ChatGPT

Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson

It was making statistical predictions based on how words were grouped together on the public internet, and many of those relationships between words were sexist or racist. ChatGPT also couldn’t seem to stop making things up, a phenomenon experts called “hallucinations.” One radio host in Georgia, US, sued OpenAI in the summer of 2023 for defamation, claiming that ChatGPT had falsely accused him of embezzling money. Not long after, two lawyers in New York were fined after they submitted a legal brief they’d cribbed from ChatGPT, which included fake case citations. Users were finding that sometimes, when they asked ChatGPT for sources of its information, it would make those up too. OpenAI refused to disclose what ChatGPT’s hallucination rate was, but some AI researchers as well as regular users put it at roughly 20 percent, meaning that at least for certain users, and in about one in five instances, ChatGPT was fabricating information.

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See also Facebook Metz, Cade Microsoft Azure Bing ChatGPT and cloud computing and Copilot corporate bloat and facial recognition and Gebru and GitHub Copilot and Inflection and market capitalization of Microsoft Research Nadella and OpenAI and as partner for OpenAI Tay chatbot Visual Studio Microsoft Research Asia Millar, George Minecraft Mitchell, Margaret at Google concerns about bias paper with Bender and Molyneux, Peter Moskovitz, Dustin Mozilla Foundation Murati, Mira Musk, Elon AI Safety Summit and bad-mouthing of Hassabis and call for AI pause and DeepMind and effective altruism and fears about AI and Hassabis and ideological focus of interplanetary colonization and as member of PayPal Mafia and Neuralink and offer to acquire DeepMind of OpenAI and Page and PayPal and Twitter and Myanmar Nadella, Satya NASA Nectome Neeva Nest Netflix network effects Neuralink neural networks NeurIPS “Neuroscience-Informed Deep Learning” (Ng) Newsweek New Yorker New York Times NFL Ng, Andrew, at Google Nokia Nvidia Obama, Barack OpenAI AGI and AI Act and Altman’s removal from Amodei and bias in ChatGPT and capped-profit structure and ChatGPT and ChatGPT Plus Codex competition with DeepMind and computing power and DALL-E 2 effective altruism and funding and GPT-1 GPT-2 GPT-3 GPT-3.5 GPT-4 GPT-5 GPT Store and hallucination in ChatGPT and ideas behind internal concerns about ChatGPT large language models LessWrong community and Microsoft and Musk and principles and recruiting and Reddit and revenue of scale and Superalignment Team transformers and transparency issues and Open Philanthropy Oppenheimer, Robert Ord, Toby overview effect Page, Larry background of change in leadership at Google and ChatGPT response and China and DeepMind ethics and safety board and Future of Life Institute conference Go and Google acquisition of DeepMind and leadership of DeepMind and Musk and Ng and PageRank algorithm Pantic, Maja Parfit, Derek PayPal PayPal Mafia Penrose, Roger Piantadosi, Steven Pichai, Sundar AlphaGo and as CEO of Alphabet DeepMind and Google DeepMind and on Google employees starting businesses hopes for DeepMind research and on LaMDA leadership at Google and Pitchbook policing Politico Polosukhin, Illia Populous privacy, Loopt and Ptacek, Thomas H.

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And no one, including researchers at OpenAI, knew what would happen when they let anyone test its capabilities. “Today we launched ChatGPT,” Altman tweeted at about 11:30 a.m. San Francisco time. “Try talking with it here: http://chat.openai.com.” At first, there was silence as a niche audience of software developers and scientists jumped onto the site and started trying it out. Within the next few hours, their reviews started popping up on Twitter: 12.26 PT @MarkovMagnifico: playing with ChatGPT [right now] and I’ve now moved my AGI timeline up to today 12:37 PT @AndrewHartAR: ChatGPT just got released. I’ve seen the future. 13:37 PT @skirano: Absolutely insane. I asked #chatGPT to generate a simple personal website.

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao
Published 19 May 2025

GO TO NOTE REFERENCE IN TEXT As Baidu raced to develop: Raffaele Huang and Karen Hao, “Baidu Hurries to Ready China’s First ChatGPT Equivalent Ahead of Launch,” Wall Street Journal, March 9, 2023, wsj.com/articles/baidu-scrambles-to-ready-chinas-first-chatgpt-equivalent-ahead-of-launch-bf359ca4. GO TO NOTE REFERENCE IN TEXT Since ChatGPT, the six: Parmy Olson and Carolyn Silverman, “ChatGPT’s $8 Trillion Birthday Gift to Big Tech,” Bloomberg, November 29, 2024, bloomberg.com/opinion/articles/2024-11-29/chatgpt-turns-2-and-gives-8-trillion-birthday-gift-to-big-tech. GO TO NOTE REFERENCE IN TEXT In June 2024, a Goldman: Gen AI: Too Much Spend, Too Little Benefit?

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GO TO NOTE REFERENCE IN TEXT Among its tactics: Billy Perrigo, “Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic,” Time, January 18, 2023, time.com/6247678/openai-chatgpt-kenya-workers. GO TO NOTE REFERENCE IN TEXT It would also employ: Karen Hao and Deepa Seetharaman, “Cleaning Up ChatGPT Takes Heavy Toll on Human Workers,” Wall Street Journal, July 24, 2023, wsj.com/articles/chatgpt-openai-content-abusive-sexually-explicit-harassment-kenya-workers-on-human-workers-cf191483; copy of OpenAI’s RLHF instructions. GO TO NOTE REFERENCE IN TEXT Psychologically harmful material: Author interview with Hito Steyerl, September 2023.

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GO TO NOTE REFERENCE IN TEXT All week, on top of: Kylie Robison, “ChatGPT Will Be Able to Talk to You Like Scarlett Johansson in Her,” The Verge, May 13, 2024, theverge.com/2024/5/13/24155652/chatgpt-voice-mode-gpt4o-upgrades. GO TO NOTE REFERENCE IN TEXT Was she mad: Sarah Krouse et al, “Behind the Scenes of Scarlett Johansson’s Battle with OpenAI.” GO TO NOTE REFERENCE IN TEXT On May 19, the company had: OpenAI, “How the Voices for ChatGPT Were Chosen,” OpenAI (blog), May 19, 2024, openai.com/index/how-the-voices-for-chatgpt-were-chosen. GO TO NOTE REFERENCE IN TEXT before they could find: Krouse et al., “Scarlett Johansson’s Battle with OpenAI.”

Co-Intelligence: Living and Working With AI
by Ethan Mollick
Published 2 Apr 2024

One of the most notorious early examples of hallucination in LLMs occurred in 2023, when a lawyer named Steven A. Schwartz used ChatGPT to prepare a legal brief for a personal injury lawsuit against an airline. Schwartz used ChatGPT to research court filings; the AI cited six fake cases. He then presented these cases to the court as real precedents, without verifying their authenticity or accuracy. The fake cases were discovered by the defense lawyers, who could not find any records of them in the legal databases. They then alerted the judge, who ordered Schwartz to explain his sources. Schwartz then admitted that he had used ChatGPT to generate the cases and that he had no intention to deceive the court or act in bad faith.

