.php. Facebook cofounder Chris Hughes: The Economic Security Project, https://economicsecurityproject.org/.

pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

“Why would you want to compete with some existing company, be incrementally more efficient, and make the world only a slightly better place?” In 2014, they took a $250,000 investment and moved out to Y-Combinator in Mountain View, California, for a stint and demo. Started by the entrepreneur Paul Graham, YC was a kind of reverse Hotel California for nerds—hard to get into and easy to leave. Thiel told the Y-Combinator in 2015 to seek “apparently bad ideas” that “were actually good,” like Dropbox (cloud-based storage) and Airbnb (cloud-based lodgings). Both began nebulously at YC and now have a total market cap of many billions of dollars.

Low power is imperative in an age of mobility serving the mobility of human minds and bodies. Balaban, the oldest member of the trio, was still almost as precocious as Russell and Sohmers. Like Buterin, he was fluent in Mandarin and software. But he missed out on a possible Thiel Fellowship when he went to Beijing instead to start a “Y-Combinator” incubator fund. Befriending Danielle and Mike, then still with the Thiel Fellowship, Balaban became the adult supervisor of the younger fellows while working on a wearable, hands-free camera embedded in a baseball hat. The three young men often talked technology and libertarian philosophy late into the Atherton nights and worked on their companies nearly all other hours.

But a crucible of deep learning preceded this triumphal proposal scene. Fluent in Mandarin, Balaban went to Beijing in 2010 as a college senior, taking a semester off from studying computer science and economics at the University of Michigan. In China, he helped found “a clone,” as he describes it, of the Y-Combinator startup accelerator. He named it for Yuan Fen, the Chinese concept of the fate that brings people together. He had the educational experience of eventually watching the venture fizzle because of conflicts among the founders. Returning to Michigan, he took his degree and headed for Silicon Valley—after Beijing, “the real deal.”

pages: 324 words: 89,875

Modern Monopolies: What It Takes to Dominate the 21st Century Economy
by Alex Moazed and Nicholas L. Johnson
Published 30 May 2016

Even though the software is expensive, “dentists realize significant cost savings after deploying the system. The company, Dentasoft, grows quickly into a $10mm annual revenue business, goes public, and trades up to a billion dollar valuation.”2 But the story doesn’t end there. Next, two young entrepreneurs go to startup accelerator Y Combinator and create a low-cost version of Dentasoft. Their product, Dent.io, includes more modern software and a mobile app that allow dentists to manage their offices remotely. Dent.io gets to market quickly at a price point of $5,000 per year. Many dentists switch to the newer, cheaper entrant. Dentasoft misses its quarterly forecast, and its stock crashes.

Quora’s early employees and beta testers continued this trend until the amount of content and activity on the platform became self-sustaining. For Reddit, founders Alexis Ohanian and Steve Huffman used basically the same strategy. The two founded Reddit back in June 2005. It was one of the first startups out of Y Combinator, which today is widely recognized as the leading startup accelerator. The founders resorted to some old-school marketing to get their first users, printing out stickers and putting them up everywhere they traveled. But without any content on the site, no one was sticking around.10 So Ohanian and Huffman started posting all the content themselves.

Alyson Shontell, “Here’s What the First Hires at Apple, Google and Other Top Tech Companies Are Doing Now,” Entrepreneur, November 1, 2014, http://www.entrepreneur.com/article/239115. 21. Jackson, The PayPal Wars. 22. Quoted in Liz Gannes, “Airbnb Now Wants to Check Your Government ID,” All Things, April 30, 2013, http://allthingsd.com/20130430/airbnb-now-wants-to-check-your-government-id/. 23. Katie Roof, “Checkr Is Raising $30M+ For Its Background Checking API, Y Combinator Investing,” TechCrunch, October 13, 2015, http://techcrunch.com/2015/10/13/checkr-series-b/#.e9v15e:kySL. 24. To be fair, this problem isn’t unique to platforms. It’s a hiring risk for any business. The risk just becomes more obvious when you’re dealing with a huge decentralized network rather than a smaller, centralized linear business. 25.

pages: 848 words: 227,015

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

On one side are the safe and silent places, the home, the well-regulated role in business, industry, and the professions; on the other are all those activities that generate expression, requiring the individual to lay himself on the line and place himself in jeopardy. —Erving Goffman Sam Altman always knew where the action was. “You know how a dog will run around a room sniffing anything interesting? Sam does that with technology, just as constantly and automatically,” said Paul Graham, the polymathic English programmer who cofounded Y Combinator. Y Combinator is the world’s most prestigious startup accelerator, what you’d get if you took the rough average between Andreessen Horowitz, a summer camp for gifted-and-talented math nerds, and Shark Tank. The process is intrinsically a long shot. Would-be founders often apply to YC with little more than the seed of an idea.

GO TO NOTE REFERENCE IN TEXT most interesting founders: Paul Graham, “Five Founders,” April 2009, paulgraham.com/5founders.html. GO TO NOTE REFERENCE IN TEXT later handpicked Altman: Paul Graham, “Sam Altman for President,” Y Combinator, February 21, 2014, ycombinator.com/blog/sam-altman-for-president. GO TO NOTE REFERENCE IN TEXT he was fired: Elizabeth Dwoskin and Nitasha Tiku, “Altman’s Polarizing Past Hints at OpenAI Board’s Reason for Firing Him,” The Washington Post, November 22, 2023, washingtonpost.com/technology/2023/11/22/sam-altman-fired-y-combinator-paul-graham. GO TO NOTE REFERENCE IN TEXT “Technology happens because”: Cade Metz, “The ChatGPT King Isn’t Worried, but He Knows You Might Be,” The New York Times, March 31, 2023, sec.

GO TO NOTE REFERENCE IN TEXT Chapter ∞: Termination “Looking for where the action is”: Erving Goffman, Interaction Ritual: Essays on Face-to-Face Behavior (New York: Pantheon Books, 1982), 268. GO TO NOTE REFERENCE IN TEXT said Paul Graham: My interview with Graham was conducted by email. GO TO NOTE REFERENCE IN TEXT of YC companies:: Y Combinator, ycombinator.com. GO TO NOTE REFERENCE IN TEXT frenetic minutes to pitch: Antonio García Martínez, Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley, Kindle ed. (New York: Harper, 2016), 104–5. GO TO NOTE REFERENCE IN TEXT his company Loopt: Liz Games, “Loopt’s Sam Altman on Why He Sold to Green Dot for $43.4M,” AllThingsD, March 9, 2012, allthingsd.com/20120309/green-dot-buys-location-app-loopt-for-43-4m.

pages: 232 words: 63,846

Traction: How Any Startup Can Achieve Explosive Customer Growth
by Gabriel Weinberg and Justin Mares
Published 5 Oct 2015

So if you’re in enterprise software, [initial traction] may be two or three early customers who are paying a bit; if you’re in consumer software the bar might be as high as hundreds of thousands of users. You can always get more traction. The whole point of a startup is to grow rapidly. Getting traction means moving your growth curve up and to the right as best you can. Paul Graham, founder of startup accelerator Y Combinator, puts it like this: A startup is a company designed to grow fast. Being newly founded does not in itself make a company a startup. Nor is it necessary for a startup to work on technology, or take venture funding, or have some sort of “exit.” The only essential thing is growth. Everything else we associate with startups follows from growth.

Without you this book would not be possible: Jimmy Wales, Cofounder of Wikipedia Alexis Ohanian, Cofounder of reddit Eric Ries, Author of The Lean Startup Rand Fishkin, Founder of Moz Noah Kagan, Founder of AppSumo Patrick McKenzie, CEO of Bingo Card Creator Sam Yagan, Cofounder of OkCupid Andrew Chen, Investor in 500 Startups Dharmesh Shah, Founder of HubSpot Justin Kan, Founder of Justin.tv Mark Cramer, CEO of Surf Canyon Colin Nederkoorn, CEO of Customer.io Jason Cohen, Founder of WP Engine Chris Fralic, Partner at First Round Capital Paul English, CEO of Kayak Rob Walling, Founder of MicroConf Brian Riley, Cofounder of SureStop Steve Welch, Cofounder of DreamIt Jason Kincaid, Blogger at TechCrunch Nikhil Sethi, Founder of Adaptly Rick Perreault, CEO of Unbounce Alex Pachikov, Evernote Founding Team David Skok, Partner at Matrix Ashish Kundra, CEO of myZamana David Hauser, Founder of Grasshopper Matt Monahan, CEO of Inflection Jeff Atwood, Cofounder of Discourse Dan Martell, CEO of Clarity Chris McCann, Founder of Startup Digest Ryan Holiday, Exec at American Apparel Todd Vollmer, Enterprise Sales Veteran Sandi MacPherson, Founder of Quibb Andrew Warner, Founder of Mixergy Sean Murphy, Founder of SKMurphy Satish Dharmaraj, Partner at Redpoint Ventures Garry Tan, Partner at Y Combinator Steve Barsh, CEO of PackLate Michael Bodekaer, Cofounder of Smartlaunch Each of you played a critical role in shaping this book and making it a useful resource. We apologize if we left anyone off this list. We’d also like to thank our early readers for their helpful comments and feedback, as well as Eric Nelson, Michael Zakhar, and Brian Spadora for their editing help.

See also specific channels overview, 2–7 traction development, 9–12, 17 traction goals, 12–15, 18, 35–36, 139 defining your Critical Path, 37–38 traction subgoals, 36 traction testing, 21–22, 27–34 inner ring tests, 22–23, 28–31 middle ring tests, 21, 27–28, 209–13 online tools, 32–33 targets, 33–34 traction thinking, 2, 8–18 50 percent rule, 8–12 fund-raising, 15–16 moving the needle, 12–15 targets, 17–18 when to “pivot,” 16–17 trade show booths, 179–81, 182 trade shows, 7, 22, 175–82, 212–13 strategy, 176–77 tactics, 177–81 targets, 181–82 Trainyard, 168, 169 transit advertising, 88 Tribal Fusion, 75 TripAdvisor, 98 Trust Me, I’m Lying (Holiday), 3, 49, 50 Tumblr, 80 TV advertising, 88–90 Twilio, 183 Twitter, 170–71, 184–85 email marketing, 112–13 reaching out to reporters online, 53–54 social ads, 4, 31, 78, 79 targeting blogs, 46 Uber, 57, 94, 120, 121 Unbounce, 5, 29, 102–7, 110 unconventional PR, 3–4, 57–64, 210 case study of David Hauser, 62–63 customer appreciation, 58, 59–61 publicity stunts, 57–59 targets, 63–64 Upfront Ventures, 154, 178–79 Upromise, 160–61 URL Builder, 69 UserTesting.com, 125 UserVoice, 120 Vero, 112 vertical search sites, 161 Vine, 170 viral coefficient, 121–23, 126, 211 viral cycle time, 121, 123, 128 viral loops, 119–21, 123–24, 126, 127–28 viral marketing, xi, 5, 23, 118–25, 211 strategy, 119–23 tactics, 123–26 targets, 127–28 viral pockets, 126, 128 Virgin Galactic, 57–58 Visual Website Optimizer, 29, 70 Vollmer, Todd, 151 Volpe, Mike, 100 Walling, Rob, 7, 186–87, 188–89 Wall Street, 52 Walmart, 82, 160 Warby Parker, 79 Washington Post, 48, 142, 144–45 Weebly, 120 Wendy’s, 89 WePay, 58–59 WhatsApp, 120 widgets, 99, 129, 133 Wikipedia, 7, 98, 199, 201 Williams, Evan, 184–85 Wilson, Fred, 106 Winfrey, Oprah, 50 word of mouth, 120, 127 WordPress, 114, 131 WP Engine, 4, 113–14, 131, 175 writer’s block, 105 Yagan, Sam, 5, 103, 104–5 Yahoo!, 137–38 Y Combinator, 2 yellow pages, ads in, 86 Yelp, 98, 184, 188, 198, 201, 202 YouTube, 4, 44, 46, 59, 81, 120–21, 170 Zappos, 61, 159, 160 Zuckerberg, Mark, 191 Zynga, 31 Looking for more? Visit Penguin.com for more about this author and a complete list of their books. Discover your next great read!

pages: 359 words: 96,019

How to Turn Down a Billion Dollars: The Snapchat Story
by Billy Gallagher
Published 13 Feb 2018

With Snapchat, Kan distills an entire day down to two to three minutes of the most interesting ten-second photos and videos. Kan leaves his messages open for his eleven thousand followers and typically gets ten messages an hour.2 In May 2016, Kan worked as a partner at the prestigious startup incubator Y Combinator; he let his followers apply to take over his Snapchat account for an hour and pitch their startup for funding from Y Combinator. Eventually, Kan and Y Combinator funded three startups from over four hundred applicants. * * * Venture capital money isn’t just headed to companies pitching on Snapchat. Investors are funding Snapchat-content companies, too. On an unusually windy afternoon in March 2016, I grabbed a coffee from Groundwork Coffee Co. in Venice, a couple blocks down the boardwalk from Snapchat’s main headquarters.

Miami (Michael Salzhauer) Thiel, Peter third-party content (Snapchat Discover) Thompson, Nicholas Thorning-Schmidt, Helle TigerText (app) Tinder Trainor, Meghan Trump, Donald Turley, Ben Turner, Elizabeth Turner, Sarah Twitter demographics of users innovation and Snapchat account at Snapchat compared with txtWeb Uber Valleywag (Gawker blog) Van Natta, Owen Vanity Fair Venice, California Venmo Vergence Labs Vine (app) virtual private network (VPN) Viterbi, Andrew VMWare Vollero, Drew Warner Music Group WeChat (app) Weiner, Anthony Wendell, Peter WhatsApp Whisper (app) White, Emily Wiley, Marcus Wilson, Ryan (ThankYouX) Wolf, Michelle Y Combinator Yahoo Yelp YesJulz (Julieanna Goddard) Yik Yak (app) YouTube Zedd Zero to One (Masters and Thiel) Zuckerberg, Mark ABOUT THE AUTHOR BILLY GALLAGHER is an MBA candidate at Stanford’s Graduate School of Business. Previously, he was a member of the investment team at Khosla Ventures and a writer at TechCrunch, which he joined as a Stanford sophomore, writing a profile of a popular startup on campus: Snapchat.

pages: 274 words: 75,846

The Filter Bubble: What the Internet Is Hiding From You
by Eli Pariser
Published 11 May 2011

And the pressure of the venture capitalists breathing down your neck to “monetize” doesn’t always offer much space for rumination on social responsibility. The $50 Billion Sand Castle Once a year, the Y Combinator start-up incubator hosts a daylong conference called Startup School, where successful tech entrepreneurs pass wisdom on to the aspiring audience of bright-eyed Y Combinator investees. The agenda typically includes many of the top CEOs in Silicon Valley, and in 2010, Mark Zuckerberg was at the top of the list. Zuckerberg was in an affable mood, dressed in a black T-shirt and jeans and enjoying what was clearly a friendly crowd.

Walker social capital social graph Social Graph Symposium Social Network, The Solove, Daniel solution horizon Startup School Steitz, Mark stereotyping Stewart, Neal Stryker, Charlie Sullivan, Danny Sunstein, Cass systematization Taleb, Nassim Nicholas Tapestry TargusInfo Taylor, Bret technodeterminism technology television advertising on mean world syndrome and Tetlock, Philip Thiel, Peter This American Life Thompson, Clive Time Tocqueville, Alexis de Torvalds, Linus town hall meetings traffic transparency Trotsky, Leon Turner, Fred Twitter Facebook compared with Últimas Noticias Unabomber uncanny valley Upshot Vaidhyanathan, Siva video games Wales, Jimmy Wall Street Journal Walmart Washington Post Web site morphing Westen, Drew Where Good Ideas Come From (Johnson) Whole Earth Catalog WikiLeaks Wikipedia Winer, Dave Winner, Langdon Winograd, Terry Wired Wiseman, Richard Woolworth, Andy Wright, David Wu, Tim Yahoo News Upshot Y Combinator Yeager, Sam Yelp You Tube LeanBack Zittrain, Jonathan Zuckerberg, Mark Table of Contents Title Page Copyright Page Dedication Introduction Chapter 1 - The Race for Relevance Chapter 2 - The User Is the Content Chapter 3 - The Adderall Society Chapter 4 - The You Loop Chapter 5 - The Public Is Irrelevant Chapter 6 - Hello, World!

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The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace
Published 17 Jul 2016

IBM Watson’s best-known work today is in the medical sector, but it is also carrying out large-scale projects in food safety with Mars, and in personality profiling for recruitment firms and dating apps.[xcviii] In December 2015, Elon Musk and Sam Altman, president of the technology incubator Y Combinator announced the formation of a new company called Open AI. They had recruited a clutch of the top machine learning professionals despite the efforts of Google and Facebook to hang onto them with eye-watering financial offers. There is some uncertainty about whether other companies controlled by Musk and Altman (like Tesla and Solar City) will have privileged access to technologies developed at Open AI, but the thrust of the company is to make advanced AI techniques more widely available in the hope that will de-risk them.

We have to go to Silicon Valley to find an experiment specifically designed to explore the impact of UBI in the context of a jobless future when machine intelligence has automated most of what we currently do for a living. Just such an experiment was announced in January 2016 by Sam Altman, president of the seed capital firm Y Combinator, which gave a start in life to Reddit, AirBnB and DropBox. Altman's task is not trivial: he will have to figure out a way to quantify the satisfaction his guinea pigs derive from their UBI, and whether they are doing anything useful with their time.[cccv] Socialism? With all these experiments bubbling up, the concept of UBI has become a favourite media topic, but it is controversial.

utm_content=buffer71a7e&utm_medium=social&utm_source=plus.google.com&utm_campaign=buffer [ccciii] http://www.fastcoexist.com/3052595/how-finlands-exciting-basic-income-experiment-will-work-and-what-we-can-learn-from-it [ccciv] http://www.latimes.com/world/europe/la-fg-germany-basic-income-20151227-story.html [cccv] http://www.vox.com/2016/1/28/10860830/y-combinator-basic-income [cccvi] https://en.wikipedia.org/wiki/Sodomy_laws_in_the_United_States#References [cccvii] http://blogs.wsj.com/washwire/2015/03/09/support-for-gay-marriage-hits-all-time-high-wsjnbc-news-poll/ [cccviii] http://www.huffingtonpost.com/2009/05/06/majority-of-americans-wan_n_198196.html [cccix] http://blogs.seattletimes.com/today/2013/08/washingtons-pot-law-wont-get-federal-challenge/ [cccx] http://www.bbc.co.uk/news/magazine-35525566 [cccxi] https://medium.com/basic-income/wouldnt-unconditional-basic-income-just-cause-massive-inflation-fe71d69f15e7#.3yezsngej [cccxii] http://streamhistory.com/die-rich-die-disgraced-andrew-carnegies-philosophy-of-wealth/ [cccxiii] http://www.forbes.com/sites/greatspeculations/2012/12/05/how-i-know-higher-taxes-would-be-good-for-the-economy/#5b0c080b3ec1 [cccxiv] http://taxfoundation.org/article/what-evidence-taxes-and-growth [cccxv] https://en.wikipedia.org/wiki/Laffer_curve [cccxvi] http://www.bbc.co.uk/news/uk-politics-26875420 [cccxvii] A minor character in Shakespeare’s Henry VI called Dick the Butcher has the memorable line, “First thing we do, let’s kill all the lawyers.”

pages: 242 words: 73,728

Give People Money
by Annie Lowrey
Published 10 Jul 2018

There are now “basic income create-a-thons,” for programmers to get together, talk UBI, and hack poverty. Cryptocurrency enthusiasts are looking into a Bitcoin-backed basic-income program. A number of young millionaire tech founders are funding a basic-income pilot among the world’s poorest in Kenya. The start-up accelerator Y Combinator is sending no-strings-attached cash to families in a few states as part of a research project. And Chris Hughes, a founder of Facebook, has plowed $10 million into an initiative to explore UBI and other related policies, something he is calling the Economic Security Project. “The community is evolving as we speak from a small group of people who say, This is it, to a large group of people who say, Hey, there may be something here,” he told me.

“When people join start-ups or work in tech, there’s an aspirational nature to it. But very few CEOs are happy with the idea that their work is going to cause a lot of stress and harm.” Yet the boosterism also does seem to be ignited by a real concern that we are in the midst of a profound economic and technological revolution. Sam Altman, the president of Y Combinator, recently spoke at a poverty summit cohosted by Stanford, the White House, and the Chan Zuckerberg Initiative, the Facebook billionaire’s charitable institution. “There have been these moments where we have had these major technology revolutions—the Agricultural Revolution, the Industrial Revolution, for example—that have really changed the world in a big way,” he said.

From technology to Trump, it is a time of greater uncertainty and change,” Kathleen Wynne, Ontario’s premier, said, announcing the pilot. “Our goal is clear. We want to find out whether a basic income makes a positive difference in people’s lives. Whether this new approach gives them the ability to begin to achieve their potential.” Here in the United States, Y Combinator is expanding its basic-income pilot out of Oakland. The group plans to select three thousand people, dividing them into one group that will receive $1,000 a month for up to five years and a second that will receive $50 a month. “Sometimes you hear that if we don’t see huge life transformations that it would be worthless, that kind of thing,” Elizabeth Rhodes, who is leading the study, told me.

pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal , Joshua Gans and Avi Goldfarb
Published 16 Apr 2018

Impatient about the pace at which government responds to technological advances, industry leaders have offered policy suggestions and, in some cases, have acted. Bill Gates advocated for a tax on robots that replace human labor. Sidestepping what would normally be government’s purview, the high-profile startup accelerator Y Combinator is running experiments on providing a basic income for everyone in society.2 Elon Musk organized a group of entrepreneurs and industry leaders to finance Open AI with $1 billion to ensure that no single private-sector company could monopolize the field. Such proposals and actions highlight the complexity of these social issues.

James Vincent, “Elon Musk Says We Need to Regulate AI Before It Becomes a Danger to Humanity,” The Verge, July 17, 2017, https://www.theverge.com/2017/7/17/15980954/elon-musk-ai-regulation-existential-threat. 2. Chris Weller, “One of the Biggest VCs in Silicon Valley Is Launching an Experiment That Will Give 3000 People Free Money Until 2022,” Business Insider, September 21, 2017, http://www.businessinsider.com/y-combinator-basic-income-test-2017-9. 3. Stephen Hawking, “This Is the Most Dangerous Time for Our Planet,” The Guardian, December 1, 2016, https://www.theguardian.com/commentisfree/2016/dec/01/stephen-hawking-dangerous-time-planet-inequality. 4. “The Onrushing Wave,” The Economist, January 18, 2014, https://www.economist.com/news/briefing/21594264-previous-technological-innovation-has-always-delivered-more-long-run-employment-not-less. 5.

See autonomous vehicles sensors, 15, 44–45, 105 Shevchenko, Alex, 96 signal vs. noise, in data, 48 Simon, Herbert, 107 simulations, 187–188 skills, loss of, 192–193 smartphones, 129–130, 155 Smith, Adam, 54, 65 The Snows of Kilimanjaro (Hemingway), 25–26 society, 3, 19, 209–224 control by big companies and, 215–217 country advantages and, 217–221 inequality and, 212–214 job loss and, 210–212 Solow, Robert, 123 Space Shuttle Challenger disaster, 143 sports, 117 camera automation and, 114–115 sabermetrics in, 56, 161–162 spreadsheets, 141–142, 163, 164 Standard & Poor’s, 36–37 statistics and statistical thinking, 13, 32–37 economic thinking vs., 49–50 human weaknesses in, 54–58 stereotypes, 19 Stern, Scott, 169–170, 218–219 Stigler, George, 105 strategy, 2, 18–19 AI-first, 179–180 AI’s impact on, 153–166 boundary shifting in, 157–158 business transformation and, 167–178 capital and, 170–171 cheap AI and, 15–17 data and, 174–176 economics of, 165 hybrid corn adoption and, 158–160 judgment and, 161–162 labor and, 171–174 learning, 179–194 organizational structure and, 161–162 value capture and, 162–165 strokes, predicting, 44–46, 47–49 Sullenberger, Chesley “Sully,” 184 supervised learning, 183 Sweeney, Latanya, 195, 196 Tadelis, Steve, 199 Taleb, Nassim Nicholas, 60–61 The Taming of Chance (Hacking), 40 Tanner, Adam, 195 task analysis, 74–75, 125–131 AI canvas and, 134–139 job redesign and, 142–145 Tay chatbot, 204–205 technical support, 90–91 Tencent Holdings, 164, 217, 218 Tesla, 8 Autopilot legal terms, 116 navigation apps and, 89 training data at, 186–187 upgrades at, 188 Tesla Motor Club, 111–112 Thinking, Fast and Slow (Kahneman), 209–210 Tinder, 189 tolerance for error, 184–186 tools, AI, 18 AI canvas and, 134–138 for deconstructing work flows, 123–131 impact of on work flows, 126–129 job redesign and, 141–151 usefulness of, 158–160 topological data analysis, 13 trade-offs, 3, 4 in AI-first strategy, 181–182 with data, 174–176 between data amounts and costs, 44 between risks and benefits, 205 satisficing and, 107–109 simulations and, 187–188 strategy and, 156 training data for, 43, 45–47 data risks, 202–204 in decision making, 74–76, 134–138 by humans, 96–97 in-house and on-the-job, 185 in medical imaging, 147 in modeling skills, 101 translation, language, 25–27, 107–108 trolley problem, 116 truck drivers, 149–150 Tucker, Catherine, 196 Tunstall-Pedoe, William, 2 Turing, Alan, 13 Turing test, 39 Tversky, Amos, 55 Twitter, Tay chatbot on, 204–205 Uber, 88–89, 164–165, 190 uncertainty, 3, 103–110 airline industry and weather, 168–169, 170 airport lounges and, 105–106 business boundaries and, 168–170 contracts in dealing with, 170–171 in e-commerce delivery times, 157–158 reducing, strategy and, 156–157 strategy and, 165 unknown knowns, 59, 61–65, 99 unknown unknowns, 59, 60–61 US Bureau of Labor Statistics, 171 US Census Bureau, 14 US Department of Defense, 14, 116 US Department of Transportation, 112, 185 Validere, 3 value, capturing, 162–165 variables, 45 omitted, 62 Varian, Hal, 43 variance, 34–36 fulfillment industry and, 144–145 taming complexity and, 103–110 Vicarious, 223 video games, 183 Vinge, Vernor, 221 VisiCalc, 141–142, 163, 164 Wald, Abraham, 101 Wanamaker, John, 174–175 warehouses, robots in, 105 Watson, 146 Waymo, 95 Waze, 89–90, 106, 191 WeChat, 164 Wells Fargo, 173 Windows 95, 9–10 The Wizard of Oz, 24 work flows AI tools’ impact on, 126–129 decision making and, 133–140 deconstructing, 123–131 iPhone keyboard design and, 129–130 job redesign and, 142–145 task analysis, 125–131 World War II bombing raids, 100–102 X.ai, 97 Xu Heyi, 164 Yahoo, 216 Y Combinator, 210 Yeomans, Mike, 117 YouTube, 176 ZipRecruiter, 93–94, 100 About the Authors AJAY AGRAWAL is professor of strategic management and Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management and the founder of the Creative Destruction Lab. He is also a research associate at the National Bureau of Economic Research in Cambridge, Massachusetts, and cofounder of The Next 36 and Next AI entrepreneurship programs.

Battling Eight Giants: Basic Income Now
by Guy Standing
Published 19 Mar 2020

Results will not emerge until late 2019. One early conclusion, however, is that scope for local variants of basic income pilots should be allowed and incorporated into any proposed British pilot programme, to broaden the evidence base. (10) California One much-reported pilot has been hatched in California, funded by Y-Combinator Research and run by tech entrepreneurs. The original plan was to give a sample of people in the city of Oakland $1,000 a month. But after three years of preparatory work, its project director  Appendix A 97 announced that it would not be conducted in Oakland after all but in two regions in two states.

See Britain UK benefit system 104 UN Climate Summit 34 unconditional cash transfers 90, 97 under-qualified employees 46–7 unemployment 56, 61, 103, 105, 106 unfree market system 13 UNILAB 80 unionism 84 United States 9, 10, 16, 20, 76, 78, 81, 87, 97, 106, 109 Universal Basic Income (UBI) 113 universal basic services (UBS) 30, 108–14 Universal Credit (UC) 4, 10, 17, 24, 28, 39–52, 58, 71, 73, 81, 84 ‘universal individual credits’ 58 ‘universal minimum inheritance’ 75 unpaid work 36, 53, 67 Unum 50 Voice organization 79, 84 wage 14 differentials 15 rates 33 subsidy 105–6 Wall Street Journal 37 Warren, Elizabeth 101 ‘weakness-of-will’ 75 wealth gap 9 welfare assistance schemes 29 welfare benefits 27, 29, 41 welfare state 62, 64, 100, 109 welfare system 41, 88 welfare tourism 6 Willetts, David 76 Wolf, Martin 123 n.15 women 15–16, 36, 43, 62 Work Capability Assessment test 50 Working Tax Credits 81 World Bank 60 World Development Report 60 World Economic Forum 31, 119 n.63 Y-Combinator Research 96 Young, Charlie 124 n.12 zero-hours contracts 16, 45 140  141 142  143 144  145 146

pages: 270 words: 79,180

The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit
by Marina Krakovsky
Published 14 Sep 2015

“I started making these introductions without any expectation that these favors would be returned,” Nozad says. He points out, for example, that he likes to help entrepreneurs even if he’s not a shareholder of their company. His relationship with Dropbox, which started at an event at the start-up accelerator Y Combinator and began to blossom over Persian tea at the rug gallery, was never transactional, either. “It’s not like an agency—‘I’ll introduce you to these Hollywood producers, and if you raise money, I’ll get five percent of the profit you make at the end of the day’—it never was like that.”6 At one point, as I began posing a question about “deal flow,” a piece of VC jargon that had slipped from his lips, he interrupted to make sure I didn’t get the wrong idea.

He is not implying that large funds typically put all their eggs in one basket—all VCs hold a portfolio of companies—only that large investments tend to make you more cautious, which means that your portfolio will be less likely to yield exceptional returns. “Larger firms with more partners that invest more money per deal are always going to be more risk-averse,” Maples says. Paul Graham, founder of Y Combinator and likewise a believer in investing at the seed stage, put it more bluntly in his essay “A Theory of VC Suckage.” Each deal is for several million dollars, Graham argues, because management fees give firms an incentive to build up large funds. That, Graham writes, “explains why VCs take so agonizingly long to make up their minds, and why their due diligence feels like a body cavity search.

Grainger, 141 Walmart, 8, 140, 196 warmth, 9, 12–13, 112 Watchdogs, 77–8, 85–6, 88, 94 Watts, Duncan, 156–7 Wealthfront, 127 Weiner, Jeff, 23 Whately, Richard, 71 Williams, Evan, 129 Williamson, Oliver, 104 Willman, Hubert, 190–2 Willow-Wear, 61 Wiltermuth, Scott, 192 Wolf, Martin, 190 Wolfe, Mike, 47–52, 57–9, 64–5, 70, 72 Wood, Ann Whitley, 58–65, 72, 89, 164, 169 Xanadu art gallery, 113–14, 116, 119, 135 Y Combinator, 20, 125 YouTube, 4, 134 Zero to One (Thiel), 129 Zillow, 4 ZocDoc, 5, 38, 142–3 Zuckerberg, Mark, 23, 121

pages: 439 words: 131,081

The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds and Our World
by Max Fisher
Published 5 Sep 2022

That same year, Renée DiResta, the tech investor who would later track Facebook anti-vaxxers in her spare time, noticed Grove’s 10x mandate morphing into a strange new business model very different from the one that had produced companies like Intel. She had first seen this change at one of the Valley’s all-important investment conferences, put on by a tech-startup accelerator called Y Combinator, where founders mingle with the money brokers who might bankroll them. “YC Demo Day was like getting an invite to the Oscars,” she said. The annual show-and-tell by graduates from Y Combinator’s incubator “wasn’t something that anybody could just walk into.” Low-ranking investors like DiResta, without the power to write a check on the spot, weren’t welcome, but her boss, after missing his flight, asked her to go in his place.

My Instinct in What You’re Uncovering Here Is Probably Less an Issue at a Partner Level, at a Company to Company Level, and More of What You’re Saying. Which Is Like, You Have Someone on the Front Lines That’s Just Saying Something That’s Inappropriate.” 4 “Why are most chess masters”: “Startup Advice for Entrepreneurs from Y Combinator,” Mark Coker, VentureBeat, March 26, 2007. 5 “in software, you want to invest”: “The Hardest Lessons for Startups to Learn,” talk by Paul Graham to Y Combinator Startup School, April 2006. 6 The perks were intended: The Code: Silicon Valley and the Remaking of America, Margaret O’Mara, 2019: 201, 271–272. 7 “spent a few years”: Zucked: Waking Up to the Facebook Catastrophe, Roger McNamee: 2019: 48. 8 “Their impact transformed”: Ibid. 9 “I no longer believe”: “The Education of a Libertarian,” Peter Thiel, CatoUnbound.com, April 13, 2009. 10 corporation-run floating cities: “Mouthbreathing Machiavellis Dream of a Silicon Reich,” Corey Pein, The Baffler, May 19, 2014. 11 incumbents: A widely used term of art, it is referenced, for example, in “The History of Progress Is a History of Better Monopoly Businesses Replacing Incumbents,” Zero to One, Thiel and Masters, 2014: 33. 12 “This problem is one”: Open Hearing on Foreign Influence Operations’ Use of Social Media Platforms, Select Committee on Intelligence of the United States Senate, August 1, 2018. 13 “Responsibility for the”: Ibid. 14 “One of the biggest issues social”: “A Blueprint for Content Governance and Enforcement,” Mark Zuckerberg, Facebook, November 15, 2018. www.facebook.com/notes/751449002072082 15 “even when they tell us”: Ibid. 16 overhauled its algorithm: “Facebook Overhauls News Feed in Favor of ‘Meaningful Social Interactions,’” Julia Carrie Wong, The Guardian, January 11, 2018. 17 Likes were worth one point: “Five Points for Anger, One for a ‘Like’: How Facebook’s Formula Fostered Rage and Misinformation,” Jeremy B.

pages: 302 words: 95,965

How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs
by Tim Draper
Published 18 Dec 2017

It is simple to get incorporated on LegalZoom or Clerky, get legal advice on LawTrades, and apply to Draper University or an accelerator like Boost.vc, Y Combinator or TechStars. It is simple to list your company on AngelList or Crowdfunder and attract people to invest angel money with you. It is easy to list products on ProductHunt, Kickstarter or Indiegogo to see if there are customers interested in what you are doing. Legal terms are getting standardized and easy to research, terms like “SAFE” (Standard Agreement of Future Equity--innovated by Y Combinator) notes, “KISS” (innovated by 500 Startups) and our favorite with Draper Associates, “Series Seed” (with our addition of “TATS [Tradeable Automated Term Sheet],” which you can find at www.lawtrades.com).

Who will pay you and when? When will you have to pay and to whom? Customers who pay in advance can make your company enormously successful, while customers who pay late can cripple you. One example of cash flow business modeling comes from my interaction with Pebble. I met Eric Migicovsky at an early Y Combinator event. A tall lanky European, Eric distinguished himself as being the only presenter who dared to create a hardware company. Venture capitalists at the time were wary of hardware companies because those companies had to buy inventory in order to sell products and that required a lot of cash. But I backed him because I saw something heroic, earnest, determined and visionary in him.

pages: 579 words: 183,063

Tribe of Mentors: Short Life Advice From the Best in the World
by Timothy Ferriss
Published 14 Jun 2017

Drew Houston TW: @drewhouston FB: /houston dropbox.com DREW HOUSTON is CEO and co-founder of Dropbox. After graduating from MIT in 2006, he turned his frustration with carrying USB drives and emailing files to himself into a demo for what became Dropbox. In early 2007, he and co-founder Arash Ferdowsi applied to tech accelerator Y Combinator. Dropbox went on to become one of the fastest-growing startups in YC history. Dropbox now has more than 500 million registered users and employs more than 1,500 people in 13 global offices. What is the book (or books) you’ve given most as a gift, and why? Or what are one to three books that have greatly influenced your life?

When I was 24, I came across a website that says most people live for about 30,000 days—and I was shocked to find that I was already 8,000 days down. So you have to make every day count. I’d give the same advice today, but I would clarify that it’s not just about passion or following your dreams. Make sure the problem you become obsessed with is one that needs solving and is one where your contribution can make a difference. As Y Combinator says, “Make something people want.” In the last five years, what have you become better at saying no to? What new realizations and/or approaches helped? This was a hard thing for me to learn; I like helping people. But realizing a couple things made a big difference: You have a lot less time than you think, and you’re not spending your time the way you think you are.

Muneeb Ali TW: @muneeb muneebali.com MUNEEB ALI is the co-founder of Blockstack, a new decentralized Internet where users control their data, and apps run without remote servers. Muneeb received his PhD in computer science from Princeton University, specializing in distributed systems. He went through Y Combinator—considered the Harvard/SEAL Team Six of startup incubators—and has worked in the systems research group at Princeton and PlanetLab, the world’s first and largest cloud computing test bed. Muneeb was awarded a J. William Fulbright fellowship and gives guest lectures on cloud computing at Princeton.

pages: 359 words: 97,415

Vanishing Frontiers: The Forces Driving Mexico and the United States Together
by Andrew Selee
Published 4 Jun 2018

The founders developed an online diagnostic test that rural clinics with no doctor present can use to diagnose a range of diseases. An app reads the patient’s blood sample, analyzes it, and shares it with a doctor at a remote location, who can provide a diagnosis. “We were the first Mexican start-up accepted in Y-Combinator,” Silicon Valley’s most prestigious technology accelerator, says José Luis Nuño, one of the company’s founders. That experience helped put them in touch with venture capitalists willing to back them. Now, with several millions of dollars in investment from the Gates Foundation and US and Mexican venture capital funds, Unima is about ready to initiate production of the diagnostic test in its own plant in Guadalajara.

It’s essentially a digital complement to walking canes, using ultrasound to send signals to the user about obstacles in the environment that go beyond what a cane can detect. It’s young founder, Marco Trujillo, got the idea while doing community service at a school for the blind in Guadalajara. His big break came when Sunu was admitted into MassChallenge, Boston’s equivalent of Y-Combinator, which has strong links to health-care technology. This experience opened up investment from US as well as Mexican-based venture funds. Most of Sunu’s sales are in the United States, though the technology was developed in Mexico. This is just a sampling of the many start-ups taking hold in Mexico.