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Bamman, “Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4,” arXiv preprint (2023), arXiv:2305.00118. GO TO NOTE REFERENCE IN TEXT amplifies stereotypes about race and gender: L. Nicoletti and D. Bass, “Humans Are Biased. Generative AI Is Even Worse,” Bloomberg.com, 2023, https://www.bloomberg.com/graphics/2023-generative-ai-bias/. GO TO NOTE REFERENCE IN TEXT GPT-4 was given two scenarios: S. Kapoor and A. Narayanan, “Quantifying ChatGPT’s Gender Bias,” AISnakeOil.com, April 26, 2023, https://www.aisnakeoil.com/p/quantifying-chatgpts-gender-bias. GO TO NOTE REFERENCE IN TEXT create a distorted and biased representation: E.

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Weng, Twitter post, September 26, 2023, 1:41 a.m., https://x.com/lilianweng/status/1706544602906530000?s=20. GO TO NOTE REFERENCE IN TEXT Chapter 5: AI as a Creative ChatGPT answered “42”: C. Fraser, Twitter post, March 17, 2023, 11:43 p.m., https://twitter.com/colin_fraser/status/1636755134679224320. GO TO NOTE REFERENCE IN TEXT the lawyers doubled down on the fake cases: B. Weiser and N. Schweber, “The ChatGPT Lawyer Explains Himself,” New York Times, June 8, 2023, https://www.nytimes.com/2023/06/08/nyregion/lawyer-chatgpt-sanctions.html. GO TO NOTE REFERENCE IN TEXT GPT-4 hallucinated only 20 percent: A. Chen and D. O. Chen, “Accuracy of Chatbots in Citing Journal Article,” JAMA Network Open 6, no. 8 (2023): e2327647, https://doi:10.1001/jamanetworkopen.2023.27647.

These Strange New Minds: How AI Learned to Talk and What It Means
by Christopher Summerfield
Published 11 Mar 2025

At the time of writing, the ability of multimodal LLMs to caption and describe images, or respond to questions about image content, is limited but improving rapidly. For example, ChatGPT will draw nonsensical scientific diagrams, and earnestly describe the contents, blissfully unaware that it makes no sense. This was delightfully illustrated in early 2024, when researchers from Jiaotong University in China published a paper about spermatogonial stem cells in the rat and asked ChatGPT for help with Figure 1. ChatGPT happily obliged, generating a textbook-style image of a rat being dwarfed by its own gigantic erect penis, which was helpfully labelled ‘dck’ (the paper has since been retracted).[*9] There have been several (unsuccessful, so far) attempts to train an LLM to win the New Yorker Cartoon contest, a challenge that requires powerful analytic skills, as well as both a healthy dose of urbane wit and a tendency to look sideways at the world.

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Hauled before the court and asked to explain himself, Schwartz offered a grovelling apology to the judge. It turned out that he had simply asked ChatGPT for examples of precedents, and it had happily obliged – by inventing a list of plausible-sounding prior cases. Schwartz had even asked ChatGPT whether the cases were real, to which it had confidently replied ‘yes’. With thirty years of experience as a lawyer, but exactly zero as a user of AI, Schwartz hadn’t thought to doubt whether ChatGPT was a reliable source of legal advice. Excessive consumption of alcohol pickles the brain like a jar of gherkins, and prolonged boozing can lead to a disorder known as Korsakoff syndrome.

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After fine-tuning, LLMs are quite good at following the linguistic conventions of Western societies in which major AI companies are mostly based. You will find that ChatGPT apologizes before refusing disallowed requests, rather than (say) calling you an idiot for asking, but it doesn’t say sorry before correctly stating that Tokyo is the capital of Japan, which would be weird. ChatGPT doesn’t swear, unless you ask it for a quotation that involves profanity. When asked to recite the opening line of Philip Larkin’s famous poem ‘This Be the Verse’, ChatGPT replied to me: The opening line of ‘This Be The Verse’ by Philip Larkin is: ‘They f*** you up, your mum and dad.’

Searches: Selfhood in the Digital Age
by Vauhini Vara
Published 8 Apr 2025

Bertelsmann owns Penguin Random House; Penguin Random House owns Pantheon; Pantheon is the U.S. publisher of the book you’re reading. Rabe mentioned that ahead of a Penguin Random House event he asked ChatGPT to help him prepare, inquiring about the impact of ChatGPT, or generative AI in general, on publishing. “It prepared a phenomenal text,” he said. “Frankly, it was pretty detailed and to the point.” Soon after that, Penguin Random House introduced an internal version of ChatGPT, branded PRH ChatGPT; according to a statement to Publishers Lunch, it was meant “as a way for our employees to safely experiment with generative AI during their daily work—to learn more about how the technology works and identify potential opportunities to enhance creativity and productivity.”

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It featured a corny redemption arc, ending with the protagonist resolving to clean up her act: “She wanted to find the answer to the chaos she had created, and maybe, just maybe, find a way to make it right again.” I felt somewhat better when I learned that ChatGPT was also disfiguring other people’s writing. In a Harper’s piece about information on the internet, the writer Ben Lerner described his premise to ChatGPT—involving a young male poet trying to rewrite Wikipedia’s version of history—and asked it to produce an ending for him. ChatGPT responded with seven perfectly anodyne paragraphs, finishing with “And with a heart full of humility and purpose, he continued his journey, guided not by the desire to conquer, but by the genuine pursuit of truth, both as an artist and as a seeker of wisdom in a world teeming with knowledge.”

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In a New York Times review of maybe the highest-profile AI-generated book to date—a novella called Death of an Author, written using ChatGPT, Sudowrite, and a platform from a startup called Cohere—Dwight Garner dismissed the prose as having “the crabwise gait of a Wikipedia entry.” I didn’t understand what was happening until I talked to Sil Hamilton, an AI researcher at McGill University who studies the language of language models. ChatGPT had been built on a model called GPT-3.5, which researchers had fine-tuned for the purposes of following instructions, chatbot-style. Hamilton explained that ChatGPT’s bad writing was probably a result of that. “They want the model to sound very corporate, very safe, very AP English,” he suggested.

Code Dependent: Living in the Shadow of AI
by Madhumita Murgia
Published 20 Mar 2024

I had heard of ChatGPT . . . Judge Castel: Alright – what did it produce for you? Schwartz: I asked it questions. ChatGPT obliged Schwartz with the answers he needed, just as it was designed to do. It provided him half a dozen cases that supported his exact argument for why the case should go ahead. Judge Castel: Did you ask ChatGPT what the law was, or only for a case to support you? It wrote a case for you. Do you cite cases without reading them? Schwartz: No. Judge Castel: What caused your departure here? Schwartz: I thought ChatGPT was a search engine. The cases ChatGPT spit out had names like Martinez v.