See Hazelton, Pennsylvania Valdés, Guillermo, 169–170, 171, 173–174, 175 Vasconcelos, José, 209–210 Venegas, Julieta, 216 venture capital, 6, 97, 99, 101, 102, 103, 107–108, 109–110, 111–112, 272 Videgaray, Luis, 200 Villanueva, Fernando, 78–79 Villaraigosa, Antonio, 266 Villas de Salvárcar massacre, 165, 166, 167, 169 virtual border crossing, 281 visas, 4, 30, 47, 100, 191, 248, 265 Vitro, 57, 76 Volvo, 54 voting, 17, 19, 21, 53, 164, 264, 266, 267, 278 wage pressure, 187 See also low-wage jobs Wallace, Roger, 114–115, 116, 117 Walmart, 77, 85, 96 Wanzek, Terry, 49, 50, 53, 54 Wilson, Christopher, 58, 61 Wilson, Woodrow, 211 wind power, 117, 121–122, 123, 124 Wizeline, 99–100, 106 Woldenberg, José, 278 Women’s National Basketball League (WNBA), 250 Wood, Duncan, 120, 122, 124, 130, 132 work ethic, 78, 79 World Cup, 47, 251, 270 World Series, 2, 272–273 World Trade Organization, 63, 98 Wozniak, Steve, 41 writing community, 213–214 Y-Combinator, 102, 103 Young Jaguars for Good, 165–166 Zacatecan Federation of Southern California, 188–190, 192 Zeta (newspaper), 135, 139, 140 Zetas, 138, 171–174, 175, 176

pages: 340 words: 100,151

Secrets of Sand Hill Road: Venture Capital and How to Get It
by Scott Kupor
Published 3 Jun 2019

Not only did the absolute cost of servers, networking, storage, data center space, and applications begin to fall, but the procurement method evolved from up-front purchasing to much cheaper “renting” with the advent of what is known as cloud computing. As a startup, these changes are very significant, as they mean that the amount of money you need to raise from VCs to get started is much less than in the past. Y Combinator Cracks Open the “Black Box” The second material transformation in the startup ecosystem was the advent of an incubator known as Y Combinator (or YC for short). Started in 2005 by Paul Graham and Jessica Livingston, YC basically created startup school. Cohorts of entrepreneurs joined a “YC batch,” working in an open office space together and going through a series of tutorials and mentorship sessions over a three-month period to see what might come out the other end.

See dot.com boom/bust term sheets, 140–169, 170–188 on aggregate proceeds, 142, 278 antidilution provisions in, 165–167, 280–281 on board of directors, 171–173, 281 on capitalization, 154, 278 on confidentiality, 285 on conversion/auto-conversion to common shares, 160–165, 280 on co-sale agreements, 181 on D&O insurance, 183, 284 on dividends, 154–155 drag-along provisions in, 182–183, 252, 284 on employee and consultant agreements, 187 and go-shop provisions, 239 on information rights, 282 on legal counsel and fees, 286 on liquidation preference, 155–159, 279 and no-shop provisions, 187–188, 239, 285 on preferred shares, 141–142 on price per share, 147–149, 278 on pro rata investments, 178–180, 283 on protective provisions, 173–177, 281–282 and recapitalizations, 281, 282 on redemption rights, 159 on registration rights, 178, 282 on right-of-first-refusal, 180–181 sample, 141, 277–286 on stock purchase agreement, 284 on stock restriction, 180–182, 283 on vesting, 183–187, 284 on voting rights, 167–169, 281 Tesla, 110 timing in startup world, importance of, 14 Tiny Speck, 137 Trados case, 220–231 and common shareholders, 221–222, 223 and conflict of board, 222–226 decision on, 227–228 distribution of proceeds from acquisition, 221 and entire fairness rule, 222, 226–229 guidelines stemming from, 225–226 and management incentive plan, 221, 226–227 takeaways from, 228–231 transfer restrictions, 98–99 Uber, 102, 172–173 United States and venture capital, 3, 271, 275 university endowments, 54–55, 56–57, 71 unrelated business income (UBIT), 93–94 use of proceeds, 278 vacation policies, 244–245 VA Linux, 264–265 valuation, 118–123 and antidilution provisions in term sheet, 165–167 and convertible notes, 144 pre- and post-money, 147–149 of very-early-stage startups, 153–154 valuation marks, understanding, 76–83 venture-backed companies economic impact of, 3–4, 41 exiting options of (see acquisitions; initial public offerings) five largest US market capitalization companies, 25, 41 and information asymmetry, 5, 140, 275 VC’s relationship with, 2–3, 4–5 venture capital (VC) and ability to raise new funds, 67–68 as asset class, 29–30 batting average of, 37–40 cardinal sins of, 44, 50–51, 179–180 competition for, 271–272 distribution of returns for, 30–32, 31, 35, 38, 40 and dot.com boom/bust, 64–65 early years in Silicon Valley, 19–20 as endorsement of a company, 43–44 equity financing as basis of, 26–27, 28 and evolution of VC industry, 270–273 extensions of last round of, 233 and institutional investors, 40–41 life cycle of, 7–8, 114–115, 268 and life cycle of fund, 152 measuring success of, 36–40 median ten-year returns in, 30 and multiple funds, 67 potential replacements for, 273–274 relationship of LPs to, 69–71 reserves set aside by, 66–67 restricted nature of, 35–36 risks inherent in, 39 rounds of, 34–35, 66–67, 115–117, 138–139, 151–152 signaling in, 32–33, 35, 37 size of industry, 40–41 and state of fund, 83–84 three professional roles in, 29 and Yale University endowment, 62–63, 64–65 as zero-sum game, 33–35 venture capitalists average duration of relationship with, 5, 115 creating incentives for, 114–115 as dual fiduciaries, 201–202 exit of, following IPO, 266–267 and failure to invest in winners, 33 funding from (see difficult financings; raising money from venture capitalists; term sheets) goals of, 114–115, 126, 139 and information asymmetry, 5, 140, 275 and opportunity costs, 43–44, 83, 212–213, 223 over-involvement with company, 203 and pitches (see pitching to venture capitalists) role of, 2–3, 29, 274–275 vesting accelerated, 99–101, 186–187, 250–251 and acquisitions, 250–251 and founders, 95–97, 99–101, 183, 186, 205–206 and general partners (GPs), 89 and term sheets, 183–187, 284 VMware, 132 voting on authorization of new classes of stock, 176 on corporate actions, 176 protective provisions on, 173–177 voting rights, 167–169, 281 WARN statutes, 243–244 waterfall valuation method, 77, 78–79 Waymo, 187 whaling industry, 53 winding down the company, 243–246 working capital, 150 Yale University endowment, 54, 59–65 Y Combinator (YC), 20–21 zero-sum game, venture capital as, 33–35 ABCDEFGHIJKLMNOPQRSTUVWXYZ ABOUT THE AUTHOR Scott Kupor is the managing partner of Andreessen Horowitz. He has overseen the firm's rapid growth to one hundred fifty employees and more than $7 billion in assets under management.

pages: 346 words: 97,330

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass
by Mary L. Gray and Siddharth Suri
Published 6 May 2019

He asked company founders to design products and services that made a market for themselves, filling a societal need rather than using profits from a popular product to fund philanthropy. Parikh had some powerful fans, like the venture capitalists behind the Bay Area incubator Y Combinator. Students who enrolled in Parikh’s class had a chance to compete for real financial backing through Y Combinator. Philipp Gutheim, Anand Kulkarni, Prayag Narula, and Dave Rolnitzky took their classroom project, MobileWorks, and won Y Combinator’s summer 2011 competition, giving them enough money to bankroll a group of engineers, a marketing campaign, and a network of on-demand workers serving as virtual assistants from around the world.

pages: 198 words: 53,264

Big Mistakes: The Best Investors and Their Worst Investments
by Michael Batnick
Published 21 May 2018

GoPro went public in 2014 at a valuation just below $3 billion.12 He didn't have the chance to invest in GoPro, but Sacca said no to some of the most well‐known and storied businesses of the decade. “One of my constant recurring nightmares is about the stuff I passed on.” He tells a story about the time he met with Dropbox, whom he met while they were still in Y Combinator's early‐stage start‐up program. He didn't think they could beat Google, which was developing its own file‐sharing service, Drive. He went so far as to recommend that Dropbox pursue a different path. Lucky for Dropbox, they didn't take his advice. Sacca estimates his decision to not invest in Dropbox cost him “hundreds of millions of dollars.”13 At close to a $10 billion valuation, Dropbox is one of the biggest misses of Sacca's career.

Steel, shares orders, 17 US stock portfolio, diversification, 109 U.S. stocks, intra‐year decline, 147 Valeant Pharmaceuticals, 113 Ackman targeting, 90 performance, S&P500 comparison, 113 shares, decline, 114 Valuation metrics, 160 Value at risk (VAR), 41–42 Value investing, function, 10 Value investors, problems, 58 Vanguard 500 Index returns, 52 size, 47 Vanguard Group, Inc., 51 VeriSign, Druckenmiller purchase, 104 Vranos, 133 Washington Post stocks, problems, 58 Wayne, Ronald, 148 Webster & Company bankruptcy, 31 problems, 30 Webster, Samuel Charles, 29 Wellington Fund, 48 merger, 49 operation, at‐cost basis, 51 Wellington Management, Bogle firing, 51 Wendy's, stock appreciation, 89 Wesco Financial, purchase, 142 Wheeler, Munger & Company, 141 Whiz Kids Take Over, The, 49 “Who Wants to Be a Millionaire” (Ackman), 90–91 Winning the Loser's Game (Ellis), 99 Woodman, Nick, 150 WordPress, 149 World War I, global monetary system, 122 Wozniak, Steve, 148 Wright Aeronautical, business demonstration, 3 Xerox, trading level, 70 Yahoo!, gains, 57 Y Combinator, 150 Zacks, Richard, 27 Zweig, Jason, 3 Zynga, trading, 157 WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA.

pages: 169 words: 56,250

Startup Communities: Building an Entrepreneurial Ecosystem in Your City
by Brad Feld
Published 8 Oct 2012

They heard the calls from Peter Thiel to drop out of college (http://startuprev.com/o2). They were fascinated with TechStars, Y Combinator, and similar programs. As we tell entrepreneurs, when there is a crisis, there is great opportunity for innovation. So at MIT we took some of our own medicine and explored what we could do to meet this challenge of making the academic environment more conducive to successful entrepreneurial development. We looked at Stanford, Berkeley, Harvard, the University of Michigan, and the University of Washington. We discussed the issue with students and saw their high level of interest in TechStars, Y Combinator, Dogpatch Labs, General Assembly, and Rock Health.

Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions
by Temple Grandin, Ph.d.
Published 11 Oct 2022

I totally flipped out watching his operation lift off. I think Musk is more than a visual thinker. It’s clear he’s that rare mind that can both design and build; like Jenney, he has vision but also the skills to implement: object and spatial. I wasn’t surprised to learn from a recent interview with Y Combinator that Musk spends 80 percent of his time in the engineering and design departments of SpaceX and Tesla developing next-generation products. “My time is almost entirely with the engineering team . . . dealing with aesthetics and look-and-feel things.” He knows every bolt on his rockets. For him, it’s all about building good stuff that works.

Redshift, May 3, 2018. https://web.archive.org/web/20201127180130/https://redshift.autodesk.com/architecture-vs-engineering/. Fraser, D. C. “Memorial Tribute—J. Halcombe Laning.” National Academy of Engineering. https://www.nae.edu/29034/Dr-J-Halcombe-Laning. Friedman, J. “How to Build a Future Series: Elon Musk.” Y Combinator. https://www.ycombinator.com/future/elon/. Fuller, T. “No Longer an Underdog Team, a Deaf High School Team Takes California by Storm.” New York Times, November 16, 2021. “Germany’s Wendelstein 7-X Stellarator Proves Its Confinement Efficiency.” Nuclear Newswire, August 17, 2021. http://www.ans.org/news/article-3166/germanys-wendelstein-7x-stellarator-proves-its-confinement-efficiency/.

O., 252 Wise, Steven M., 244 Witelson, Sandra Freedman, 189 Wonder, Stevie, 83 Woods Hole Oceanographic Institution, 178 Woolley, Anita Williams, 142 work ethic, 66, 76, 133, 151, 159 work skills, 98–99, 113, 116 work with one’s hands, 46, 49–55, 90, 110, 113 World War II, 123, 126, 180 WorldSkills competitions, 113–14 Wozniak, Steve, 6, 17, 124–25, 139–40, 183 Wright, Orville and Wilbur, 85, 155–56 writing skills, 10, 28, 58, 67–68, 146, 179 Y Y Combinator, 138 Yale University, 44, 109, 168, 177 Yeats, William Butler, 176 Young, Thomas, 147–48 Yuan, Eric, 148 Z Zeman, Adam, 39–42 Zimmer, Carl, 40, 42 Zoom, 148–49 Zuckerberg, Mark, 70, 124, 163–64, 190 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z About the Author Temple Grandin is a professor of animal science at Colorado State University and the author of the New York Times bestsellers Animals in Translation, Animals Make Us Human, The Autistic Brain, and Thinking in Pictures, which became an HBO movie starring Claire Danes.

pages: 271 words: 62,538

The Best Interface Is No Interface: The Simple Path to Brilliant Technology (Voices That Matter)
by Golden Krishna
Published 10 Feb 2015

Rachel Metz, “Every Step You Take, Tracked Automatically,” MIT Technology Review, February 12, 2013. http://www.technologyreview.com/news/510491/every-step-you-take-tracked-automatically/ 32 Sumathi Reddy, “Why We Keep Losing Our Keys,” Wall Street Journal, April 14, 2014. http://online.wsj.com/news/articles/SB10001424052702304117904579501410168111866 33 Esure, “We’re a Bunch of ‘Losers,’” esure.com, March 21, 2012. http://www.esure.com/media_centre/archive/wcmcap_100800.html 34 “Y Combinator-backed Lockitron aims to replace physical keys entirely by letting you control your door lock with your phone . . .” Alexia Tsotsis, “Lockitron Lets You Unlock Your Door with Your Phone,” TechCrunch, May 13, 2011. http://techcrunch.com/2011/05/13/lockitron-lets-you-unlock-your-door-with-your-phone/ 35 “It works like this.

UX (user experience), 46–47, 80, 103 uniqueness, understanding, 169–170 University of Washington statistics, 128 user input vs. machine input, 139–140 relying on, 135 username, invalidity of, 131–132 users, considering needs of, 114 UX (user experience) design, explained, 30–31 UX/UI job listings, 45–46 V vending-machine interface, 43–44 Visa’s payWave, 107 Vitulli, Clark, 121 voice recognition, 175–176 W Want, Roy, 139 Weeks, Jack, 173 Weiser, Mark, 137–138, 140–141 Weiss, Rick, 65 Whirlpool washing machine commercial, 151, 154 white out, 76 Whitman, Meg, 46 Wi-Fi, Airport service, 188–189 WIMP (windows, icons, menus, pointers), 77 Winfrey, Oprah, 63 wireframes for car controls, 115 familiarity of, 116 Wood, Molly, 191 work day, number of hours in, 147 World Chess Championship, 129 X Xerox PARC research, 137–139 Y Y Combinator, 102 * * * Let’s keep chatting. Well, thanks for reading my book. I really appreciate it. The conversation continues through the hashtag #NoUI, and at www.nointerface.com. And don’t hesitate to reach out to me at golden.krishna@gmail.com or through Twitter via @goldenkrishna. You can also check out other great books by Peachpit at www.peachpit.com

pages: 246 words: 68,392

Gigged: The End of the Job and the Future of Work
by Sarah Kessler
Published 11 Jun 2018

Martin Luther King Jr., the conservative economist Friedrich Hayek, and President Richard Nixon had all supported this idea, and modern boosters were no less varied. They included Andy Stern, the former president of the SEIU; the libertarian economist Charles Murray; and Robert Reich, the Bill Clinton–era labor secretary, who was fond of comparing the gig economy to a sweatshop. The tech incubator Y Combinator had recently committed to running a UBI experiment in California to understand how it worked. Facebook cofounder Chris Hughes endorsed UBI in a book. Terrence Davenport, though, was not a fan. He was nearly exasperated at what he saw as the ignorance inherent in the idea. “Do you know about the opioid crisis in this country?”

See labor and trade unions United Construction Trades and Industrial Employees Union Universal Basic Income (UBI) UPS Upwork (freelance marketplace) US Department of Labor USA Today venture capital gig economy and Google Ventures Managed by Q and TechCrunch Disrupt and Uber and venture capitalists VentureBeat (blog) Walker, Anthony Walmart Warner, Mark Warren, Elizabeth Washington Post Washio (on-demand laundry startup) WeFuel (on-demand fuel startup) Weil, David Winthrop Rockefeller Foundation Wired (magazine) Woodhead, Carole workers advocacy groups workers’ compensation Xchange Leasing Y Combinator (tech incubator) Yelp (user review website) Zaarly (online marketplace) Zirtual (virtual assistant services) Zuckerberg, Mark About the Author SARAH KESSLER is a reporter at Quartz, where she writes about the future of work. Before joining Quartz in 2016, she covered the gig economy as a senior writer at Fast Company and managed startup coverage at Mashable.

pages: 238 words: 73,824

Makers
by Chris Anderson
Published 1 Oct 2012

But the point is that the path from “inventor” to “entrepreneur” is so foreshortened it hardly exists at all anymore. Indeed, startup factories such as Y Combinator now coin entrepreneurs first and ideas later. Their “startup schools” admit smart young people on the basis of little more than a PowerPoint presentation. Once admitted, the would-be entrepreneurs are given spending money, whiteboards, and desk space and told to dream up something worth funding in three weeks. Most do, which says as much about the Web’s ankle-high barriers to entry as it does about the genius of the participants. Over the past six years, Y Combinator has funded three hundred such companies, with such names as Loopt, Wufoo, Xobni, Heroku, Heyzap, and Bump.

pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More
by Luke Dormehl
Published 4 Nov 2014

Smith” language—to be adversarial, even when this might not be the case at all. 22 Fisher, Daniel. “Silicon Valley Sees Gold in Internet Legal Services.” Forbes, October 5, 2011. forbes.com/sites/danielfisher/2011/10/05/silicon-valley-sees-gold-in-internet-legal-services/. 23 Casserly, Meghan. “Can This Y-Combinator Startup’s Technology Keep Couples Out of Divorce Court?” Forbes, April 10, 2013. forbes.com/sites/meghancasserly/2013/04/10/wevorce-y-combinator-technology-divorce-court/. 24 “After Beta Period, Wevorce Software for Making Every Divorce Amicable Is Now Generally Available Nationwide.” May 22, 2013. marketwired.com/press-release/after-beta-period-wevorce-software-making-every-divorce-amicable-is-now-generally-available-1793675.htm. 25 Turkle, Sherry.

pages: 293 words: 78,439

Dual Transformation: How to Reposition Today's Business While Creating the Future
by Scott D. Anthony and Mark W. Johnson
Published 27 Mar 2017

The titles grow more ominous over time. In 2013, Dave Ulmer drew on his experience at several large companies to detail The Innovator’s Extinction. His cover blurb? “How natural selection and best intentions will drive your company into the grave.” Another voice is that of Paul Graham, the founder of Y Combinator, a leading incubator that helped spur Dropbox, Airbnb, and hundreds of other companies. Graham perhaps summed up the zeitgeist best when he said, “Running a startup is like being punched in the face repeatedly, but working for a large company is like being waterboarded.” Coauthor Scott Anthony believed all this when he packed up his family and moved them to Singapore in 2010.

See Singapore Post (SingPost) Singtel, 24, 51, 53, 135–137, 140, 185 curiosity at, 142–150 exposing leaders to new thinking at, 145–147 reinforcing curiosity at, 147–150 Singtel Learning Fiesta, 146 Siri, 67 Skype, 136 Southern New Hampshire University (SNHU), 58, 59 space race, 115–116, 132 stakeholders, 11, 167–168 communicating to, 195–196 Starbucks, 57, 67 The Startup Owner’s Manual (Blank and Dorf), 153 startups disruption and, 58–59 established businesses versus, 72–73 Stone, Biz, 49, 138 strategic opportunity areas, 123–127, 216 strategy fitness landscape and, 5–8 future-back approach to, 129–133 at Manila Water, 117–128 predictability and, 137–139 story at the center of, 171–173 transformations A and B and, 16–20 The Structure of Scientific Revolutions (Kuhn), 68 Systrom, Kevin, 2 talent bringing in special-purpose, 44 capability development via external hires, 66–69 communicating to, 195–196 Tan, Wilson, 51 Taobao, 201–202 Tata Sons, 149 Tay Soo Meng, 135 TechCrunch, 206 Temasek, 136 template, goals and boundaries, 123, 215 Tencent, 106, 202 Tesla, 178 Theranos, 61 3Com, 13 Thrun, Sebastian, 205 Tiddler (Donaldson and Scheffler), 171 Time Warner, 96 transfer pricing, 85 transformation A, 27–45 at Adobe, 31–32, 33 aggressive implementation of, 43–45 balancing with transformation B, 173–175 business model blueprint, 214 business model innovation in, 40–42 crises of conflict in, 163–168 at Deseret Media, 29–31 determining job to be done after, 36–39 distinguishing from other strategic choices, 16–20 driving, 36–45 in dual transformation equation, 12 interface management and, 75, 80–87 leaders of the critical role of, 187 metrics in, 42–43 at Netflix, 32–36 systems for, 80–82 trigger point for, 36–37 at Xerox, 14 transformation B, 47–70 “aliens” and “diplomats” in, 68–69 at Amazon, 53–55 balancing with transformation A, 173–175 business model iterative development in, 59, 63–66 capability development in, 66–69 consumption patterns transformation in, 61–62 crises of commitment in, 161–163 crises of conflict in, 163–168 distinguishing from other strategic choices, 16–20 in dual transformation equation, 12 in higher education, 55–58 identifying constrained markets in, 59–63 interface management and, 75, 80–87 keys to success in, 58–69 at Netflix, 69–70 at SingPost, 50–53 systems for, 80–82 at Xerox, 14 transformation blurbs, 129 transformation maps, 211–212 transparency, embracing, 153–154 TripAdvisor, 50 Trustwave, 188 TurboTax, 132 Turner Broadcasting System, 2, 95–99 Turner Classic Movie, 99 Turner Entertainment Networks (TEN), 95–99 decision making at, 102, 109 early warning signs at, 108 focus at, 117 Tushman, Michael, 53, 54 TVinContext, 99 Twitter, 49, 138 Tyson, Mike, 64 Uber, 205 UK-92480, 138–139 Ulmer, Dave, 71–72 Unbreakable Kimmy Schmidt, 35 universal resource locators (URLs), 3 value, business model innovation and, 40 Vasquez, Robbie, 127 venture capitalists, 103–104, 112, 140–141 corporate, 143–144 Verizon, 49 Viagra, 138–139 Viki, 143 von Braun, Wernher, 116 vPost, 52 Walgreen, Charles R., Sr., 60 Walgreens, 60–61 Wanamaker, John, 67 warning signs, 102–113 assessment table, 213 catalysts, 104–105 circumstances, 103–104 how to spot, 107–110 impact, 106–107 underestimation of, 120 Wasson, Gregory, 60–61 Watson supercomputer, 70, 204 WebMD, 100 WeChat, 106 Welch, Jack, 177 WhatsApp, 48, 136 white space, 63, 66 will.i.am, 151 Williams, Ev, 49, 138 Wilson, Joseph C., 13 World Media Enterprises, 156 Wright brothers, 64–65 Xerox, 13–16 acquisitions and partnerships at, 67 arbitration at, 86 business model innovation at, 42, 63–64 capabilities link at, 14–15 focus at, 117 postdisruption job to be done at, 39 transformation journey at, 182 Xerox Global Services (XGS), 14, 63–64, 86 Yahoo, 49 Y Combinator, 72 Yelp, 50 Young Broadcasting, 156 YouTube, 97, 105, 108 Zillow, 50 Zipcar, 205 Zuckerberg, Mark, 48, 97 Acknowledgements From the team The central idea in Dual Transformation—that leaders need to simultaneously reposition today’s business and create tomorrow’s—has been core to each of our professional careers since 2000.

pages: 300 words: 76,638

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future
by Andrew Yang
Published 2 Apr 2018

This is hugely indicative because of the enormous sample size—Iran has 80 million people, equivalent to the combined total of New York, California, and Florida—over an extended period of time. Most recently, a small trial launched in the United States. Starting in early 2017 in Oakland, California, Sam Altman, the head of the technology firm Y Combinator, is giving 100 households in Oakland approximately $1,000 to $2,000 per month for about a year to measure the impacts on recipients. The goal is to roll out a larger five-year trial afterward. Sam and his friends are giving away $2 million and hiring researchers just to see what will happen. I love the fact that Sam is putting up the resources to study this problem.

Medicare—the government-provided health care program for Americans 65 and over—essentially serves this role for senior citizens and has successfully driven down costs and provided quality care for tens of millions. Most everyone loves Medicare—it’s politically bulletproof. Sam Altman, the head of Y Combinator, suggests rolling out Medicare across the population by gradually lowering the eligibility age over time. A gradual phase-in would give the industry time to plan and adjust. This is an excellent way forward, and a “Medicare-for-all” movement is currently gathering steam. There would inevitably remain a handful of private options for the super-affluent, but most everyone would use the generalized care.

pages: 258 words: 74,942

Company of One: Why Staying Small Is the Next Big Thing for Business
by Paul Jarvis
Published 1 Jan 2019

Because a company’s interests may not always align with the interests of its backers. Worse, investor interests may not always align with what’s best for a business’s end customers. Capital infusion can also leave a business with less control, resilience, speed, and simplicity—the main traits required for companies of one. Paul Graham, the cofounder of Y Combinator (one of the largest and most notable VC firms for startups) explains that VCs don’t invest millions in companies because that’s what those companies might need; rather, they invest the amount that their own VC business requires to see growth in their own portfolios, coming from the few companies that actually give them a positive return.

See purpose Williams, Ev, 178 win-win relationships, 79, 96, 103, 115–17, 151, 188, 202–7 Womersley, Katie, 72 Word of Mouth Marketing Association, 152 word-of-mouth publicity, 107, 109, 110, 115–16, 144, 152–56 WordPress, 35, 176 work commitment to, 86–87 in company of one, 200–202 defined, 16 engaging work traits, 83 passion and, 82 workaholism, 54–56 World Domination Summit, 189 Y Y Combinator, 28 YouTube, 176 Z Zafirovski, Mike, 21 Zander, Ed, 177 Zanuck, Darryl, 177 Zingales, Luigi, 121 Zuckerberg, Mark, 47, 69, 193 About the Author Beginning as a corporate web designer and internet consultant, Paul Jarvis first spent years working with top professional athletes like Warren Sapp, Steve Nash, and Shaquille O’Neal with their online presence, and with large companies like Yahoo, Microsoft, Mercedes-Benz and Warner Music.

pages: 267 words: 72,552

Reinventing Capitalism in the Age of Big Data
by Viktor Mayer-Schönberger and Thomas Ramge
Published 27 Feb 2018

Limited experiments in administering a UBI are underway in Finland and the Netherlands, and they will produce some empirical data about the effects of a UBI on human motivation. Switzerland held a national referendum on a UBI, but voters rejected the very generous scheme that was proposed (around $2,000 per month for every Swiss citizen). Beginning in 2016, start-up accelerator Y Combinator even sponsored a small project in the United States designed to investigate whether receiving a basic income would have an effect on people’s desire to work. Canada experimented with a version of basic income back in the 1970s, when it gave a monthly check to every eligible family in the small town of Dauphin, Manitoba.

See universal basic income UniCredit bank, 136 Unilever, 75 United Kingdom, 134, 147, 164 United States banking crisis in, 134, 135 capital share of, 185 corporate taxes in, 197–198 health care sector in, 213 labor market of, 184, 185, 186, 195 market concentration in, 164 stock market investment options in, 143 subprime mortgage crisis in (see subprime mortgage crisis) universal basic income proposed in, 190, 191 universal basic income (UBI), 189–193, 205–206 University of Pennsylvania’s Wharton School, 36 Upstart, 151 Upwork, 3 used car market, 40 venture capital (VC) firms, 141, 142–143, 216 Vocatus, 55 Volkswagen, 182 Volvo, 182 Wall Street Journal, 203 Walmart, 28, 52 Walt Disney Company, 69 Watson (machine learning system), 109, 111, 113–114, 115, 117, 163, 183 Watt, James, 111, 113 wealth tax, 187 Webvan, 112 WeChat, 147, 163 Wedgwood, Josiah, 94 welfare reducing transactions, 73 Wenger, Albert, 156, 189 Wenig, Devin, 1–2, 209 Wharton School, 36 Which?, 52 Wiener, Norbert, 159–160, 179 Wikipedia, 21–22 Windows, 166 Wired magazine, 181 WordPress, 161 work. See labor market Y Combinator, 191 Yahoo, 2–3, 6 Yamaha, 30 Yegge, Steve, 88 YouTube, 67, 68–69 ZestFinance, 151 Zetsche, Dieter, 110, 121 Zopa, 152 Zulu Trade, 152

pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global
by Rebecca Fannin
Published 2 Sep 2019

Dressed in his classic white shirt emblazoned with a Baidu logo, he put a positive spin on Baidu’s latest innovations in artificial intelligence—not so easy to be upbeat since he had to step back into the CEO seat after his star hire Lu left in May 2018 to launch and run a China offshoot of US accelerator Y Combinator. And that was only a year after AI superstar Andrew Ng departed Baidu for a new AI mission in Silicon Valley. As Li spoke, flashy surround-sound videos displayed Baidu’s new technologies for autonomous driving, smart-city projects in Beijing and Shanghai, and voice-activated speakers and lights.

Its content technology sorts and tags more than 200,000 articles and videos daily and personalizes news feeds based on analysis of data obtained through the users’ locations, phone model, and click history. Users open the app and access news through Toutiao’s 4,000 media partnerships without following other accounts, unlike Facebook or Twitter. Anu Hariharan, a partner with Y Combinator’s Continuity Fund in San Francisco, likens Toutiao to YouTube and technology news aggregator Techmeme in one. She finds the most interesting thing about Toutiao to be how it uses machine-and deep-learning algorithms to serve up personalized, high-quality content without any user inputs, social graphs, or product purchase history to rely on.19 From Sea to Shining Sea ByteDance has been moving up in recent years with content deals and smart acquisitions, fulfilling founder Zhang’s mission of making his startup borderless.

pages: 240 words: 78,436

Open for Business Harnessing the Power of Platform Ecosystems
by Lauren Turner Claire , Laure Claire Reillier and Benoit Reillier
Published 14 Oct 2017

As the presidential campaign was in full swing and both sides were keen to show support for their favourite candidates, the Airbnb team decided to sell special edition Cheerios cereal boxes for both presidential candidates called ‘Obama O’s’ and ‘Cap’n McCains’ for $40 each. They made $30,000 in a few weeks. By early 2009, they were invited to join the Y Combinator, one of the leading incubator programmes in San Francisco, and got $20,000 of funding from well-known angel investor Paul Graham. A seed round of $600,000 led 2 Introduction to platform businesses by Sequoia Capital followed shortly afterwards.1 Even so, the business did not take off. The Airbnb team realized that the photos of places advertised on their website were not appealing.

• • • • • • • • • • Notes 1 Telegraph, 7 September 2012, www.telegraph.co.uk/technology/news/9525267/AirbnbThe-story-behind-the-1.3bn-room-letting-website.html. 2 Fast Company, www.fastcompany.com/3017358/most-innovative-companies-2012/ 19airbnb. Introduction to platform businesses 9 3 Following the Sequoia round, Airbnb went on to raise a series A round of $7.2 million in 2010. Wall Street Journal, 25 July 2011, http://blogs.wsj.com/venturecapital/2011/ 07/25/airbnb-from-y-combinator-to-112m-funding-in-three-years/. 4 VentureBeat, 19 June 2014, http://venturebeat.com/2014/06/19/uber-and-airbnbsincredible-growth-in-4-charts/ and Airbnb website at www.airbnb.co.uk/about/ about-us. 5 www.airbnb.co.uk/about/about-us. 6 CB Insights, 1 August 2016, www.wired.com/2015/12/airbnb-confirms-1-5-billionfunding-round-now-valued-at-25-5-billion/.

pages: 624 words: 127,987

The Personal MBA: A World-Class Business Education in a Single Volume
by Josh Kaufman
Published 2 Feb 2011

Join me at personalmba.com to explore these ideas in more detail and learn how to apply them to your daily life and work. Let’s begin. 2 VALUE CREATION Make something people want . . . There’s nothing more valuable than an unmet need that is just becoming fixable. If you find something broken that you can fix for a lot of people, you’ve found a gold mine. —PAUL GRAHAM, FOUNDER OF Y COMBINATOR, VENTURE CAPITALIST, AND ESSAYIST AT PAULGRAHAM.COM Every successful business creates something of value. The world is full of opportunities to make other people’s lives better in some way, and your job as a businessperson is to identify things that people don’t have enough of, then find a way to provide them.

If you have enough profit to do the things you need to do to keep the business running and make it worth your time, you’re successful, no matter how much revenue your business brings in. Sufficiency is the point where a business is bringing in enough profit that the people who are running the business find it worthwhile to keep going for the foreseeable future. Paul Graham, venture capitalist and founder of Y Combinator (an early-stage venture capital firm), calls the point of sufficiency “ramen profitable”—being profitable enough to pay your rent, keep the utilities running, and buy inexpensive food like ramen noodles. You may not be raking in millions of dollars, but you have enough revenue to keep building your venture without going under.

See Working with others testing Working with others attribution error authority bystander apathy clanning commander’s intent commitment and consistency communication overhead comparative advantage convergence and divergence golden trifecta importance, feeling of incentive-caused bias management modal bias option orientation planning fallacy and power Pygmalion effect reasons for action, giving recommended reading referrals safety, feeling of social proof social signals Wozniak, Steve Y Combinator Zappos

pages: 310 words: 34,482

Makers at Work: Folks Reinventing the World One Object or Idea at a Time
by Steven Osborn
Published 17 Sep 2013

Migicovsky: We decided to go on Kickstarter mainly because we were unable to raise money from standard, more typical investment sources. We joined a program called Y Combinator, which is an incubator in Silicon Valley. That was our first connection to the Valley. In Canada, we were initially trying to raise money from angel or seed investors, and it was nearly impossible. The only funding sources that I found in Canada were my parents and the government. We were unable to convince any private investors. It was tough because as a hardware company, we had to physically make things. We finally got the chance to go down to Silicon Valley to join the Y Combinator program in early 2011.3 We thought it would be a massive change in our ability to raise money.

We finally got the chance to go down to Silicon Valley to join the Y Combinator program in early 2011.3 We thought it would be a massive change in our ability to raise money. Silicon Valley and is where people dream big and they believe in random, crazy, and potentially difficult ideas. After going through three months of Y Combinator, we went into the demo day and tried again to raise money. I talked to tens, if not hundreds of investors and was only able to raise money from a couple of people. It was tough. I thought it would be a little bit easier in the Valley to get money. It turned out it wasn’t. Osborn: The economy was still pretty bad around 2009. It was pretty tough to raise money at that point in, even in the Valley.

pages: 864 words: 272,918

Palo Alto: A History of California, Capitalism, and the World
by Malcolm Harris
Published 14 Feb 2023

“Alisher Usmanov: What Makes the Russian Britain’s Richest Person?” Guardian, April 22, 2013. 28. Juliette Garside, “Russia’s Richest Man Cashes In on Facebook Share Recovery,” Guardian, September 5, 2013. 29. Michael Arrington, “Start Fund: Yuri Milner, SV Angel Offer EVERY New Y Combinator Startup $150k,” TechCrunch, January 28, 2011, https://social.techcrunch.com/2011/01/28/yuri-milner-sv-angel-offer-every-new-y-combinator-start-up-150k. 30. James F. Peltz and Tracey Lien, “Russian Billionaire Yuri Milner’s Early Backing of Facebook, Twitter Had Kremlin Ties,” Los Angeles Times, November 7, 2017. 31. Bloomberg News, “Saudi Arabian Prince Says He Bought Stake in Apple,” New York Times, April 2, 1997. 32.

The London Sunday Times named Alisher Usmanov not only Russia’s richest man but also, with his London mansion, Britain’s richest man, displacing Indian steel magnate Lakshmi Mittal.27 Usmanov channeled his generic iron-ore monopoly profits into Anglophone brand plays, including the Arsenal Football Club (30 percent) and Apple, pushing $100 million into the iPhone maker and putting a stop to a dangerous and somewhat unexplained slide in investor confidence.28 Milner continued pumping up tech valuations, investing in Facebook-based game maker Zynga, discount coupon site Groupon, and music streamer Spotify. He put $380 million in Twitter, and in 2011 he teamed with famed Silicon Valley angel Ron Conway to offer $150,000 to each and every start-up in the Bay Area tech accelerator Y Combinator, laying down a bet on the whole regional ecosystem.29 When it came out in 2017 that a significant amount of DST’s capital originated with the Russian state, the news yielded shrugs in the industry.30 No one could suck that kind of money out of the country without close ties to the government.vii And besides, sovereign wealth funds invest in Silicon Valley all the time.

One reason they were excited was Yahoo’s revenue growth. So they invested in new internet start-ups. The start-ups then used the money to buy ads on Yahoo to get traffic. Which caused yet more revenue growth for Yahoo, and further convinced investors the internet was worth investing in.”3 In 2005, he and some colleagues from the Yahoo! deal opened Y Combinator, an accelerator that traded advice, connections, and a little cash to start-ups in return for shares. Theirs was the best-organized institution—angel investing as a for-profit university—but there were more casual setups, too. The fictional prototype is Erlich Bachman, the extroverted bullshit artist at the center of Mike Judge’s HBO clown-era Palo Alto satire, Silicon Valley.

pages: 252 words: 78,780

Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us
by Dan Lyons
Published 22 Oct 2018

VCs claim that they make decisions based entirely on the strength of the company’s ideas, and without any regard for race or gender. But can you guess where the members of the White Man Club tend to put their money? “I can be tricked by anyone who looks like Mark Zuckerberg” is how Paul Graham, the founder of Y Combinator, a top Silicon Valley start-up incubator, once famously put it. Graham later claimed he was joking, but a glance through the roster of Y Combinator portfolio companies turns up an awful lot of nerdy young Zuckerberg clones. As for why there are so few women in venture capital, Michael Moritz, a partner at Sequoia Ventures, once said that it’s not because of gender bias but that “What we’re not prepared to do is to lower our standards.”

pages: 291 words: 90,771

Upscale: What It Takes to Scale a Startup. By the People Who've Done It.
by James Silver
Published 15 Nov 2018

‘That would also be TransferWise’s challenge, probably not for the next five years, but sometime after their ten-year anniversary - but they’ll be well prepared for it, I’m sure.’ It’s a challenge which Seedcamp itself - once the European disruptor and answer to Silicon Valley’s Y Combinator, but now in its second decade - also faces. While Sohoni’s startup blazed a trail as Europe’s first seed-stage startup accelerator, according to the European Accelerator Report there are now at least 113 tech accelerators across Europe - a host of rivals and also-rans against which Seedcamp must continue to stand out. Today, like Y Combinator, it has been repositioned as a seed fund. ‘People are invested in our old story because it’s emotional and it was well known [in the industry].

pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

When your team is taking smaller bites, there’s a feeling of momentum, and the electricity of that is palpable. Other teams see it. They feel it. And they want it for themselves. None of this is to say that scale doesn’t matter, just that it’s not our first priority. Paul Graham, one of the cofounders of the startup incubator Y Combinator, advises his startups to “do things that don’t scale.” What he means is that in the early days of anything new, worrying about scale can prevent us from learning, growing, and being remarkable. Think systemically. Act locally. And let scale happen. Learn by Doing We are all experts in our current way of working.

L. and Associates, 16, 69–70, 79, 142 Gorilla Glass, 103 Gould, Stephen Jay, 103 governance meetings, 122 governing constraints, 46 Gower, Bob, 222 Graham, Benjamin, 30 Graham, Paul, 230 Grant, Adam, 142 gratitude, 148 Gray, Dave, 196 G Suite, 135 Haier, 76, 80 Hamel, Gary, 26 Hammond, Robert, 188 Handelsbanken, 13, 94, 227–28 Hansson, David Heinemeier, 68–69 Harrison, Scott, 224–25 Hawk, Tony, 259 healthcare industry, 34–35 Buurtzorg in, 13, 34–36, 38, 79, 105, 144, 218 Heath, Chip and Dan, 212 Heppner, Frank, 46 Herzberg, Frederick, 165 hierarchies, 77–78, 258 High Line, 188 Hillaker, Harry, 88 Hillman, James, 36 hiring, 79, 142–43 Hoffman, Reid, 88 Holacracy, 71, 122, 202 HolacracyOne, 89 Human Side of Enterprise, The (McGregor), 39–41 Husney, Jordan, 89 Huxley, Aldous, 22 hygiene factors, 165, 173 ICBD (Intentions, Concerns, Borders, and Dreams), 222–23 IDEO, 142–43 incentive compensation, 171–72 Indie.vc, 253–54 influence, 78 information, 14, 54, 127–37 information symmetry, 130, 134, 170, 190 initial public offerings (IPOs), 254, 255 Innosight, 29 innovation, 14, 54, 102–9, 188 innovator’s dilemma, 91 integration vs. functions, 79–80 Integrative Decision Making (IDM), 71–73 internet, 84, 131 Intrinsic Motivation (Deci), 42 investment, 251–55 James, LeBron, 143, 172 Jamieson, Alex, 222 Jobs, Steve, 103 job satisfaction, 165 Johnson, Steven, 189 Joint Special Operations Command (JSOC), 128–29, 130 Kahneman, Daniel, 165 Kanigel, Robert, 22 Kegan, Robert, 152–53 knee-jerk reactions, 28 Kotter, John, 186 KPMG, 32 Kroger, 59 Kroghrud, Ivar, 147 labor productivity growth, 33–34 Lahey, Lisa, 152–53 Laloux, Frederic, 105 language, 217 Lasseter, John, 191 lattice organizations, 142 leader, role of, 223–28 leadership gap, 166 Leading Change (Kotter), 186 Lean Change Management (Little), 201 Lean Startup method, 107–8 learning, 152, 153, 156–57, 160, 162, 200 by doing, 230–31 faster, 88 games and activities for, 200 retrospectives and, 123–24 validated, 108 Legacy Organizations, 5, 21, 38, 47, 59, 237, 258 authority in, 66 decision making in, 69 information in, 129, 131 measurement in, 60 membership in, 140 operating systems of, 12 performance targets and, 97 strategy in, 86 liminal space, 196, 197, 201 Little, Jason, 201 Little Book of Beyond Budgeting, The (Morlidge), 96–97 locus of control, 154, 155 Long-Term Stock Exchange (LTSE), 254–55 looping, 193, 201–16, 229, 236 and conducting experiments, 213–16 and proposing practices, 207–12 and sensing tensions, 202–6 Lyft, 169 Machiavelli, Niccolò, 248 Made to Stick (Heath and Heath), 212 management, 26–27, 81 innovations in, 20 open-book, 130 org charts for, 7–9, 24, 77, 78, 81, 114, 189 market pay, 167–68 Marquet, David, 67 Maslow, Abraham, 38 Masters of Scale, 88 mastery, 14, 54, 151–62 maturity, 154, 155–56, 255–56 McChrystal, Stanley, 128–29, 130, 197 McGregor, Douglas, 39–41, 158 McKeown, Greg, 62 McKinsey, James O., 24–25, 95 McKinsey & Company, 24, 32, 143, 187 Medium, 84–85, 86 meetings, 3–4, 6, 119 moratorium on, 123 in OS Canvas, 14, 54, 118–26 structures in, 124–25 membership, 14, 54, 138–50 mergers and acquisitions (M&A), 32, 33 metrics, 60–61 Meyer, Erin, 258 Microsoft, 170 microwave, 103 Mindset (Dweck), 154 mindsets: fixed and growth, 154–55 see also Complexity Conscious mindset; People Positive mindset minimum viable policy, 68–69 Mitchell, Melanie, 129 Mitra, Sugata, 257 Morlidge, Steve, 96–97 Morning Star Company, 13, 55–56, 99, 168 motivation, 41–42, 64, 74, 165, 173 multiplayer software, 135 Münsterberg, Hugo, 25 murmuration, 194 Netflix, 113, 167–68, 219 Netscape, 59 networks, dynamic, 77–78 New York Summit, 147 New York Times, 147, 165 Nietzsche, Friedrich, 179 noncompete clauses, 144 Office of Strategic Services, U.S., 6–7 Ohno, Taiichi, 20, 111 OKR (objectives and key results), 87–88 Oktogonen Foundation, 94 Olympic basketball team, U.S., 172 one-on-ones, 121–22 OODA loop, 88, 90 Operating Manual for Spaceship Earth (Fuller), 247 operating system, organizational (OS), 12–13, 17, 18, 43, 215 agility and, 19 changing, see change economic, 246–47, 248 evolutionary, see Evolutionary Organizations management innovations and, 20 Operating System Canvas (OS Canvas), 14, 53–57 authority, 14, 54, 63, 65–74 compensation, 14, 54, 163–73 how to use, 174, 270–72 information, 14, 54, 127–37 innovation, 14, 54, 102–9, 188 mastery, 14, 54, 151–62 meetings, 14, 54, 118–26 membership, 14, 54, 138–50 purpose, 14, 54, 68–64, 67, 85 resources, 14, 54, 93–101 strategy, 14, 54, 83–92 structure, 14, 54, 75–82, 111 workflow, 14, 54, 110–17 operating systems, 9 for traffic flow, see traffic flow organizational debt, 27–29, 91 organizations, 255 agility in, 19, 20, 28–29 as complex systems, 45, 187–88 cooperatives, 250 decentralized autonomous, 250–51 entry/exit rates of, 33 evolutionary, see Evolutionary Organizations governance of, 122 investment and, 251–55 lattice, 142 legacy, see Legacy Organizations longevity of, 29–30 mergers and acquisitions, 32, 33 new forms of incorporation, 248–51, 252 operating systems of, see operating system, organizational org charts, 7–9, 24, 77, 78, 81, 114, 189 return on assets of, 31, 32 as set of membranes, 139–40 three structures of, 78 OS Canvas, see Operating System Canvas Ostrom, Elinor, 98 over statements, 89 Page, Larry, 136 Patagonia, 85, 130–31, 133, 249, 259 pay, see compensation People Positive mindset, 13, 36–43, 53, 55–57, 190, 195, 199, 244, 258–59, 267 authority and, 74 compensation and, 173 information and, 137 innovation and, 109 mastery and, 162 meetings and, 126 membership and, 150 purpose and, 64 resources and, 101 strategy and, 92 structure and, 82 workflow and, 117 Percolate, 131–32 performance, 46 individual, 158–60, 172 Petrarch, 224 Pflaeging, Niels, 78, 180, 189–90 Pixar, 119–20, 191–92 planning, 91, 95, 96, 100 see also strategy Plato, 3 Play-Doh, 103 polycentric governance, 98 PopSugar, 135 practices, proposing, 207–12 priming, 193, 197–201, 236 Principles (Dalio), 152 Principles of Scientific Management, The (Taylor), 23–24, 29 priorities, 88–89 profit, 59–60 Project Aristotle, 221 projects, 113, 114, 117 management of, 112, 237 sprints and, 115, 237–38 status of, 121, 132 work in progress and, 115–16, 132 proposing practices, 207–12 psychological safety, 219–23, 236 purpose, 14, 54, 68–64, 67, 85 push vs. pull, 131–32 Quaroni, Guido, 192 railroads, 8, 22–23 Raworth, Kate, 246–47 Ready, The, 17–19, 123, 125, 143, 149, 174, 190, 217 recruiting and hiring, 79, 142–43 Reddit, 135 red team, 90–91 REI, 85 Reinventing Organizations (Laloux), 105 relatedness, 42 relief, 236–37 reputation, 78 resistance, 233–34 resources, 14, 54, 93–101 retrospectives, 123–24 return on assets (ROA), 31, 32 Rework (Fried and Hansson), 68–69 Ries, Eric, 107–8, 254, 255 risk, 68, 122, 132, 231 barbell strategy and, 86–87, 105–6 ritual, 143 Robertson, Brian, 202 Rogers, Carl, 38 roles, 72, 77, 80, 81, 111, 141, 157 decision making and, 72, 73 mixing of, 157–58 Rotter, Julian B., 154 roundabouts, 10–12, 13, 47, 55 Ruimin, Zhang, 76 Russell, Bertrand, 247 Ryan, Richard, 42 sabotage, 5–7 safety, psychological, 219–23, 236 Sahlberg, Pasi, 12 Saint-Exupéry, Antoine de, 212 salary, 164, 165, 168 see also compensation Salary.com, 170 Salesforce, 119 S&P 500, 29–30, 60 Santa Fe, USS, 67 Santa Fe Institute, 29 scaling change, 234–39 scenario planning, 90 Schaar, Tom, 259 Scientific Management, 22–24, 26, 48 Scott, Kim, 120 scribes, 122–23 Securities and Exchange Commission (SEC), 104, 255 self-determination theory, 42 self-employment, 33 self-evaluation, 154 self-management, 16–17 self-set pay, 168 Semler, Ricardo, 245, 258 Seneca, 189 Senge, Peter, 153, 202 sensing, 202–6, 231–32 signal-controlled intersections, 9–12, 13, 46, 55 Simple Sabotage Field Manual, 7 Sinek, Simon, 222 Sisodia, Raj, 60 Slack, 119, 134, 135 SLAM teams, 80 Snowden, Dave, 156, 188–89 Sociocracy, 70–71, 122 space: creating, 224–26, 228 holding, 226–28 liminal, 196, 197, 201 Spencer, Percy, 103 Spotify, 112–13, 160, 218 spread, 217–18 sprints, 115, 237–38 standards vs. defaults, 106–7 Starbucks, 85 startups, 27–28, 33, 76–77, 107, 197, 254 Lean Startup method, 107–8 status quo, 48, 90–91, 233 steering metrics, 60–61 stocks, 30–31 strategy, 14, 54, 83–92 strategy+business, 76 strategy review meetings, 3–4 structure, 14, 54, 75–82, 111 Svenska Handelsbanken, 13, 94, 227–28 Taleb, Nassim Nicholas, 86–88, 106 targets, 95, 97, 101 Taylor, Frederick Winslow, 21–24, 26, 29, 111, 153, 186, 257 teams, 79, 82, 113, 117, 141, 142, 172, 225–26 Ballpoint game for, 199–200 charters for, 144–45 dynamic, 81 gratitude and, 148 ICBD technique for, 222–23 red, 90–91 retrospectives and, 123–24 rituals and, 143 SLAM, 80 sprints and, 115, 237–38 status updates and, 121 teams of, 77, 197 work in progress and, 115–16, 132 technology, 256–57 TED, 128, 246, 257 telephone, 103 Teller, Astro, 49 tensions, sensing, 202–6 Tesla, Inc., 62, 86 Theory X and Theory Y, 39–41, 130 Thomison, Tom, 89 tipping point, 216 Torvalds, Linus, 132 Toyota, 20, 111, 235 TPG, 253 traffic flow, 9–12, 45 roundabouts for, 10–12, 13, 47, 55 signal-controlled intersections for, 9–12, 13, 46, 55 tragedy of the commons, 98 training, 6, 156–57 transparency, 129, 130–31, 134, 136, 190, 195, 258 compensation and, 168, 169–71, 173 radical, 152, 154 trust, 236 twenty percent time, 107 Twitter, 84 Urwick, Lyndall, 25 User Manual to Me, 147–48 value creation, 78, 111–14, 160 Valve Software, 66, 107 Vang-Jensen, Frank, 227 Vanguard, 48 venture capital, 253 Vrba, Elisabeth, 103 VUCA, 43 wages, 34, 166 see also compensation Wallander, Jan, 94, 227 Warby Parker, 96 waterline principle, 69–70, 72 Weber, Max, 25 WeWork, 87 Whole Foods, 59, 61, 170, 259 Wikipedia, 140 Williams, Ev, 84–85 workflow, 14, 54, 110–17 working in public, 132 work in progress (WIP), 115–16, 132 World War II, 6–7 Wright, Orville and Wilbur, 103 Y Combinator, 230 Zanini, Michele, 26 Zappos, 144 al-Zarqawi, Abu Musab, 129 Zobrist, Jean-François, 37, 42–43 Zuckerberg, Mark, 88 ABCDEFGHIJKLMNOPQRSTUVWXYZ About the Author Aaron Dignan is the founder of The Ready, an organization design and transformation firm that helps institutions like Johnson & Johnson, Charles Schwab, Kaplan, Microsoft, Lloyds Bank, Citibank, Edelman, Airbnb, Cooper Hewitt Smithsonian Design Museum, and charity: water change the way they work.

pages: 669 words: 210,153

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers
by Timothy Ferriss
Published 6 Dec 2016

But please know that it’s often the tiny details that really thrill someone enough to make them tell all their friends about you. Spirit animal: Black bear * * * Alexis Ohanian Alexis Ohanian (TW/IG: @alexisohanian, alexisohanian.com) is perhaps best known for being a co-founder of Reddit and Hipmunk. He was in the very first class of Y Combinator, arguably the world’s most selective startup “accelerator,” where he is now a partner. He is an investor or advisor in more than 100 startups, an activist for digital rights (e.g., SOPA/PIPA), and the best-selling author of Without Their Permission. “You Are a Rounding Error” “[I had] an executive at Yahoo!

A Damn-Giving Assignment of Less Than 15 Minutes Improve a notification email from your business (e.g., subscription confirmation, order confirmation, whatever): “Invest that little bit of time to make it a little bit more human or—depending on your brand—a little funnier, a little more different, or a little more whatever. It’ll be worth it, and that’s my challenge.” (See Derek Sivers’s best email ever on page 192.) ✸ One of his questions for founders who apply to Y Combinator: “What are you doing that the world doesn’t realize is a really big fucking deal?” Giving Feedback to Founders—How Do You Express Skepticism? Alexis has many approaches, of course, but I liked this example of what Cal Fussman (page 495) might call “letting the silence do the work”: “I really think a lot can be conveyed with a raised eyebrow.”

The math especially doesn’t work if you screw it up like I did by getting overexcited and dropping $50K on your first investment. Here’s how I did a course correction and dealt with this problem: First, I invested very small amounts in a few select startups, ideally those in close-knit “seed accelerator” (formerly called “incubator”) networks like Y Combinator and Techstars. Then, I did my best to deliver above and beyond the value of my investment. In other words, I wanted the founders to ask themselves, “Why the hell is this guy helping us so much for a ridiculously small amount of equity?” This was critical for establishing a reputation as a major value-add, someone who helped a lot for very little.

pages: 299 words: 91,839

What Would Google Do?
by Jeff Jarvis
Published 15 Feb 2009

Idealab, founded by nonstop entrepreneur Bill Gross, has launched a large number of companies as an incubator, including Overture (which became the basis for Yahoo’s—and, indirectly, Google’s—search-ad industry), PetSmart, Picasa (now Google’s photo software), Citysearch, and the electric-car company Aptera Motors. Both incubators provide space, office services, advice, and money. Then there is a series of next-generation incubators built to advise and invest in new web 2.0 enterprises. These include Y Combinator, which funds small entrepreneurs and helps them get from idea to company; Seed Camp, which runs regular competitions for start-up help in Europe; and Betaworks, which funds and advises early start-ups. Investors still need to reach into the dorms at MIT and Stanford—or farther back into my son’s high school—where ideas are hatching.

See vendor relationship management Waghorn, Rick, 56 Wales, Jimmy, 60, 87 Wall Street Journal, 129 Wal-Mart, 54–55, 101 Washlet, 181 Wattenberg, Laura, 233 Weinberger, David, 3, 82, 96–97, 137, 149, 232 Westlaw, 224 widgets, 36–37 Wikia, 60 Wikileaks.org, 92–93 Wikinomics (Tapscott), 113, 151, 225 Wikipedia communities and, 50 growth of, 66 mistakes in, 92–93 open-source and, 60 speed of, 106 wikitorials, 86–87 Williams, Evan, 105–6 Williams, Raymond, 63 Wilson, Fred, 35, 176, 189–92, 225, 237 Wine.com, 158 WineLibrary.TV, 157 The Winner Stands Alone (Coelho), 142 Wired, 33 wireless access, 166 airlines and, 182–83 wireless spectrum, 166 The Wisdom of Crowds (Surowiecki), 88 The Witch of Portobello (Coelho), 142–43 WNYC, 128 Wojcicki, Anne, 205 Wolf, Maryanne, 235 World Economic Forum, 48, 113 Wyman, Bob, 211 Yahoo, 5, 36, 58 China and, 99–100 communities and, 50 Yang, Jerry, 36 Y Combinator, 193 youth, 191–94, 212 YouTube, 6, 20, 33, 37 Zappos, 161 Zara, 103–4 Zazzle, 180 Zell, Sam, 129 zero-based budgeting, 79–80 Zillow, 75, 80, 187 Zipcar, 176 Zopa, 196 Zuckerberg, Mark, 4, 48–53, 94–95 About the Author Jeff Jarvis is the proprietor of one of the Web’s most popular and respected blogs about the internet and media, Buzzmachine.com.

pages: 374 words: 89,725

A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas
by Warren Berger
Published 4 Mar 2014

Here is where the two dreamers ran headfirst into conventional wisdom. Initially, no one, outside of Chesky, Gebbia, and a third partner they brought on, thought this was an idea that made business sense or was worth supporting. Paul Graham, a renowned angel investor in Silicon Valley who runs the start-up incubator firm Y Combinator, believed quite simply, “No one would want to stay in23 someone else’s bed.” The idea that would eventually become Airbnb was challenging a basic assumption: that you needed established, reputable hotels to provide accommodation for out-of-town visitors. Those paying close attention might have noticed that just a few years prior to this, lots of people held similar assumptions about cars—you could buy them, you could rent them, but there was no practical way to share them.

When they noticed that many of the visitors to their site were asking about foreign cities, this led to a big question: Why are we limiting this to the U.S.? What if we go global? Within less than two years, they were in more than a hundred countries, doing a million bookings, and flush with more than one hundred million investment dollars. They had even won over early skeptics such as Y Combinator’s Graham, who became one of their seed investors. These days, Gebbia and Chesky are asking a whole new set of questions about whether it’s feasible to create a “sharing economy.” At the core of this idea is the fundamental question Why should we, as a society, continue to buy things that we really don’t need to own?

pages: 326 words: 91,559

Everything for Everyone: The Radical Tradition That Is Shaping the Next Economy
by Nathan Schneider
Published 10 Sep 2018

He cited recent basic-income experiments in India that showed promise for combating poverty among people the tech economy has left behind. Diamandis later reported having been “amazed” by the potential.12 That year, also, celebrity investor Marc Andreessen told New York magazine that he considered basic income “a very interesting idea,” and Sam Altman of the elite startup accelerator Y Combinator called its implementation an “obvious conclusion.”13 Those were just the early salvos. What people generally mean by universal basic income is the idea of giving everyone enough money to provide for the necessities of life. Imagine, say, a $20,000 check every year for every US citizen. The idea appeals to hopeful longings for a humane, egalitarian sort of commonwealth—a recognition that people, including those who are currently poor, will know better than any top-down welfare program what to spend the money on.

That same week, an article appeared in the Atlantic making a “conservative case for a guaranteed basic income” on the basis of devolving federal powers.16 This is one of those rare notions that is sneaking into plausibility from both the political left and right, more quickly than many proponents expected. The idea gets less utopian by the moment. Barack Obama mentioned basic income approvingly in the waning days of his presidency. Governments from Finland to Hawaii are exploring policy options, and Y Combinator’s nonprofit arm is funding a private experiment of its own in Oakland. For years, the journalist and entrepreneur Peter Barnes has been calling for a universal dividend funded through the use of common goods, particularly a tax on carbon emissions; now, governments in places from California and Oregon to the District of Columbia have considered plans to implement such a system.

pages: 350 words: 90,898

A World Without Email: Reimagining Work in an Age of Communication Overload
by Cal Newport
Published 2 Mar 2021

Curious to figure out where their time was going, they cobbled together some scripts to monitor their behavior. As Robby Macdonell, the current CEO, explained to me, their experiment became popular in their social circles: “We were hearing from more and more people who wished they could see what their application use actually looked like.” In the winter of 2008, the idea was accepted by the prestigious Y Combinator incubator, and the company was born. The primary purpose of RescueTime is to provide individual users with detailed feedback on their behavior so they can find ways to be more productive. Because the tool is a web application, however, all this data is stored in central servers, which makes it possible to aggregate and analyze the time use habits of tens of thousands of users.

See also academic sector teams attention capital principle and, xx, 133–34 in auto industry, 97–98 and email communication, 19–20, 31–32 and hyperactive hive mind, 24 and in-person communication, 54–55, 223–26 management of, 110–11 new workflow approaches and, xx, 108–9 optimal size of, 85–88 predictable time off (PTO) and, 37 and production process, 144–51 and status meeting protocols, 209–10 task boards and, 153–55, 157, 164, 166–67, 244 vested in new workflows, 125–27, 133 and working in sprints, 234–39 technological determinism, 73–77, 83 technology, 188, 221 changes in, 257–58, 260–61 and diminishment of specialization, 256 in early medieval times, 71–73, 76–77 email and, 69–77 and extreme programming (XP), 222–27, 233–34, 241, 246 history of, 68–77, 215–19 and increased overload, 256 investment in, 216–17, 235–36 office culture and, 75–76 and pair programming, 224–26 revolution in, 247 start-ups of, 196–200, 222 and support staff, 247 ticketing systems and, 130–31 unintended results of, 73–77, 215–19 See also computers; digital telephone answering service for, 121–27 communicating by, 78, 81–82, 87, 127, 160 separation from, 45–46 See also smartphones Tenner, Edward, 215–17, 219, 246 text messages, 61, 67, 206, 227 Thrive, 46 Thrive Away, 46–47 ticketing systems, 26–28, 130–31 time budgeting of, 239–46 driven by email, 105, 107–8 management of, 56–57, 98–100, 188–90 tracking of, xvi–xvii, 8–13 Time magazine, 21 Tomlinson, Ray, 203 Torah, the, 43 Trello, 105–8, 111, 124, 132–33, 148, 155, 159, 161–62, 166 Truman, Harry, 21 Tube messaging system, 63–65 Turing, Alan M., 81, 181 Turkle, Sherry, 54 Twitter, 30, 227 University College London, 40 University of California, Irvine, xvi–xvii, 7–8 University of California, Los Angeles, 72 University of Maryland, 208–9 University of Southern California, 205 Upwork, 190 vacation, 43, 46, 113 Vaden, Rory, 174 value production, 221 and administrative vs. specialized work, 246–47, 254, 256 attention capital principle and, 103, 110 human brain and, 113, 121, 218, 227 maximizing it, xx–xxi, 29, 101, 194, 252 of specialized professionals, 226–29, 232–34, 251–53, 256 and working in sprints, 238 Van Vleck, Tom, 203 Vanderkam, Laura, 230–31 venture capitalists, 196–97 videoconferencing, 32 Wall Street Journal, The, 100, 205, 207–8 War Department, 21–23 websites, 10, 106, 144, 193 designers of, 206, 230 developers of, 10–12, 197–99 updating them, 171 See also specific names Wharton, 86–87, 238 White, Lynn Jr., 72–73 Why Things Bite Back: Technology and the Revenge of Unintended Consequences (Tenner), 215–17 William Morris Agency, 66 women, and service tasks, 245 Woodward, Greg, 222–26, 233 work reform movements, 115 Work the System (Carpenter), 122, 125 workday desk work vs. meetings, 8–9 divided into five-minute buckets, 12, 184–85 divided into working spheres, 57–58 eight-hours long, 225–26 five-hours long, 100–102 structured around hive mind, xvii workflows, xix–xxii adding complexity to, 117–18 alternative ones, 61, 194 broken ones, 91 changing work execution and, 124–27 creating better ones, 92–93, 112–14, 219–20, 260–61 differentiating them, 111–12 frustrations over shifting of, 116–17 haphazardly constructed, 239 implementing new ones, 108–9 informal, 137–38, 141–43, 145 key elements of, 110–11, 186 to minimize communication overload, 112–14 to minimize context switches, 112–14, 152 new approaches to, 105–8, 110–11, 120–29, 133, 145 old-fashioned, 248 optimized, 220, 222–23 overhauling them, 199, 233 personal, 124, 127–33 project-focused, 154, 162 structured, 137–40, 150–51, 249 tracking revenue of, 114 transformed by email, xvi–xvii unstructured, 194, 258 See also hyperactive hive mind workflow; project-board workflow workloads deep-to-shallow work ratios and, 243–44 increased by email, 55–61 keeping track of, 56–57 overloaded, 56–61, 219–22, 228 and working in sprints, 238 workplace, recent history of, 215–19 works in progress (WIP) limit, 164–65 workweek dedicated to sprints, 236–37 and separating support self/specialist self, 255–56 standard schedule for, 192 traditional forty hours in, 225–26 See also 4-Hour Workweek, The (Ferriss) writing profession, 19, 204–5, 216, 227, 234 written language, 48–49 x.ai, 188, 192 XP methodology, 222–27, 233–34, 241, 246 Y Combinator, 10 YouTube, 165 Zeratsky, John, 236 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z About the Author Cal Newport is an associate professor of computer science at Georgetown University, where he specializes in the theory of distributed systems, as well as a New York Times bestselling author who writes for a broader audience about the intersection of technology and culture.

pages: 102 words: 27,769

Rework
by Jason Fried and David Heinemeier Hansson
Published 9 Mar 2010

The authors live by the credo ‘keep it simple, stupid’ and Rework possesses the same intelligence—and irreverence—of that simple adage.” —John Maeda, author of The Laws of Simplicity “Rework is like its authors: fast-moving, iconoclastic, and inspiring. It’s not just for startups. Anyone who works can learn from this.” —Jessica Livingston, partner, Y Combinator; author, Founders at Work INTRODUCTION FIRST The new reality TAKEDOWNS Ignore the real world Learning from mistakes is overrated Planning is guessing Why grow? Workaholism Enough with “entrepreneurs” GO Make a dent in the universe Scratch your own itch Start making something No time is no excuse Draw a line in the sand Mission statement impossible Outside money is Plan Z You need less than you think Start a business, not a startup Building to flip is building to flop Less mass PROGRESS Embrace constraints Build half a product, not a half-assed product Start at the epicenter Ignore the details early on Making the call is making progress Be a curator Throw less at the problem Focus on what won’t change Tone is in your fingers Sell your by-products Launch now PRODUCTIVITY Illusions of agreement Reasons to quit Interruption is the enemy of productivity Meetings are toxic Good enough is fine Quick wins Don’t be a hero Go to sleep Your estimates suck Long lists don’t get done Make tiny decisions COMPETITORS Don’t copy Decommoditize your product Pick a fight Underdo your competition Who cares what they’re doing?

pages: 344 words: 96,020

Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success
by Sean Ellis and Morgan Brown
Published 24 Apr 2017

The company’s cloud-based file storage and sharing service had built up a good early fan base, concentrated primarily among the tech-savvy community centered in Silicon Valley. Even before the product was completely built, Houston had pushed a video prototype online illustrating how the service would work, which had earned him the backing of the powerful Y Combinator start-up incubator and drawn a flood of early adopters. It became pretty clear that Houston was on to something when the waiting list he was keeping for the beta version grew from 5,000 to 75,000 in a blink of an eye when a second video was posted on news aggregator site Digg and went viral.1 The next wave of users who signed up after the public launch were happy with the service, but Houston was still running into a wall trying to break out beyond the tech elite.

As Facebook’s original growth team lead, Chamath Palihapitiya, wisely cautioned in one talk, “If you can’t be extremely clinical and extremely unemotionally detached from the thing that you’re building, you will make these massive mistakes and things won’t grow because you don’t understand what’s happened.”9 To clarify how dedication to improving a North Star metric helps make difficult decisions about how to spend time and resources, let’s look at how the Airbnb founders decided to conduct an experiment they thought might generate more nights booked—their North Star. To begin, they looked at their data to identify markets where bookings were lagging and, to their surprise, discovered that New York City was underachieving. Clearly, New York is a major tourist destination, so they dug in, with early investor Paul Graham of Y Combinator, to analyze why bookings weren’t stronger. Reviewing the apartment listings for the city, cofounder Joe Gebbia recalls that “the photos were really bad. People were using camera phones and taking Craigslist-quality pictures. Surprise! No one was booking because you couldn’t see what you were paying for.”

pages: 443 words: 98,113

The Corruption of Capitalism: Why Rentiers Thrive and Work Does Not Pay
by Guy Standing
Published 13 Jul 2016

On the other side of the Atlantic, the provincial government in Ontario, Canada, is planning a basic income experiment, and the provinces of Quebec and Alberta have indicated interest. There are also private initiatives that show up the timidity of politicians. In California, Sam Altman, president of Y Combinator, a start-up ‘accelerator’, has committed funds to a five-year basic income experiment. GiveDirectly, a charity that channels money directly from online donors to recipients, has moved from giving random individuals a basic income to more community-oriented experiments in Africa. In Germany, a crowdfunding scheme selects individuals by lottery to receive a basic income for a year.

W. 1 Phillips curve 1 ‘pig cycle’ effects 1 Piketty, Thomas 1, 2 Pinochet, Augusto 1, 2, 3 platform debt 1 Plato 1 plutocracy 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 Polanyi, Karl 1 policing 1 political consultancy 1 Politico magazine 1 Ponzi schemes 1 Poor Law Amendment Act (1834) 1 POPS (privately owned public spaces) 1 Portfolio Recovery Associates 1 ‘postcapitalism’ 1 poverty traps 1, 2, 3 precariat and commons 1, 2, 3, 4, 5 and debt 1, 2 and democracy 1, 2 emergence of 1 growth of 1, 2 and rentier platforms 1, 2, 3 revolt of see revolt of precariat predatory creditors 1 ‘primitive rebel’ phase 1 Private Landlords Survey (2010) 1 privatisation and commons 1, 2, 3, 4, 5, 6, 7, 8, 9 and debt 1, 2 and democracy 1 and neo-liberalism 1 and rentier platforms 1 and revolt of precariat 1 and shaping of rentier capitalism 1, 2, 3, 4, 5, 6, 7 professionalism 1 ‘profit shifting’ 1 Property Law Act (1925) 1 Proudhon, Pierre-Joseph 1 Public and Commercial Services Union 1 PricewaterhouseCoopers (PwC) 1, 2, 3. 4, 5, 6 QE (quantitative easing) 1, 2, 3, 4, 5, 6 Quayle, Dan 1 QuickQuid 1 Reagan, Ronald 1, 2 reCAPTCHA security system 1 ‘recognition’ phase 1 ‘redistribution’ phase 1 Regeneron Pharmaceuticals 1 rentier platforms and automation 1 and cloud labour 1 and commodification 1 and ‘concierge’ economy 1 ecological and safety costs 1 and occupational dismantling 1 and on-call employees 1 and precariat 1, 2, 3 and revolt of precariat 1, 2 and ‘sharing economy’ 1, 2, 3, 4 and underpaid labour 1 and venture capital 1 rentiers ascendency of 1, 2 and British Disease 1 classical images of 1 and commons see commons and debt 1, 2, 3, 4, 5, 6, 7 and democracy 1, 2, 3, 4, 5, 6, 7 digital/tasking platforms see rentier platforms ‘euthanasia’ of 1, 2, 3, 4, 5, 6, 7 lies of rentier capitalism 1, 2, 3 revolt of precariat see revolt of precariat shaping of see shaping of rentier capitalism subsidies for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ‘representation’ phase 1 ‘repression effect’ 1 Research of Gartner 1 revolt of precariat and basic income systems 1 and commons 1, 2, 3, 4, 5 ‘euthanasia’ of rentiers 1, 2, 3, 4, 5 inequality of rentier capitalism 1, 2, 3 and intellectual property 1, 2, 3 and neo-liberalism 1, 2, 3, 4, 5, 6 organisational forms 1 potential growth of movement 1 progressive political reengagement 1, 2 and rentier platforms 1, 2 rights as demands 1 sovereign wealth funds 1 wage and labour regulation 1, 2 ‘right to buy’ schemes 1, 2, 3, 4 Robbins, Lionel 1 Rockefeller, David 1 Rockefeller, John D. 1 Rolling Stone 1 Romney, Mitt 1 Roosevelt, Franklin D. 1 Ross, Andrew 1 Ross, Michael 1 Rothermere, Viscount 1, 2 Royal Bank of Scotland 1, 2 Royal Mail 1 Royal Parks 1 Rubin, Robert 1, 2 Rudd, Amber 1 Ruralec 1 Ryan, Conor 1 Sainsbury, Lord 1 Samsung 1, 2, 3 Sanders, Bernie 1, 2, 3 Sassen, Saskia 1 school–business partnerships 1 Schröder, Gerhard 1 Schwab Holdings 1 Schwarz, Dieter 1 Scottish Water 1 Second Gilded Age 1, 2, 3 Securitas 1 securitisation 1, 2, 3 selective tax rates 1 Selma 1 shaping of rentier capitalism branding 1 Bretton Woods system 1, 2, 3 and copyright 1 and ‘crony capitalism’ 1, 2, 3 dispute settlement systems 1, 2, 3 global architecture of rentier capitalism 1 lies of rentier capitalism 1 and neo-liberalism 1, 2 patents 1 and privatisation 1, 2, 3, 4, 5, 6, 7 and ‘shock therapy’ 1, 2 trade and investment treaties 1 ‘sharing economy’ 1, 2, 3, 4, 5, 6 Shelter 1 ‘shock therapy’ 1, 2, 3, 4 Shore Capital 1 Sierakowski, Slawomir 1, 2, 3, 4 silicon revolution 1 Simon, Herbert 1 Sirius Minerals 1 Skoll Centre for Social Entrepreneurship 1 Sky UK 1, 2 SLABS (student loan asset-backed securities) 1, 2 Slim, Carlos 1, 2 Smith, Adam 1 Snow, John 1 Social Care Act (2012) 1 social commons 1, 2, 3 social dividend systems 1, 2 social housing 1 ‘social income’ 1, 2, 3, 4, 5, 6, 7, 8 social strike 1 SoFi (Social Finance) 1 Solidarność (Solidarity) movement 1 South West Water 1 sovereign wealth funds 1 spatial commons 1, 2 Speenhamland system 1, 2, 3 Spielberg, Steven 1 Springer 1 ‘squeezed state’ 1 Statute of Anne (1710) 1 Statute of Monopolies (1624) 1 StepChange 1 Stevens, Simon 1 ‘strategic’ debt 1 strike action/demonstrations 1, 2, 3 student debt 1, 2 subsidies 1 and austerity 1, 2 and bank ‘bailouts’ 1 and charities 1 and ‘competitiveness’ 1 direct subsidies 1 and moral hazards 1 and ‘non-dom’ status 1 and quantitative easing 1, 2 for rentiers 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 selective tax rates 1 and sovereign wealth funds 1 subsidised landlordism 1 tax avoidance and evasion 1 tax breaks 1, 2, 3, 4, 5 tax credits 1 Summers, Larry 1, 2 Sun, The 1, 2 Sunday Telegraph 1 Sunday Times 1 Sutton Trust 1 ‘sweetheart deals’ 1 tasking platforms see rentier platforms TaskRabbit 1, 2, 3, 4, 5 Tatler magazine 1 tax avoidance/evasion 1 tax breaks 1, 2, 3, 4, 5 tax credits 1, 2, 3 Tax Justice Network 1 Tax Research UK 1 Taylor & Francis 1 Tennessee Valley Authority 1 ‘tertiary time’ regime 1 Tesco 1 Texas Permanent School Fund 1 Textor, Mark 1 Thames Water 1 Thatcher, Margaret 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 The Bonfire of the Vanities 1 The Constitution of Liberty 1 The General Theory of Employment, Interest and Money 1 The Innovator’s Dilemma 1 think tanks 1 ‘thinner’ democracy 1 ‘Third-Way’ thinking 1, 2, 3 Times, The 1 TISA (Trade in Services Agreement) 1 Tottenham Court Road underground station 1 TPP (Trans-Pacific Partnership) 1, 2, 3 Trades Union Congress 1, 2 ‘tragedy of the commons’ 1 ‘tranching’ of loans 1 Treaty of Detroit (1950) 1, 2 Treuhand 1 TRIPS (Agreement on Trade-Related Aspects of Intellectual Property Rights) 1, 2, 3, 4 trolling (of patents) 1 Trump, Donald 1, 2 TTIP (Trans-Atlantic Trade and Investment Partnership) 1, 2, 3, 4 Turnbull, Malcolm 1 Turner, Adair 1 Twain, Mark 1 Uber 1, 2, 3, 4, 5, 6, 7 ‘ultra-loose’ monetary policy 1 underpaid labour 1 UNESCO (UN Educational, Scientific and Cultural Organization) 1 UNHCR (UN refugee agency) 1 Unison 1 Unite 1 UnitedHealth Group 1 universal credit scheme 1 universal justice 1 UpCounsel 1 Upwork 1, 2 Uruguay Round 1, 2, 3 USPTO (US Patent and Trademark Office) 1 Vattenfall 1 Veblen, Thorstein 1 venture capital 1 Veolia 1 Vero Group 1 Victoria, Queen 1 Villeroy de Galhau, François 1 Vlieghe, Gertjan 1 Warner Chappell Music 1 Watt, James 1 welfare abuse/fraud 1 Wilde, Oscar 1 Wilson, Fergus 1 Wilson, Judith 1 WIPO (World Intellectual Property Organization) 1, 2, 3, 4, 5, 6 Wolf, Martin 1, 2 Wolfe, Tom 1 Wonga 1, 2 Work Capability Assessment 1 Work Programme 1 World Bank 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 World Economic Forum 1 world heritage sites 1 Wriglesworth Consultancy 1 WTO (World Trade Organization) 1, 2, 3, 4, 5, 6 Y Combinator 1 Yanukovych, Viktor 1 Yukos 1 de Zayas, Alfred-Maurice 1 van Zeeland, Marcel 1 Zell, Sam 1 zero-hours contracts 1, 2, 3 Zipcar 1 Copyright First published in Great Britain in 2016 by Biteback Publishing Ltd Westminster Tower 3 Albert Embankment London SE1 7SP Copyright © Guy Standing 2016 Guy Standing has asserted his right under the Copyright, Designs and Patents Act 1988 to be identified as the author of this work.

pages: 368 words: 96,825

Bold: How to Go Big, Create Wealth and Impact the World
by Peter H. Diamandis and Steven Kotler
Published 3 Feb 2015

But mostly it’s an at-a-glance alert system—using its big flat screen to notify you about incoming cellular messages. The inPulse developed a core fan base, but because the first iteration worked only with a BlackBerry (this was 2008 and Migicovsky, like BlackBerry, was Canadian), it didn’t go big. Yet there was enough early initial traction that Migicovsky decided to move the project to Y Combinator in Silicon Valley, which is also where he found the seed money to start manufacturing an updated version of the inPulse. And that’s when he hit the wall. Some great customer feedback had led to further rounds of design improvements, which resulted in an entirely new watch, the Pebble. It’s a great watch.