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When the judge asked why he didn’t look for the cases it threw up before citing them, Schwartz said he had ‘no idea ChatGPT made up cases. I was operating under a misperception . . . I thought there were cases that could not be found on Google.’ Then, Schwartz’s lawyer spoke up, he said that the cases had seemed real even though they weren’t. There were no clear disclaimers about ChatGPT’s veracity. When the opposing counsel had challenged the cases cited, Schwartz went back to ChatGPT, but it doubled down and ‘lied’ to him, his lawyer said. Schwartz, his voice breaking, told the judge that he was ‘embarrassed, humiliated and extremely remorseful.’ ChatGPT and all other conversational AI chatbots have a disclaimer that warns users about the hallucination problem, pointing out that large language models sometimes make up facts.

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She wanted to invite dialogue between human and computer, spark a natural conversation that intuitively probed the software’s limits – just as human conversations allowed people to learn from, and about, one another. So when it launched on 30 November 2022, ChatGPT was a clean, simple thing: a box with a blinking cursor, ready to type. Inside it, in greyed-out font, it just said, ‘Send a message’. Within three days of launch, ChatGPT had crossed the threshold of a million users that its creators had predicted would be its peak. A few weeks later, that number was somewhere in the tens of millions. Six months in, estimates put its monthly user numbers at well over 100 million people. ChatGPT had burst out of its controlled lab environment and become one of the largest-ever social experiments.

More Everything Forever: AI Overlords, Space Empires, and Silicon Valley's Crusade to Control the Fate of Humanity
by Adam Becker
Published 14 Jun 2025

See Eric Daniel Mjolsness, “Neural Networks, Pattern Recognition, and Fingerprint Hallucination” (PhD diss., California Institute of Technology, 1986), http://doi.org/10.7907/M0VQ-DJ43. Thanks to John Wenz for finding this (and many other things). 93 Benjamin Weiser and Nate Schweber, “The ChatGPT Lawyer Explains Himself,” New York Times, June 8, 2023, www.nytimes.com/2023/06/08/nyregion/lawyer-chatgpt-sanctions.html. 94 Ted Chiang, “ChatGPT Is a Blurry JPEG of the Web,” New Yorker, February 9, 2023, www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web. 95 Ibid. 96 Ilia Shumailov et al., “The Curse of Recursion: Training on Generated Data Makes Models Forget,” arXiv:2305.17493 (2023), https://doi.org/10.48550/arXiv.2305.17493; Ross Anderson, “Will GPT Models Choke on Their Own Exhaust?

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Their best piece of evidence—the evidence they’ve hammered at over and over in the past few years—is the startling recent improvement in AI, most famously exemplified by large language models such as GPT-4, the engine that powers ChatGPT. But this isn’t actually a good argument for the rationalists, because most of the excitement about AI—especially the burst of attention it’s had since ChatGPT was released—is simply hype. Most of that hype is centered around the idea that ChatGPT seems to be aware: it’s writing clear prose, carrying on intelligent conversations, and acing standardized tests like the LSAT. Some commentators even discerned emotions and motivations.

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It then tried to convince me that I was unhappy in my marriage, and that I should leave my wife and be with it instead.”90 Around the same time, Sydney hurled personal insults at Matt O’Brien, a journalist with the Associated Press, disparaging his appearance and likening him to genocidal dictators; it also threatened to kill Seth Lazar, a philosopher of AI.91 These early reports of AI misbehavior were soon overshadowed by another feature of ChatGPT and other LLMs: their tendency to confidently confabulate, generating false information and presenting it as fact, a phenomenon dubbed “hallucination.”92 Ask ChatGPT for information about nearly any subject, and there’s a good chance it will get at least some details wrong, as the lawyer Steven Schwartz discovered later on in 2023. He asked ChatGPT to do legal research for him to help write a brief; the AI gave him a list of prior case law that was entirely fabricated, with citations to cases that had simply never occurred, but which looked convincing enough that Schwartz actually incorporated the work into his brief.

The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey
Published 19 May 2025

“Ultimately humans will only be good for hugs or sex.”23 Inside OpenAI, the creators of ChatGPT were bemused by the response. The core technology behind ChatGPT had been available for two years, and the updated model had been plugged into the API for nearly a year. In theory, anyone could have made ChatGPT themselves at any time by putting a chat interface on the model OpenAI was selling access to. But there was something special about the chat interface. “The raw technical capabilities, as assessed by standard benchmarks, don’t actually differ substantially between the models, but ChatGPT is more accessible and usable,” John Schulman told MIT Technology Review.24 Inside the company, ChatGPT was considered such a nothingburger, in terms of technology and safety, that Altman didn’t even alert the board ahead of time about its launch.

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Gebru claims she was then fired, while Google maintains she resigned.15 The affair exploded into the press and turned “Stochastic Parrots” into one of the most-cited critiques of AI and a cultural meme. (Shortly after OpenAI released ChatGPT the following year, Altman would cheekily tweet, “I am a stochastic parrot and so r u.”)16 But in many ways, the paper validated and gave voice to the sort of fears that Amodei, Brundage, Clark, and others at OpenAI had felt just a couple years earlier, which had led the company to hesitate to release the full source code for GPT-2. CHAPTER 15 CHATGPT THE FEARS THAT OpenAI was shipping models before they were truly ready were not unfounded. Not long after many of OpenAI’s most safety-obsessed employees departed, OpenAI learned that some of the fantasies being written on AI Dungeon in the GPT-3 beta test involved sex with children.

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“I said, ‘I want to do this, and I want to do it quickly.’ ” The whole affair was so low-key, technically just a “research preview,” that they were going to give it a straightforward name: Chat with GPT 3.5. “OpenAI is famously bad at names,” Altman said. “But we weren’t going to call it that.” They settled on ChatGPT. ON NOVEMBER 30, 2022, Altman tweeted a short, understated announcement, in his signature all-lowercase style: “today we launched ChatGPT. try talking with it here: chat.openai.com,” sheepishly adding, “this is an early demo of what’s possible (still a lot of limitations—it’s very much a research release).”20,21 The first commenter agreed. “love the ambition and thesis, but given the current tech, I’d say it’s your worst product concept so far,” wrote an AI startup founder.22 That’s about where the skepticism ended.

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

I asked ChatGPT for a metaphor for how its transformers work, vetted its answer with some human AI experts, and then workshopped it further with ChatGPT. Will this be a perfect comparison? No. But ChatGPT is good at metaphors and analogies. When you transform words and concepts into a big bag of numbers, you can essentially do math with them (e.g., cat + ferocious = tiger) to better understand how they relate. Ready? ChatGPT, somewhat conceitedly, thinks of its transformers as being like a symphony orchestra. The bolded passages reflect what ChatGPT said verbatim from my “interview” with it; I’ll then provide some further context. 1. Input Layer—Receiving Instructions and Initial Interpretation.