Craig, 64, 65–66 Vicarious, 167, 295n video games, 38, 45, 117, 144 video surveillance, 43 Virgin Atlantic, 124, 125, 126 Virgin Galactic, 96–97, 115, 125, 127 Virgin Management group, 111, 127, 128 Virgin Music, 124, 125 voice recognition, 58 Voltaire, 275 Vor-Tek, 252–53 vWorker, 149 Wachs, Eli, 258 Walmart, 72, 133 Wardenclyffe (Tesla’s laboratory), 178 Watson (IBM supercomputer), 56–57, 59 Waze, 47 web browsers, 11, 27 Wendy Schmidt Oil Cleanup XCHALLENGE, 247, 250, 251–53, 262, 263, 264 Weston, Graham, 50, 51, 257 Wikipedia, 11, 156, 291n Wilson, Rainn, 200, 207 Winning the Oil Endgame (Lovins), 222 Wired, 10, 15, 43, 135–36, 138, 144, 194, 224, 255 Wojcicki, Susan, 84 X.com, see PayPal XPRIZE competitions, 54, 96, 109, 112, 172, 244–45, 248–49, 255, 262, 265, 267, 272, 299n Ansari XPRIZE, 76, 96, 115, 127, 134, 246, 249, 253, 260, 261, 262, 263, 264, 265, 266, 267, 268 Google Lunar, 139, 249 Qualcomm Tricorder XPRIZE, 253 Wendy Schmidt Oil Cleanup XCHALLENGE of, 247, 250, 251–53, 262, 263, 264 XPRIZE Foundation, xi, xv, 115, 139, 237, 250, 257, 267, 269, 279, 299n Yahoo!, 15, 167 Y Combinator, 176 YouTube, 128, 135, 138, 154, 213, 254 Yucatan Peninsula, Mexico, ix Zappos, 80 Zero-G, 96, 110 Zip2, 117 Zooniverse, 145–46, 221, 228 Zuckerberg, Mark, 167 Simon & Schuster 1230 Avenue of the Americas New York, NY 10020 www.SimonandSchuster.com Copyright © 2015 by PHD Ventures All rights reserved, including the right to reproduce this book or portions thereof in any form whatsoever.

pages: 331 words: 95,582

Golden Gates: Fighting for Housing in America
by Conor Dougherty
Published 18 Feb 2020

It’s the same vaguely idealistic “we promote economic empowerment” pitch given by finance start-ups across the Bay Area, perhaps because RealCrowd is itself a product of Silicon Valley. The company’s CEO, Adam Hooper, went to Penn State and brokered commercial real estate deals in Sacramento before entering Y Combinator, the famed “accelerator” that serves as a kind of exclusive boot camp for start-up founders, in 2013. A year later RealCrowd raised $1.6 million in seed funding from investors including Initialized Capital, a San Francisco venture capital firm, and Paul Buchheit, a former Google engineer who created Gmail and is credited with suggesting “Don’t Be Evil” as the company’s former motto.

High Rent, Price Anxiety, and NIMBYism” (Hankinson), 126 Wicks, Buffy, 218–19, 225, 226 Wiener, Scott, 117–32, 134–43, 185, 186, 187, 203, 208, 211, 221, 225 SB 35 bill of, 135–40, 164 SB 50 bill of, 228–29 SB 827 bill of, 188–90, 192, 193–95, 218–19 senate campaign of, 123–25, 129–31 work ethic of, 137 Woo, Vincent, 131 Wooten, Wilma J., 154–55 work commutes, xii, xiii, xiv World War II, xiii, 40, 63, 65, 68, 145 Yahoo, 24 Y Combinator, 178–79 Yelp, 25–26, 29, 173 Yglesias, Matthew, 24 YIMBY (“yes in my backyard”), 27, 35, 38, 111, 127, 130, 131, 133, 136–38, 141–43, 186, 187, 190, 198, 200, 209–12, 214, 218–25, 228–31, 235 California YIMBY, 188, 195, 211, 218, 220 defining, 210 Proposition C and, 210–11, 224 Proposition 10 and, 210–12, 215–16, 224 SB 827 bill and, 188–90, 192, 193–95, 218–19 YIMBY Action, 130, 138, 140, 209–11, 216, 219, 220 YIMBY Congress, 126–28, 141 YIMBY Gala, 143, 209 YIMBY Market Urbanists, 212, 215 YIMBY PAC, 130 YIMBY Socialists, 212, 215 YIMBYtown conference in Boston, 227–30, 234–35 YIMBYtown conference in Boulder, 35–38, 106, 210, 225 YIMBYtown conference in Oakland, 210 Yom Kippur War, 84 Zeta Communities, 160, 162 zoning and land-use rules, 8–9, 19, 21, 23, 24, 30–31, 85, 108, 109, 134, 136, 149, 157, 167, 235 exclusionary, 9, 23, 31, 86, 108, 193 gentrification and, 193 permits, 120–21, 158 SB 50 bill and, 228–29 SB 827 bill and, 188–90, 192, 193–95, 218–19 ABCDEFGHIJKLMNOPQRSTUVWXYZ ABOUT THE AUTHOR Conor Dougherty is an economics reporter at The New York Times.

pages: 100 words: 31,338

After Europe
by Ivan Krastev
Published 7 May 2017

In the technological dystopia that we see dawning, there will be no jobs left for human beings. According to a recent UK government study, over the next thirty years, 43 percent of current jobs in the EU will be automated. How society will function when work is a privilege and not a right or duty is not a theoretical question. Y Combinator, a big start-up incubator, has already announced it will conduct a basic income experiment with roughly one hundred families in Oakland, California, giving them between $1,000 and $2,000 a month for up to a year, no strings attached, to see what people do when they do not need to work to earn a living.

pages: 139 words: 35,022

Roads and Bridges
by Nadia Eghbal

Docker’s 2014 revenue was less than $10 million.[52] Npm is a package manager to help Node.js developers share and manage their projects, released in 2010. Npm raised nearly $11M in funding since 2014 from True Ventures and Bessemer Ventures, among others. Their business model focuses on paid features that support privacy and security. Meteor is a JavaScript framework that was first released in 2012. It was incubated by Y Combinator, a prestigious startup accelerator that also incubated companies like AirBnB and Dropbox. Meteor has received over $30M in funding to date from firms including Andreessen Horowitz and Matrix Partners.[53] Meteor’s business model focuses on an enterprise platform called Galaxy, released in October 2015, for operating and managing Meteor applications.[54] The venture funding approach is relatively new, and growing rapidly.

pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations
by Nicholas Carr
Published 5 Sep 2016

Jeff Bezos and Elon Musk dream of establishing Learyesque space colonies, celestial Burning Mans. Peter Thiel is slightly more down to earth. His Seasteading Institute hopes to set up floating technology incubation camps on the ocean, outside national boundaries. “If you can start a new business, why can you not start a new country?” he asks. In a speech last fall at the Y Combinator Startup School, venture capitalist Balaji Srinivasan channeled Leary when he called for “Silicon Valley’s Ultimate Exit”—the establishment of a new country beyond the reach of the U.S. government and other allegedly failed states. “You know, they fled religious persecution, the American Revolutionaries which left England’s orbit,” Srinivasan said, referring to the Pilgrims.

(Starr), 218 When We Are No More (Rumsey), 325–27 Whitman, Walt, 20, 183, 184 wicks, 229–30 Wiener, Anthony, 315 wiki, as term, 19 “wikinomics,” 84 Wikipedia, xvi, 21, 192 in fact-mongering, 58 hegemony of, 68 ideological split in, 18–20 slipshod quality of, 5–8 wiki-sects, 18 Wilde, Oscar, 174, 308 Williams, Anthony, 84 Wilson, Fred, 11 Windows Home Server, 32 Winer, Dave, 35 wings, human fascination with, 329–30, 335, 340–42 wingsuits, 341–42 Wired, xvii, xxi, 3, 4, 106, 156, 162, 174, 195, 232 Wittgenstein, Ludwig, 215 Wolf, Gary, 163 Wolf, Maryanne, 234 Wolfe, Tom, 170 work: as basis for society, 310–11, 313 in contemplative state, 298–99 efficiency in, 165–66, 237–38 job displacement in, 164–65, 174, 310 trivial alternatives to, 64 World Brain (Wells), 267 World Health Organization, 244 World of Warcraft, 59 Wozniac, Steve “Woz,” 32 Wright brothers, 299 writing: archiving of, 325–27 and invention of paper, 286–87 writing skills, changes in, 231–32, 234–35, 240 Xbox, 64, 93, 260 X-Ray Spex, 63 Yahoo, 67, 279–80 Yahoo People Search, 256 Y Combinator Startup School, 172 Yeats, William Butler, 88 Yelp, 31 Yosemite Valley, 341–42 youth culture, 10–11 as apolitical, 294–95 music and, 125 TV viewing in, 80–81 YouTube, 29, 31, 58, 75, 81, 102, 186, 205, 225, 314 technology marketing on, 108–9 Zittrain, Jonathan, 76–77 zombies, 260, 263 Zuckerberg, Mark, xvii, xxii, 53, 115, 155, 158, 215, 225 Facebook Q & A session of, 210–11, 213, 214 imagined as jackal, xv ALSO BY NICHOLAS CARR The Glass Cage The Shallows The Big Switch Does IT Matter?

pages: 397 words: 102,910

The Idealist: Aaron Swartz and the Rise of Free Culture on the Internet
by Justin Peters
Published 11 Feb 2013

“Animals,” Graham called them: tenacious and intelligent young self-starters who didn’t need Ping-Pong tables in the office as long as they had a case of Dr Pepper and a reliable Internet connection. Graham decided to test his hypothesis and, in March 2005, announced that he was soliciting applications for a project he called the Summer Founders Program—an early version of what would eventually become the renowned start-up incubator Y Combinator. “The SFP is like a summer job, except that instead of salary we give you seed funding to start your own company with your friends,” he wrote on his blog.11 Aspiring young entrepreneurs proposed ideas for start-up companies to Graham; the most promising applicants would be invited to move to Cambridge, Massachusetts, and participate in a sort of start-up summer camp.

G., 99 Westlaw database, 173 White Friars, 174 Whole Earth Catalog, 12 Wikimedia Foundation, 150 Wikipedia, 124, 173, 241 Wikler, Ben, 10, 233, 248–49, 260 and Avaaz, 203, 241, 248 and Flaming Sword of Justice, 241, 243 and Swartz’s death, 262 Swartz’s friendship with, 203, 214, 262 and Swartz’s legal woes, 202, 217 Wilcox-O’Hearn, Zooko, 125, 129, 145 Wilhelm II, Kaiser, 71 Williams, Julie Kay Hedgepeth, 27 Wilson, Christopher P., The Labor of Words, 64–65, 70 Wilson, Holmes, 152, 155, 240, 241, 243, 263 Windows operating system, 106 Winer, Dave, 8, 131 Winn, Joss, 207 Wolcott, Oliver Jr., 25, 32, 33 Woodhull, Nathan, 10, 259 WordPress, 241 work as identity, 146 WorldCat, 179 World War I, 77 World War II, 78, 82, 208 World Wide Web: anniversary of, 237–38 archiving all of, 135–36, 173 commercial potential of, 112 as infinite library, 127–28 and Internet, 98, 108–10 introduction of, 98, 108 linking capacity of, 108, 238 malignant forces vs., 238 open, collaborative, 178, 237 popularization of, 112 World Wide Web Consortium (W3C), 127–29 Wyden, Ron, 226, 231 Xerox photocopy machine, 87–88 Xerox Sigma V mainframe, 95–97, 113 Yahoo, 185 Y Combinator, 147 Young America, 50–53 Zanger, Jules, 44 SCRIBNER An Imprint of Simon & Schuster, Inc. 1230 Avenue of the Americas New York, NY 10020 www.SimonandSchuster.com Copyright © 2016 by Justin Peters All rights reserved, including the right to reproduce this book or portions thereof in any form whatsoever.

pages: 406 words: 109,794

Range: Why Generalists Triumph in a Specialized World
by David Epstein
Published 1 Mar 2019

,” their work indicated that it is better to be a scientist of yourself, asking smaller questions that can actually be tested—“Which among my various possible selves should I start to explore now? How can I do that?” Be a flirt with your possible selves.* Rather than a grand plan, find experiments that can be undertaken quickly. “Test-and-learn,” Ibarra told me, “not plan-and-implement.” Paul Graham, computer scientist and cofounder of Y Combinator—the start-up funder of Airbnb, Dropbox, Stripe, and Twitch—encapsulated Ibarra’s tenets in a high school graduation speech he wrote, but never delivered: It might seem that nothing would be easier than deciding what you like, but it turns out to be hard, partly because it’s hard to get an accurate picture of most jobs. . . .

Brewer, “Ester Ledecka Is the Greatest Olympian at the Games, Even If She Doesn’t Know It,” Washington Post, February 24, 2018, online ed. “I was doing so many different sports”: J. Drenna, “Vasyl Lomachenko: ‘All Fighters Think About Their Legacy. I’m No Different,’” Guardian, April 16, 2018, online ed. “young people are just smarter”: M. Coker, “Startup Advice for Entrepreneurs from Y Combinator,” VentureBeat, March 26, 2007. a tech founder who is fifty: P. Azoulay et al., “Age and High-Growth Entrepreneurship,” NBER Working Paper No. 24489 (2018). “No one imagined silos like that”: G. Tett, The Silo Effect: The Peril of Expertise and the Promise of Breaking Down Barriers (New York: Simon & Schuster, 2015 [Kindle ebook]).

pages: 647 words: 43,757

Types and Programming Languages
by Benjamin C. Pierce
Published 4 Jan 2002

If we replace all the numbers and arithmetic operations with lambda-terms representing them and evaluate the program, we will get the same result. Thus, in terms of their effects on the overall results of programs, there is no observable difference between the real numbers and their Church-numeral representation. [7]It is often called the call-by-value Y-combinator. Plotkin (1975) called it Z. [8]Note that the simpler call-by-name fixed point combinator Y = λf. (λx. f (x x)) (λx. f (x x)) is useless in a call-by-value setting, since the expression Y g diverges, for any g. [9]It is also possible to derive the definition of fix from first principles (e.g., Friedman and Felleisen, 1996, Chapter 9), but such derivations are also fairly intricate

—Albert Einstein * * * * * * Index symbol Î alternate notation for type membership, 92 ⇒ arrow kind, 441 ⇓ big-step evaluation, 42 Π dependent function type, 463 :: derivation of, 203 ↑ divergence, 16 dom(Г) domain of Г, 101 ⋆ "quick check" exercise, xviii ⋆⋆ easy exercise, xviii ⋆⋆⋆ moderate exercise, xviii ⋆⋆⋆⋆ challenging exercise, xviii ↛ exercise without solution, xviii → function type, 100 :: kind membership, 449 →* multi-step evaluation, 39 → one-step evaluation, 36 ⇛ parallel reduction of types, 454 L record labels, 129 R+ transitive closure of R, 17 R* reflexive, transitive closure, 17 ▸ sample output from system, 25 \ set difference, 15 ↑d() shifting, 79 σ ∘ γ substitution composition, 318 <: subtyping, 181 ↓ termination, 16 ≡ type equivalence, 447, 453 : type membership, 92 _ wildcard binder, 46, 121 α conversion, 71 * * * * * * Index A abbreviations, see also derived forms parametric type-, 439 Abel, 409 abstract data types, 11, 226, 368–372 parametric, 450–453 partially abstract, 406 vs. objects, 374–377 abstract machine, 32 with store, 160 abstract syntax, 25, 53 tree, 53 abstraction full, 143 functional, 52 type abstraction and ascription, 123 abstraction principle, 339 abstractions, protecting user-defined, 3, 5, 368–377 activation record, 174 ad-hoc polymorphism, 340 ADT, see abstract data type Algol-60, 11 Algol-68, 11 Algorithm W (Damas and Milner), 337 algorithmic subtyping, 209–213, 417–436 algorithmic typing, 213–218 aliasing, 155–157 compiler analysis of, 170 allocation of references, 154 allsome implementation, 381–387 alpha-conversion, 71 Amadio-Cardelli algorithm for recursive subtyping, 309–311 Amber, 311 rule, 311, 312 AnnoDomini, 9 annotations and uniqueness of types, 135, 141 datatype constructors as, 355 implicit, 330–331 antisymmetric relation, 16 applications of type systems, 8–9 arith implementation, 23–49 arithmetic expressions typed, 91–98 untyped, 23–44 arrays bounds checking, 7 subtyping, 198–199 arrow types, 99–100 ascription, 121–123, 193, see also casting and subtyping, 193–196 assembly language, typed, 11 assignment to references, 153, 154 associativity of operators, 53 atomic types, see base types Automath, 11 automatic storage management, see garbage collection axiom, 27 axiomatic semantics, 33 * * * * * * Index B β-reduction, 56 Barendregt convention, 75 Barendregt cube, 465 base types, 117–118 and subtyping, 200 behavioral equivalence, 64 beta-reduction, 56 big-step operational semantics, 32, 43 binary methods, 375–377 binary operations on abstract data, 375–377 strong vs. weak, 375 binary relation, 15 binder, 55 binding (OCaml datatype of bindings), 85, 113–115 bisimulation, 284 BNF (Backus-Naur form), 24 booleans, 23–44, see also Church encodings Bot type, 191–193 algorithmic issues, 220 with bounded quantification, 436 bot implementation, 220 bottom-up subexpressions of a recursive type, 304 bound variables, 55, 69–72 bounded meet, 219 bounded quantification, 11, 389–409 and intersection types, 400, 409 existential types, 406–408, 435–436 higher-order, 467–473 joins and meets, 432–435 object encodings, 411–416 typechecking algorithms, 417–436 undecidability, 427–431 with Bot type, 436 bounded type operators, 467, 473 bounds checking, see arrays boxed values, 201 boxed vs. unboxed argument passing, 341 * * * * * * Index C C, 6, 45 C#, 7, see also Java C++, 6, 226, see also Java c0, c1, c2, etc. (Church numerals), 60 calculus of constructions, 11, 465 call stack and exception handling, 173–174 call-by-name evaluation, 57 call-by-need evaluation, 57 call-by-value evaluation, 57 call-by-value Y-combinator, 65 call/cc, see continuations candidate, reducibility, 150 canonical forms lemma, 96, 105, 190, 405, 458 capture-avoiding substitution, 70 cartesian product type, 126–127 casting, 193–196, 247–264, 357, see also ascription and abstraction, 194 and reflection, 196 as substitute for polymorphism, 195–196 implementation, 196 categorial grammar, 9 category theory, 12 CCS, 34 Cecil, 226, 340 cell, see references chain, 18 channel types, 200 and subtyping, 200 chapter dependencies, xv Church encodings booleans, 58–59 in System F, 347–353 numerals, 60–63 pairs, 60, 396–400 records, 396–400 subtyping, 396–400 Church-Rosser property, 455 Church-style presentation, 111 class, 227, 231 granularity of, 231 classification, type systems as formalisms for, 2 Clean, 338 CLOS, 226, 340 closed set, 282 closed term, 55 closure, 17 property, 289 CLU, 11, 408 codomain of a relation, 16 coercion semantics for subtyping, 200–206, 224 coherence, 204–206 coinduction, 281–313 defined, 282–284 collection classes, 195–196 colored local type inference, 355 combinator, 55 combinatory logic, 76 complete induction, 19 completely bounded quantification, 431 completeness, 212 composition of substitutions, 318 compositionality, 2 comprehension notation for sets, 15 computation rules, 35, 72 computational effects, 153 concrete rule, 27 concrete syntax, 53 confluence, see Church-Rosser property congruence rules, 35, 72 conservativity of type analyses, 2, 92, 99–100 consistent set, 282 constraint types, 337 constraint-based typing rules, 321–326 constructive logic, 108 constructive type theory, 2, 11 constructors, see type operators contexts, 76–78 ML implementation, 83–85, 113–115 naming, 77 typing, 101 continuations, 178, 377 contractiveness, 300 contravariant position in a type, 185 type operator, 473 correctness by construction, 464 countable set, 15 counting subexpressions of μ-types, 304–309 course syllabi, xvii covariant position in a type, 185 type operator, 473 cube, Barendregt, 465 Curry-Howard correspondence, 2, 108–109, 341, 429 Curry-style presentation, 111 currying, 58, 73 of type operators, 440 cut elimination, 109 * * * * * * Index D Damas-Milner polymorphism, 331 dangling reference, 158 databases, 9, 142, 207 datatypes, 355, see also abstract data types constructors as type annotations, 355 parametric, 444-445 recursive, 277-278 vs. variant types, 140-142 de Bruijn indices, 75-81, 83-88, 381-387 levels, 81 pronunciation, 76 terms, 76 decidability, see also undecidability of Fω, 459-460 of kernel F<: subtyping, 423 declarative subtyping and typing relations, 210 decreasing chain, 18 definedness, 16 definitional equivalence of types, 441, 447 definitions formalization of, 441 of programming languages, 7 delegation, 227, 264 denotational semantics, 33 dependencies between chapters, xv dependent function types, 463 kinds, 445 types, 7, 11, 462-466, 473 depth of a term, 29 depth subtyping, 183 dereferencing, 154 derivable statement, 36 derivations evaluation, 36 induction on, 37 subtyping, 183-187 trees, 36, 102 typing, 94 derived forms, 51, 53, 119-121 desugaring, 121 determinacy of one-step evaluation, 37 diamond property, 455, 494 dimension analysis, 4 disjoint union, 142 divergent combinator, 65 divergeT, 145 documentation, types as, 5, 121 domain of a relation, 16 domain theory, 33 down-cast, see casting Dylan, 226 Dynamic type, 142 dynamic dispatch, 226 dynamic type testing, see casting dynamic typing, 2 * * * * * * Index E Edinburgh Logical Framework, see LF effects, 11, 153 efficiency, type systems and, 8 elaboration, 120 elimination rule, 108 encapsulation, 226 encodings, see object encodings enumerated type, 138 environment, 88 type-, 101 equi-recursive types, 280, 281 equirec implementation, 281-313 equi-recursive types, 275, 281-313 equivalence, see type equivalence equivalence, behavioral, 64 equivalence relation, 17 erasure, 109-110, 354-358 error, run-time, 42 error detection, use of types for, 4-5 evaluation, 34-43, 72-73 contexts, 261, 262 determinacy of, 37 lazy, 57 ML implementation, 47-49, 87 multi-step, 39 normalization by, 152 of nameless terms, 80-81 strategy, 35 strict vs. non-strict, 57 type-directed partial, 152 untyped lambda-calculus, 55-58 vs. reduction (terminology), 34 exceptions, 171-178 handlers, 171, 174 in Java and ML, 174 subtyping vs. polymorphism in typing of, 192 exercises, difficulty ratings, xviii existential objects, see objects, existential existential types, 11, 363-379 and modules, 364 bounded, 406-408 existential unificands, 320 expansion, 98, 108 explicit substitutions, 76, 88 explicitly typed languages, 101 exponential behavior of ML type reconstruction, 334 exposure, type-, 417-418 expressions vs. terms (termionology), 24 extended calculus of constructions, 11 Extended Static Checking, 3 extensible records, see row variables extensible variant type, 177 extensions of the simply typed lambda-calculus, 117-146 external language, 53, 120 * * * * * * Index F F, see System F Fω, see System Fω , see System F<:, see System F<: F-bounded quantification, 393, 408 F-closed set, 282 F-consistent set, 282 F1, F2, F3, etc., 461 factorial, 52 fail, 16 failure vs. undefinedness, 16 families (of terms, types), 462 Featherweight Java, 247–264 fields, see instance variables; records finalizers, 515 finding type errors, 545 finite tree type, 285 finite-state generating function, 294 first-class polymorphism, 340 fixed point, 142–145 combinator, 65 of a generating function, 282 theorem (Tarski-Knaster), 283 typing, using recursive types, 273 FJ, see Featherweight Java flattened data structures, 341 Float type, 117 fold function, 63 fomsub implementation, 467–473 formal methods, lightweight, 1 Forsythe, 11, 199 Fortran, 8, 11 fragments of System F, 358–359 fragments of System Fω, 461 free variable, 55, 69 fresh variable, 120 full abstraction, 143 full beta-reduction, 56 full F<:, 391 fullequirec implementation, 267–280 fullerror implementation, 171–178 fullfomsub implementation, 389–409, 467–473 fullfsub implementation, 389–409, 417–436 fullfsubref implementation, 411–416 fullisorec implementation, 275–278 fullomega implementation, 439–466 fullpoly implementation, 339–379 fullrecon implementation, 317–338 fullref implementation, 153–170, 225–245 fullsimple implementation, 99–111, 117–146 fullsub implementation, 181–208 fulluntyped implementation, 51–73 fullupdate implementation, 475–489 <fun>, 118 function types, 99–100 functional languages, mostly, 153 functions, 16 higher-order, 58 multi-argument, 58 on types, see type operators Funnel, 409 FX, 11 * * * * * * Index G garbage collection, 45, 158–165, 514–515 tag free, 341 general recursion, 142–145 generating function, 282 generating set, 290 generation lemma, see inversion lemma generators, classes as, 229 generics, 341 gfp algorithm, 292, 295–298 GJ, 195, 248, 409 grammar, 24 graph reduction, 57 greatest fixed point of a generating function, 283 greatest lower bound, see joins and meets greedy type inference, 355 * * * * * * Index H hash consing, 222 Haskell, 6, 45 heap, 153 hidden representation type, 364 higher-order bounded quantifiers, 468 higher-order functions, 58 higher-order polymorphism, 449–466 history of type systems, 10 hungry functions, 270 hybrid object models, 377 * * * * * * Index I identity, object, 245 identity function, 55 imperative objects, see objects, imperative implementations allsome, 381–387 arith, 23–49 bot, 220 equirec, 281–313 fomsub, 467–473 fullequirec, 267–280 fullerror, 171–178 fullfomsub, 389–409, 467–473 fullfsub, 389–409, 417–436 fullfsubref, 411–416 fullisorec, 275–278 fullomega, 439–466 fullpoly, 339–379 fullrecon, 317–338 fullref, 153–170, 225–245 fullsimple, 99–111, 117–146 fullsub, 181–208 fulluntyped, 51–73 fullupdate, 475–489 joinexercise, 223 joinsub, 218–220 purefsub, 417–436 rcdsub, 181–224 recon, 317–338 reconbase, 330 simplebool, 113–116 tyarith, 91–98 untyped, 83–88 implicit type annotations, 330–331 implicitly typed languages, 101 impredicative polymorphism, 340, 360–361 impure language features, 153 induction, 19 lexicographic, 19 logical relations proof technique, 150 mathematical foundations, 282–284 on derivations, 37 on natural numbers, 19 on terms, 29–32 inductive definitions, 23–29 inference, see type reconstruction inference rules, 26 mathematical foundations, 283 infinite types, 284–286 inheritance, 227 overrated, 245 injection into a sum type, 133 instance of an inference rule, 36 instance variables, 228, 230, 233–234 instanceof, 341 instantiation of a polymorphic function, 317–320, 342 intensional polymorphism, 340 interface, 226 interface types, 479 interfaces (in Java), 261 interference, syntactic control of, 170 intermediate language, 161 intermediate languages, typed, 11 internal language, 53, 120 internet, see web intersection types, 11, 206–207, 359, 489 and bounded quantification, 400, 409 and normal forms, 206 introduction rule, 108 invariant, 33 inversion lemma subtyping, 188 typing, 94, 104, 188, 457 iso-recursive types, 275, 280 subtyping, 311–312 * * * * * * Index J Java, 6, 8–10, 45, 119, 137, 154, 174, 177, 178, 193, 195, 196, 198–199, 226, 232, 247–264, 341, 444 exception handling in, 174 parametric polymorphism, 195 reflection, 196 JINI, 9 joinexercise implementation, 223 joins and meets, 17 algorithms for calculating, 218–220 in System F<:, 432–435 joinsub implementation, 218–220 judgment, 36 * * * * * * Index K KEA, 226 kernel F<:, 391 kinding, 439–447, 459 kinds dependent, 445 power, 445 row, 445 singleton, 445 Knaster-Tarski fixed point theorem, 283 * * * * * * Index L λ-calculus, see lambda-calculus λNB, 63-66 λ→, see simply typed lambda-calculus λω, see System λω λ<:, see simply typed lambda-calculus with subtyping label, 129 lambda cube, 465 lambda-& calculus, 226, 340 lambda-calculi, typed, 2 lambda-calculus, 51, 52 enriched, 63-68 simply typed, see simply typed lambda-calculus untyped, see untyped lambda-calculus lambda-term, 53 language definition, defined, 7 language design and type systems, 9-10 language features, pure, 153 late binding, see objects, open recursion latent type system, 2 lazy evaluation, 57 least fixed point of a generating function, 283 least upper bound, see joins and meets left-associativity of application, 54 let bindings, 124-125 let-polymorphism, 331-336, 340 exponential behavior, 334 levels, de Bruijn, 81 lexical analysis, 53 lexicographic induction, 19 lexicographic order, 19 LF, 11, 465 lfp algorithm, 294 lightweight formal methods, 1 linear logic and type systems, 11, 109 linking, 367 lists, 146 Church encoding, 350-353, 500 defined using recursive types, 267-270 polymorphic functions for, 345-347 subtyping, 197 local type inference, 355 location, 159 logic and type systems, 108 origins, 11 type systems in, 2 logical framework, 465 logical relations, 149 * * * * * * Index M μ, see least fixed point μ notation for recursive types, 299–304 marshaling, 252, 341 Martin-Löf type theory, see constructive type theory match function, 131 matching, pattern-, 130–131 matching relation on object types, 480 mathematics, formalization of, 11 meaning of terms, 32–34 meet, see joins and meets membership checking for (co-)inductively defined sets, 290–298 Mercury, 338 message, 226 meta-mathematics, 24 metalanguage, 24 metatheory, 24 metavariables, 24 naming conventions, 565 method, 226, 228 invocation, 226 multi-, see multi-method override, 233, 264 Milner-Mycroft Calculus, 338 minimal types, 218, 418–420 minimal typing theorem, 419 mini-ML, 337 ML, 6, 8, 9, 11, 174, 177 exception handling in, 174 history, 336–338 module system, 379 parametric datatypes, 445 polymorphism, 331–336 ML implementations evaluation, 87 simply typed lambda-calculus, 113–116 subtyping, 221–224 System F, 381–387 untyped arithmetic expressions, 45–49 untyped lambda-calculus, 83–88 ML-style polymorphism, 340 modal logics, 109 model checking, 1, 284 Modula-3, 7 modularity, 3 module systems, 364, 379, 465 monads, 153 monitoring, run-time, 1 monotone function, 282 monotype, 359 most general unifier, 327 mostly functional languages, 153 multi-argument functions, 58 multi-method, 226, 340 multiple inheritance, 489 multiple representations (of object types), 226 multi-step evaluation, 39 mutually recursive functions, 144 types, 253 v, see greatest fixed point nameless form, see de Bruijn indices naming context, 77 naming conventions for metavariables and rules, 565–566 narrowing lemmas, 401, 425 National Science Foundation, xx natural deduction, 26 natural semantics, 32, 34, 43 natural-number induction, 19 nested induction, 19 NextGen, 196 nominal type systems, 251–254, 312 normal forms, 38 and intersection types, 206 uniqueness of, 39 normal order, 56 normalization, 149–152 by evaluation, 152 strong, 152 normalization theorem, 39, 152, 353 numeric values, 40 NuPRL, 11 * * * * * * Index O object calculus, 11, 51, 184, 248, 251 object language, 24 Objective Caml see OCaml, xvii objects, 228, 368 as recursive records, 272 bounded quantification and, 411–416 encodings vs. primitive objects, 262–263 existential, 372–377, 475–489 hybrid object models, 377 identity, 245 imperative, 157, 225–245, 411–416 interface types, 479 Java-style, 247–264 matching relation on object types, 480 object-oriented programming, defined, 225–227 open recursion, 227, 235–244 purely functional, 372–377, 475–489 vs. abstract data types, 374–377 OCaml, xvii, 7, 45, 208, 231, 489 OCaml implementations, see ML implementations occur check, 327, 338 omega, 65 open recursion, see objects, open recursion operational semantics, 32, see also evaluation big-step, 43 small-step, 42 operator associativity, 53 operator precedence, 53 Option type, 137–138 order, well-founded, 18 ordered sets, basic definitions, 16–18 ordinary induction, 19 overloading, 340 finitary, 206 overriding of method definitions, 233 * * * * * * Index P P(S) powerset of S, 15 package, existential, 364 pairs, 126–127 Church encodings, see Church encodings, pairs parallel reduction, 454 parametric abbreviation, 439 data type, 142, 444 polymorphism, 319, 340 parametricity, 359–360 parentheses and abstract syntax, 25, 52 parsing, 53 partial evaluation, 109 partial function, 16 partial order, 17 partially abstract types, 406 Pascal, 11 pattern matching, 130–131 PCF, 143 Pebble, 465 Penn translation, 204 Perl, 6 permutation, 18 permutation lemma, 106 permutation rule for record subtyping, 184 performance issues, 201–202 pi-calculus, 51 Pict, 200, 356, 409 Pizza, 195 pointer, 154, see references arithmetic, 159 pointwise subtyping of type operators, 468 polarity, 473 PolyJ, 195 polymorphic functions for lists, 345–347 identity function, 342 recursion, 338 update, 482–485 polymorphism, 331 ad hoc, 340 data abstraction, 368–377 existential, see existential types existential types, 363–379 exponential behavior of ML-style, 334 F-bounded, 393, 408 higher-order, 449–466 impredicativity and predicativity, 360–361 intensional, 340 ML-style, 331–336 parametric, 339–361 parametricity, 359–360 predicative, 360 prenex, 359 rank-2, 359 safety problems with references, 335–336 stratified, 360 subtype, see subtyping universal, see universal types varieties of, 340–341 polytype, 359 portability, types and, 7 positive subtyping, 489 Postscript, 6 power types, 445, 472 precedence of operators, 53 predecessor for Church numerals, 62 predicate, 15 predicative polymorphism, 360–361 prenex polymorphism, 359 preorder, 17 preservation of a predicate by a relation, 16 preservation of shapes under type reduction, 456 preservation of types during evaluation, 95–98, 107, 168, 173, 189, 261, 353, 404, 457 preservation of typing under type substitution, 318 principal type, 317, 329–330 types theorem, 329 typing, 337 unifier, 327 principal solution, 329 principle of safe substitution, 182 product type, 126–127 programming languages Abel, 409 Algol-60, 11 Algol-68, 11 Amber, 311 C, 6, 45 C#, 7 C++, 6, 226 Cecil, 226, 340 Clean, 338 CLOS, 226, 340 CLU, 11, 408 Dylan, 226 Featherweight Java, 247–264 Forsythe, 11, 199 Fortran, 8, 11 Funnel, 409 FX, 11 GJ, 195, 248, 409 Haskell, 6, 45 Java, 6, 8–10, 45, 119, 137, 154, 174, 177, 178, 193, 195, 196, 198–199, 226, 232, 247–264, 341, 444 KEA, 226 Mercury, 338 ML, 6, 8, 9, 11, 174, 177, see also OCaml, Standard ML Modula-3, 7 NextGen, 196 Objective Caml, see OCaml OCaml, xvii, 7, 208, 231, 489 Pascal, 11 Pebble, 465 Perl, 6 Pict, 200, 356, 409 Pizza, 195 PolyJ, 195 Postscript, 6 Quest, 11, 409 Scheme, 2, 6, 8, 45 Simula, 11, 207 Smalltalk, 226 Standard ML, xvii, 7, 45 Titanium, 8 XML, 9, 207, 313 progress theorem, 38, 95–98, 105, 169, 173, 190, 262, 353, 405, 458 projection (from pairs, tuples, records), 126–131 promotion, 418 proof, defined, 20 proof-carrying code, 9 proof theory, 2 proper types, 442 propositions as types, 109 pure λ→, 102 pure lambda-calculus, 51 pure language features, 153 pure type systems, xiv, 2, 444, 466 purefsub implementation, 417–436 * * * * * * Index Q qualified types, 338 quantification, see polymorphism Quest, 11, 409 * * * * * * Index R ramified theory of types, 2 range of a relation, 16 rank-2 polymorphism, 359 raw type, 248 rcdsub implementation, 181–224 reachableF, 294 recon implementation, 317–338 reconbase implementation, 330 reconstruction, see type reconstruction record kinds, 445 records, 129–131 Cardelli-Mitchell calculus, 207 Church encoding, 396–400 concatenation, 207 row variables, 208, 337 recursion, 65–66, 142–145 fixed-point combinator, 65 polymorphic, 338 recursive types, 253, 267–280 Amadio-Cardelli algorithm, 309–311 and subtyping, 279 equi-recursive vs. iso-recursive, 275 history, 279–280 in ML, 277–278 in nominal systems, 253 metatheory, 281–313 μ notation, 299–304 subtyping, 281–290, 298–313 type reconstruction, 313, 338 recursive values from recursive types, 273 redex, 56 reduce function, 63 reducibility candidates, 150 reduction vs. evaluation (terminology), 34 references, 153–170 allocation, 154 and subtyping, 199–200 assignment, 154 dereferencing, 154 subtyping, 198 type safety problems, 158 type safety problems with polymorphism, 335–336 refinement types, 207 reflection, 196, 252 and casting, 196 reflexive closure, 17 reflexive relation, 16 reflexivity of subtyping, 182 region inference, 8 regular trees, 298–299 relation, 15 logical, see logical relations removenames, 78 representation independence, 371 representation of numbers by Church numerals, 67 representation type (of an object), 230 restorenames, 78 row kinds, 445 row variables, 11, 208, 337, 489 rule computation, 35, 72 congruence, 35, 72 naming conventions, 565 schema, 27 rule, inference, 27 rule schema, 27 rules B-IFFALSE, 43 B-IFTRUE, 43 B-ISZEROSUCC, 43 B-ISZEROZERO, 43 B-PREDSUCC, 43 B-PREDZERO, 43 B-SUCC, 43 B-VALUE, 43 CT-ABS, 322, 542 CT-ABSINF, 330 CT-APP, 322, 542 CT-FALSE, 322 CT-FIX, 543 CT-IF, 322 CT-ISZERO, 322 CT-LETPOLY, 332 CT-PRED, 322 CT-PROJ, 545 CT-SUCC, 322 CT-TRUE, 322 CT-VAR, 322, 542 CT-ZERO, 322 E-ABS, 502 E-APP1, 72, 103, 160, 166, 186, 343, 392, 446, 450, 470, 502, 503 E-APP2, 72, 103, 160, 166, 186, 343, 392, 446, 450, 470, 502 E-APPABS, 72, 81, 103, 160, 166, 186, 342, 343, 392, 446, 450, 470, 502, 503 E-APPERR1, 172 E-APPERR2, 172 E-APPRAISE1, 175 E-APPRAISE2, 175 E-ASCRIBE, 122, 194 E-ASCRIBE1, 122 E-ASCRIBEEAGER, 123 E-ASSIGN, 161, 166 E-ASSIGN1, 161, 166 E-ASSIGN2, 161, 166 E-CASE, 132, 136 E-CASEINL, 132, 135 E-CASEINR, 132, 135 E-CASEVARIANT, 136 E-CAST, 258 E-CASTNEW, 258 E-CONS1, 147 E-CONS2, 147 E-DEREF, 161, 166 E-DEREFLOC, 161, 166 E-DOWNCAST, 195 E-FIELD, 258 E-FIX, 144 E-FIXBETA, 144 E-FLD, 276 E-FUNNY1, 40 E-FUNNY2, 40 E-GC, 514 E-HEAD, 147 E-HEADCONS, 147 E-IF, 34 E-IF-WRONG, 42 E-IFFALSE, 34 E-IFTRUE, 34 E-INL, 132, 135 E-INR, 132, 135 E-INVK-ARG, 258 E-INVK-RECV, 258 E-INVKNEW, 258 E-ISNIL, 147 E-ISNILCONS, 147 E-ISNILNIL, 147 E-ISZERO, 41 E-ISZERO-WRONG, 42 E-ISZEROSUCC, 41 E-ISZEROZERO, 41 E-LET, 124, 131, 335 E-LETV, 124, 131, 332 E-NEW-ARG, 258 E-PACK, 366, 452 E-PAIR1, 126 E-PAIR2, 126 E-PAIRBETA1, 126 E-PAIRBETA2, 126 E-PRED, 41 E-PRED-WRONG, 42 E-PREDSUCC, 41, 48 E-PREDZERO, 41 E-PROJ, 128, 129, 187 E-PROJ1, 126 E-PROJ2, 126 E-PROJNEW, 258 E-PROJRCD, 129, 187, 201, 484 E-PROJTUPLE, 128 E-RAISE, 175 E-RAISERAISE, 175 E-RCD, 129, 187, 484 E-REF, 162, 166 E-REFV, 162, 166 E-SEQ, 120 E-SEQNEXT, 120 E-SUCC, 41 E-SUCC-WRONG, 42 E-TAIL, 147 E-TAILCONS, 147 E-TAPP, 343, 392, 450, 470 E-TAPPTABS, 342, 343, 385, 392, 450, 470 E-TRY, 174, 175 E-TRYERROR, 174 E-TRYRAISE, 175 E-TRYV, 174, 175 E-TUPLE, 128 E-TYPETEST1, 195 E-TYPETEST2, 195 E-UNFLD, 276 E-UNFLDFLD, 276 E-UNPACK, 366 E-UNPACKPACK, 366, 367, 452 E-UPDATEV, 484 E-VARIANT, 136 E-WILDCARD, 507 K-ABS, 446, 450, 470 K-ALL, 450, 470 K-APP, 446, 450, 470 K-ARROW, 446, 450, 470 K-SOME, 452 K-TOP, 470 K-TVAR, 446, 450, 470 M-RCD, 131 M-VAR, 131 P-RCD, 509 P-RCD', 509 P-VAR, 509 Q-ABS, 446, 451, 471 Q-ALL, 451, 471 Q-APP, 446, 451, 471 Q-APPABS, 441, 446, 451, 471 Q-ARROW, 446, 451, 471 Q-REFL, 446, 451, 471 Q-SOME, 452 Q-SYMM, 446, 451, 471 Q-TRANS, 446, 451, 471 QR-ABS, 454 QR-ALL, 454 QR-APP, 454 QR-APPABS, 454 QR-ARROW, 454 QR-REFL, 454 S-ABS, 468, 471 S-ALL, 392, 394, 395, 427, 471 S-AMBER, 311 S-APP, 468, 471 S-ARRAY, 198 S-ARRAYJAVA, 198 S-ARROW, 184, 186, 211, 392, 471 S-ASSUMPTION, 311 S-BOT, 192 S-EQ, 468, 471 S-INTER1, 206 S-INTER2, 206 S-INTER3, 206 S-INTER4, 206 S-LIST, 197 S-PRODDEPTH, 187 S-PRODWIDTH, 187 S-RCD, 211 S-RCDDEPTH, 183, 187, 484 S-RCDPERM, 184, 187 S-RCDVARIANCE, 484 S-RCDWIDTH, 183, 187, 484 S-REF, 198 S-REFL, 182, 186, 211, 392 S-REFSINK, 199 S-REFSOURCE, 199 S-SINK, 199 S-SOME, 406, 476, 556 S-SOURCE, 199 S-TOP, 185, 186, 211, 392, 471 S-TRANS, 183, 186, 209, 211, 392, 471 S-TVAR, 392, 394, 471 S-VARIANTDEPTH, 197 S-VARIANTPERM, 197 S-VARIANTWIDTH, 197 SA-ALL, 422, 424 SA-ARROW, 212, 422, 424 SA-BOT, 220 SA-RCD, 212 SA-REFL-TVAR, 422, 424 SA-TOP, 212, 422, 424 SA-TRANS-TVAR, 422, 424 T-ABS, 101, 103, 167, 186, 343, 392, 447, 451, 471 T-APP, 102, 103, 167, 181, 186, 343, 392, 447, 451, 471 T-ASCRIBE, 122, 194 T-ASSIGN, 159, 165, 167, 199 T-CASE, 132, 136 T-CAST, 530 T-CONS, 147 T-DCAST, 259 T-DEREF, 159, 165, 167, 199 T-DOWNCAST, 194 T-EQ, 441, 447, 451 T-ERROR, 172 T-EXN, 175 T-FALSE, 93 T-FIELD, 259 T-FIX, 144 T-FLD, 276 T-HEAD, 147 T-IF, 93, 102, 218 T-INL, 132, 135 T-INR, 132, 135 T-INVK, 259 T-ISNIL, 147 T-ISZERO, 93 T-LET, 124, 332, 509 T-LETPOLY, 332, 333 T-LOC, 164, 167 T-NEW, 259 T-NIL, 147 T-PACK, 365, 366, 406, 452 T-PAIR, 126 T-PRED, 93 T-PROJ, 128, 129, 187, 484 T-PROJ1, 126 T-PROJ2, 126 T-RCD, 129, 187, 484 T-REF, 159, 165, 167 T-SCAST, 259 T-SEQ, 120 T-SUB, 182, 186, 209, 392, 471 T-SUCC, 93 T-TABS, 342, 343, 392, 395, 451, 471 T-TAIL, 147 T-TAPP, 342, 343, 392, 395, 451, 471 T-TRUE, 93 T-TRY, 174, 175 T-TUPLE, 128 T-TYPETEST, 195 T-UCAST, 259 T-UNFLD, 276 T-UNIT, 119, 167 T-UNPACK, 366, 406, 435, 452 T-UPDATE, 484 T-VAR, 101, 103, 167, 186, 259, 343, 392, 447, 451, 471 T-VARIANT, 136, 197 T-WILDCARD, 507 T-ZERO, 93 TA-ABS, 217, 419 TA-APP, 217, 419 TA-APPBOT, 220 TA-IF, 220, 526 TA-PROJ, 217 TA-PROJBOT, 220 TA-RCD, 217 TA-TABS, 419 TA-TAPP, 419 TA-UNPACK, 436 TA-VAR, 217, 419 XA-OTHER, 418 XA-PROMOTE, 418 run-time code generation, 109 run-time error, 42 trapped vs. untrapped, 7 run-time monitoring, 1 * * * * * * Index S safety, 3, 6-8, 95-98 problems with references, 158 problems with references and polymorphism, 335-336 satisfaction of a constraint set by a substitution, 321 saturated sets, 150 Scheme, 2, 6, 8, 45 units, 368 scope, 55 scoping of type variables, 393-394 second-order lambda-calculus, 341, 461 security, type systems and, 9 self, 227, 234-244, 486-488 semantics alternative styles, 32-34 axiomatic, 33 denotational, 33 operational, 32 semi-unification, 338 semistructured databases, 207 sequences, basic notations, 18 sequencing notation, 119-121 and references, 155 sets, basic operations on, 15 sharing, 445, 465 shifting (of nameless terms), 78-80 ML implementation, 85-87 side effects, 153 simple theory of types, 2 simple types, 100 simplebool implementation, 113-116 simply typed lambda-calculus, 2, 11, 99-111 extensions, 117-146 ML implementation, 113-116 pure, 102 with type operators, 445 Simula, 11, 207 single-field variant, 138-140 singleton kinds, 441, 445, 465 size of a term, 29 small-step operational semantics, 32, 42 Smalltalk, 226 soundness, see safety soundness and completeness, 212 of algorithmic subtyping, 423 of constraint typing, 325 Source and Sink constructors, 199 spurious subsumption, 253 Standard ML, xvii, 7, 45 statement, 36 static distance, 76 static vs. dynamic typing, 2 store, 153 store typing, 162-165 stratified polymorphism, 360 streams, 270-271 strict vs. non-strict evaluation, 57 String type, 117 strong binary operations, 376 strong normalization, 152, 353 structural operational semantics, 32, 34 structural unfolding, 489 structural vs. nominal type systems, 251-254 stuck term, 41 stupid cast, 259-260 subclass, 227, 232 subexpressions of μ-types, 304-309 subject expansion, 98, 108 subject reduction, see preservation subscripting conventions, 566 subset semantics of subtyping, 182, 201-202 substitution, 69-72, 75-81, 83-88 capture-avoiding, 70 ML implementation, 85-87 type-, 317 substitution lemma, 106, 168, 189, 453 substitution on types, 342 ML implementation, 382 subsumption, 181-182 postponement of, 214 subtraction of Church numerals, 62 subtype polymorphism, see subtyping subtyping, 181-224, see also bounded quantification Top and Bot types, 191-193 algorithm, 209-213, 417-436 algorithmic, in nominal systems, 253 and ascription, 193-196 and base types, 200 and channel types, 200 and objects, 227 and references, 199-200 and type reconstruction, 338, 355 and variant types, 196-197 arrays, 198-199 coercion semantics, 200-206 depth, 183 higher-order, 11, 467-473 intersection types, 206-207 iso-recursive types, 311-312 joins and meets in System F<:, 432-435 lists, 197 ML implementation, 221-224 objects, 229-230 positive, 489 power types, 472 record permutation, 184 recursive types, 279, 281-290, 298-313 references, 198 reflexivity, 182 subset semantics, 182, 201-202 subtype relation, 182-187 transitivity, 183 type operators, 467-473 undecidability of System F<:, 427-431 union types, 206-207 vs. other forms of polymorphism, 341 width, 183 sum types, 132-135 super, 234 supertype, 182 support, 290 surface syntax, 53 syllabi for courses, xvii symmetric relation, 16 syntactic control of interference, 170 syntactic sugar, 121 syntax, 26-29, 52-55, 69 ML implementation, 46-47, 383-385 syntax-directedness, 209 System F, 11, 339-361 fragments, 358-359 history, 341 ML implementation, 381-387 System Fω, 449-466 and higher-order logic, 109 fragments, 461 System , 467-473 System F<:, 389-409 kernel and full variants, 391 System λω, 445-447 * * * * * * Index T T, see terms tag, type-, 2 tag-free garbage collection, 341 tagged representation of atomic values, 201 tagging creating new types by, 133 tail recursion, 296 TAL, 11 Tarski-Knaster fixed point theorem, 283 termination measure, 39 terminology, reduction vs. evaluation, 34 terms, 24, 26 and expressions (terminology), 24 closed, 55 depth, 29 induction on, 29–32 inductive definition of (nameless form), 77 ML implementation, 46, 83–85 nameless form, see de Bruijn indices size, 29 stuck, 41 theorem proving, types in, 9, 464 this, see self thunk, 239 TinkerType, xx Titanium, 8 Top type, 185, 191–193 top-down subexpressions of a recursive type, 304 Top[K], 468 total function, 16 total order, 17 transitive closure, 17, 289 transitive relation, 16 transitivity and coinduction, 288–290 transitivity of subtyping, 183 translucent types, 11 trapped vs. untrapped errors, 7 tree, 538 abstract syntax, 25 derivation, 36 regular, 298–299 type, 285 treeof, 300 tuples, 126–129 two-counter machines, 430 tyarith implementation, 91–98 typability, 93, 109–110, 354–357 type abstraction, 342 type annotations, 3, 10, 111 type application, 342 type classes, 337, 338 type constructors, see type operators type destructors, 489 type environment, 101 type equivalence, 447, 453–456 type erasure, 110, 354 type errors, 3 finding, 545 type exposure, 417–418 type inference, see type reconstruction type names, 251 type operators, 100, 439–447 bounded, 473 co- and contravariant, 473 definition equivalence, 441 in nominal systems, 254 quantification over, 449–466 subtyping, 467–473 type reconstruction, 317–338, 354–357 colored local type inference, 355 greedy, 355 history, 336–338 local type inference, 355 recursive types, 313, 338 subtyping, 338, 355 type safety, see safety type scheme, 359 type substitution, 317 ML implementation, 382 type systems and efficiency, 8 and portability, 7 and security, 9 and theorem provers, 9, 464 applications, 8–9 as formal methods, 1 category theory and, 12 defined, 1–4 history, 10 in mathematics and logic, 2 language design and, 9–10 role in computer science, 1–4 type tags, 2, 196, 252 type theory, see type systems constructive, 2 type variables, 319–320 type-assignment systems, 101 type-directed partial evaluation, 152 type-erasure semantics, 357 type-passing semantics, 357 typecase, 341 typed arithmetic expressions, 91–98 typed assembly language, 11 typed intermediate languages, 11 typed lambda-calculi, 2 types, 92 typing context, 101 typing derivations, 94 desugaring of, 125 semantics defined on, 111, 200–206 typing relation, 92–95, 100–103 algorithm, 213–218 ML implementation, 113–116 properties, 104–108 * * * * * * Index U undecidability of full type reconstruction for System F, 354 of partial type reconstruction for System F, 354 of subtyping for System F<:, 427–431 undefinedness vs. failure, 16 unification, 321, 326–329 union types, 142, 206–207 disjoint, 142 uniqueness of normal forms, 39 uniqueness of types, 94, 104, 511 and annotations, 135, 141 and sums, 134–135 Unit type, 118–119 unit value, 118–119 units (in Scheme), 368 universal domain, 273 universal set, 282 universal types, 339–361 unsafe declarations, 7 untyped implementation, 83–88 untyped arithmetic expressions, 23–44 untyped lambda-calculus, 11, 51–73 representation using recursive types, 273–275 up-cast, see casting update, polymorphic, 482–485 * * * * * * Index V value, 34, 57 numeric, 40 value restriction, 336, 358 variable capture, 70 variables bound, 55, 69-72 free, 55 variant types, 132-142 and subtyping, 196-197 extensible, 177 single-field, 138-140 vs. datatypes, 140-142 * * * * * * Index W weak binary operations, 375 weak head reduction, 460 weak pointers, 515 weak type variable, 336 weakening lemma, 106 web resources, xx well-formed context, 459 well-founded order, 18 set, 18 well-typed term, 93 width subtyping, 183 wildcard bindings, 119-121 witness type, 364 wrong, 42, 73 * * * * * * Index X XML, 9, 207, 313 * * * * * * Index Y Y combinator, 65 Year 2000 problem, 9 * * * * * * Index Z Z combinator, 65 * * * * * * List of Figures Preface Figure P-1: Chapter Dependencies Figure P-2: Sample Syllabus for an Advanced Graduate Course Chapter 1: Introduction Figure 1-1: Capsule History of Types in Computer Science and Logic Chapter 3: Untyped Arithmetic Expressions Figure 3-1: Booleans (B) Figure 3-2: Arithmetic Expressions (NB) Chapter 5: The Untyped Lambda-Calculus Figure 5-1: The Predecessor Function's "Inner Loop" Figure 5-2: Evaluation of factorial c3 Figure 5-3: Untyped Lambda-Calculus (λ) Chapter 8: Typed Arithmetic Expressions Figure 8-1: Typing Rules for Booleans (B) Figure 8-2: Typing Rules for Numbers (NB) Chapter 9: Simply Typed Lambda-Calculus Figure 9-1: Pure Simply Typed Lambda-Calculus (λ→) Chapter 11: Simple Extensions Figure 11-1: Uninterpreted Base Types Figure 11-2: Unit Type Figure 11-3: Ascription Figure 11-4: Let Binding Figure 11-5: Pairs Figure 11-6: Tuples Figure 11-7: Records Figure 11-8: (Untyped) Record Patterns Figure 11-9: Sums Figure 11-10: Sums (With Unique Typing) Figure 11-11: Variants Figure 11-12: General Recursion Figure 11-13: Lists Chapter 13: References Figure 13-1: References Chapter 14: Exceptions Figure 14-1: Errors Figure 14-2: Error Handling Figure 14-3: Exceptions Carrying Values Chapter 15: Subtyping Figure 15-1: Simply Typed Lambda-Calculus with Subtyping (λ<:) Figure 15-2: Records (Same as Figure 11-7) Figure 15-3: Records and Subtyping Figure 15-4: Bottom Type Figure 15-5: Variants and Subtyping Chapter 16: Metatheory of Subtyping Figure 16-1: Subtype Relation with Records (Compact Version) Figure 16-2: Algorithmic Subtyping Figure 16-3: Algorithmic Typing Chapter 19: Case Study: Featherweight Java Figure 19-1: Featherweight Java (Syntax and Subtyping) Figure 19-2: Featherweight Java (Auxiliary Definitions) Figure 19-3: Featherweight Java (Evaluation) Figure 19-4: Featherweight Java (Typing) Chapter 20: Recursive Types Figure 20-1: Iso-Recursive Types (λμ) Chapter 21: Metatheory of Recursive Types Figure 21-1: Sample Tree Types.

(Church numerals), 60 calculus of constructions, 11, 465 call stack and exception handling, 173–174 call-by-name evaluation, 57 call-by-need evaluation, 57 call-by-value evaluation, 57 call-by-value Y-combinator, 65 call/cc, see continuations candidate, reducibility, 150 canonical forms lemma, 96, 105, 190, 405, 458 capture-avoiding substitution, 70 cartesian product type, 126–127 casting, 193–196, 247–264, 357, see also ascription and abstraction, 194 and reflection, 196 as substitute for polymorphism, 195–196 implementation, 196 categorial grammar, 9 category theory, 12 CCS, 34 Cecil, 226, 340 cell, see references chain, 18 channel types, 200 and subtyping, 200 chapter dependencies, xv Church encodings booleans, 58–59 in System F, 347–353 numerals, 60–63 pairs, 60, 396–400 records, 396–400 subtyping, 396–400 Church-Rosser property, 455 Church-style presentation, 111 class, 227, 231 granularity of, 231 classification, type systems as formalisms for, 2 Clean, 338 CLOS, 226, 340 closed set, 282 closed term, 55 closure, 17 property, 289 CLU, 11, 408 codomain of a relation, 16 coercion semantics for subtyping, 200–206, 224 coherence, 204–206 coinduction, 281–313 defined, 282–284 collection classes, 195–196 colored local type inference, 355 combinator, 55 combinatory logic, 76 complete induction, 19 completely bounded quantification, 431 completeness, 212 composition of substitutions, 318 compositionality, 2 comprehension notation for sets, 15 computation rules, 35, 72 computational effects, 153 concrete rule, 27 concrete syntax, 53 confluence, see Church-Rosser property congruence rules, 35, 72 conservativity of type analyses, 2, 92, 99–100 consistent set, 282 constraint types, 337 constraint-based typing rules, 321–326 constructive logic, 108 constructive type theory, 2, 11 constructors, see type operators contexts, 76–78 ML implementation, 83–85, 113–115 naming, 77 typing, 101 continuations, 178, 377 contractiveness, 300 contravariant position in a type, 185 type operator, 473 correctness by construction, 464 countable set, 15 counting subexpressions of μ-types, 304–309 course syllabi, xvii covariant position in a type, 185 type operator, 473 cube, Barendregt, 465 Curry-Howard correspondence, 2, 108–109, 341, 429 Curry-style presentation, 111 currying, 58, 73 of type operators, 440 cut elimination, 109 * * * * * * Index D Damas-Milner polymorphism, 331 dangling reference, 158 databases, 9, 142, 207 datatypes, 355, see also abstract data types constructors as type annotations, 355 parametric, 444-445 recursive, 277-278 vs. variant types, 140-142 de Bruijn indices, 75-81, 83-88, 381-387 levels, 81 pronunciation, 76 terms, 76 decidability, see also undecidability of Fω, 459-460 of kernel F<: subtyping, 423 declarative subtyping and typing relations, 210 decreasing chain, 18 definedness, 16 definitional equivalence of types, 441, 447 definitions formalization of, 441 of programming languages, 7 delegation, 227, 264 denotational semantics, 33 dependencies between chapters, xv dependent function types, 463 kinds, 445 types, 7, 11, 462-466, 473 depth of a term, 29 depth subtyping, 183 dereferencing, 154 derivable statement, 36 derivations evaluation, 36 induction on, 37 subtyping, 183-187 trees, 36, 102 typing, 94 derived forms, 51, 53, 119-121 desugaring, 121 determinacy of one-step evaluation, 37 diamond property, 455, 494 dimension analysis, 4 disjoint union, 142 divergent combinator, 65 divergeT, 145 documentation, types as, 5, 121 domain of a relation, 16 domain theory, 33 down-cast, see casting Dylan, 226 Dynamic type, 142 dynamic dispatch, 226 dynamic type testing, see casting dynamic typing, 2 * * * * * * Index E Edinburgh Logical Framework, see LF effects, 11, 153 efficiency, type systems and, 8 elaboration, 120 elimination rule, 108 encapsulation, 226 encodings, see object encodings enumerated type, 138 environment, 88 type-, 101 equi-recursive types, 280, 281 equirec implementation, 281-313 equi-recursive types, 275, 281-313 equivalence, see type equivalence equivalence, behavioral, 64 equivalence relation, 17 erasure, 109-110, 354-358 error, run-time, 42 error detection, use of types for, 4-5 evaluation, 34-43, 72-73 contexts, 261, 262 determinacy of, 37 lazy, 57 ML implementation, 47-49, 87 multi-step, 39 normalization by, 152 of nameless terms, 80-81 strategy, 35 strict vs. non-strict, 57 type-directed partial, 152 untyped lambda-calculus, 55-58 vs. reduction (terminology), 34 exceptions, 171-178 handlers, 171, 174 in Java and ML, 174 subtyping vs. polymorphism in typing of, 192 exercises, difficulty ratings, xviii existential objects, see objects, existential existential types, 11, 363-379 and modules, 364 bounded, 406-408 existential unificands, 320 expansion, 98, 108 explicit substitutions, 76, 88 explicitly typed languages, 101 exponential behavior of ML type reconstruction, 334 exposure, type-, 417-418 expressions vs. terms (termionology), 24 extended calculus of constructions, 11 Extended Static Checking, 3 extensible records, see row variables extensible variant type, 177 extensions of the simply typed lambda-calculus, 117-146 external language, 53, 120 * * * * * * Index F F, see System F Fω, see System Fω , see System F<:, see System F<: F-bounded quantification, 393, 408 F-closed set, 282 F-consistent set, 282 F1, F2, F3, etc., 461 factorial, 52 fail, 16 failure vs. undefinedness, 16 families (of terms, types), 462 Featherweight Java, 247–264 fields, see instance variables; records finalizers, 515 finding type errors, 545 finite tree type, 285 finite-state generating function, 294 first-class polymorphism, 340 fixed point, 142–145 combinator, 65 of a generating function, 282 theorem (Tarski-Knaster), 283 typing, using recursive types, 273 FJ, see Featherweight Java flattened data structures, 341 Float type, 117 fold function, 63 fomsub implementation, 467–473 formal methods, lightweight, 1 Forsythe, 11, 199 Fortran, 8, 11 fragments of System F, 358–359 fragments of System Fω, 461 free variable, 55, 69 fresh variable, 120 full abstraction, 143 full beta-reduction, 56 full F<:, 391 fullequirec implementation, 267–280 fullerror implementation, 171–178 fullfomsub implementation, 389–409, 467–473 fullfsub implementation, 389–409, 417–436 fullfsubref implementation, 411–416 fullisorec implementation, 275–278 fullomega implementation, 439–466 fullpoly implementation, 339–379 fullrecon implementation, 317–338 fullref implementation, 153–170, 225–245 fullsimple implementation, 99–111, 117–146 fullsub implementation, 181–208 fulluntyped implementation, 51–73 fullupdate implementation, 475–489 <fun>, 118 function types, 99–100 functional languages, mostly, 153 functions, 16 higher-order, 58 multi-argument, 58 on types, see type operators Funnel, 409 FX, 11 * * * * * * Index G garbage collection, 45, 158–165, 514–515 tag free, 341 general recursion, 142–145 generating function, 282 generating set, 290 generation lemma, see inversion lemma generators, classes as, 229 generics, 341 gfp algorithm, 292, 295–298 GJ, 195, 248, 409 grammar, 24 graph reduction, 57 greatest fixed point of a generating function, 283 greatest lower bound, see joins and meets greedy type inference, 355 * * * * * * Index H hash consing, 222 Haskell, 6, 45 heap, 153 hidden representation type, 364 higher-order bounded quantifiers, 468 higher-order functions, 58 higher-order polymorphism, 449–466 history of type systems, 10 hungry functions, 270 hybrid object models, 377 * * * * * * Index I identity, object, 245 identity function, 55 imperative objects, see objects, imperative implementations allsome, 381–387 arith, 23–49 bot, 220 equirec, 281–313 fomsub, 467–473 fullequirec, 267–280 fullerror, 171–178 fullfomsub, 389–409, 467–473 fullfsub, 389–409, 417–436 fullfsubref, 411–416 fullisorec, 275–278 fullomega, 439–466 fullpoly, 339–379 fullrecon, 317–338 fullref, 153–170, 225–245 fullsimple, 99–111, 117–146 fullsub, 181–208 fulluntyped, 51–73 fullupdate, 475–489 joinexercise, 223 joinsub, 218–220 purefsub, 417–436 rcdsub, 181–224 recon, 317–338 reconbase, 330 simplebool, 113–116 tyarith, 91–98 untyped, 83–88 implicit type annotations, 330–331 implicitly typed languages, 101 impredicative polymorphism, 340, 360–361 impure language features, 153 induction, 19 lexicographic, 19 logical relations proof technique, 150 mathematical foundations, 282–284 on derivations, 37 on natural numbers, 19 on terms, 29–32 inductive definitions, 23–29 inference, see type reconstruction inference rules, 26 mathematical foundations, 283 infinite types, 284–286 inheritance, 227 overrated, 245 injection into a sum type, 133 instance of an inference rule, 36 instance variables, 228, 230, 233–234 instanceof, 341 instantiation of a polymorphic function, 317–320, 342 intensional polymorphism, 340 interface, 226 interface types, 479 interfaces (in Java), 261 interference, syntactic control of, 170 intermediate language, 161 intermediate languages, typed, 11 internal language, 53, 120 internet, see web intersection types, 11, 206–207, 359, 489 and bounded quantification, 400, 409 and normal forms, 206 introduction rule, 108 invariant, 33 inversion lemma subtyping, 188 typing, 94, 104, 188, 457 iso-recursive types, 275, 280 subtyping, 311–312 * * * * * * Index J Java, 6, 8–10, 45, 119, 137, 154, 174, 177, 178, 193, 195, 196, 198–199, 226, 232, 247–264, 341, 444 exception handling in, 174 parametric polymorphism, 195 reflection, 196 JINI, 9 joinexercise implementation, 223 joins and meets, 17 algorithms for calculating, 218–220 in System F<:, 432–435 joinsub implementation, 218–220 judgment, 36 * * * * * * Index K KEA, 226 kernel F<:, 391 kinding, 439–447, 459 kinds dependent, 445 power, 445 row, 445 singleton, 445 Knaster-Tarski fixed point theorem, 283 * * * * * * Index L λ-calculus, see lambda-calculus λNB, 63-66 λ→, see simply typed lambda-calculus λω, see System λω λ<:, see simply typed lambda-calculus with subtyping label, 129 lambda cube, 465 lambda-& calculus, 226, 340 lambda-calculi, typed, 2 lambda-calculus, 51, 52 enriched, 63-68 simply typed, see simply typed lambda-calculus untyped, see untyped lambda-calculus lambda-term, 53 language definition, defined, 7 language design and type systems, 9-10 language features, pure, 153 late binding, see objects, open recursion latent type system, 2 lazy evaluation, 57 least fixed point of a generating function, 283 least upper bound, see joins and meets left-associativity of application, 54 let bindings, 124-125 let-polymorphism, 331-336, 340 exponential behavior, 334 levels, de Bruijn, 81 lexical analysis, 53 lexicographic induction, 19 lexicographic order, 19 LF, 11, 465 lfp algorithm, 294 lightweight formal methods, 1 linear logic and type systems, 11, 109 linking, 367 lists, 146 Church encoding, 350-353, 500 defined using recursive types, 267-270 polymorphic functions for, 345-347 subtyping, 197 local type inference, 355 location, 159 logic and type systems, 108 origins, 11 type systems in, 2 logical framework, 465 logical relations, 149 * * * * * * Index M μ, see least fixed point μ notation for recursive types, 299–304 marshaling, 252, 341 Martin-Löf type theory, see constructive type theory match function, 131 matching, pattern-, 130–131 matching relation on object types, 480 mathematics, formalization of, 11 meaning of terms, 32–34 meet, see joins and meets membership checking for (co-)inductively defined sets, 290–298 Mercury, 338 message, 226 meta-mathematics, 24 metalanguage, 24 metatheory, 24 metavariables, 24 naming conventions, 565 method, 226, 228 invocation, 226 multi-, see multi-method override, 233, 264 Milner-Mycroft Calculus, 338 minimal types, 218, 418–420 minimal typing theorem, 419 mini-ML, 337 ML, 6, 8, 9, 11, 174, 177 exception handling in, 174 history, 336–338 module system, 379 parametric datatypes, 445 polymorphism, 331–336 ML implementations evaluation, 87 simply typed lambda-calculus, 113–116 subtyping, 221–224 System F, 381–387 untyped arithmetic expressions, 45–49 untyped lambda-calculus, 83–88 ML-style polymorphism, 340 modal logics, 109 model checking, 1, 284 Modula-3, 7 modularity, 3 module systems, 364, 379, 465 monads, 153 monitoring, run-time, 1 monotone function, 282 monotype, 359 most general unifier, 327 mostly functional languages, 153 multi-argument functions, 58 multi-method, 226, 340 multiple inheritance, 489 multiple representations (of object types), 226 multi-step evaluation, 39 mutually recursive functions, 144 types, 253 v, see greatest fixed point nameless form, see de Bruijn indices naming context, 77 naming conventions for metavariables and rules, 565–566 narrowing lemmas, 401, 425 National Science Foundation, xx natural deduction, 26 natural semantics, 32, 34, 43 natural-number induction, 19 nested induction, 19 NextGen, 196 nominal type systems, 251–254, 312 normal forms, 38 and intersection types, 206 uniqueness of, 39 normal order, 56 normalization, 149–152 by evaluation, 152 strong, 152 normalization theorem, 39, 152, 353 numeric values, 40 NuPRL, 11 * * * * * * Index O object calculus, 11, 51, 184, 248, 251 object language, 24 Objective Caml see OCaml, xvii objects, 228, 368 as recursive records, 272 bounded quantification and, 411–416 encodings vs. primitive objects, 262–263 existential, 372–377, 475–489 hybrid object models, 377 identity, 245 imperative, 157, 225–245, 411–416 interface types, 479 Java-style, 247–264 matching relation on object types, 480 object-oriented programming, defined, 225–227 open recursion, 227, 235–244 purely functional, 372–377, 475–489 vs. abstract data types, 374–377 OCaml, xvii, 7, 45, 208, 231, 489 OCaml implementations, see ML implementations occur check, 327, 338 omega, 65 open recursion, see objects, open recursion operational semantics, 32, see also evaluation big-step, 43 small-step, 42 operator associativity, 53 operator precedence, 53 Option type, 137–138 order, well-founded, 18 ordered sets, basic definitions, 16–18 ordinary induction, 19 overloading, 340 finitary, 206 overriding of method definitions, 233 * * * * * * Index P P(S) powerset of S, 15 package, existential, 364 pairs, 126–127 Church encodings, see Church encodings, pairs parallel reduction, 454 parametric abbreviation, 439 data type, 142, 444 polymorphism, 319, 340 parametricity, 359–360 parentheses and abstract syntax, 25, 52 parsing, 53 partial evaluation, 109 partial function, 16 partial order, 17 partially abstract types, 406 Pascal, 11 pattern matching, 130–131 PCF, 143 Pebble, 465 Penn translation, 204 Perl, 6 permutation, 18 permutation lemma, 106 permutation rule for record subtyping, 184 performance issues, 201–202 pi-calculus, 51 Pict, 200, 356, 409 Pizza, 195 pointer, 154, see references arithmetic, 159 pointwise subtyping of type operators, 468 polarity, 473 PolyJ, 195 polymorphic functions for lists, 345–347 identity function, 342 recursion, 338 update, 482–485 polymorphism, 331 ad hoc, 340 data abstraction, 368–377 existential, see existential types existential types, 363–379 exponential behavior of ML-style, 334 F-bounded, 393, 408 higher-order, 449–466 impredicativity and predicativity, 360–361 intensional, 340 ML-style, 331–336 parametric, 339–361 parametricity, 359–360 predicative, 360 prenex, 359 rank-2, 359 safety problems with references, 335–336 stratified, 360 subtype, see subtyping universal, see universal types varieties of, 340–341 polytype, 359 portability, types and, 7 positive subtyping, 489 Postscript, 6 power types, 445, 472 precedence of operators, 53 predecessor for Church numerals, 62 predicate, 15 predicative polymorphism, 360–361 prenex polymorphism, 359 preorder, 17 preservation of a predicate by a relation, 16 preservation of shapes under type reduction, 456 preservation of types during evaluation, 95–98, 107, 168, 173, 189, 261, 353, 404, 457 preservation of typing under type substitution, 318 principal type, 317, 329–330 types theorem, 329 typing, 337 unifier, 327 principal solution, 329 principle of safe substitution, 182 product type, 126–127 programming languages Abel, 409 Algol-60, 11 Algol-68, 11 Amber, 311 C, 6, 45 C#, 7 C++, 6, 226 Cecil, 226, 340 Clean, 338 CLOS, 226, 340 CLU, 11, 408 Dylan, 226 Featherweight Java, 247–264 Forsythe, 11, 199 Fortran, 8, 11 Funnel, 409 FX, 11 GJ, 195, 248, 409 Haskell, 6, 45 Java, 6, 8–10, 45, 119, 137, 154, 174, 177, 178, 193, 195, 196, 198–199, 226, 232, 247–264, 341, 444 KEA, 226 Mercury, 338 ML, 6, 8, 9, 11, 174, 177, see also OCaml, Standard ML Modula-3, 7 NextGen, 196 Objective Caml, see OCaml OCaml, xvii, 7, 208, 231, 489 Pascal, 11 Pebble, 465 Perl, 6 Pict, 200, 356, 409 Pizza, 195 PolyJ, 195 Postscript, 6 Quest, 11, 409 Scheme, 2, 6, 8, 45 Simula, 11, 207 Smalltalk, 226 Standard ML, xvii, 7, 45 Titanium, 8 XML, 9, 207, 313 progress theorem, 38, 95–98, 105, 169, 173, 190, 262, 353, 405, 458 projection (from pairs, tuples, records), 126–131 promotion, 418 proof, defined, 20 proof-carrying code, 9 proof theory, 2 proper types, 442 propositions as types, 109 pure λ→, 102 pure lambda-calculus, 51 pure language features, 153 pure type systems, xiv, 2, 444, 466 purefsub implementation, 417–436 * * * * * * Index Q qualified types, 338 quantification, see polymorphism Quest, 11, 409 * * * * * * Index R ramified theory of types, 2 range of a relation, 16 rank-2 polymorphism, 359 raw type, 248 rcdsub implementation, 181–224 reachableF, 294 recon implementation, 317–338 reconbase implementation, 330 reconstruction, see type reconstruction record kinds, 445 records, 129–131 Cardelli-Mitchell calculus, 207 Church encoding, 396–400 concatenation, 207 row variables, 208, 337 recursion, 65–66, 142–145 fixed-point combinator, 65 polymorphic, 338 recursive types, 253, 267–280 Amadio-Cardelli algorithm, 309–311 and subtyping, 279 equi-recursive vs. iso-recursive, 275 history, 279–280 in ML, 277–278 in nominal systems, 253 metatheory, 281–313 μ notation, 299–304 subtyping, 281–290, 298–313 type reconstruction, 313, 338 recursive values from recursive types, 273 redex, 56 reduce function, 63 reducibility candidates, 150 reduction vs. evaluation (terminology), 34 references, 153–170 allocation, 154 and subtyping, 199–200 assignment, 154 dereferencing, 154 subtyping, 198 type safety problems, 158 type safety problems with polymorphism, 335–336 refinement types, 207 reflection, 196, 252 and casting, 196 reflexive closure, 17 reflexive relation, 16 reflexivity of subtyping, 182 region inference, 8 regular trees, 298–299 relation, 15 logical, see logical relations removenames, 78 representation independence, 371 representation of numbers by Church numerals, 67 representation type (of an object), 230 restorenames, 78 row kinds, 445 row variables, 11, 208, 337, 489 rule computation, 35, 72 congruence, 35, 72 naming conventions, 565 schema, 27 rule, inference, 27 rule schema, 27 rules B-IFFALSE, 43 B-IFTRUE, 43 B-ISZEROSUCC, 43 B-ISZEROZERO, 43 B-PREDSUCC, 43 B-PREDZERO, 43 B-SUCC, 43 B-VALUE, 43 CT-ABS, 322, 542 CT-ABSINF, 330 CT-APP, 322, 542 CT-FALSE, 322 CT-FIX, 543 CT-IF, 322 CT-ISZERO, 322 CT-LETPOLY, 332 CT-PRED, 322 CT-PROJ, 545 CT-SUCC, 322 CT-TRUE, 322 CT-VAR, 322, 542 CT-ZERO, 322 E-ABS, 502 E-APP1, 72, 103, 160, 166, 186, 343, 392, 446, 450, 470, 502, 503 E-APP2, 72, 103, 160, 166, 186, 343, 392, 446, 450, 470, 502 E-APPABS, 72, 81, 103, 160, 166, 186, 342, 343, 392, 446, 450, 470, 502, 503 E-APPERR1, 172 E-APPERR2, 172 E-APPRAISE1, 175 E-APPRAISE2, 175 E-ASCRIBE, 122, 194 E-ASCRIBE1, 122 E-ASCRIBEEAGER, 123 E-ASSIGN, 161, 166 E-ASSIGN1, 161, 166 E-ASSIGN2, 161, 166 E-CASE, 132, 136 E-CASEINL, 132, 135 E-CASEINR, 132, 135 E-CASEVARIANT, 136 E-CAST, 258 E-CASTNEW, 258 E-CONS1, 147 E-CONS2, 147 E-DEREF, 161, 166 E-DEREFLOC, 161, 166 E-DOWNCAST, 195 E-FIELD, 258 E-FIX, 144 E-FIXBETA, 144 E-FLD, 276 E-FUNNY1, 40 E-FUNNY2, 40 E-GC, 514 E-HEAD, 147 E-HEADCONS, 147 E-IF, 34 E-IF-WRONG, 42 E-IFFALSE, 34 E-IFTRUE, 34 E-INL, 132, 135 E-INR, 132, 135 E-INVK-ARG, 258 E-INVK-RECV, 258 E-INVKNEW, 258 E-ISNIL, 147 E-ISNILCONS, 147 E-ISNILNIL, 147 E-ISZERO, 41 E-ISZERO-WRONG, 42 E-ISZEROSUCC, 41 E-ISZEROZERO, 41 E-LET, 124, 131, 335 E-LETV, 124, 131, 332 E-NEW-ARG, 258 E-PACK, 366, 452 E-PAIR1, 126 E-PAIR2, 126 E-PAIRBETA1, 126 E-PAIRBETA2, 126 E-PRED, 41 E-PRED-WRONG, 42 E-PREDSUCC, 41, 48 E-PREDZERO, 41 E-PROJ, 128, 129, 187 E-PROJ1, 126 E-PROJ2, 126 E-PROJNEW, 258 E-PROJRCD, 129, 187, 201, 484 E-PROJTUPLE, 128 E-RAISE, 175 E-RAISERAISE, 175 E-RCD, 129, 187, 484 E-REF, 162, 166 E-REFV, 162, 166 E-SEQ, 120 E-SEQNEXT, 120 E-SUCC, 41 E-SUCC-WRONG, 42 E-TAIL, 147 E-TAILCONS, 147 E-TAPP, 343, 392, 450, 470 E-TAPPTABS, 342, 343, 385, 392, 450, 470 E-TRY, 174, 175 E-TRYERROR, 174 E-TRYRAISE, 175 E-TRYV, 174, 175 E-TUPLE, 128 E-TYPETEST1, 195 E-TYPETEST2, 195 E-UNFLD, 276 E-UNFLDFLD, 276 E-UNPACK, 366 E-UNPACKPACK, 366, 367, 452 E-UPDATEV, 484 E-VARIANT, 136 E-WILDCARD, 507 K-ABS, 446, 450, 470 K-ALL, 450, 470 K-APP, 446, 450, 470 K-ARROW, 446, 450, 470 K-SOME, 452 K-TOP, 470 K-TVAR, 446, 450, 470 M-RCD, 131 M-VAR, 131 P-RCD, 509 P-RCD', 509 P-VAR, 509 Q-ABS, 446, 451, 471 Q-ALL, 451, 471 Q-APP, 446, 451, 471 Q-APPABS, 441, 446, 451, 471 Q-ARROW, 446, 451, 471 Q-REFL, 446, 451, 471 Q-SOME, 452 Q-SYMM, 446, 451, 471 Q-TRANS, 446, 451, 471 QR-ABS, 454 QR-ALL, 454 QR-APP, 454 QR-APPABS, 454 QR-ARROW, 454 QR-REFL, 454 S-ABS, 468, 471 S-ALL, 392, 394, 395, 427, 471 S-AMBER, 311 S-APP, 468, 471 S-ARRAY, 198 S-ARRAYJAVA, 198 S-ARROW, 184, 186, 211, 392, 471 S-ASSUMPTION, 311 S-BOT, 192 S-EQ, 468, 471 S-INTER1, 206 S-INTER2, 206 S-INTER3, 206 S-INTER4, 206 S-LIST, 197 S-PRODDEPTH, 187 S-PRODWIDTH, 187 S-RCD, 211 S-RCDDEPTH, 183, 187, 484 S-RCDPERM, 184, 187 S-RCDVARIANCE, 484 S-RCDWIDTH, 183, 187, 484 S-REF, 198 S-REFL, 182, 186, 211, 392 S-REFSINK, 199 S-REFSOURCE, 199 S-SINK, 199 S-SOME, 406, 476, 556 S-SOURCE, 199 S-TOP, 185, 186, 211, 392, 471 S-TRANS, 183, 186, 209, 211, 392, 471 S-TVAR, 392, 394, 471 S-VARIANTDEPTH, 197 S-VARIANTPERM, 197 S-VARIANTWIDTH, 197 SA-ALL, 422, 424 SA-ARROW, 212, 422, 424 SA-BOT, 220 SA-RCD, 212 SA-REFL-TVAR, 422, 424 SA-TOP, 212, 422, 424 SA-TRANS-TVAR, 422, 424 T-ABS, 101, 103, 167, 186, 343, 392, 447, 451, 471 T-APP, 102, 103, 167, 181, 186, 343, 392, 447, 451, 471 T-ASCRIBE, 122, 194 T-ASSIGN, 159, 165, 167, 199 T-CASE, 132, 136 T-CAST, 530 T-CONS, 147 T-DCAST, 259 T-DEREF, 159, 165, 167, 199 T-DOWNCAST, 194 T-EQ, 441, 447, 451 T-ERROR, 172 T-EXN, 175 T-FALSE, 93 T-FIELD, 259 T-FIX, 144 T-FLD, 276 T-HEAD, 147 T-IF, 93, 102, 218 T-INL, 132, 135 T-INR, 132, 135 T-INVK, 259 T-ISNIL, 147 T-ISZERO, 93 T-LET, 124, 332, 509 T-LETPOLY, 332, 333 T-LOC, 164, 167 T-NEW, 259 T-NIL, 147 T-PACK, 365, 366, 406, 452 T-PAIR, 126 T-PRED, 93 T-PROJ, 128, 129, 187, 484 T-PROJ1, 126 T-PROJ2, 126 T-RCD, 129, 187, 484 T-REF, 159, 165, 167 T-SCAST, 259 T-SEQ, 120 T-SUB, 182, 186, 209, 392, 471 T-SUCC, 93 T-TABS, 342, 343, 392, 395, 451, 471 T-TAIL, 147 T-TAPP, 342, 343, 392, 395, 451, 471 T-TRUE, 93 T-TRY, 174, 175 T-TUPLE, 128 T-TYPETEST, 195 T-UCAST, 259 T-UNFLD, 276 T-UNIT, 119, 167 T-UNPACK, 366, 406, 435, 452 T-UPDATE, 484 T-VAR, 101, 103, 167, 186, 259, 343, 392, 447, 451, 471 T-VARIANT, 136, 197 T-WILDCARD, 507 T-ZERO, 93 TA-ABS, 217, 419 TA-APP, 217, 419 TA-APPBOT, 220 TA-IF, 220, 526 TA-PROJ, 217 TA-PROJBOT, 220 TA-RCD, 217 TA-TABS, 419 TA-TAPP, 419 TA-UNPACK, 436 TA-VAR, 217, 419 XA-OTHER, 418 XA-PROMOTE, 418 run-time code generation, 109 run-time error, 42 trapped vs. untrapped, 7 run-time monitoring, 1 * * * * * * Index S safety, 3, 6-8, 95-98 problems with references, 158 problems with references and polymorphism, 335-336 satisfaction of a constraint set by a substitution, 321 saturated sets, 150 Scheme, 2, 6, 8, 45 units, 368 scope, 55 scoping of type variables, 393-394 second-order lambda-calculus, 341, 461 security, type systems and, 9 self, 227, 234-244, 486-488 semantics alternative styles, 32-34 axiomatic, 33 denotational, 33 operational, 32 semi-unification, 338 semistructured databases, 207 sequences, basic notations, 18 sequencing notation, 119-121 and references, 155 sets, basic operations on, 15 sharing, 445, 465 shifting (of nameless terms), 78-80 ML implementation, 85-87 side effects, 153 simple theory of types, 2 simple types, 100 simplebool implementation, 113-116 simply typed lambda-calculus, 2, 11, 99-111 extensions, 117-146 ML implementation, 113-116 pure, 102 with type operators, 445 Simula, 11, 207 single-field variant, 138-140 singleton kinds, 441, 445, 465 size of a term, 29 small-step operational semantics, 32, 42 Smalltalk, 226 soundness, see safety soundness and completeness, 212 of algorithmic subtyping, 423 of constraint typing, 325 Source and Sink constructors, 199 spurious subsumption, 253 Standard ML, xvii, 7, 45 statement, 36 static distance, 76 static vs. dynamic typing, 2 store, 153 store typing, 162-165 stratified polymorphism, 360 streams, 270-271 strict vs. non-strict evaluation, 57 String type, 117 strong binary operations, 376 strong normalization, 152, 353 structural operational semantics, 32, 34 structural unfolding, 489 structural vs. nominal type systems, 251-254 stuck term, 41 stupid cast, 259-260 subclass, 227, 232 subexpressions of μ-types, 304-309 subject expansion, 98, 108 subject reduction, see preservation subscripting conventions, 566 subset semantics of subtyping, 182, 201-202 substitution, 69-72, 75-81, 83-88 capture-avoiding, 70 ML implementation, 85-87 type-, 317 substitution lemma, 106, 168, 189, 453 substitution on types, 342 ML implementation, 382 subsumption, 181-182 postponement of, 214 subtraction of Church numerals, 62 subtype polymorphism, see subtyping subtyping, 181-224, see also bounded quantification Top and Bot types, 191-193 algorithm, 209-213, 417-436 algorithmic, in nominal systems, 253 and ascription, 193-196 and base types, 200 and channel types, 200 and objects, 227 and references, 199-200 and type reconstruction, 338, 355 and variant types, 196-197 arrays, 198-199 coercion semantics, 200-206 depth, 183 higher-order, 11, 467-473 intersection types, 206-207 iso-recursive types, 311-312 joins and meets in System F<:, 432-435 lists, 197 ML implementation, 221-224 objects, 229-230 positive, 489 power types, 472 record permutation, 184 recursive types, 279, 281-290, 298-313 references, 198 reflexivity, 182 subset semantics, 182, 201-202 subtype relation, 182-187 transitivity, 183 type operators, 467-473 undecidability of System F<:, 427-431 union types, 206-207 vs. other forms of polymorphism, 341 width, 183 sum types, 132-135 super, 234 supertype, 182 support, 290 surface syntax, 53 syllabi for courses, xvii symmetric relation, 16 syntactic control of interference, 170 syntactic sugar, 121 syntax, 26-29, 52-55, 69 ML implementation, 46-47, 383-385 syntax-directedness, 209 System F, 11, 339-361 fragments, 358-359 history, 341 ML implementation, 381-387 System Fω, 449-466 and higher-order logic, 109 fragments, 461 System , 467-473 System F<:, 389-409 kernel and full variants, 391 System λω, 445-447 * * * * * * Index T T, see terms tag, type-, 2 tag-free garbage collection, 341 tagged representation of atomic values, 201 tagging creating new types by, 133 tail recursion, 296 TAL, 11 Tarski-Knaster fixed point theorem, 283 termination measure, 39 terminology, reduction vs. evaluation, 34 terms, 24, 26 and expressions (terminology), 24 closed, 55 depth, 29 induction on, 29–32 inductive definition of (nameless form), 77 ML implementation, 46, 83–85 nameless form, see de Bruijn indices size, 29 stuck, 41 theorem proving, types in, 9, 464 this, see self thunk, 239 TinkerType, xx Titanium, 8 Top type, 185, 191–193 top-down subexpressions of a recursive type, 304 Top[K], 468 total function, 16 total order, 17 transitive closure, 17, 289 transitive relation, 16 transitivity and coinduction, 288–290 transitivity of subtyping, 183 translucent types, 11 trapped vs. untrapped errors, 7 tree, 538 abstract syntax, 25 derivation, 36 regular, 298–299 type, 285 treeof, 300 tuples, 126–129 two-counter machines, 430 tyarith implementation, 91–98 typability, 93, 109–110, 354–357 type abstraction, 342 type annotations, 3, 10, 111 type application, 342 type classes, 337, 338 type constructors, see type operators type destructors, 489 type environment, 101 type equivalence, 447, 453–456 type erasure, 110, 354 type errors, 3 finding, 545 type exposure, 417–418 type inference, see type reconstruction type names, 251 type operators, 100, 439–447 bounded, 473 co- and contravariant, 473 definition equivalence, 441 in nominal systems, 254 quantification over, 449–466 subtyping, 467–473 type reconstruction, 317–338, 354–357 colored local type inference, 355 greedy, 355 history, 336–338 local type inference, 355 recursive types, 313, 338 subtyping, 338, 355 type safety, see safety type scheme, 359 type substitution, 317 ML implementation, 382 type systems and efficiency, 8 and portability, 7 and security, 9 and theorem provers, 9, 464 applications, 8–9 as formal methods, 1 category theory and, 12 defined, 1–4 history, 10 in mathematics and logic, 2 language design and, 9–10 role in computer science, 1–4 type tags, 2, 196, 252 type theory, see type systems constructive, 2 type variables, 319–320 type-assignment systems, 101 type-directed partial evaluation, 152 type-erasure semantics, 357 type-passing semantics, 357 typecase, 341 typed arithmetic expressions, 91–98 typed assembly language, 11 typed intermediate languages, 11 typed lambda-calculi, 2 types, 92 typing context, 101 typing derivations, 94 desugaring of, 125 semantics defined on, 111, 200–206 typing relation, 92–95, 100–103 algorithm, 213–218 ML implementation, 113–116 properties, 104–108 * * * * * * Index U undecidability of full type reconstruction for System F, 354 of partial type reconstruction for System F, 354 of subtyping for System F<:, 427–431 undefinedness vs. failure, 16 unification, 321, 326–329 union types, 142, 206–207 disjoint, 142 uniqueness of normal forms, 39 uniqueness of types, 94, 104, 511 and annotations, 135, 141 and sums, 134–135 Unit type, 118–119 unit value, 118–119 units (in Scheme), 368 universal domain, 273 universal set, 282 universal types, 339–361 unsafe declarations, 7 untyped implementation, 83–88 untyped arithmetic expressions, 23–44 untyped lambda-calculus, 11, 51–73 representation using recursive types, 273–275 up-cast, see casting update, polymorphic, 482–485 * * * * * * Index V value, 34, 57 numeric, 40 value restriction, 336, 358 variable capture, 70 variables bound, 55, 69-72 free, 55 variant types, 132-142 and subtyping, 196-197 extensible, 177 single-field, 138-140 vs. datatypes, 140-142 * * * * * * Index W weak binary operations, 375 weak head reduction, 460 weak pointers, 515 weak type variable, 336 weakening lemma, 106 web resources, xx well-formed context, 459 well-founded order, 18 set, 18 well-typed term, 93 width subtyping, 183 wildcard bindings, 119-121 witness type, 364 wrong, 42, 73 * * * * * * Index X XML, 9, 207, 313 * * * * * * Index Y Y combinator, 65 Year 2000 problem, 9 * * * * * * Index Z Z combinator, 65 * * * * * * List of Figures Preface Figure P-1: Chapter Dependencies Figure P-2: Sample Syllabus for an Advanced Graduate Course Chapter 1: Introduction Figure 1-1: Capsule History of Types in Computer Science and Logic Chapter 3: Untyped Arithmetic Expressions Figure 3-1: Booleans (B) Figure 3-2: Arithmetic Expressions (NB) Chapter 5: The Untyped Lambda-Calculus Figure 5-1: The Predecessor Function's "Inner Loop" Figure 5-2: Evaluation of factorial c3 Figure 5-3: Untyped Lambda-Calculus (λ) Chapter 8: Typed Arithmetic Expressions Figure 8-1: Typing Rules for Booleans (B) Figure 8-2: Typing Rules for Numbers (NB) Chapter 9: Simply Typed Lambda-Calculus Figure 9-1: Pure Simply Typed Lambda-Calculus (λ→) Chapter 11: Simple Extensions Figure 11-1: Uninterpreted Base Types Figure 11-2: Unit Type Figure 11-3: Ascription Figure 11-4: Let Binding Figure 11-5: Pairs Figure 11-6: Tuples Figure 11-7: Records Figure 11-8: (Untyped) Record Patterns Figure 11-9: Sums Figure 11-10: Sums (With Unique Typing) Figure 11-11: Variants Figure 11-12: General Recursion Figure 11-13: Lists Chapter 13: References Figure 13-1: References Chapter 14: Exceptions Figure 14-1: Errors Figure 14-2: Error Handling Figure 14-3: Exceptions Carrying Values Chapter 15: Subtyping Figure 15-1: Simply Typed Lambda-Calculus with Subtyping (λ<:) Figure 15-2: Records (Same as Figure 11-7) Figure 15-3: Records and Subtyping Figure 15-4: Bottom Type Figure 15-5: Variants and Subtyping Chapter 16: Metatheory of Subtyping Figure 16-1: Subtype Relation with Records (Compact Version) Figure 16-2: Algorithmic Subtyping Figure 16-3: Algorithmic Typing Chapter 19: Case Study: Featherweight Java Figure 19-1: Featherweight Java (Syntax and Subtyping) Figure 19-2: Featherweight Java (Auxiliary Definitions) Figure 19-3: Featherweight Java (Evaluation) Figure 19-4: Featherweight Java (Typing) Chapter 20: Recursive Types Figure 20-1: Iso-Recursive Types (λμ) Chapter 21: Metatheory of Recursive Types Figure 21-1: Sample Tree Types.

pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things
by Daniel Kellmereit and Daniel Obodovski
Published 19 Sep 2013

In the next chapter we will take a look at the investment attractiveness of the M2M space. 27 Luke Dempsey, “Monty Python’s Flying Circus: Complete and Annotated … All the Bits,” Python Productions, Ltd., 159. (Source: http://en.wikipedia.org/wiki/Kilimanjaro_Expedition.) 28 Donald A. Norman, The Design of Everyday Things (New York: Basic Books, 1988). Chapter 7 WHERE TO INVEST All creative people want to do the unexpected. ~ Hedy Lamarr According to Paul Graham of Y Combinator, the best way to get start-up ideas is not to think of start-up ideas. Instead, one should focus on problems one has firsthand experience with.29 This is great advice for both start-up and corporate entrepreneurs, but what about investors? How would investors know where to put their money if they are not familiar with the space and specific problems?

pages: 412 words: 128,042

Extreme Economies: Survival, Failure, Future – Lessons From the World’s Limits
by Richard Davies
Published 4 Sep 2019

What made him a winner was his big idea – a new way to grow plants – and the fact that Estonia is a country that loves innovators: Ajujaht is one of many inventors’ contests and translates roughly as ‘brain hunt’. The victorious Mr Lepp received a prize of €30,000, and significant media coverage. Seven years later his company, Click and Grow, has 35 staff and recently raised $9 million in funding, including investments from Y Combinator, an influential Silicon Valley investment fund. Mr Lepp shows me the first part of his invention. It looks like a massive pack of paracetamol designed for giants – flat tinfoil on one side with a series of large plastic bubbles on the other. Rather than holding a pill, each of the capsules contains a clump of soil, shaped like the root ball that comes out when you empty a dried plant pot.

Abercrombie, Sir Patrick 203 Aceh 2–39, 10, 331, 332, 333, 334, 335 ‘building back better’ 24–5, 29–31, 42 civil war 32–3 education 13, 31 financial system 20–22 history 17–18 Memorandum of Understanding (MOU) 32, 33 tsunami 2–3, 6, 12–14, 15, 16, 18–19, 23 ageing populations 6, 212–49, 331 agglomeration see industrial agglomeration AI see artificial intelligence Akita, Japan 212–49 ageing population/ low birth rate 7, 213–25, 227–49, 331 suicides 225–6 Allende, Salvador 296–8, 301 amoral familism 196, 202 Anglo-Dutch wars 25 Angola: Kongo people 83 Angola (Louisiana penitentiary) 5, 76–104, 331, 335, Angolite, The 80 Argentina 110, 144, 291, 303 Arkwright, Richard 267 Arrol, Sir William 191 artificial intelligence (AI) 245, 268–9, 270, 284, 286, 287, 378 automation: and job losses 253 see also technology Azraq refugee camp 57–67, 71, 72, 144, 334, 340, 348–9 Bajo Chiquito, Panama 106, 108–9, 1112, 133, 136, 139 Banda Aceh 13, 16, 18, 20, 26–7, 34–5 Bandal, Kinshasa 144, 162 Bandudu, Congo 164, 165 banks 97, 99 in Aceh 19, 21, 22 Chilean 296, 297, 302 in Kinshasa/ Congo 151, 158 online 99, 278 Panamanian 131 Barbour, Mary 203, 366 barter economy, prison 89–90 Bevan, Aneurin 201 birth rates, falling 215–16, 226–7, 233, 247 Blockbuster Video 97 blood circulation (William Harvey) 3–4 borders: and conservation of common resources 126–7 Borland, Francis: History of Darien 107 Brazil: ageing population 213, 214 Brazzaville, Congo 174–5 Bruce, Robert 203 Brumberg, Richard 218 buccaneers and Darien 112–14 business start-up rates 54 Calabria, negative social integration 195–6 Calton, Glasgow 179, 190, 191, 192 Cambridge University 26, 182 Cameron, Verney Lovett 141, 143, 149 cannabinoids, synthetic 93–4, 95–6, 352 cartels, Chilean 321–3 Casement, Roger: on Congo Free State 150 cash vs. barter 89–90 Castro, Fidel 298 Castro, Sergio de 301 centenarians, Japanese 215, 216 Chesterton, George Laval 77 Chicago Boys 294–5, 296, 300, 301, 314, 325 El Ladrillo (economic plan) 301–5, 315–16, 317, 323–4, 325–6 protests against 305, 317 Chile Allende period 296–8, 301 education 294, 295, 302, 304–5, 310, 311–12, 312, 313–17, 318, 324, 326, 327 national income 291–3 nationalization 296–7 Pinochet dictatorship 298, 300–1, 305, 322, 383 tsunami 15 see also Chicago Boys ‘Chilean Winter’ 317–18 Clyde shipyards 178–9, 181, 183–4, 185 Cold Bath Fields prison 91 Coleridge, Samuel Taylor 113 Colombian peace accord (2016) 111, 134 common resources and conservation 124–5 depletion paradox 122–39 overgrazed land 122–3 and self-regulation 125, 126–8 Confucian ethics 220 Congo, Democratic Republic of ‘Crisis’ 151–8 GDP per capita 153, 173 independence (1960) 151 unemployment 142–3 see also Kinshasa; Mobutu, Sese Seko; Zaire consumerism as slavery 319 copper mining 143, 151, 156, 296, 323–4 corruption 133 in Kinshasa 143, 145–6, 148, 159–61, 168, 333, 361 credit: and poverty 308–10 Crompton, Samuel 267 crop rotation 279 Cunard Line 185 currencies cacao beans 91 cigarette papers 91 cigarettes/tobacco 92, 95 coffee 77, 96, 100 commodities 90–91 ‘dot’ payment system 97–100 dual-currency system 166–7 ‘EMAK’ (edible mackerel) 92 postage stamps 92 in prisons 91–101 ramen noodles 92 roles played (Jevons) 90 on Rossel Island 91 salt 91 Yoruk people 91 Cut Nyak Dhien 35 Dael, Syria: refugees 42–4 Dagahaley settlement, Kenya 45, 46 Dampier, William 113, 114 Daraa: and Syrian civil war 44 Darien Gap 6, 106, 107–39, 332, 333, 334 borders and common resource conservation 126–7 buccaneers’ accounts 112–14 eco-tourism 132 environmental damage 6, 120–21, 129–31 ethnic rivalry 126–8 externalities 131, 138, 183, 186, 332 illegal immigrants 132–7 market failure 109–10, 122–3, 129, 138 Scottish disaster 114–15, 133, 137–8 Darien National Park 126, 132 deaths lonely 225, 226, 236, 237, 248 premature (‘Glasgow effect’) 192–3 suicide 194, 213, 224, 225–6, 236, 248, 366 see also life expectancy digital divide 254, 281, 377 digital ID 277, 279 digital infrastructure, Estonian 259 drugs in Angola (prison) 81, 82, 88, 93–4, 95–6, 97, 99, 100, 101, 352 in Chile 306, 310, 322 in Darien 110, 111, 128, 134, 135 in Scotland 191–2, 193 in Tallinn 206 Dunlop, John Boyd 150 Durkheim, Emile: La Suicide 194, 196, 206 e-democracy (Estonia) 284, 287 e-Residency (Estonia) 277–8, 279, 283, 287, 379 education in Aceh 13, 31 in Chile/Santiago 295, 302, 304–5, 310, 311–12, 312, 313–17, 326, 327 in Italy 195 in Japan 220, 223, 229 in Louisiana 81 in Zaatari camp 67, 71, 349 see also universities Embera tribe 108, 109, 111, 119, 127, 128, 129, 133, 136, 137, 138–9, 357 entrepreneurs 331 in Aceh 19, 22, 23, 24, 27, 30, 39 in Akita, Japan 236–7, 238 in Angola (prison) 89, 102–3 Chilean 295, 296 in Darien 5, 114 Estonian 270, 275, 278–9, 281 in Glasgow 181, 182 in Kinshasa 162, 171 in Zaatari camp 43, 46, 54, 55–8, 62–3, 71 environmental damage see Darien Gap Estonia 256–7, 259 Ajujaht competition 252, 260, 275, 276, 278, 283–3 companies 281 economic revival 275–87 e-Government services 254–5 as ESSR 257–9, 272–4 labour shortage 280 Russia border 271–2 Russian population 272–4, 281–3 technology 252–6, 259–87 externalities 183, 206 Darien Gap 131, 138, 183, 186, 332 Glasgow 183–4, 186, 189–90, 333 and markets 332 extractive economy 122–39 Fairfield Heritage 349 Fairfield shipyard 178, 186, 189, 200, 206 FARC guerrillas 111, 132, 133, 134–5, 137, 355, 357 Ffrench-Davis, Ricardo 302 Foljambe, Joseph 265–6 Force Publique 150 foreign aid 23, 27–9, 54, 170 foreign exchange traders 166–7 Franklin, Isaac 83 free markets 128, 131, 174, 296, 300–3, 316, 320, 326–7, 331–2, 356 Frente Amplio coalition 318, 384 Friedman, Milton 289, 295, 303, 319, 326, 383, 384 GAM (Gerakan Aceh Merdeka) freedom fighters 18, 32, 346 Gbadolite 159 GDP see Gross Domestic Product Gécamines 155–6 Geddes, Reay: report 189–90 gender roles, Japanese 223–4, 232 Germany 187, 195, 222, 227, 247, 249, 292, 302, 360 Glasgow 6–7, 176, 177–207, 333 culture 180 drug users 191–2 externalities 183–4, 186, 189–90, 333 population density 197 shipbuilding 178–9, 181, 184–6, 187–8, 189, 190–91, 199–200, 206–7, 333, 334 tenement homes and social capital 196, 197–202, 205, 335 unemployment 190 see also Calton; Gorbals; Govan and below Glasgow City Council (GCC) 202–4 Glasgow City Improvement Trust 202–3, 366 ‘Glasgow effect’, isolation 205–6 Glassford, John 181 Glenlee 179 gold in Aceh 17, 20–22, 37, 332, 334 in the Congo 143 in Darien 109, 113, 117, 120, 356 Golden Island 114–15 Good Neighbor Policy (USA) 294, 383 Goodyear, Charles 150 Gorbals, Glasgow 176, 191, 192, 204, 205, 367 Govan, Glasgow 176, 178, 184, 186, 192, 197–8, 201–3, 206, 207 Great Depression 26 Gross Domestic Product (GDP) 26 Aceh 27, 37–8 Chile 316 Congo 153, 173 Estonia 259 Hagadera refugee camp, Kenya 45 Han, Byung-Chul 319 Harberger, Arnold ‘Alito’ 295, 305, 326 Hargreaves, James 266, 267 Harris, Walter 115 Harvey, William 1, 3–4, 5, 6, 329, 330, 336 Heinla, Ahti 263–4, 268, 282, 284, 285 Hinohara, Shigeaki 211 housing 90 Aceh 12–13, 16, 19, 24, 26, 27, 28, 29–30, 26, 38, 39 Akita, Japan 223, 228, 229, 230, 232, 233, 236–7, 239, 248 Azraq and Zaatari camps 44, 45, 48, 54, 55, 59, 61, 63, 70, 71 Chile 296, 297, 300, 302, 204, 306, 207, 308, 326 Darien 118, 139 Glasgow 197–9, 202–6 Kinshasa 142 Louisiana 95, 102 human capital 38–9, 168, 305, 335, 346–7 human rights abuses 300–1 Hyakumoto, Natsue 235 ID cards, personal data 260–61 Ifo refugee camp, Kenya 45 incarceration rates, USA 76–7, 78 industrial agglomeration 182–6, 200, 206, 330–31, 333, 365 inequality 6, 18, 254, 331, 337 in Chile 6, 291–2, 292, 293, 297, 298, 304, 308, 311, 317, 318, 324–7 intergenerational (Japan) 221–3, 238, 248 informal economies 122–5, 214–15, 331, 333–4, 336 Aceh 21–2, 24, 30, 31, 34, 37 Akita 233, 248 Chile 297, 306–7, 310, 323 Darien 122, 128, 129 Estonia 258 and Glasgow 204, 206, 334 Italy 196, 336 Kinshasa 142, 146, 148, 163–6, 167–8, 170, 173–5, 334 in prisons 77, 78–9, 86–7, 91, 93, 96, 99, 100–1, 102 in Zaatari camp 43, 45, 47, 57, 61, 64, 71, 72, 86 Innophys 245 innovation in Chile 315 and currency 97, 99–100 and economies 43, 79, 80, 87, 100, 122, 162, 333, 334 in Estonia 252, 256–7, 258–87 in Glasgow 179, 180, 182, 185, 188, 192, 201 technological 97–8, 183, 187, 252, 256–7, 258–87 intergenerational inequality (Japan) 221–3, 238, 248 International African Association (IAA) 149 International Cooperation Administration (ICA) 294 International Monetary Fund 303 inventions 265–6 in Estonia 252–3, 260, 265, 275–6, 282–3 isolation, ‘Glasgow effect’ 205–6 Italy 195–6, 201, 202, 335–6, 366 ageing population 213, 220, 222, 243, 331 population decline 227, 230, 233, 249 ivory trade 149 Jackie Chan Village 35–7, 39 Jackson, Giorgio 317–20 Jadue, Daniel 322, 332 Japan ageing population 6, 213–25, 227–49, 331 common forest conservation 124, 125 education 220, 223, 229 shipyards innovation/ competition 187–8, 189 tsunamis 15 Japan Football Association (JFA) 212–13 Jendi, Mohammed 54–5, 56, 71 Jevons, William Stanley 75, 89–90, 99, 352 Kabila family 154, 161, 162, 173 Kajiwara, Kenji 238 Kakuma refugee camp, Kenya 45 Kalanick, Travis 57 Kasa-Vubu, Joseph 151 Katanga 143, 151 Katumba refugee camp, Tanzania 45 Kenya: refugee camps 45, 46 Keynes, John Maynard 5, 7 Kinshasa 6, 140, 141–75, corruption 143, 145–6, 148, 159–61, 168, 333, 361 informal economy 142, 146, 148, 163, 166, 167–8, 170, 173, 334 natural wealth 143 pillages 157–8 police 159–61 roads as informal markets 163–6 tax system 145–6, 147–8, 16 Kirkaldy, David 4, 5, 6, 330 Kuala Lumpur 293 Kuna tribe 126, 340 Laar, Mart 258 labour pools, industrial agglomeration 183, 184–5, 200 Ladrillo, El see Chicago Boys Lagos 293 Lampuuk 2–3, 6, 13, 14, 22–3, 26, 32, 33, 35, 37, 345 Lancashire 266, 267 Las Condes 288, 290, 293, 304, 306, 307, 308, 309, 321, 322, 325 Lasnamäe, Tallinn 272, 281 Le Corbusier: Cité radieuse 203 Leontief, Wassily: Machines and Man 251, 377 Leopold II, King of the Belgians 149–50 Lhokgna 10, 12–13, 14, 26, 27–8, 29, 31, 33, 34, 35, 38, 345 life-cycle hypothesis 218–19, 248 life expectancy Glasgow 179, 190, 191–3 Japan 215 Russia 273–4 Swaziland 179 Lima 293 Liverpool 89, 177, 192, 193, 205–6 Livingstone, David 148–9 Lloyd, William Forster 122–3 lonely deaths 225, 226, 236, 237, 248 Louisiana 74, 76, 81 Department of Public Safety and Corrections 83 Prison Enterprises 83–4, 85, 351 State Penitentiary see Angola Lüders, Rolf 293, 295, 304, 305, 325 Lumumba, Patrice 151 machine learning 268–70 Makarova, Marianna 272, 274 Malacca Strait 10, 17,. 18, 35, 39 Malahayati, Admiral Laksamana 34–5 Maluku steel mill, Kinshasa 155, 156–7 Manchester 192, 193, 205–6 market economies Chile 297, 302, 305, 317 prison 78, 79, 87, 89, 100, 101, 103 markets 71, 122, 332–3, 336 Aceh 20–22, 36–7, 38, 144, 331 Azraq camp 62–4, 71, 144 Chile 295, 296, 297, 298–9, 304, 309, 319, 320–23 Darien 122, 126–7, 128, 129, 131, 138 free 128, 131, 174, 296, 300–3, 316, 320, 326–7, 331–2, 356 Glasgow 181, 190 Japan 232, 233, 248, 249 Kinshasa 143, 145, 146–7, 162, 163–6, 167, 173, 174 Zaatari supermarkets 48–53, 64, 348 Marshall, Alfred 182–3, 184, 185, 186, 187, 189, 190, 194, 200, 206, 329, 330, 365 Maslow, Abraham 41, 65–7, 68, 71, 72, 286, 319, 326, 349 Meikle, Andrew 266 Melvin, Jean 197, 198, 199, 200, 201, 202, 205 ménage lending system 201, 334 Menger, Carl 90, 99, 352 Michelin brothers 150 military coup, Pinochet’s 298 Mill, John Stuart 11, 38, 335, 346–7 minimum wages 94, 267, 296, 307–8, 310 Mishamo refugee camp, Tanzania 45 Mississippi River 74, 76 Mobutu, Sese Seko (formerly Joseph-Désiré) 141, 151–2, 154–9, 161, 162, 166, 173, 297, 333, 360–61 Modigliani, Franco 218–19, 372 Mojo (synthetic cannabis) 92–4, 95–6, 97 monopolies, facilitated 319 Montgomery, Hugh 3–4 Moore, Gordon 269 Morgan, Henry 112–13 Narva, Estonia 250, 271, 272, 274, 283, 287, 378 National Health Service 201–2 nationalization 187, 296, 301–2, 383 natural disasters: and economic growth 24–5 New Caledonia 114, 356 New Orleans 74, 76, 79, 93, 101, 102, 103 Ninagawa, Yukio 234–5 norms, economics and 196, 200, 201, 323, 334, 336 obesity 81, 309, 326, 351 opportunism: and depletion of common resources 126–38 Organization for Economic Cooperation and Development (OECD) 291, 316, 326, 377 Ostrom, Elinor 123–5, 137 Pan-American Highway 106, 110, 111, 115–17, 118–19, 121, 139, 355 Panama 106, 108-9, 110, 111, 113, 117, 118, 121, 130, 131, 356–7 see also Darien Gap; FARC guerrillas Panian refugee camp, Pakistan 45 Paro robot 243–5 Paterson, William: A Proposal to Plant a Colony in Darien 107 pawn shops 200, 334, 367 Penguins’ Revolution 317 pepper: global boom 17, 345 Pepper robot 246–7 personal data 260–61 Petty, William 25–6, 38n, 346 Piñera, Sebastián 309 Pinochet, General Augustine 298, 300–1, 305, 322, 383 pirate economies see informal economies population 122, 125, 330, 347 Aceh 14, 16, 18 Chile/Santiago 291, 324 China 76 Congo/Kinshasa 143, 150 Dael 42 Darien Gap 126, 128 Estonia 255, 256, 265, 272 Glasgow 179, 197 Greece 238 Japan 226–7, 229 Portugal 238 refugee camps 44, 45, 49, 57, 348 Sweden 238 US prisons 76–7 see also ageing populations Portugal 213, 227, 230, 233, 238, 243, 249, 291, 331, 351, 360 poverty Chile 291, 293, 300, 301, 303–4, 305, 208, 311. 15. 326 Congo/Kinshasa 143, 144, 160, 169, 11, 173 Glasgow 192 Italy 195 Japan 220, 226, 233, 248 Louisiana 81, 351 prices 147–8, 302 Pride of York 207 Prisoner’s Dilemma 174 privatization 169, 173, 301–2, 315, 326, 361 Pugnido refugee camp, Ethiopia 45 Putnam, Robert 195–6, 201, 202, 335–6, 366 Rahmatullah mosque, Aceh 14 rainforest destruction 121, 128–31 Rand, Rait 260, 275–6, 283, 284 Red Road Estate, Glasgow 203 refugee camps 45, 46, 55, 173 see also Azraq; Zaatari Reid, Alexander 180 resilience 3, 5, 6, 13, 16, 22, 31, 34, 35–9, 78, 103, 109, 122, 123, 146, 170, 248, 293, 325, 333–7, 384 Revolutionary Armed Forces of Colombia see FARC Rideau, Wilbert 79–80, 82, 87–8, 100, 351 Rio Chucunaque 117, 119 robotics/ robots and care 243–4, 245–7, 248 delivery robots 262–4 for egalitarian economies 284–5 human overseers/ minders 280 ‘last-mile problem’ 264 machine learning 268–70 Sony AIBO robotic dogs 245 trams, driverless 264 Roosevelt, Franklin D. 294, 356 rosewood trees 120, 128, 138 rubber trade 149–50 Russian-Estonians 272–4, 281–4, 286–7 salarymen, retired 223–4, 228, 248 Samuel, Arthur 269 Santiago 7, 288, 289–327 see also Chile schools/ schooling markets 165, 311–15 Scotland Darien disaster 114–15, 133, 137–8 see also Glasgow self-governance 125–8 shipbuilding 178–9, 181, 184–6, 187–8, 189–91, 199–200 Sikkut, Siim 259, 277, 284 Skype 254, 263, slavery 82–6 smuggling 42, 46–8, 68 social capital 195–6, 199, 200, 202, 323, 325, 335–6, 366 social inequality 142–3, 324–5 Somalia 15 South Korea 213, 214, 220, 227, 233, 247, 319, 373 Spain 115, 137, 213, 222, 227, 243, 331 Spice (synthetic cannabis) 352 Spice Islands 17 Spiers, Alexander 181 Spinning Jenny 267, 269, 274, 378 Sri Lanka 15, 17, 49 Stanley, Henry Morton 148–9 Stanyforth, Disney 266 Starship Technologies 262–4, 269, 280 stateless people 255 store cards, prepaid 97–8 students 81, 168, 218, 221, 223, 236–7, 238, 248, 282, 283, 294–5, 304–5, 311–14, 315–18 suicide 194, 213, 224, 225–6, 236, 248, 366 Sumatra 17-18, see also Aceh supermarkets, Zaatari 48–53, 64, 348 Swing Riots 266, 378 synthetic cannabis see Mojo; Spice Takahashi, Kiyoshi 235, 236 Tallinn 7, 250, 251–87 Russian population 272–4, 281–4, 286–7 start-up paradise 254 Tallinn, Harry 278, 282–3 Tanzania: refugee camps 45 taxation 25, 346 Aceh 32 Chile 295, 302, 307, 315–17, 325 Darien 111, 130 Estonia 256–7, 259, 273, 278, 287 Glasgow 190 Japan 220, 231 Kinshasa 145–6, 147–8, 151, 152, 158, 161–2, 165, 167–8, 169, 173–4 in Zaatari refugee camp 48, 56 Tay Bridge collapse 5 teak trees 116, 130–31, 138, 333, 356, 357 technology and inequality 253–4 innovation 97–8, 183, 187, 256–7, 258–9 spill-overs 183, 189 and unemployment 253, 262, 270, 279, 286, 287, 377, 379 tectonic plates 13–14 tenement buildings, Glaswegian 196, 197–202, 205, 335 Thailand 15, 144, 213 tobacco 77, 85–6, 92, 95, 100, 143, 156, 181, 191, 202, 365 Tomaya, Yoichi 235 Törbel, Switzerland: forest conservation 124 towerblocks 203, 204, 205 trade in prison 97–100 in Zaatari camp 43–57, 67–70 see also markets traditions, economic resilience and 21, 22, 24, 34, 196, 336 trust 148, 150, 174, 196, 199, 201, 206, 248, 261, 295, 321, 323, 325, 335 Tshisekedi, Félix 154 tsunamis 2–3, 12–14, 15, 16, 18–19, 22–3, 25 Tull, Jethrow 266 Turkey 28, 58, 144, 213 Uber 57 Ukegawa, Sachiko 234 underground economies 77–9, 87–101 see also informal economies unemployment 64–5, 142–3, 190, 275 Chile 290, 297, 302, 307, 311 Congo 142, 359 Estonia 270, 273, 275, 279, 283, 379 Glasgow 179, 190, 191 and technology 253, 262, 270, 279, 286, 287, 377, 379 United Kingdom 4, 18, 26, 181, 187, 188, 199, 213, 223, 278, 335 agriculture 265, 267 housing 232 jails 86, 91, 96, 352 National Health Service 201, 203 population 226 and technology 253, 254, 257, 260, 262, 264 see also Glasgow; Scotland United Nations High Commissioner for Refugees (UNHCR) 44, 46, 48, 54, 57, 72, 348 World Food Programme (WFP), and Zaatari 48, 49–50 universities Aceh 13, 33, 34 Akita, Japan 221, 223 Chile 294, 305, 313, 314, 315, 316–17, 318, 324, 326 Congo/Kinshasa 151, 160, 166, 168 Estonia 275, 282, 283 Upper Clyde Shipbuilders (UCS) 189 urbanization: and agglomeration forces 330–31 United States 26, 54, 76, 83, 93, 213, 223, 253, 262, 279, 292, 294, 297–8 prisons 76–7, 78, 81, 91–2, see also Angola population 226 and technology 260, 262, 264, 267, 269, 276 USAID 28, 29 Valdez, Samuel 121, 128–9, 130 Vallejo, Camila 317–18, 384 Van Gogh, Vincent 180 Vatter, Ott 277, 278 Viik, Linnar 257, 258–60, 261–2 Wafer, Lionel 113–14, 134, 355 Waisbluth, Mario 313 Walpole, Sir Spencer: A History of England 177 Walsh, David: History, Politics and Vulnerability … 177 Watanabe, Hiroshi 234 wealth 4–5, 159, 218–19, 324–5, 329, 334–6 nation’s 25, 38n, 346–7 natural 109, 132, 143 workforce 184–5, 264–8, 275, 297 World Bank 303, 305, 346 World Health Organization (WHO) 63, 215 World Trade Organization 303 Wounan tribe 126, 127 X-Road data system 261, 274–5, 279, 283, 377 Y Combinator 252 Yamamoto, Ryo 236–7 Yaviza, Panama 110, 111, 116–20, 127, 132, 135, 138, 144, 356 Yida refuge camp, South Sudan 45 Zaatari Syrian refugee camp 6, 40, 41–73, 86, 89, 100, 163, 173, 308, 331, 332, 334, 335, 348, 349 declining population 57 education 67, 71, 349 informal economy 43, 45, 47, 57, 61, 64, 71, 72, 86 smuggler children 42, 46–8, 68 supermarkets 48–53, 64, 348 trade development 43–57, 67–70, 71, 72 UNHCR cedes control 44–6 Zaire 152, 154, 155–6, 159, 361 Zorrones 324 TRANSWORLD PUBLISHERS 61–63 Uxbridge Road, London W5 5SA penguin.co.uk Transworld is part of the Penguin Random House group of companies whose addresses can be found at global.penguinrandomhouse.com.