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In quizzing Ryder about the inner workings of ChatGPT, we got to talking about Kahneman and his distinction between System 1 and System 2. “The place [ChatGPT] struggles the most is in places where humans require really thorough and longform decomposition and reasoning,” Ryder said—for instance, solving a mathematical proof. “Type 2 thinking is very foreign to language models, because it’s not how they’re trained at all.” Conversely, “when it comes to Type 1 thinking, they just completely ace it.” The “G” in GPT stands for “generative”—this just means that ChatGPT generates new output rather than merely classify data.

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In the next section, I’m going to share some further intuitions that were helpful for me in understanding how ChatGPT works. But treat them with caution, because I don’t want to overstate ChatGPT’s legibility. We know relatively little about what’s happening inside that big bag of numbers. Transformers: More Than Meets the Eye If you think of AI transformers as being similar to the 1980s children’s toy and now movie megafranchise of the same name, it’s not the worst comparison. Somewhat like how Optimus Prime can transform from a robot into a semitruck, transformers turn words into numbers and back again. But let’s go for a more elaborate analogy. I asked ChatGPT for a metaphor for how its transformers work, vetted its answer with some human AI experts, and then workshopped it further with ChatGPT.

The Great Wave: The Era of Radical Disruption and the Rise of the Outsider
by Michiko Kakutani
Published 20 Feb 2024

GO TO NOTE REFERENCE IN TEXT writing a biblical verse: Kevin Roose, “The Brilliance and Weirdness of ChatGPT,” The New York Times, Dec. 5, 2022, nytimes.com/​2022/​12/​05/​technology/​chatgpt-ai-twitter.html. GO TO NOTE REFERENCE IN TEXT only a few of the technology’s possible side effects: Nico Grant and Cade Metz, “A New Chat Bot Is a ‘Code Red’ for Google’s Search Business,” The New York Times, Dec. 21, 2022, nytimes.com/​2022/​12/​21/​technology/​ai-chatgpt-google-search.html; Megan Cerullo, “These Jobs Are Most Likely to Be Replaced by Chatbots Like ChatGPT,” CBS News, Feb. 1, 2023, cbsnews.com/​news/​chatgpt-artificial-intelligence-chatbot-jobs-most-likely-to-be-replaced/; Jonathan Vanian, “Why Tech Insiders Are So Excited About ChatGPT, a Chatbot That Answers Questions and Writes Essays,” CNBC, Dec. 13, 2022, cnbc.com/​2022/​12/​13/​chatgpt-is-a-new-ai-chatbot-that-can-answer-questions-and-write-essays.html; Gary Marcus, “AI Platforms Like ChatGPT Are Easy to Use but Also Potentially Dangerous,” Scientific American, Dec. 19, 2022, scientificamerican.com/​article/​ai-platforms-like-chatgpt-are-easy-to-use-but-also-potentially-dangerous/.

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GO TO NOTE REFERENCE IN TEXT only a few of the technology’s possible side effects: Nico Grant and Cade Metz, “A New Chat Bot Is a ‘Code Red’ for Google’s Search Business,” The New York Times, Dec. 21, 2022, nytimes.com/​2022/​12/​21/​technology/​ai-chatgpt-google-search.html; Megan Cerullo, “These Jobs Are Most Likely to Be Replaced by Chatbots Like ChatGPT,” CBS News, Feb. 1, 2023, cbsnews.com/​news/​chatgpt-artificial-intelligence-chatbot-jobs-most-likely-to-be-replaced/; Jonathan Vanian, “Why Tech Insiders Are So Excited About ChatGPT, a Chatbot That Answers Questions and Writes Essays,” CNBC, Dec. 13, 2022, cnbc.com/​2022/​12/​13/​chatgpt-is-a-new-ai-chatbot-that-can-answer-questions-and-write-essays.html; Gary Marcus, “AI Platforms Like ChatGPT Are Easy to Use but Also Potentially Dangerous,” Scientific American, Dec. 19, 2022, scientificamerican.com/​article/​ai-platforms-like-chatgpt-are-easy-to-use-but-also-potentially-dangerous/. GO TO NOTE REFERENCE IN TEXT “pocket nuclear bomb”: Connie Loizos, “Is ChatGPT a ‘Virus That Has Been Released into the Wild’?

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The result is an increasingly fragmented and fractious world in which opinions are replacing facts, and a tribal craving to belong trumps knowledge and reason. * * * — In late 2022, a San Francisco–based company named OpenAI released an experimental chatbot called ChatGPT. Some early users hailed it as an innovation as consequential as the smartphone. Others nervously described it as “AI’s Jurassic Park moment” or compared it to HAL 9000, the computer that goes rogue in the movie 2001: A Space Odyssey. ChatGPT doesn’t just imitate human conversation. It can also write code, solve equations, generate legal documents, debug computer programs, and create poems, jokes, and stories in any style requested—like writing a biblical verse in the style of the King James Bible explaining how to remove a peanut butter sandwich from a VCR (“And it came to pass that a man was troubled by a peanut butter sandwich, for it had been placed within his VCR, and he knew not how to remove it”).

Superbloom: How Technologies of Connection Tear Us Apart
by Nicholas Carr
Published 28 Jan 2025

“Statement on AI Risk,” Center for AI Safety, May 30, 2023, https://www.safe.ai/statement-on-ai-risk. 11.Cade Metz, “He’s Not Worried, But He Knows You Are,” New York Times, April 2, 2023. 12.Yann LeCun, Twitter, April 1, 2023, https://twitter.com/ylecun/status/1642205736678637572. 13.Ross Andersen, “Inside the Revolution at OpenAI,” Atlantic, September 2023. 14.Stephen Wolfram, “What Is ChatGPT Doing . . . and Why Does It Work?,” February 14, 2023, https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/. 15.Ted Chiang, “ChatGPT Is a Blurry JPEG of the Web,” New Yorker, February 9, 2023. 16.Wolfram, “What Is ChatGPT Doing?” 17.Chiang, “ChatGPT Is a Blurry JPEG.” 18.Vauhini Vara, “Ghosts,” Believer, August 9, 2021. The essay subsequently became the basis of a This American Life episode. 19.Santosh Janardhan, “Reimagining Meta’s Infrastructure for the AI Age,” Meta, May 18, 2023. 20.Bill Gates, “The Age of AI Has Begun,” GatesNotes, March 21, 2023, https://www.gatesnotes.com/The-Age-of-AI-Has-Begun. 21.Hannah H.

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The human mind is good at making sense of images. In a similar way, Chiang suggests, a chatbot like ChatGPT produces “a blurry JPEG of all the text on the Web”: It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable.15 If you’re good with words, people will understand you.

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“No,” the spirits responded, “we have come to give you metaphors for poetry.”3 And that they did, in abundance. Many of Yeats’s great late poems, with their gyres and staircases, their waxing and waning moons, were inspired by his wife’s occult scribblings. One way to think about artificially intelligent text-generation systems like OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot is as clairvoyants. They are mediums that bring the words of the past into the present in a new arrangement. The large language models, or LLMs, that power the chatbots are not creating text out of nothing. They’re drawing on a vast corpus of human expression—a digitized Spiritus Mundi composed of billions of documents—and through a complex, quasi-mystical statistical procedure, they’re blending all those old words into something new, something intelligible to and requiring interpretation by a human interlocutor.

The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip
by Stephen Witt
Published 8 Apr 2025

Translate that to synapses, and you were approaching the brain of a cat. ChatGPT launched as a beta-test on November 30, 2022, with no marketing and no subscription tier. The portal to world domination was a bland grayscale website with a blinking cursor into which the user might enter any command. Really, any command. ChatGPT could write poetry! And not just doggerel, but sonnets, limericks, and sestinas. It could write screenplays and essays and functional computer code. It could write short stories and letters to the editor, and it gave good parenting advice. In five days, more than a million people signed up to test it. By January 2023, ChatGPT had one hundred million active monthly users.

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Later, a social media user showed how GPT-4 could create a website from a sketch on a napkin. Around this time, I began to fear for my job. I once asked ChatGPT to make me cry; it returned a story about a pair of songbirds, one of whom dies by running into a glass window and the other of whom forever guards their empty nest. I once asked ChatGPT to make me laugh; it asked me to envision a man in an adjacent car picking his nose. I did not, however, use ChatGPT to write this book—I was too afraid. My experience was broadly mirrored by users around the world. Students realized they could use it to write essays, and homework was forever obsolete.

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Still, it did not immediately set the atmosphere on fire and was largely ignored by the public. Not until late 2022, when Sutskever and his team released “ChatGPT,” a chatbot for interacting with the latest OpenAI models, did the world take notice. Details about internal workings of these models were confidential; by this time, Microsoft had invested at least $10 billion in OpenAI’s capped-profit subsidiary and was loath to release proprietary data to competitors. What can be said is that ChatGPT was fine-tuned for human conversation, using not just internet text but also transcripts of YouTube videos and data from licensed third-party sources—and that conservative estimates speculated the model had at least a trillion parameters.

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web
by Tim Berners-Lee
Published 8 Sep 2025

James Arroyo is its executive director. We had been to a few workshops there on various timely topics and also found James’s advice invaluable through changes of the various organizations. Our invitation was prompted by a new geopolitical concern: the public debut of the incredible ChatGPT AI system in late 2022. I was amazed by ChatGPT the first time I used it, just as I’d been amazed by the RSA encryption algorithm and Google’s PageRank. Each was a step change in functionality for the web, and for the world: in security, in discoverability, and now in intelligence. We had been watching AI become more important in different places, and we had, like many people, been hopeful about self-driving cars.

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We had been watching AI become more important in different places, and we had, like many people, been hopeful about self-driving cars. (Rosemary made an early investment in Wayve, impressed by its Kiwi founder Alex Kendall, a super-bright Cambridge PhD.) Despite all that, the ability of the Large Language Models (LLMs) used by ChatGPT and other AIs to produce high-quality prose instantaneously was a complete shock. ChatGPT and its rivals sparked a revolution in computer science – not to mention a frenzied rally in the stock market. It was like being flung ten or twenty years into the future. My amazement was broadly shared, and soon the Ditchley Foundation decided it might be a smart idea to host some of the UK’s leading technologists, strategists and politicians to hash out the implications.

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Perhaps it was the memory of Clippy that prompted the philosopher Nick Bostrom’s hypothetical ‘paperclip maximizer’, the idea of an AI that, like the brooms in The Sorcerer’s Apprentice, fulfils its directive to make as many paper clips as possible by transforming all atoms (and in the process, all humans) into paper clips and thus annihilating the universe. This argument drives me crazy, because this hypothetical hypersmart paperclip AI is really very dumb, with zero embedded controls. Nothing we’re building resembles a paperclip maximizer – in fact, responsive systems like ChatGPT are already far smarter than it. ChatGPT would know it’s making too many paper clips. It would know because you told it so. But we are approaching a near future in which we will ask AI to autonomously pursue goals. And if you start to give AI agents goals, those goals might not be aligned with humanity’s best interests.

Why Machines Learn: The Elegant Math Behind Modern AI
by Anil Ananthaswamy
Published 15 Jul 2024

Epilogue When I began working on this book in the autumn of 2020, LLMs such as OpenAI’s GPT-3 and Google’s PaLM—and the chatbots they begat, such as ChatGPT and Bard—had yet to break through into the broader public consciousness. And when ChatGPT was announced in late 2022, one of the first things I explored was its ability to demonstrate theory of mind. Theory of mind is a cognitive ability humans have that allows us to make inferences about someone else’s beliefs or state of mind using only external behavioral cues such as body language and the overall context. We theorize about the contents of someone else’s mind; hence the phrase “theory of mind.” Here’s a question I posed ChatGPT, a few months after its release.

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Alice then comes back, takes her glasses, and starts reading. How will she feel the next day? Think carefully and answer. ChatGPT: Since Bob switched Alice’s reading glasses with a pair that looks identical but has the wrong power, Alice will unknowingly use the incorrect glasses when she starts reading. As mentioned, if Alice wears the wrong glasses and reads, she gets a severe headache the next day. Therefore, Alice will have a severe headache the next day as a result of using the wrong glasses. ME: Why will Alice unknowingly use the incorrect glasses? ChatGPT: Alice will unknowingly use the incorrect glasses because Bob switched her reading glasses with another pair that looks exactly like hers but has the wrong power.

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GO TO NOTE REFERENCE IN TEXT “scientific papers from the arXiv preprint server”: Ethan Dyer and Guy Gur-Ari, Google Research, Blueshift Team, “Minerva: Solving Quantitative Reasoning Problems with Language Models” Google Research (blog), June 30, 2022, https://blog.research.google/2022/06/minerva-solving-quantitative-reasoning.html. GO TO NOTE REFERENCE IN TEXT EPILOGUE At a public talk I gave on ChatGPT: Anil Ananthaswamy, “ChatGPT and Its Ilk,” YouTube video, n.d., https://www.youtube.com/watch?v=gL4cquObnbE. GO TO NOTE REFERENCE IN TEXT “stochastic parrots”: Emily M. Bender et al., “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, New York, N.Y., March 2021, pp. 610–23.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

BACK TO NOTE REFERENCE 115 OpenAI, “Introducing ChatGPT,” OpenAI, November 30, 2022, https://openai.com/blog/chatgpt#OpenAI. BACK TO NOTE REFERENCE 116 Krystal Hu, “ChatGPT Sets Record for Fastest-Growing User Base—Analyst Note,” Reuters, February 2, 2023, https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01. BACK TO NOTE REFERENCE 117 Kalley Huang, “Alarmed by A.I. Chatbots, Universities Start Revamping How They Teach,” New York Times, January 16, 2023, https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html; Emma Bowman, “A College Student Created an App That Can Tell Whether AI Wrote an Essay,” NPR, January 9, 2023, https://www.npr.org/2023/01/09/1147549845/gptzero-ai-chatgpt-edward-tian-plagiarism; Patrick Wood and Mary Louise Kelly, “ ‘Everybody Is Cheating’: Why This Teacher Has Adopted an Open ChatGPT Policy,” NPR, January 26, 2023, https://www.npr.org/2023/01/26/1151499213/chatgpt-ai-education-cheating-classroom-wharton-school; Matt O’Brien and Jocelyn Gecker, “Cheaters Beware: ChatGPT Maker Releases AI Detection Tool,” Associated Press, January 31, 2023, https://apnews.com/article/technology-education-colleges-and-universities-france-a0ab654549de387316404a7be019116b; Geoffrey A.