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What We Owe the Future: A Million-Year View
by William MacAskill
Published 31 Aug 2022

Whole-body cryopreservation with the Alcor Life Extension Foundation costs $220,000; it costs less than half that if one merely preserves one’s head.76 Some entrepreneurs hope to abandon meat-based bodies altogether and live on in digital form through computer emulation of their brains. Nectome, a Y Combinator–funded start-up that preserves brains with the hope that future generations will scan and upload them, counts Silicon Valley entrepreneur Sam Altman as a customer. Nectome’s founder, Robert McIntyre, describes the service as “100% fatal.”77 If the aim of locking in values and the desire for immortality have been so common throughout history, then we should expect many people to have those aspirations in the future, too.

At the time of his appointment, he was the youngest associate professor of philosophy in the world. He has focused his research on moral uncertainty, effective altruism, and future generations. A TED speaker and past Forbes 30 Under 30 social entrepreneur, he also cofounded the nonprofits Giving What We Can, the Centre for Effective Altruism, and Y Combinator-backed 80,000 Hours, which together have moved over £200 million to effective charities. He is the author of Doing Good Better and lives in Oxford. A Oneworld Book First published in Great Britain, the Republic of Ireland and Australia by Oneworld Publications, 2022 This ebook edition published 2022 Published by arrangement with Basic Books, an imprint of Perseus Books LLC, a subsidiary of Hachette Book Group, Inc.

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The Kickstarter Handbook: Real-Life Success Stories of Artists, Inventors, and Entrepreneurs
by Steinberg, Don
Published 14 Aug 2012

Here are some of these types of sources: AlphaLab, Pittsburgh, PA (alphalab.org) Bootup Labs, Vancouver, BC (bootuplabs.com) Capital Factory, Austin, TX (capitalfactory.com) DreamIt Ventures, Philadelphia, PA (dreamitventures.com) Good Company Ventures, Philadelphia, PA (goodcompanygroup.org) Junto Partners, Salt Lake City, UT (juntopartners.com) Seed Hatchery, Memphis, TN (seedhatchery.com) TechStars, Boulder, CO (techstars.com) Y-Combinator, Mountain View, CA (ycombinator.com) KICKSTARTER CAMPAIGNS are a lot of work, so you’ll want to make a good plan well in advance of the launch date. This Prelaunch Worksheet asks important questions you’ll need to answer before presenting your project to the world. Some of these answers will need to be input directly into Kickstarter.com.

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Hooked: How to Build Habit-Forming Products
by Nir Eyal
Published 26 Dec 2013

With both Pinterest and Instagram, tiny teams generated huge value—not by cracking hard technical challenges, but by solving common interaction problems. Likewise, the fast ascent of mobile devices, including tablets, has spawned a new revolution in interface changes—and a new generation of start-up products and services designed around mobile user needs and behaviors. To uncover where interfaces are changing, Paul Buchheit, a partner at Y Combinator, encourages entrepreneurs to “live in the future.”10 A profusion of interface changes are just a few years away. Wearable technologies like Google Glass, the Oculus Rift virtual reality goggles, and the Pebble smartwatch promise to change how users interact with the real and digital worlds. By looking forward to anticipate where interfaces will change, the enterprising designer can uncover new ways to form user habits.

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The Facebook Effect
by David Kirkpatrick
Published 19 Nov 2010

Fall 2005 Page 149 As the school year resumed in the fall of 2005: Michael Arrington, “85% of College Students Use Facebook,” TechCrunch, September 7, 2005, www.techcrunch.com/2005/09/07/85-of-college-students-use-facebook/ (accessed November 15, 2009). 151 One new group was called “You’re Still in High School … ”: John Cassidy, “Me Media: How Hanging Out On The Internet Became Big Business,” New Yorker, May 15, 2006, http://www.newyorker.com/archive/2006/05/15/060515fa_fact_cassidy (accessed December 11, 2009). 151 At the beginning of the school year, Facebook had nearly doubled: Owen Van Natta, interview with author, May 15, 2007. 152 Ever vigilant about competitors: Angwin, Stealing MySpace, 140, 177. 153 Zuckerberg was dismissive: Ibid., 177. 156 By early 2010 Facebook was hosting: email from Brandee Barker, Facebook public relations, February 24, 2010. 8. The CEO Page 166 “I want to stress the importance of being young”: Mark Coker, “Start-Up Advice For Entrepreneurs, From Y Combinator Startup School,” Venturebeat, March 26, 2007, http://venturebeat.com/2007/03/26/start-up-advice-for-entrepreneurs-from-y-combinator-start-up-school/ (accessed November 28, 2009). 169 But at the end of March, BusinessWeek’s online edition: Steve Rosenbush, “Facebook’s on the Block,” BusinessWeek, March 28, 2006, http://www.businessweek.com/technology/content/mar2006/tc20060327_215976.htm (accessed November 15, 2009). 170 But to Zuckerberg, what was more significant: Ibid. 171 Another imitator, which launched around the same time in China: Baloun, Inside Facebook, 95. 173 He also quoted a sociologist who speculated: Cassidy, “Me Media.” 174 who he had met while: Lacy, 162. 174 After some negotiation, Zuckerberg: Lacy, 162. 176 A week after the program launched: Rob Walker, “A For-Credit Course,” New York Times, September 30, 2007, http://www.nytimes.com/2007/09/30/magazine/30wwInconsumed-t.html (accessed December 27, 2009). 177 As part of the deal the ad giant: email from Brandee Barker, Facebook public relations, December 11, 2009. 9. 2006 Page 184 Peter Thiel, older but very sympathetic: Lacy, 165. 186 Some nights, unable to sleep: David Kushner, “The Baby Billionaires of Silicon Valley,” Rolling Stone, November 16, 2006, http://rollingstone.com/news/story/12286036/the_baby_billionaires_of_silicon_valley (accessed November 28, 2009). 186 “I hope he doesn’t sell it”: Kevin Colleran, interview with the author. 190 Within about three hours the group’s membership: Tracy Samantha Schmidt, “Inside the Backlash Against Facebook,” Time, September 6, 2006, www.time.com/time/nation/article/0,8599,1532225,00.html (accessed December 11, 2009). 190 And there were about five hundred other protest groups: Brandon Moore, “Student users say new Facebook feed borders on stalking,” Arizona Daily Wildcat, September 8, 2006, http://wildcat.arizona.edu/2.2257/student-users-say-new-facebook-feed-borders-on-stalking-1.177273 (accessed December 11, 2009). 190 “Chuck Norris come save us”: Layla Aslani, “Users Rebel Against Facebook Feature,” Michigan Daily, September 7, 2006, http://www.michigandaily.com/content/users-rebel-against-facebook-feature (accessed December 11, 2009). 190 “You shouldn’t be forced to have a Web log”: Moore, “Student Users.” 190 “I’m really creeped out”: Aslani, “Users Rebel.” 191 But Zuckerberg, in New York on a promotional trip: Andrew Kessler, “Weekend Interview with Facebook’s Mark Zuckerberg,” Wall Street Journal, March 24, 2007, http://www.andykessler.com/andy_kessler/2007/03/wsj_weekend_int.html (accessed December 11, 2009). 10.

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The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order
by Paul Vigna and Michael J. Casey
Published 27 Jan 2015

Plug and Play is the brainchild of Saeed Amidi, a garrulous Iranian immigrant who started the accelerator in 2006 and has turned the successful idea into a global franchise that recruits start-ups worldwide. Plug and Play units are now in Canada, Spain, Singapore, Jordan, Dagestan, Russia, Poland, and Mexico, as well as at four other sites in the United States. A number of these kinds of programs exist around the world and in the Valley: Boost, Hero City, Y Combinator, 500 Startups, all with roughly the same idea. The difference between “incubator” and “accelerator” is somewhat vague, but the main idea behind the latter is to move fast. Billing itself as “Silicon Valley in a box,” Plug and Play brings together start-ups, corporations, venture capital, and universities all in one place and bangs out companies.

Gox and trust industries Turing Festival 20Mission Twitter Uber U-Haul Ulbricht, Ross Ultimate Frisbee unbanked people Unenumerated Unfair Trade, The (Casey) UnionPay Union Square Partners United Kingdom Utah utilities value: of bitcoins of coins of cryptocurrencies of dollar of gold intrinsic of money van der Laan, Wladimir Vaurum venture capitalists (VCs) Ver, Roger Verisign Verizon Vessenes, Peter VHS Virgin Group VirtEx Visa Vodafone Volabit Volcker, Paul Voltaire Voorhees, Erik voting Wall Street Wall Street Journal Walmart Washington State wealth bitcoin and Wealth of Nations, The (Smith) Web Designs WeChat Wedbush Securities Weill, Sanford Wei Dai Weimar Republic welfare state Wells Fargo Western Union Whelan, Jason Whelan, John WikiLeaks Wikipedia Willard, Rik William III, King Williams, Mark T. Wilson, Cody Wilson, Fred Winklevoss, Cameron and Tyler Wise, Josh Women’s Annex Wood, Gavin work World Bank Wright, Frank Lloyd Wuille, Pieter Xapo XIPH Foundation Xpert Financial XRP Y2K threat Yahoo Yang, Jerry Yap Y Combinator Yellen, Janet Yermack, David YouTube YTCracker Yunus, Muhammad ZeroBlock Zhang, Ng Zimbabwe Zimmerman, Phil Zobro, Jonathan Zoosk Zuckerberg, Mark Zug Also by Michael J. Casey The Unfair Trade Che’s Afterlife ABOUT THE AUTHORS Paul Vigna is a markets reporter for The Wall Street Journal, covering equities and the economy.

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Imaginable: How to See the Future Coming and Feel Ready for Anything―Even Things That Seem Impossible Today
by Jane McGonigal
Published 22 Mar 2022

In March 2021, the National Academies of Sciences, Engineering, and Medicine recommended that the US government establish a $200 million federal research program to investigate solar geoengineering.6 The Solar Radiation Management Governance Initiative is another signal: it promotes and funds geoengineering research in the most climate-vulnerable countries in the global south. Since 2018, it has funded half a million dollars’ worth of SRM research in eight countries: Argentina, Bangladesh, Benin, Indonesia, Iran, Ivory Coast, Jamaica, and South Africa.7 Meanwhile, Y Combinator, Silicon Valley’s largest incubator, is requesting proposals from geoengineering-focused start-ups.8 And in the summer of 2021, IEEE Spectrum, the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences, published an article under the headline: “Engineers: You Can Disrupt Climate Change; Decarbonization, Carbon Capture, and Solar-Radiation Management Will Provide Work for Decades to Come.”

Here Are 11 Last-Ditch Ways We Could Hack the Planet to Reverse That Trend,” Business Insider, April 20, 2019, https://www.businessinsider.com/geoengineering-how-to-reverse-climate-change-2019-4. 6 Jeff Tollefson, “US Urged to Invest in Sun-Dimming Studies as Climate Warms,” Nature, March 29, 2021, https://www.nature.com/articles/d41586-021-00822-5. 7 Solar Radiation Management Governance Initiative, accessed August 27, 2021, https://www.srmgi.org/. 8 Dana Varinsky, “Silicon Valley’s Largest Accelerator Is Looking for Carbon-Sucking Technologies—Including One That Could Become ‘the Largest Infrastructure Project Ever,’ ” Business Insider, October 27, 2018, https://www.businessinsider.com/silicon-valley-accelerator-y-combinator-startups-remove-co2-2018-10. 9 David Fork and Ross Koningstein, “Engineers: You Can Disrupt Climate Change,” IEEE Spectrum, June 28, 2021, https://spectrum.ieee.org/energy/renewables/engineers-you-can-disrupt-climate-change. 10 “Climate-Related Geoengineering and Biodiversity: Technical and Regulatory Matters on Geoengineering in Relation to the CBD; COP Decisions,” Convention on Biological Diversity, March 23, 2017, https://www.cbd.int/climate/geoengineering/. 11 Natalie L.

pages: 427 words: 134,098

Wonder Boy: Tony Hsieh, Zappos, and the Myth of Happiness in Silicon Valley
by Angel Au-Yeung and David Jeans
Published 25 Apr 2023

But even fairy tales have an ending. Tony’s time at LinkExchange was soon marred by internal politics and drama that led to lessons learned. In 1997, as the dot-com era was in full swing, Ali had started discussions with Viaweb, a newer startup led by Paul Graham (who would later launch the storied startup accelerator Y Combinator), about merging their businesses. Just as the discussions were starting to finalize, Jerry Yang came knocking again. He offered $125 million for Yahoo to buy LinkExchange. It was a lot of money, and enough to do away with any previous hesitations Tony, Ali, and Sanjay might have had. They said yes to Yahoo, signed a term sheet, and agreed to cut off talks with Viaweb.

See nitrous oxide WHISKY (Warehouse Inventory System in Kentucky) “Who Will Save Your Soul” (song) Wilke, Jeff William Morris Endeavor Williams, Elissa Williams, Evan Williams, Tyler Winfrey, Oprah Winter Camp Wired Wolfington, Ryan Women Who Work (Ivanka Trump) Woodbridge, James World War II World Wide Web Xanax XTR studio Y2K Yahoo Yale University Yang, Andrew Yang, Jerry Y Combinator Yeh, Connie Yosemite camping trip Young, Natalie Zappos Alfred Lin departs from Amazon buys Burning Man and corporate culture of customer service and death of Tony and Downtown Project and Elissa Williams discovers, in Vegas extranet created for “FAT” (Fred Mossler, Alfred Lin, and Tony) leadership team of financial crisis of 2008 and founding of, and early growth founding of, idea pitched by Nick Swinmurn Fred departs from Fred joins, as shoe guy Holacracy and Jewel’s Whole Human program for Kanye West and Las Vegas headquarters in old City Hall market-based dynamics and Mark Guadagnoli hired to train staff of meetings and Michelle D’Attilio and mini-promotions and moves to Henderson, Las Vegas suburb moves to Las Vegas downtown name created parties and events at Simon Sinek’s message to StyleTread mimics Sundance Film Festival and Tony as CEO of Tony asks Victor Oviedo to join Tony dates employees at Tony decides to go all in on Tony hires, then lets go, brothers Andy and Dave at Tony’s drug problems and decline and Tony seen as cult leader at Tony’s resignation from Tyler informs leadership of Tony’s breakdown Tyler Williams hired by Van Ness Avenue complex and warehouse and shoe stores bought by Wells Fargo loans of 2003 and WHISKY and Zappos Insights Zappos Monopoly Zappos University Zappos yearbook ZDTV (cable channel) Zech, Ava Zhitomirskiy, Ilya Zipline (formerly Romotive) Zynga ABOUT THE AUTHORS Angel Au-Yeung is a reporter for the Wall Street Journal and a former staff writer for Forbes.

pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection
by Jacob Silverman
Published 17 Mar 2015

.* Given that Mark Zuckerberg has said that connectivity is a human right, does requiring patrons to log into Facebook to get free WiFi impinge on their rights, or does it merely place Facebook access on the same level of humanitarianism? All of the world’s information, every opportunity, every fact, every business on earth. Such widely shared self-regard has made it seem embarrassing to claim more modest goals for one’s business. A document sent out to members of Y Combinator, the industry’s most sought-after start-up incubator, instructed would-be founders: “If it doesn’t augment the human condition for a huge number of people in a meaningful way, it’s not worth doing.” As long as we have the informational appetite, more data will always seem axiomatic—why wouldn’t one collect more, compute more?

(Lanier), 328 WiFi, 323–24 Wikipedia, 198 Winnebago Man (documentary), 72 Winogrand, Garry, 48 women and abusive labor practices in Asia, 266n and revenge porn, 210 and shadow work, 271 targeting ads by gender in the physical world, 298–99 tracking feelings of unattractiveness, 304 warning other women about deadbeat men, 191 Wonkblog (Washington Post), 105–7, 123, 124 Wood, Graeme, 213 World Economic Forum (WEF), 281–82, 328–29, 330–31 World Wide Web. See Internet Wu, Tim, 2, 67 Yahoo, 28, 96 Yang, Zoe, 78–80, 81, 82 Y Combinator, 324 YouTube, 13, 15, 70–71, 84, 361 Zakas, Laimonas, 353–54 Zengotita, Thomas de, 120, 346 Zipcar, 236 Zuckerberg, Mark claims for Facebook, 6 on companies over countries, 6 on Facebook’s supply of data, vii on frictionless sharing, 12 on human beings as cells of a single organism, 12, 376n on maintaining two identities, 159 on privacy, 287–88, 292 Shreateh’s post on Zuckerberg’s Facebook page, 354–55 Zuckerberg, Randi, 159 Zuckerberg’s Law, 288 About the Author JACOB SILVERMAN’S work has been published in the New York Times, the Los Angeles Times, Slate, the Atlantic, the New Republic, and many other publications.

pages: 229 words: 61,482

The Gig Economy: The Complete Guide to Getting Better Work, Taking More Time Off, and Financing the Life You Want
by Diane Mulcahy
Published 8 Nov 2016

We can’t deepen our connections with others and devote significant attention to our important relationships if we’re also checking email or watching the clock because we’re scheduled for something else in 10 minutes. To be our most effective and efficient selves and to create the time to invest in our priorities, we need reasonably sized blocks of time. One way to create that time is to apply the framework of Maker vs. Manager schedules to our calendars. Paul Graham of Y Combinator introduced the concept in his 2009 blog post “Maker’s Schedule, Manager’s Schedule.”7 I’ll summarize the concepts he introduces, but it’s worth reading it in its entirety. The Manager’s Schedule The Manager’s Schedule is the one most familiar to us, as it’s common for traditional employees and management (as the name implies) in corporations.

pages: 196 words: 61,981

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
by Xiaowei Wang
Published 12 Oct 2020

The same fear of automation drives a public discourse that glints with a subterfuge: that being human is the only thing that makes us special. The project of making AI a natural, evolutionary force continues. In this state of optimized life, we are told humans will be free from work. Silicon Valley claims it has anticipated this mass unemployment by automation, with places like Y Combinator piloting universal basic income programs. Individuals would get a monthly stipend to pay rent and purchase things, keeping a consumer-driven economy afloat. The promise being advertised to us about an AI labor force is that we will be free, and we will also be able to optimize our own tiny human lives—maybe for freedom, for true happiness. 6.

pages: 265 words: 69,310

What's Yours Is Mine: Against the Sharing Economy
by Tom Slee
Published 18 Nov 2015

They were inundated with requests and realized there may be a market for this kind of thing, and so “Airbed and Breakfast” was born. Since then, the story has been one of hard work and growth. Running up the limit on multiple credit cards to finance the very beginnings, they got an early investment from Paul Graham’s Y-Combinator fund. Struggling to get the site to take off, they went out to their biggest city (New York) and got the hosts to have professional photos taken of their rooms to make them more appealing; the bookings increased, and professional photography continues to be the most effective way for a host to attract guests.

pages: 270 words: 64,235

Effective Programming: More Than Writing Code
by Jeff Atwood
Published 3 Jul 2012

If you’re looking for good programming blogs to sharpen your saw (or at least pique your intellectual curiosity), I know of two excellent programming specific link aggregation sites that can help you find them. The first is Hacker News, which I recommend highly. Hacker News is the brainchild of Paul Graham, so it partially reflects his interests in Y Combinator and entrepreneurial stuff like startups. Paul is serious about moderation on the site, so in addition to the typical Digg-style voting, there’s a secret cabal (I like to think of it as The Octagon, “no one will admit they still exist!”) of hand-picked editors who remove flagged posts. More importantly, the conversation on the site about the articles is quite rational, with very little noise and trolling.

pages: 222 words: 70,132

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy
by Jonathan Taplin
Published 17 Apr 2017

But for now there are few constraints on Tech capitalism. The monopoly profits of this new era have been very, very good to a few men. The Forbes 400 list, which ranks American wealth, places Bill Gates, Larry Ellison, Larry Page, Jeff Bezos, Sergey Brin, and Mark Zuckerberg in the top ten. The Silicon Valley venture capitalist Paul Graham (CEO of Y Combinator), in a 2016 blog post, was quite open about celebrating income inequality. He wrote, “I’ve become an expert on how to increase economic inequality, and I’ve spent the past decade working hard to do it. Not just by helping the 2500 founders YC has funded. I’ve also written essays encouraging people to increase economic inequality and giving them detailed instructions showing how.”

pages: 232 words: 71,237

Kill It With Fire: Manage Aging Computer Systems
by Marianne Bellotti
Published 17 Mar 2021

In later chapters, we’ll tackle navigating the organization and strategies to advance your goals. 1. Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press, 1988), 15. 2. “Matt Cutts on the US Digital Service and Working at Google for 17 Years,” Y Combinator, December 4, 2019, https://blog.ycombinator.com/matt-cutts-on-the-us-digital-service-and-working-at-google-for-17-years/. 3. Dependency trees can be quite complicated, and traversing the whole graph is a lot of work without a lot of payoff. Make a list of the application’s direct dependencies and what those packages depend on, and then accept the risk that there might be a problem in nodes further down and move on. 4.

pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives
by Steven Levy
Published 12 Apr 2011

In early 2007, it heard about an innovative start-up that was working on a web-based presentation program that had some even niftier features than the one Google was developing internally. Wayne Crosby and Robby Walker had begun a company called Zenter. Funded by $15,000 from a start-up incubator called Y Combinator, they set out to create their web-based program in four months. They were working out of a small apartment in Mountain View with almost no furniture: their dining room table was a large Styrofoam box that had once held a case of Lean Cuisine meals that Walker’s father had sent them so they wouldn’t starve.

C., The Google Book, 348–49 virtual machine, 208–9 virtual reality, 60 Vogenthaler, Alex, 1–2 voice recognition, 219, 234, 236 Waddell, Matt, 137 Wagner, Dana, 331, 345, 347 Wagoner, Rick, 113 Wajda, Andrzej, 245 Walk, Hunter, 251, 263–64 Walker, Craig, 233–34, 235, 236 Walker, Robby, 203 Wang, Yonggang, 299–300 Weaver, Warren, 62 web: emergence of, 14–15 free posting and links, 15 growth of, 15, 22, 35, 42 as infinite database, 15–16 net neutrality, 222 rating system for, 16 social networking on, 369–83 stickiness in, 30 world changed by, 20 see also Internet web connectivity analysis, 39 WebGuerrilla, 56 web links, 10, 15–16, 17, 18–24, 25–26, 27, 34, 37–38, 51, 53, 59, 217 web searches: AltaVista, 19–20 comprehensiveness in, 52–53, 58 crawling, 19, 23, 34, 41, 42, 52 formatting of, 19 indexing in, 20, 21–22, 26, 41–43, 52 keywords in, 21–22 links in, 21–22 as magic, 32 quality of, 19, 20–24, 52, 61–62 ranking, 18 relevance in, 52 by servers, 19 spam in, 25 speed in, 47, 52 user data in logs of, 45–48, 59 Web 2.0 movement, 238, 243 Weihl, Bill, 37, 196–97 White Pages, 50 Whitman, Meg, 318 Whitt, Richard, 222–25 Whitten, Alma, 269–70 Whyte, William H., The Organization Man, 162 Wi-Fi networks, 343, 383, 384 Wikipedia, 240, 241 Wilkin, John, 352 Williams, Evan, 374, 376 Williams, Robin, 246–47 Winograd, Terry, 14, 16, 17, 28, 31 wireless communication, 214–16, 384 Wissman, Adam, 103 Wittgenstein, Ludwig, 48 Wojcicki, Anne, 126, 253 Wojcicki, Susan, 128, 235, 356 and advertising, 78, 79, 95, 98, 101, 102, 104, 115, 119, 174, 335 house of, 34, 125–26, 133, 139 Wong, Nicole, 175–76, 178, 309, 338–39, 379–80 Wooki, 240–41 word processing, 201, 202 words, defined by content, 48 word stuffing, 25 World Economic Forum, 283–84 World Wide Web, see Internet; web Wright, Johanna, 58, 59, 68 Writely, 201 Writers Guild of America, 361 Wu, Dandan, 288 Wu, Qing, 119–20 Xerox PARC (Palo Alto Research Center), 37 Xue, Rohnsin, 289 Yahoo: and China, 273, 284, 285, 286 and competition, 98, 99, 220, 332, 380 and eGroups, 30 and email, 168, 172, 180 and Flickr, 239 founding of, 31 funding of, 73 and Google, 44–45, 57, 151 meetings with, 28 and Microsoft, 343–44, 346, 380 and Overture, 98–99 and YouTube, 247, 248 Yang, Jerry, 28, 71, 344 Y Combinator, 203 Yeo, Boon-Lock, 301–2 Yoshka (dog), 36 YouTube, 242–52, 328, 372 and China, 298, 305 and copyrights, 244, 245, 251, 261 formation of, 243 Google acquisition of, 199, 247–52 and Google management, 251, 260–65 and Google Video, 242–47, 249, 263 Insight project, 264 profitability of, 264, 383 and U.S. politics, 317–18 Yuanchao, Li, 306 Zenter, 203 Zenzu Consulting, 195 Zhu, Julie, 303, 312 Zuckerberg, Mark, 369–70, 374, 381, 382

pages: 1,007 words: 181,911

The 4-Hour Chef: The Simple Path to Cooking Like a Pro, Learning Anything, and Living the Good Life
by Timothy Ferriss
Published 1 Jan 2012

CULINARY CRAM SCHOOL—THE BLUEPRINT We’ll work on the most central techniques throughout this book, but if you want to replicate our madness, here’s the blueprint. Find a chef coach to lead the way. CULINARY CRAM SCHOOL—LESSON PLAN TECHNIQUE: BUTCHERY TECHNIQUE: COOKING TECHNIQUE: CUTTING TECHNIQUE: EGG COOKERY TECHNIQUE: GARDE MANGER TECHNIQUE: SAUCE MAKING LONGER-TERM LEARNING SUGGESTIONS Y Combinator, quietly tucked away off highway 101 just miles from Google headquarters, is named after one of the coolest ideas in computer science: a program that runs programs. Cofounded in 2005 by Paul Graham, Robert Morris, Trevor Blackwell, and Jessica Livingston, YC offers small amounts of capital ($14,000–$20,000) to founders in exchange for, on average, 6% of each company.

Brian Chesky, cofounder of Airbnb, says of Paul Graham, the godfather of YC: “Just as [legendary music producer John] Hammond found Bob Dylan when he was a bad singer no one knew, Graham can spot potential.” If Graham can spot potential, the question I had was: how does he do it? The answer: YC has funded more than 450 start-ups since 2005. They have a far better sample size than most venture capitalists. I vividly remember my first visit to Y Combinator Demo Day. The only photograph I took was of a graph labeled “The Process” on a whiteboard, reflective of a good data set, which follows. For many reasons, it fascinated and amused me. First and foremost, I’d sketched out an eerily similar graph for language learning in 2005, which follows. This sketch, covering roughly eight months, represented my Japanese learning curve from 1992.

pages: 276 words: 78,094

Design for Hackers: Reverse Engineering Beauty
by David Kadavy
Published 5 Sep 2011

They do whatever it takes to achieve their visions. They’re entrepreneurial. They value skills and knowledge over titles and experience. At the forefront of the hacker movement is the Hacker News community (http://news.ycombinator.com), a news aggregation site contributed to by followers of Paul Graham’s Y Combinator entrepreneurial incubator program. The program tends to fund small teams of hackers who have used their skills and hacker attitude to build cool products that solve problems: UserVoice (www.uservoice.com) democratizes customer support; Reddit (www.reddit.com) democratizes news; Dropbox (www.dropbox.com) provides an easy, automatic backup solution; and AirBNB (www.airbnb.com) turns extra bedrooms into places for travelers to stay.

pages: 256 words: 73,068

12 Bytes: How We Got Here. Where We Might Go Next
by Jeanette Winterson
Published 15 Mar 2021

If digital social passports become normal, and if such passports can be used to decide who goes where, who does what, gets what, pays what (China is mooting charging systems that offer discounts to exemplary citizens), then how we live changes collectively, as well as individually – and perhaps it will make us less compassionate too. We won’t know what’s in the data of the person turned away or turned down or charged double, and likely we will feel it must be justified – mustn’t it? And we all like to feel superior to others. * * * Elon Musk and Sam Altman (CEO of the start-up funder Y Combinator) launched OpenAI in 2015 as a non-profit organisation promoting more inclusive AI – more benefits for more people – and to explore safe AGI. (We don’t want a Skynet situation.) Musk, who has since left the organisation due to what he calls conflicts of interest, is notably worried about artificial general intelligence – the point where AI becomes an autonomous self-monitoring system.

pages: 302 words: 73,946

People Powered: How Communities Can Supercharge Your Business, Brand, and Teams
by Jono Bacon
Published 12 Nov 2019

People Powered provides a clear and thoughtful blueprint for others looking to tap into this potential and unlock benefits for their own organizations. —Jim Whitehurst, President and CEO, Red Hat; Author, The Open Organization In my profession, building networks is all about nurturing relationships for the long term. Jono Bacon has authored the recipe on how to do this, and you should follow it. —Gia Scinto, Head of Talent, Y Combinator Continuity Communities are the future of business, technology, and collaboration. Jono Bacon’s experience, approach, and candor is critical reading for harnessing this future. —Jim Zemlin, Executive Director, The Linux Foundation If you want to harness the power of your customers, People Powered should be the first book you open.

Buy Then Build: How Acquisition Entrepreneurs Outsmart the Startup Game
by Walker Deibel
Published 19 Oct 2018

If there is a single word in this book that shouldn’t be there, trust me that Tucker told me with conviction to remove it. My Publishing Manager Katherine Sears could not have been a better partner. She started Booktrope, a disruptive market network for selfpublished authors. After raising over seven figures of capital and going through Y-Combinator, the # 1 accelerator program in the world, she met early success. After almost two years of running a successful, aggressively growing company she was faced with a change in product-market fit, and the startup capitulated through no fault of her team. She understands first-hand how a great product, all-star team, world class training, and amazing idea can fail in execution from external sources.

pages: 284 words: 75,744

Death Glitch: How Techno-Solutionism Fails Us in This Life and Beyond
by Tamara Kneese
Published 14 Aug 2023

Whereas most public seminars on end-of-life planning and estate law are intended for retirees, a newer cohort of digital estate–planning companies is hoping to attract younger and more diverse audiences. AngelList, a who’s who of companies that have received substantial funding from accelerators like Y Combinator, has a number of digital death companies in its inventory, including Estate Assist in San Francisco and the Lake Alabaster Box, based in Austin, which promises “estate planning for the 99% who couldn’t afford it until now.”51 Increasingly, digital estate–planning startups position themselves as affordable alternatives to more traditional services offered by banks, insurance companies, and law firms.

The Code: Silicon Valley and the Remaking of America
by Margaret O'Mara
Published 8 Jul 2019

It was about that “pattern recognition” so fatefully identified by John Doerr, looking for the next Stanford or Harvard dropout with a wild but brilliant idea. Of all those assertions, Doerr’s slip-up came closest to the heart of the Valley’s secret. “West Coast investors aren’t bolder because they’re irresponsible cowboys, or because the good weather makes them optimistic,” wrote Paul Graham, founder of the Valley’s most influential tech incubator, Y Combinator, in 2007. “They’re bolder because they know what they’re doing.” The Valley power players knew the tech, knew the people, and knew the formula that worked. They looked for “grade-A men” (who very occasionally were women) from the nation’s best engineering and computer science programs, or from the most promising young companies, and who had validation from someone else they already knew.