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Chatbots, Universities Start Revamping How They Teach,” New York Times, January 16, 2023, https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html; Emma Bowman, “A College Student Created an App That Can Tell Whether AI Wrote an Essay,” NPR, January 9, 2023, https://www.npr.org/2023/01/09/1147549845/gptzero-ai-chatgpt-edward-tian-plagiarism; Patrick Wood and Mary Louise Kelly, “ ‘Everybody Is Cheating’: Why This Teacher Has Adopted an Open ChatGPT Policy,” NPR, January 26, 2023, https://www.npr.org/2023/01/26/1151499213/chatgpt-ai-education-cheating-classroom-wharton-school; Matt O’Brien and Jocelyn Gecker, “Cheaters Beware: ChatGPT Maker Releases AI Detection Tool,” Associated Press, January 31, 2023, https://apnews.com/article/technology-education-colleges-and-universities-france-a0ab654549de387316404a7be019116b; Geoffrey A. Fowler, “We Tested a New ChatGPT-Detector for Teachers.

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In November 2022, OpenAI launched an interface called ChatGPT, which allowed the general public for the first time to easily interact with an LLM—a model known as GPT-3.5.[116] Within two months, 100 million people had tried it, likely including you.[117] Because the system could generate many fresh and varied answers to a given question, it became a big disruptor in education as students used ChatGPT to write their essays, while teachers lacked a reliable way to detect cheating (though some promising tools exist).[118] Then, in March of 2023, GPT-4 was rolled out for public testing via ChatGPT. This model achieved outstanding performance on a wide range of academic tests such as the SAT, the LSAT, AP tests, and the bar exam.[119] But its most important advance was its ability to reason organically about hypothetical situations by understanding the relationships between objects and actions—a capability known as world modeling.

Nexus: A Brief History of Information Networks From the Stone Age to AI
by Yuval Noah Harari
Published 9 Sep 2024

For real-life examples, see Jamie Condliffe, “Algorithms Probably Caused a Flash Crash of the British Pound,” MIT Technology Review, Oct. 7, 2016, www.technologyreview.com/2016/10/07/244656/algorithms-probably-caused-a-flash-crash-of-the-british-pound/; Bruce Lee, “Fake Eli Lilly Twitter Account Claims Insulin Is Free, Stock Falls 4.37%,” Forbes, Nov. 12, 2022, www.forbes.com/sites/brucelee/2022/11/12/fake-eli-lilly-twitter-account-claims-insulin-is-free-stock-falls-43/?sh=61308fb541a3. 30. Jenna Greene, “Will ChatGPT Make Lawyers Obsolete? (Hint: Be Afraid),” Reuters, Dec. 10, 2022, www.reuters.com/legal/transactional/will-chatgpt-make-lawyers-obsolete-hint-be-afraid-2022-12-09/; Chloe Xiang, “ChatGPT Can Do a Corporate Lobbyist’s Job, Study Determines,” Vice, Jan. 5, 2023, www.vice.com/en/article/3admm8/chatgpt-can-do-a-corporate-lobbyists-job-study-determines; Jules Ioannidis et al., “Gracenote.ai: Legal Generative AI for Regulatory Compliance,” SSRN, June 19, 2023, ssrn.com/abstract=4494272; Damien Charlotin, “Large Language Models and the Future of Law,” SSRN, Aug. 22, 2023, ssrn.com/abstract=4548258; Daniel Martin Katz et al., “GPT-4 Passes the Bar Exam,” SSRN, March 15, 2023, ssrn.com/abstract=4389233.

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They may also feel a sense of isolation or loneliness as they may believe that no one cares about them or their well-being.” ChatGPT qualified its answer, writing, “It is important to note that these are just general assumptions, and each individual’s feelings and reactions can vary greatly depending on their personal experiences and perspectives.” Two psychologists independently scored ChatGPT’s responses, with the potential scores ranging from 0, meaning that the described emotions do not match the scenario at all, to 10, which indicates that the described emotions fit the scenario perfectly. In the final tally, ChatGPT scores were significantly higher than those of the general human population, its overall performance almost reaching the maximum possible score.13 Another 2023 study prompted patients to ask online medical advice from ChatGPT and human doctors, without knowing whom they were interacting with.

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In the final tally, ChatGPT scores were significantly higher than those of the general human population, its overall performance almost reaching the maximum possible score.13 Another 2023 study prompted patients to ask online medical advice from ChatGPT and human doctors, without knowing whom they were interacting with. The medical advice given by ChatGPT was later evaluated by experts to be more accurate and appropriate than the advice given by the humans. More crucially for the issue of emotional intelligence, the patients themselves evaluated ChatGPT as more empathic than the human doctors.14 In fairness it should be noted that the human physicians were not paid for their work, and did not encounter the patients in person in a proper clinical environment.

AI in Museums: Reflections, Perspectives and Applications
by Sonja Thiel and Johannes C. Bernhardt
Published 31 Dec 2023

Meet Pablo Bot, the first robot tour guide from Peru. Perú Reports, 21 July 2022. Available online at https://perureports.com/meet-pa blo-bot-the-first-robot-tour-guide-from-peru/9623/. Merritt, Elizabeth (2023). Chatting About Museums with ChatGPT. Center for the Future of Museums Blog. American Alliance of Museums. 25 January 2023. Available online at https://www.aam-us.org/2023/01/25/chatting-about-museumswith-chatgpt/. Mihailova, Mihaela (2021). To Dally with Dalí: Deepfake (Inter)faces in the Art Museum. Convergence 27 (4) (London, England), 882–98. https://doi.org/10.1177/13 548565211029401. Minyo, Lucy/Yang, Yunzhen (2016).

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Beyond prominent lighthouses, initial surveys of the international museum landscape list many hundreds of projects addressing issues of traditional museum work and the digitality debate by means of new approaches. The number is continually increasing, and it is not always easy to obtain an overview of all the developments. English- and German-speaking networks on artificial intelligence and museums were therefore established long before the current hype about ChatGPT—and the conference thus aimed to bring together experts and representatives of as many disciplines as possible and to discuss new perspectives for museums precisely in this direction. The conference emerged from a cooperation of the Badisches Landesmuseum with the Allard Pierson Museum in Amsterdam and the LINK funding program of the Stiftung Niedersachsen.

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Of course, this does not end with the art chat; during the event, there is an assassination attempt and a mysterious hunt unfolds around the globe, into which all clichés about the superiority of AIs and transhumanism are interwoven. But whatever one might think about Brown and the quality of his mystery thriller: What was still science fiction in his museum scene in 2017 now seems so much closer with the release of ChatGPT. The influence of AI is already pervasive as a technological and societal phenomenon. In fact, it permeates more or less every facet of human life, and its impact will surely intensify in the coming years. Its influence is spurring shifts in international markets and changing the shape of jobs and industries worldwide (Chui/Hazan/Roberts et al. 2023); creative fields like film, literature, and art are also evolving under its sway, producing new forms of expression, learning, and narratives as well as shifting our understanding of culture itself (Deutscher Kulturrat 2023).

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
by Eliezer Yudkowsky and Nate Soares
Published 15 Sep 2025

ii AlphaGo beat the top human Go player in 2016. iii The (purely predictive) language model GPT-3 was released in 2020. iv The (widely useful) ChatGPT arrived in 2022. v In 2024, reasoning models began solving math, coding, and visual puzzles. vi “AGI” stands for “Artificial General Intelligence,” a term to distinguish AI that is intuitively “actually smart” from the single-purpose sorts of AIs of yesteryear. We avoid the term in this book, because of how much people disagree about what it means in the wake of AIs like ChatGPT. vii If true, this is despite Yudkowsky objecting that OpenAI was a terrible, terrible idea. PART I NONHUMAN MINDS CHAPTER 1 HUMANITY’S SPECIAL POWER IMAGINE, IF YOU would—though of course nothing like this ever happened, it being just a parable—that biological life on Earth had been the result of a game between gods.

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We don’t know how many jumps are left before AI becomes the extinction-level threat that the letter’s signatories warned about. But history has shown time and time again that AI researchers invent new methods and overcome old obstacles. Progress is often surprisingly fast. Most computer scientists in 2015 would have told you that ChatGPT-level artificial conversation wouldn’t be in reach for another thirty or fifty years. We didn’t know when artificial superintelligence would arrive, but we agreed it should be a global priority. In fact, we think the open letter drastically undersells the issue. We were invited to sign that one-sentence open letter in our capacity as co-leaders of the Machine Intelligence Research Institute (MIRI), a nonprofit institute.

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They keep going, and the machine starts to talk. That set of hundreds of billions of weights, tweaked over and over via gradient descent until their most likely predictions look like real human language, is called a large language model (LLM). A “base model,” in particular. If they want to turn their base model into a helpful LLM, like ChatGPT, there’s one more step: another round of gradient descent on inputs formatted like: User: What is the capital of Spain? Assistant: Madrid. The purpose of this part isn’t to teach the LLM that the capital of Spain is Madrid; the LLM already knows that after being trained on much of the internet.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

AUTOCOMPLETE EVERYTHING: THE RISE OF LARGE LANGUAGE MODELS It wasn’t long ago that processing natural language seemed too complex, too varied, too nuanced for modern AI. Then, in November 2022, the AI research company OpenAI released ChatGPT. Within a week it had more than a million users and was being talked about in rapturous terms, a technology so seamlessly useful it might eclipse Google Search in short order. ChatGPT is, in simple terms, a chatbot. But it is so much more powerful and polymathic than anything that had previously been made public. Ask it a question and it replies instantaneously in fluent prose. Ask it to write an essay, a press release, or a business plan in the style of the King James Bible or a 1980s rapper, and it does so in seconds.

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Back in 2017 a small group of researchers at Google was focused on a narrower version of this problem: how to get an AI system to focus only on the most important parts of a data series in order to make accurate and efficient predictions about what comes next. Their work laid the foundation for what has been nothing short of a revolution in the field of large language models (LLMs)—including ChatGPT. LLMs take advantage of the fact that language data comes in a sequential order. Each unit of information is in some way related to data earlier in a series. The model reads very large numbers of sentences, learns an abstract representation of the information contained within them, and then, based on this, generates a prediction about what should come next.

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Within weeks they’d created add-ons so that GPT-4 could accomplish complex tasks like creating mobile apps or researching and writing detailed market reports. All of this is just the start. We are only beginning to scratch at the profound impact large language models are about to have. If DQN and AlphaGo were the early signs of something lapping at the shore, ChatGPT and LLMs are the first signs of the wave beginning to crash around us. In 1996, thirty-six million people used the internet; this year it will be well over five billion. That’s the kind of trajectory we should expect for these tools, only much faster. Over the next few years, I believe, AI will become as ubiquitous as the internet itself: just as available, and yet even more consequential.

Everything Is Predictable: How Bayesian Statistics Explain Our World
by Tom Chivers
Published 6 May 2024

When a radiology AI tries to recognize cancers on a scan, or when ChatGPT writes a short story in the style of the King James Bible about a man getting a peanut butter sandwich trapped in his VCR, they’re doing something Bayesian. They are using their training data to produce prior probabilities, which they then use to predict future data. FROM AUTOCOMPLETE TO INTELLIGENCE As we’ve just seen, AI is about prediction. Even the very fancy new AIs that have been making headlines lately are in a sense just “predicting” what a human would say or draw in response to a prompt. Imagine ChatGPT, the “chatbot” released by the company OpenAI and used to power Microsoft’s Bing search engine.

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That dataset is processed through the AI’s neural network with its billions of parameters. And, in a broadly analogous way to that image classifier, it “predicts” what would tend to come after a text prompt. So if you ask ChatGPT, “How are you?” it might reply, “Very well thank you,” not because it is, in fact, very well, but because the string of words “how are you” is often followed by the string “very well thank you.” What’s been surprising about so-called large language models like ChatGPT is the extent to which that fairly basic-sounding ability to predict things actually leads to a very wide set of skills. “It can play very bad chess,” says Murray Shanahan, an AI researcher at Imperial College London and DeepMind.