News & World Report, 265 Valentine, Don, 97, 149, 190, 308–9 Varian, Sigurd and Russell, 19 Varian Associates, 19, 31, 36, 38, 52 Vector Graphic, 145, 237 VeriSign, 310, 311 Very Large Scale Integration (VLSI), 207–8, 225 Video Brain, 142 Vietnam War, 51, 65, 86, 89, 92, 100, 110, 115, 118, 127, 224, 232, 245, 247, 301, 327 VisiCalc, 188, 227, 235, 240, 241 Volcker, Paul, 171 Walker, Eric, 25 Walkman, 206–7, 216, 224 Wall Street Journal, 90, 101, 104, 150, 178, 183–84, 189, 192, 233, 275, 310, 358, 390 Wang, An, 111, 204, 279 Wang Laboratories, 86, 110, 111, 168, 187, 192, 203, 235, 279 WarGames, 245–48, 256 Warner Communications, 108 Warren, Earl, 81 Warren, Jim, 140, 146, 150, 186 Washington Post, 170, 209, 243, 261 Watson, Emmett, 271–72 Watson, Thomas J., Jr., 13, 36, 48, 236 Watson, Thomas J., Sr., 36 Wayne, Ron, 148 Web 2.0, 361, 373 Weinberger, Caspar, 245 WELL, The, 258, 286, 287, 369 Wells, Katherine, 319 West Coast Computer Faire, 140, 150–51, 157, 186 West Coast Electronics Manufacturers Association (WEMA), 34, 35, 164, 166, 168 Westinghouse, 25, 26 White, Mark, 265 Whitney, Dick, 160 Whole Earth Catalog, 118, 128, 150, 257 Wick, Charles, 196 Widlar, Bob, 97, 112 Wiener, Norbert, 56, 119 Wikipedia, 368–69 Wilson, Charlie, 25 Wilson, John, 78 Wilson, Rand, 264 Wilson Sonsini Goodrich & Rosati, 79, 164, 305, 342, 344–45, 354–55 Winamp, 357 Winblad, Ann, 274, 345 Winograd, Terry, 248, 254, 352, 353, 355, 369 Wired, 303, 327, 333 Wirth, Tim, 192–94, 215, 216, 222, 304 Wojcicki, Susan, 354 Wordsworth, William, 148 World Altair Computer Convention, 139–40 World’s Fair, 48, 49, 153 World War II, 7, 11, 18, 20–24, 26, 28, 34, 37, 54, 70, 78, 81, 352 World Wide Web, 20, 258, 287, 289, 303, 305, 307–10, 332, 341, 342, 353, 363, 371 Wozniak, Steve, 138, 139, 146–51, 154, 157, 178, 181, 189, 224, 232, 285, 304, 404 Wyly, Sam, 58 Xerox, 76, 128–31, 133, 147, 231, 234, 248 Xerox PARC, see Palo Alto Research Center Y2K, 347–48 Yahoo!, 309, 352–54, 361, 362, 366, 368–70 Yammer, 395 Yang, Jerry, 283, 284, 308–9, 316, 377 Y Combinator, 399 Young, John, 213, 215, 223, 237–38, 295–97 YouTube, 365, 369, 403 Yu, Albert, 141–42 Zschau, Ed, 95–96, 166, 168, 170, 171, 197, 221–22, 224, 225, 250, 261–62, 331, 334–36 Zuckerberg, Mark, 367, 368, 370–74, 392, 393, 402–4 ABCDEFGHIJKLMNOPQRSTUVWXYZ ABOUT THE AUTHOR Margaret O'Mara is Professor of History at the University of Washington.

pages: 403 words: 87,035

The New Geography of Jobs
by Enrico Moretti
Published 21 May 2012

.” [>] For example, studies have shown: Baumgardner, “Physicians’ Services and the Division of Labor Across Local Markets.” [>] Think about the history of Facebook: In a recent interview, Zuckerberg criticized several aspects of Silicon Valley’s culture that he does not like but admitted that “Facebook would not have worked if I had stayed in Boston.” Interview at Y Combinator’s Startup School, October 29, 2011. [>] The size of labor markets: Wheeler, “Local Market Scale and the Pattern of Job Changes Among Young Men”; Bleakley and Lin, “Thick-Market Effects and Churning in the Labor Market.” [>] In a recent study of changing family structure: Costa and Kahn, “Power Couples.” [>] “where Ericsson has more than 1,200 employees”: Clark, “Overseas Tech Firms Ramp Up Hiring in Silicon Valley.” [>] One study finds that the likelihood: Sorenson and Stuart, “Syndication Networks and the Spatial Distribution of Venture Capital Investment.” [>] “to be closer”: Delo, “When the Car-Rental Fleet Is Parked in Your Driveway.” [>] “it is tough to get funding”: Gelles, “All Roads Lead to the Valley.” [>] “There’s a lot of support”: Interview, “The Changing Role of the Venture Capitalist,” Marketplace, NPR, January 18, 2011. [>] “to be close to the action”: Kissack, “Electric Vehicle Companies Tap Silicon Valley Cash.” [>] “Knowledge flows are invisible”: Quoted in Jaffe, Trajtenberg, and Henderson, “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” [>] In 1993 three economists: Ibid. [>] Excluding citations that come from the same company: Thompson, “Patent Citations and the Geography of Knowledge Spillovers.” [>] “cricket spills over”: Lohr, “Silicon Valley Shaped by Technology and Traffic.” [>] Citations are highest: Belenzon and Schankerman, “Spreading the Word.” [>] Geographical distance seems to impede: Adams and Jaffe, “Bounding the Effects of R&D.” [>] Pierre Azoulay, Joshua Graff Zivin, and Jialan Wang: Azoulay, Graff Zivin, and Wang, “Superstar Extinction.” [>] When a team of Harvard Medical School doctors: Lee, Brownstein, Mills, and Kohane, “Does Collocation Inform the Impact of Collaboration?”

pages: 290 words: 119,172

Beginning Backbone.js
by James Sugrue
Published 15 Dec 2013

Companies Using Backbone Some of the brightest companies in the world use Backbone to power their latest applications. Let’s take a look at three of these companies and find out why they have chosen Backbone. You’ll find many more case studies listed at the official Backbone web site. 11 Chapter 1 ■ An Introduction to Backbone.js Airbnb Airbnb is one of Y Combinator’s greatest success stories, providing a collaborative sharing service for people to rent living space across 192 countries. Airbnb has used Backbone in a number of its products, from its mobile web application to web site features including wish lists and matching and in its own internal applications.

pages: 232

A Discipline of Programming
by E. Dijkstra
Published 15 Feb 1976

If we start with a pair (X, Y) such that GCD(X, Y) == 713, then there exists no pair (x, y) satisfying condition (2), i.e. for those values of X and Y condition (2) reduces to F; and that means that the machine in question cannot be used to compute the GCD(X, Y) for that pair of values of X and Y.) For many (X, Y) combinations, many states satisfy (2). In the case that o < X < 500 and 0 < Y < 500, the trivial choice is x == X and y == Y. It is a choice that can be made without any evaluation of the GCD-function, even without appealing to the fact that the GCD-function is a symmetric function of its arguments. The condition that characterizes the set of all initial states such that activation will certainly result in a properly terminating happening leaving the system in a final state satisfying a given post-condition is called "the weakest pre-condition corresponding to that post-condition".

pages: 361 words: 81,068

The Internet Is Not the Answer
by Andrew Keen
Published 5 Jan 2015

Having bought a $37.5 million, 89-acre property in Half Moon Bay, a coastal town just south of San Francisco, Khosla unilaterally declared independence and blocked all public access to a much-loved local beach beside his property.74 Balaji Srinivasan, a Stanford University lecturer and startup entrepreneur, has taken the secession fantasy one crazy step further. At one of Paul Graham’s “Failure Central” Y Combinator startup events, Srinivasan pitched the concept of what he called “Silicon Valley’s Ultimate Exit,” a complete withdrawal of Silicon Valley from the United States. “We need to build opt-in society, outside the US, run by technology,” is how he described a ridiculous fantasy that would turn Silicon Valley into a kind of free-floating island that Wired’s Bill Wasik satirizes as the “offshore plutocracy of Libertaristan.”75 And one group of “Libertaristanians” at the Peter Thiel–funded, Silicon Valley–based Seasteading Institute, founded by Patri Friedman, a former Google engineer and the grandson of the granddaddy of free-market economics, Milton Friedman, has even begun to plan floating utopias that would drift off the Pacific coast.76 Behind all these secession fantasies is the very concrete reality of the secession of the rich from everyone else in Silicon Valley.

pages: 352 words: 87,930

Space 2.0
by Rod Pyle
Published 2 Jan 2019

“How to Poop in Space: NASA Unveils Winners of the Space Poop Challenge.” Space.com, February 15, 2017. CHAPTER 8: SPACE EXPLORATION TECHNOLOGIES COR P. 50Masunaga, Samantha. “SpaceX track record ‘right in the ballpark’ with 93% success rate.” Los Angeles Times, September 1, 2016. 51Sam Altman interview with Elon Musk for Y Combinator, September 2016. 52Dillow, Clay. “The Great Rocket Race.” Fortune, October 2016. 53Brown, Alex. “Why Elon Musk Is Suing the U.S. Air Force.” The Atlantic, June 5, 2014. 54De Selding, Peter. “SpaceX’s reusable Falcon 9: What are the real cost savings for customers?” SpaceNews, April 25, 2016. 55This figure is quoted across a wide range.

pages: 297 words: 84,009

Big Business: A Love Letter to an American Anti-Hero
by Tyler Cowen
Published 8 Apr 2019

In 2017, some of the critics pointed to Juicero, a $400 Wi-Fi–enabled juicer that has been called “the absurd avatar of Silicon Valley hubris.” (The company later went under.) Scott Alexander, one of my favorite bloggers (on the internet, of course), set out to rebut this charge. Here is what he found: I looked at the latest batch of 52 startups from legendary Silicon Valley startup incubator Y Combinator. Thirteen of them had an altruistic or international development focus, including Neema, an app to help poor people without access to banks gain financial services; Kangpe, online health services for people in Africa without access to doctors; Credy, a peer-to-peer lending service in India; Clear Genetics, an automated genetic counseling tool for at-risk parents; and Dost Education, helping to teach literacy skills in India via a $1/month course.

pages: 288 words: 86,995

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

From the onset, OpenAI has attracted some of the field’s top researchers, including Ilya Sutskever, who was part of the team from Geoff Hinton’s University of Toronto Lab that built the neural network that triumphed at the 2012 ImageNet competition. In 2019, Sam Altman, who was then in charge of Silicon Valley’s highest profile startup incubator, Y-Combinator, became CEO and undertook a complicated legal reshuffling that resulted in a for-profit company attached to the original nonprofit entity. This was done in order to attract enough investment from the private sector so that OpenAI could fund massive investment in computational resources and compete for increasingly scarce AI talent.

pages: 292 words: 81,699

More Joel on Software
by Joel Spolsky
Published 25 Jun 2008

If you’re a web app developer and you don’t want to support the SDK everybody else is supporting, you’ll increasingly find that people won’t use your web app, because it doesn’t, you know, support cut and paste and address book synchronization and whatever weird new interop features we’ll want in 2010. Imagine, for example, that you’re Google with Gmail, and you’re feeling rather smug. But then somebody you’ve never heard of, some bratty Y Combinator startup, maybe, is gaining ridiculous traction selling NewSDK, which combines a great portable programming language 176 More from Joel on Software that compiles to JavaScript, and even better, a huge Ajaxy library that includes all kinds of clever interop features. Not just cut and paste: cool mashup features like synchronization and single-point identity management (so you don’t have to tell Facebook and Twitter what you’re doing, you can just enter it in one place).

pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity
by Douglas Rushkoff
Published 1 Mar 2016

Facebook and Google, once startups themselves, now acquire more businesses than they incubate internally. With each new generation, firms and investors leverage the startup economy more deliberately, or even cynically. After all, a win is a win. Take OMGPop, a gaming Web site startup that won a spot in the Y Combinator incubator to build social games. It soon enjoyed moderate success with a Facebook game but then couldn’t seem to get any traction. With good advice from its venture-savvy mentors—all former startup founders themselves—the company pivoted from one sector to another, looking for a sweet spot. It picked up another cohort of mentors, including the famed startup studio Betaworks, who helped steer the company toward a trending yet underserved market segment: mobile social gaming.

pages: 284 words: 92,688

Disrupted: My Misadventure in the Start-Up Bubble
by Dan Lyons
Published 4 Apr 2016

Almost by definition these companies are founded and run by young people. Young people are the ones who change the world. They’re filled with passion. They have new ideas. Venture capitalists openly admit they prefer to invest in twenty-something founders. “The cut-off in investors’ heads is thirty-two,” Paul Graham, who runs an incubator called Y Combinator, once said, adding that, “I can be tricked by anyone who looks like Mark Zuckerberg.” John Doerr, a legendary venture capitalist and partner at Kleiner Perkins, once said he liked to invest in “white male nerds who have dropped out of Harvard or Stanford and they have absolutely no social life.

pages: 294 words: 96,661

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity
by Byron Reese
Published 23 Apr 2018

All of these are more than abstract worries. There are people working on these concerns right now. Since we probably couldn’t defeat a malicious AGI given that we couldn’t ever outsmart it, our best plan is to never make a malicious AGI. To that end, Elon Musk along with Sam Altman, the president of the start-up incubator Y Combinator, cochair a nonprofit called OpenAI that has as its purpose to help usher in the era of safe and beneficial AI. The initial blog post announcing its formation states, “Because of AI’s surprising history, it’s hard to predict when human-level AI might come within reach. When it does, it’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest.”

pages: 267 words: 90,353

Private Equity: A Memoir
by Carrie Sun
Published 13 Feb 2024

“Let’s say in a year,” he said, “you’re given the opportunity to be an analyst at Carbon. Would you take it?” I shook my head, explaining that I had already been on the investment side, how in those years I had come to discover that I loved people and stories a bit more than I did numbers or finance. I mentioned an essay by one of the founders of Y Combinator, a premier start-up accelerator, on how to do what you love; later, I followed that up with an email to Gabe exploring quotes from the essay on how “prestige is just fossilized inspiration” and “you have to like what you do enough that the concept of ‘spare time’ seems mistaken.” In my spare time I wrote, read, and planned.

pages: 335 words: 96,002

WEconomy: You Can Find Meaning, Make a Living, and Change the World
by Craig Kielburger , Holly Branson , Marc Kielburger , Sir Richard Branson and Sheryl Sandberg
Published 7 Mar 2018

Essie North, current Big Change M.D., sums up exactly how elated we felt when we heard the news: “Today, the most satisfying feedback we get from our project partners is that by supporting them, when and how we did, we've influenced other organizations to do the same. An example of a similar model, but in the high-tech sector, would be the Y Combinator in Silicon Valley, which takes some of the best tech ideas and incubates them in those early stages with mentors. They can then access funding because they've proved their impact. Taking bold bets does mean you have to roll your sleeves up and get down and dirty sometimes.” You'll experience some trepidation because you are leading the way and approaching charity from a different angle—but you will encourage others to follow and therefore bring about real change.

pages: 343 words: 101,563

The Uninhabitable Earth: Life After Warming
by David Wallace-Wells
Published 19 Feb 2019

Peter Thiel may complain about the pace of technological change, but maybe he’s doing so because he’s worried it won’t outpace ecological and political devastation. He’s still investing in dubious eternal-youth programs and buying up land in New Zealand (where he might ride out social collapse on the civilization scale). Y Combinator’s Sam Altman, who has distinguished himself as a kind of tech philanthropist with a small universal-basic-income pilot project and recently announced a call for geoengineering proposals he might invest in, has reportedly made a down payment on a brain-upload program that would extract his mind from this world.

pages: 572 words: 94,002

Reset: How to Restart Your Life and Get F.U. Money: The Unconventional Early Retirement Plan for Midlife Careerists Who Want to Be Happy
by David Sawyer
Published 17 Aug 2018

[472] “use the income from that to pay for everything else”: “Early Retirement Extreme: A Philosophical and Practical... – Amazon UK.” toreset.me/472, (Kindle version) Location 1,151. [473] “Character is destiny”: “Character is Destiny – Thoughts And Ideas – Medium.” 31 Jan. 2017, toreset.me/473. [474] Charlie Munger…gave a talk to USC Business School in 1994: “Y Combinator: Elementary Worldly Wisdom.” toreset.me/474. [475] “The future belongs to those who learn more skills and combine them in creative ways”: “Quote by Robert Greene: “The future belongs to those who learn more...” toreset.me/475. [476] “It is this stamp of personality, of individual view, which is known as individuality”: “Jack London’s Wisdom on Living a Life of Thumos – The Art of Manliness.” 11 Dec. 2017, toreset.me/476.

Rockonomics: A Backstage Tour of What the Music Industry Can Teach Us About Economics and Life
by Alan B. Krueger
Published 3 Jun 2019

His second album, however, was a flop. Smith jokes that it went “double plastic.” He neglected to pay his income taxes, and the Internal Revenue Service repossessed Smith’s car and motorcycle, assessed him with a $2.8 million tax debt, and garnished his income. “Being famous and broke,” Smith recalled, “is a s****y combination, because you’re still famous and people recognize you, but they recognize you while you’re sitting on the bus.”30 On the verge of bankruptcy, Smith caught a break. A chance meeting led to an impromptu audition at Quincy Jones’s mansion in Bel Air, and the rest is history, as Smith went on to become one of Hollywood’s biggest stars.

pages: 1,076 words: 67,364

Haskell Programming: From First Principles
by Christopher Allen and Julie Moronuki
Published 1 Jan 2015

But the lambda calculus does not appear on the surface to have any means of recursion, because of the anonymity of expressions. How do you call something without a name? Being able to write recursive functions, though, is essential to Turing completeness. We use a combinator – known as the Y combinator or fixed-point combinator – to write recursive functions in the lambda calculus. Haskell has native recursion ability based on the same principle as the Y combinator. It is important to have a solid understanding of the behavior of recursive functions. In later chapters, we will see that, in fact, it is not often necessary to write our own recursive functions, as many standard higher-order functions have built-in recursion.

pages: 648 words: 108,814

Solr 1.4 Enterprise Search Server
by David Smiley and Eric Pugh
Published 15 Nov 2009

Originally from England, James discovered his passion for computer science and programming while at Cambridge University. Upon graduation, James worked as a software engineer at IBM's Hursley Park laboratory—a role which taught him many things, most importantly, his desire to work in a small company. In January 2008, James founded WebMynd Corp., which received angel funding from the Y Combinator fund, and he relocated to San Francisco. WebMynd is one of the largest installations of Solr, indexing up to two million HTML documents per day, and making heavy use of Solr's multicore features to enable a partially active index. Jerome Eteve holds a BSC in physics, maths and computing and an MSC in IT and bioinformatics from the University of Lille (France).

pages: 364 words: 99,897

The Industries of the Future
by Alec Ross
Published 2 Feb 2016

He points to the example of his technology-savvy son, Luukas, who works in government: “He’s never going to invent a billion-dollar app, but he’s in policy, and he understands the policy implications, and that is, I think, one of our problems right now: we don’t have, in Europe at least, people at the policymaking level who understand what IT is about.” * * * But what about the many children born around the world who will not have access to college? There are several resources that have arisen lately that democratize access to important programming skills. One is Codeacademy, a Y Combinator project cofounded by two 23-year-olds that teaches people how to code for free online. Codeacademy counts more than 24 million people around the globe who have used its resources. A second incredible resource is Scratch, a project of the Lifelong Kindergarten Group at the MIT Media Lab. It’s a nonprofit endeavor that teaches programming.

pages: 322 words: 106,663

Women Talk Money: Breaking the Taboo
by Rebecca Walker
Published 15 Mar 2022

I built a customer base, then wireframes, a website, and, within a few months, launched into a business that had small but growing sales. Artists offered street-art walks, street-food bloggers offered exotic food tastings, a homeless man offered homeless tours. A year into running the business, I found myself at the world’s top start-up accelerator, Y Combinator, pitching my start-up in front of an audience of five hundred investors packed into the auditorium of the Computer History Museum in Mountain View, California. “And it turns out, when you build something people want, they tell their friends about it!” I boasted, flashing up a growth curve on the PowerPoint presentation behind me that investors lovingly refer to as the “hockey stick.”

pages: 419 words: 109,241

A World Without Work: Technology, Automation, and How We Should Respond
by Daniel Susskind
Published 14 Jan 2020

While those in work rail against the unemployed, those without work also feel aggrieved toward those with it. This, in part, explains the curious reaction to Silicon Valley’s recent enthusiasm about the UBI. Mark Zuckerberg and Elon Musk have made supportive noises about the idea of a UBI; Pierre Omidyar, founder of eBay, and Sam Altman, founder of Y Combinator, have funded trials of it in Kenya and the United States.14 But their interest has been met with widespread hostility. If work were simply a means to an income, that response might seem odd: these entrepreneurs were essentially proposing that people like them should do all the hard work and give everyone else money for free.

pages: 385 words: 106,848

Number Go Up: Inside Crypto's Wild Rise and Staggering Fall
by Zeke Faux
Published 11 Sep 2023

GO TO NOTE REFERENCE IN TEXT Chapter Five: Getting Hilariously Rich “It’s enough to put the word ‘bitcoin’ on Google”: Comment on “Il Blog delle Stelle.” December 8, 2012. GO TO NOTE REFERENCE IN TEXT programmed by a sixteen- or seventeen-year-old: “Show HN: Bitcoinica—Advanced Bitcoin Trading Platform,” Y Combinator, https://news.ycombinator.com/​item?id=2973301. GO TO NOTE REFERENCE IN TEXT “you forgot to switch your brain on?”: User: urwhatuknow, “Re: [OFFICIAL]Bitfinex.com first Bitcoin P2P lending platform for leverage trading,” Bitcointalk.org, February 10, 2014. GO TO NOTE REFERENCE IN TEXT he wrote to another: User: urwhatuknow, “Re: And we have another Bitfinex Hookey THIEVING Short Squeeze!

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

So too would a range of institutes supporting diversity of innovative thinking – anything from Princeton's Institute of Advanced Study to the MIT Media Lab, the Santa Fe Institute and Japan's RIKEN and the eponymous institutes of Max Planck, Louis Pasteur and Francis Crick. But the diversity is greater still – you could argue that the structure of Oxbridge colleges enables experimental spaces, just as organisations like Y Combinator do for startup ideas, or ARPA does for technology in general. Lastly come even purer breakthrough organisations – those with a specific mission geared around executing a particular idea. Places like Bletchley Park or the Manhattan Project, which grew out of the Second World War, are emblematic here – faced with a monumental problem or challenge, they assemble a taskforce to achieve it.

pages: 356 words: 116,083

For Profit: A History of Corporations
by William Magnuson
Published 8 Nov 2022

Zuckerberg ’06: The Whiz Behind thefacebook.com,” Harvard Crimson, June 10, 2004. 4. Claire Hoffman, “The Battle for Facebook,” Rolling Stone, Sept. 15, 2010; S. F. Brickman, “Not-So-Artificial Intelligence,” Harvard Crimson, Oct. 23, 2003. 5. Steven Levy, Facebook: The Inside Story 13 (2020). 6. Interview with Mark Zuckerberg, “How to Build the Future,” Y Combinator, Aug. 16, 2016. 7. Katharine A. Kaplan, “Facemash Creator Survives Ad Board,” Harvard Crimson, Nov. 19, 2003. 8. Kaplan, “Facemash Creator Survives Ad Board”; Hoffman, “The Battle for Facebook.” 9. “Put Online a Happy Face,” Harvard Crimson, Dec. 11, 2003. 10. Nicholas Carlson, “At Last—the Full Story of How Facebook Was Founded,” Business Insider, Mar. 5, 2010. 11.

pages: 521 words: 118,183

The Wires of War: Technology and the Global Struggle for Power
by Jacob Helberg
Published 11 Oct 2021

The video was fake—a satirical warning from the comedian Jordan Peele and BuzzFeed about the danger of “synthetic content,” more commonly known as deepfakes. This type of synthetic media will render obsolete the old axiom that “seeing is believing”—with potentially devastating ramifications for the fabric of our democracy and the outcome of the Gray War. In mid-2020, I asked Daniel Gross, a partner at the start-up accelerator Y Combinator and in 2011 one of Forbes’s “30 Under 30” tech pioneers, where tech trends could be leading us. He quickly zeroed in on the rise of deepfakes. “A lot of discussion is around synthetic generation of content,” Daniel told me. “Music, movies, faces.” Because of the COVID-19 pandemic, our conversation took place by Zoom, and Daniel offered a timely illustration.

pages: 402 words: 126,835

The Job: The Future of Work in the Modern Era
by Ellen Ruppel Shell
Published 22 Oct 2018

the “social vaccine of the 21st century” Critics point to a conflict of interest: rather than promote technology that contributes to general human flourishing, Silicon Valley elites favor UBI as a publicly supported solution that does not impede their profit-making activities. See, for example, Jathan Sadowski, “Why Silicon Valley Is Embracing Universal Basic Income,” Guardian, July 14, 2017, https://www.thegu­ardian.com/​technology/​2016/​jun/​22/​silicon-valley-universal-basic-income-y-combinator. an addictive public handout Predictions that a BIG (basic income guarantee) would result in many people laying around lazily are not supported by the evidence. In particular, Brazil’s subsistence-level BIG program has resulted in very little change in workforce participation. Given a choice, most people choose to work, and the World Bank has determined that such supports even increase individual efforts to find work, as they allow people to take risks.

pages: 411 words: 119,022

Build: An Unorthodox Guide to Making Things Worth Making
by Tony Fadell
Published 2 May 2022

Elliot was dedicated to educational technology and together we built a multimedia editor for kids. And we got pretty far—a product, employees, an office. But I was still going to the library to look up the difference between an S-corporation and a C. I was green, green, green. And I had no one to ask—there were no entrepreneurial meetups back then, no Y Combinators. Google wouldn’t exist for seven years. General Magic was my chance to learn everything I could possibly want to know. To work with my heroes—the geniuses who made the Apple ][, the Lisa, the Macintosh. It was my first real job and my first real chance to change the world like Andy and Bill had.

pages: 452 words: 134,502

Hacking Politics: How Geeks, Progressives, the Tea Party, Gamers, Anarchists and Suits Teamed Up to Defeat SOPA and Save the Internet
by David Moon , Patrick Ruffini , David Segal , Aaron Swartz , Lawrence Lessig , Cory Doctorow , Zoe Lofgren , Jamie Laurie , Ron Paul , Mike Masnick , Kim Dotcom , Tiffiniy Cheng , Alexis Ohanian , Nicole Powers and Josh Levy
Published 30 Apr 2013

The magic of reddit comes from an appreciation Steve and I had from the day we launched—nothing would work without a truly empowered community. So we’d guide people to a common subreddit (r/SOPA) and see what bubbled up. I posted a quick YouTube video explaining why I was publicly in opposition: “The story of reddit, where Steve Huffman and I started it from an apartment in Medford, MA with 12k in funding from Y Combinator simply could not have happened in a world with this bill … and it’s not just reddit, it’s every single other social media site out there that would be threatened by this bill. And that is devastating. It’s something we simply cannot afford to do from an economic standpoint.” An unprecedented display of democracy in action culminated on January 18, with simultaneously offline and online protests.

pages: 444 words: 127,259

Super Pumped: The Battle for Uber
by Mike Isaac
Published 2 Sep 2019

Chapter 16: THE APPLE PROBLEM 156 BuzzFeed ran its story: Ben Smith, “Uber Executive Suggests Digging Up Dirt on Journalists,” BuzzFeedNews, November 17, 2014, https://www.buzzfeednews.com/article/bensmith/uber-executive-suggests-digging-up-dirt-on-journalists. 156 an enterprising young hacker: Average Joe, “What the Hell Uber? Uncool Bro.,” Gironsec (blog), November 25, 2014, https://www.gironsec.com/blog/2014/11/what-the-hell-uber-uncool-bro/. 156 it landed on Hacker News: “Permissions Asked for by Uber Android App,” Y Combinator, November 25, 2014, https://news.ycombinator.com/item?id=8660336. Chapter 17: “THE BEST DEFENSE . . .” 165 Kalanick also held court over “Hell”: Amir Efrati, “Uber’s Top Secret ‘Hell’ Program Exploited Lyft’s Vulnerability,” The Information, April 12, 2017, https://www.theinformation.com/articles/ubers-top-secret-hell-program-exploited-lyfts-vulnerability. 166 Those programs fell under: Kate Conger, “Uber’s Massive Scraping Program Collected Data About Competitors Around The World.”

pages: 460 words: 130,820

The Cult of We: WeWork, Adam Neumann, and the Great Startup Delusion
by Eliot Brown and Maureen Farrell
Published 19 Jul 2021

VCs were obsessed: they wanted well-spoken entrepreneurs with a strong vision, someone who could not only come up with an innovative idea but also, crucially, sell it to others and lure more funding and followers. Founders Fund even wrote on its website that “entrepreneurs who make it have a near-messianic attitude.” Money flowed to those like Brian Chesky, the magnetic co-founder and CEO of Airbnb, who had impressed his first funders at Y Combinator not with his idea—they thought people wouldn’t cede their homes to strangers—but with the creativity he exhibited in a side project, a cereal called Obama O’s. It flowed to Alex Karp, the quirky CEO of the data analysis firm Palantir, who wasn’t a data analyst—he holds a PhD in social theory—but was a fantastic storyteller.

Convergence Culture: Where Old and New Media Collide
by Henry Jenkins
Published 31 Jul 2006

The example of The Daily Prophet suggests yet another important cultural competency: role-playing both as a means of exploring a fictional realm and as a means of developing a richer understanding of yourself and the culture around y o u . These kids came to understand Harry Potter b y occupying a space w i t h i n Hogwarts; occupying such a space helped them to map more fully the rules of this fictional w o r l d and the roles that various characters played w i t h i n it. M u c h as an actor builds u p a character b y combining things discovered through research w i t h things learned through personal introspection, these kids were drawing on their o w n experiences to flesh out various aspects of Rowling's fiction. This is a k i n d of intellectual mastery than comes only through active Skenovano pro studijni ucely Why Heather Can Write participation.

pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions
by Brian Christian and Tom Griffiths
Published 4 Apr 2016

Perhaps nowhere, however, is overfitting as powerful and troublesome as in the world of business. “Incentive structures work,” as Steve Jobs put it. “So you have to be very careful of what you incent people to do, because various incentive structures create all sorts of consequences that you can’t anticipate.” Sam Altman, president of the startup incubator Y Combinator, echoes Jobs’s words of caution: “It really is true that the company will build whatever the CEO decides to measure.” In fact, it’s incredibly difficult to come up with incentives or measurements that do not have some kind of perverse effect. In the 1950s, Cornell management professor V. F. Ridgway cataloged a host of such “Dysfunctional Consequences of Performance Measurements.”

pages: 505 words: 161,581

The Founders: The Story of Paypal and the Entrepreneurs Who Shaped Silicon Valley
by Jimmy Soni
Published 22 Feb 2022

To date, no one had made much use of the PalmPilot IR ports, other than swapping notes or sinking battleships. In beaming money, Confinity had an IR port use case. In retrospect, Levchin chuckled at the idea, calling it “quaint and silly.” Years later, he joked to Jessica Livingston, author and founder of the seed stage venture firm, Y Combinator, “What would you rather do, take out five dollars and give someone their lunch share, or pull out two PalmPilots and geek out at the table?” But at the time, Levchin remembered that the idea had freshness: “It was so weird and innovative. The geek crowd was like, ‘Wow. This is the future.’ ” Lauri Schultheis was a paralegal at Wilson Sonsini Goodrich & Rosati, the law firm that Levchin and Thiel used to incorporate and handle their financing paperwork.

Likewar: The Weaponization of Social Media
by Peter Warren Singer and Emerson T. Brooking
Published 15 Mar 2018

nytmobile=0&_r=0. 222 “You’re so focused”: Deepa Seetharaman, Robert McMillan, and Georgia Wells, “Tone-Deaf: How Facebook Misread America’s Mood on Russia,” Wall Street Journal, March 2, 2018, https://www.wsj.com/articles/tone-deaf-how-facebook-misread-americas-mood-on-russia-1520006034. 222 “Political stories”: “Tell HN: Political Detox Week,” Hacker News, Y Combinator, accessed March 20, 2018, https://news.ycombinator.com/item?id=13108404. This is a fascinating discussion thread that digs deep into the Silicon Valley zeitgeist. 222 “If we could use code”: Authors’ interview with senior social media company official, Washington, DC, July 14, 2016. 223 pleas from Ukrainian activists: Volodymyr Scherbachenko, “We Support Ukraine on Facebook!

pages: 742 words: 166,595

The Barbell Prescription: Strength Training for Life After 40
by Jonathon Sullivan and Andy Baker
Published 2 Dec 2016

Shoulder impingement: objective 3D shape analysis of acromial morphologic features. Radiology 2006; 239:497-505. Cheema B, Gaul CA, Lane K, Singh MAF. Progressive resistance training in breast cancer: a systematic review of clinical trials. Br Ca Res Treat 2008;109(1):9-26. Chen Y, Zhu M, Zhang Y. Combined endurance-resistance training improves submaximal exercise capacity in elderly heart-failure patients: a systematic review of controlled trials. Int J Cardiol 2012;http://dx.doi.org/10.1016/j.ijcard.2012.09.114. Cho KY, Park H, Seo JW. The relationship between lifestyle and metabolic syndrome in obese children and adolescents.

pages: 598 words: 183,531

Hackers: Heroes of the Computer Revolution - 25th Anniversary Edition
by Steven Levy
Published 18 May 2010

“I am a sensei of the dojo, which as you may know is a grand revered master,” he says, a wide grin on his face. “Felsenstein sensei.” • • • • • • • • Greenblatt, Stallman, and Felsenstein see hacking as a set of ideals. But Paul Graham sees it as a humming economic engine. The forty-five-year-old Internet guru, himself a fanatic engineer in his day, is a cofounder of Y Combinator, an incubator for Internet startups. Twice a year, his company runs American Idol-style contests to select twenty to thirty budding companies to participate in a three-month boot camp, culminating in a demo day packed with Angel investors, VCs, and acquisition-hungry companies like Google and Yahoo.

pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

Timing Is Everything Data on the Federal Reserve’s balance sheet can be found at “Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level,” FRED Economic Data, https://fred.stlouisfed.org/series/WALCL. Federal gross public debt in the United States rose from $10.02 trillion in 2008 Q3 to $15.22 trillion in 2011 Q4. Data can be found at https://fred.stlouisfed.org/series/GFDEBTN#0. The Coinbase Bitcoin sales amount is referenced in Sean Ludwig, “Y Combinator-Backed Coinbase Now Selling over $1m Bitcoins per Month,” VentureBeat, February 8, 2013, https://venturebeat.com/2013/02/08/coinbase-bitcoin/. Coinmarketcap.com has an up-to-date list of active cryptocurrencies, their prices, and market capitalization. It does not take much to issue one’s own cryptocurrency, so there were in fact a few thousand more cryptocurrencies as of December 2020, some with trivial or zero market value.

pages: 611 words: 188,732

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom)
by Adam Fisher
Published 9 Jul 2018

Susan Wojcicki’s quotes can be found in Jefferson Graham’s USA Today story, “The House That Helped Build Google,” in July 2007. I’m CEO… Bitch Mark Zuckerberg’s quotes come from a guest lecture he gave to Harvard’s “Introduction to Computer Science” class in 2005, from an interview he gave to the Harvard Crimson in February that same year, and from Y Combinator’s Startup School event in 2007. Dustin Moskovitz’s quotes were taken from a keynote address given to the Alliance of Youth Movements Summit in December 2008, and from David Kirkpatrick’s authoritative history, The Facebook Effect. David Choe’s comments were made on The Howard Stern Show in March 2016.

pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It
by Marc Goodman
Published 24 Feb 2015

Church, Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves (New York: Basic Books, 2012); J. Craig Venter, Life at the Speed of Light: From the Double Helix to the Dawn of Digital Life (New York: Viking Adult, 2013). 47 In another example: Kim-Mai Cutler, “Glowing Plant Is One of Y Combinator’s Very First Biotech Startups,” TechCrunch, Aug. 11, 2014. 48 Her estate eventually: Rebecca Skloot, The Immortal Life of Henrietta Lacks (New York: Broadway Books, 2011); Moore v. Regents of University of California (1990) 51 Cal. 3d 120 (271 Cal. Rptr. 146, 793 P.2d 479), Justia Law, accessed Sept. 12, 2014, http://​law.​justia.​com/. 49 Why did they: A case of Moore v.

pages: 562 words: 201,502

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

The Talulah Roller Coaster: Author’s interviews with Talulah Riley, Elon Musk, Maye Musk, Kimbal Musk, Navaid Farooq, Bill Lee. Junod, “Force of His Will.” 40. Artificial Intelligence: Author’s interviews with Sam Altman, Demis Hassabis, Elon Musk, Reid Hoffman, Luke Nosek, Shivon Zilis. Steven Levy, “How Elon Musk and Y Combinator Plan to Stop Computers from Taking Over,” Backchannel, Dec. 11, 2015; Cade Metz, “Inside OpenAI, Elon Musk’s Wild Plan to Set Artificial Intelligence Free,” Wired, Apr. 27, 2016; Maureen Dowd, “Elon Musk’s Billion-Dollar Crusade to Stop the A.I. Apocalypse,” Vanity Fair, Apr. 2017; Elon Musk talk, MIT Aeronautics and Astronautics Department’s Centennial Symposium, Oct. 24, 2014; Chris Anderson interview with Elon Musk, TED Conference, Apr. 14, 2022. 41.

pages: 903 words: 235,753

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

See http://www.crimethinc.com/texts/ex/digital-utopia.html, https://www.youtube.com/watch?v=cOubCHLXT6A, and http://www.ftc.gov/sites/default/files/documents/public_statements/promoting-internet-inclusion-more-things-more-people/140107ces-iot.pdf. 32.  Balaji Srinivasa, “Silicon Valley's Ultimate Exit,” speech at the Y Combinator Startup School, De Anza College, Cupertino, CA, October 25, 2013, https://www.youtube.com/watch?v=cOubCHLXT6A. “Peacefully start an international (1) company, (2) community, (3) currency, (4) country. We are now at step 3.” (@balajis, January 3, 2014. 33.  Albert O. Hirschman, Exit, Voice, and Loyalty; Responses to Decline in Firms, Organizations, and States (Cambridge, MA: Harvard University Press, 1970). 34. 

pages: 827 words: 239,762

The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite
by Duff McDonald
Published 24 Apr 2017

Of those who were self-employed, too, there’s a good bet that many were one-man consulting shops. “The one area where they excel at starting their own firms is in the field of management consultancy,” say the authors of Gravy Training: Inside the Business of Business Schools, “and the value such firms add to the economy is debatable.”25 Thirty years later, Sam Altman, the president of Y Combinator, told the School’s 2014 Cyberposium that MBAs mistake starting a company for the next resume item and that their education trained them for running a business, not for starting one.26 That same year, at a conference organized by HBS’s venture capital and private equity club, startup investor Chamath Palihapitiya made the absurd claim that “I would bet a large amount of money that the overwhelming majority of [venture capitalists] would not look favorably on a company started by one of you.”27 Palihapitiya would lose that bet.

Frommer's California 2009
by Matthew Poole , Harry Basch , Mark Hiss and Erika Lenkert
Published 2 Jan 2009

AE, DC, MC, V. Mon–Sun 5pm– 2am. Valet parking $7. Matsuhisa JAPANESE/PERUVIAN Japanese chef/owner Nobuyuki Matsuhisa arrived in Los Angeles via P eru in 1987 and opened what may be the most cr eative restaurant in the city . A tr ue master of fish cooker y, Matsuhisa creates unusual dishes b y combining Japanese flavors with S outh American spices and salsas (he was the first to introduce Americans to y ellowtail sashimi with sliced jalapeños). B roiled sea bass with black truffles, miso-flavored black cod, sautéed squid with garlic and so y, tempura sea urchin in a shiso leaf, and D ungeness crab tossed with chiles and cr eam are just a fe w examples of the masterfully prepared dishes available, in addition to thickly sliced nigiri and creative sushi r olls.

pages: 1,737 words: 491,616

Rationality: From AI to Zombies
by Eliezer Yudkowsky
Published 11 Mar 2015

Though it does take a mature understanding to appreciate this impossibility, so it’s not surprising that people go around proposing clever shortcuts. On the AI-Box Experiment, so far I’ve only been convinced to divulge a single piece of information on how I did it—when someone noticed that I was reading Y Combinator’s Hacker News, and posted a topic called “Ask Eliezer Yudkowsky” that got voted to the front page. To which I replied: Oh, dear. Now I feel obliged to say something, but all the original reasons against discussing the AI-Box experiment are still in force . . . All right, this much of a hint: There’s no super-clever special trick to it.