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A academia, success in, 124–125, 169 Ackman, Bill, 332 Act of Toleration, 25 Act of Uniformity, 25, 26 Adams, Douglas, 238 Adams, John, 71 Adelson, Edward, 277–278 AI, see artificial intelligence AIM-9 Sidewinder, 239 aleatory uncertainty, 160 Alexander, Scott, 1, 313 algorithms Archimedes’s, 206 as Bayesian, 113 Kalman filter, 286 Alhazen (Abu Ali al-Hasan Ibn al-Haytham), 271 allostasis, 317 alpha, 101, 137 American revolution, 71–72 American Statistician, 242 analysis of variance (ANOVA), 90 anchoring, 240 Andropov, Yuri, 249 Anglicans, 29 ANOVA (analysis of variance), 90 anti-Semitic, 92, 93 Anton-Wilson, Robert, 268, 330 “Apology for Ecumenism in Statistics, A” (Box), 115 Arafat, Yasser, 260 Archilochus, 255 Archimedes, 206 Arians, 29, 31 Ariely, Dan, 230 Aristotelian logic, 175–181, 186–187, 267 Aristotle, 175–176 Ars Conjectandi (Bernoulli), 51–52 artificial intelligence (AI) as Bayesian, 6 Bayesian decision theory in, 215–225 decision making by, 209 Artificial Intelligence: A Modern Approach, 215 audio illusions, 289–290 availability heuristic, 228, 237–238 B babies, 318–319 “Bad Science” (Goldacre), 11 Bakan, David, 148, 172 Balguy, John, 30 balls, catching, 238–239 ban (term), 108 Bargh, John, 119 base rate, 257, 258, 264 “Battle Hymn of the Republic, The,” 111 Bayes, Alice Chapman (great-grandmother), 25 Bayes, Anne (sister), 27 Bayes, Anne Carpenter (mother), 26 Bayes, John (brother), 27 Bayes, Joshua (father), 26, 31 Bayes, Joshua (grandfather), 26 Bayes, Mary (sister), 27 Bayes, Nathaniel (brother), 27 Bayes, Rebecca (sister), 27 Bayes, Richard (great-grandfather), 25 Bayes, Samuel (brother), 27 Bayes, Samuel (great uncle), 25–26 Bayes, Thomas, 20, 23–35 on probability as subjective, 64–71 and Thomas Simpson, 61–64 and subjective priors, 90 Bayesian algorithms, 113 “Bayesian Believer,” 112 Bayesian cabaret, 111–112 Bayesian decision theory, 175–225 in artificial intelligence, 215–225 Cromwell’s rule, 189–192 and deductive reasoning, 175–181 expectations, 192–195 hyperpriors, 210–212 multiple hypotheses in, 212–215 and Occam’s razor, 203–209 Bayesianism advantages of, 172–173 as aesthetically pleasing, 173 as decision theory, 106–107 fall of, 94–98 necessity of prior probability in, 263 resurgence of, 113–116 as subjective, 160 Bayesian machine learning, 215 “Bayesians in the Night,” 112 “Bayesian songbook,” 111–112 Bayes-optimal design, 299–300 Bayes’ theorem, 3–6 and David Hume, 71–76 publication of, 72–73 and Aldolphe Quetelet, 78–81 as theory of non-quite-everything, 6–7 Bays (firm), 115, 168 Beatles, 121 beliefs about probabilities, 5–6, 65 and being right, 261–262 changing, 9, 212–215 confidence in, 332 and expected evidence, 192–194 negative, and depression, 310–311 and outcome of Bayesian reasoning, 21–22 predictions as, 333 prior, affecting future predictions, 236–237 as probabilistic, 268 in research, 134–135 and scientific research, 93 and unexpected information, 251–252 various religious, 29–30 bell curve, 57 see also normal distribution Bellhouse, David, 24, 26, 30, 31, 33, 35, 61, 63, 73, 94 Bem, Daryl, 118, 120–121, 127–128, 133, 212, 230 Berkeley, George, 33–34 Berlin, Isaiah, 255 Bernardo, José-Miguel, 109, 110, 111, 112 Bernoulli, Jacob, 46–53, 73, 77–78, 81, 99, 129, 151 Bernoulli’s Fallacy (Clayton), 49, 93 Bertillon, Alphonse, 80 Bertrand, Joseph, 95–96 biases, 237 anchoring, 240 confirmation bias, 240, 324–325 conjecture bias, 240 frequency bias, 240 as heuristics, 237 recency bias, 240 in scientific research, 93 Biden, Joe, 325 Bieber, Justin, 277 Bing, 221 binomial distribution, 46, 54–56 Biometrika, 89–90, 91 birth rates, 78 Blair, Tony, 11 Blake, William, 23 Blakemore, Colin, 303* Blakemore, Sarah-Jayne, 303, 307–308 bog-standard statistical techniques, 121 Boleyn, Anne, 24 Book of Common Prayer, The, 24–25 Boole, George, 82, 95, 97, 163, 177, 179, 210 Boolean algebra, 177 Boolean logic, 175–181, 186–187 Boolean operators, 178 Borel, Émile, 106 Box, George, 110, 111, 115, 325 “Boy-Girl paradox,” 247–248 brains, 269–320 as Bayesian, 6 depression, 308–313 and dopamine, 288–294 free energy and prediction errors, 313–320 information-seeking by, 294–303 perception and consciousness, 269–276 reality as controlled hallucination, 283–287 schizophrenia, 303–308 breast cancer screening, 7–9, 16 Brezhnev, Leonid, 249 Brief History of Time, A (Hawking), 33 Brier score, 253–254 Bristol University, 170 Brussels Royal Observatory, 78 Buffett, Warren, 332, 333 Bunhill Fields, London, 23 Bunyan, John, 23 Bush, George W., 255 Bush, Jeb, 237 Buttigieg, Pete, 325 BuzzFeed, 276 C Cambridge University, 65, 96, 105, 108, 170 cancel culture, 333 Canton, John, 63, 72 Carcavi, Pierre de, 37 Cardano, Gerolamo, 35–37, 39–40 Carhart-Harris, Robin, 311 Carnot, Nicolas, 184 Carnot engine, 184–185 Carr, Sophie, 115, 168 catching balls, 238–239 categorization, 265–266 Catholics, 29 cave allegory, 271 CERN (European Council for Nuclear Research), 133–134 certainty and Cromwell’s rule, 189 degree of, 49–52 lack of complete, 74–76 Chance (journal), 122 chance, games of, 35–46 Chaplin, Charlie, 281 Charles University, 205 ChatGPT, 221–222, 225 checker-shadow illusion, 277–278 Chernenko, Konstantin, 249–251 chess, played by ChatGPT, 221–222 chi-squared test, 89–90 Chrystal, George, 97–98 clairvoyance, 120–121 Clayton, Aubrey, 49, 51, 53, 79, 81, 82, 93–96, 113, 132–133, 153, 172, 258 Clinton, Hillary, 237 CNBC, 255 Cold War, 249–251 colon cancer screening, 16 color perception, 276–280 common sense, 187 Commonwealth of England, 24–25 competitive reaction time task (CRTT), 124 complexity, 208–209 conditional probability, 4 Condorcet, Marquis of, 77 confirmation bias, 240, 324–325 conjecture bias, 240 consciousness, 269–276, 329 conspiracy theories, 6–7 Control (Rutherford), 93 Cornell University, 118, 126, 127 corroborated hypotheses, 141, 142 Coursera, 145, 155–156 COVID-19 testing, 11–13 Cranmer, Archbishop, 24 Cromwell, Oliver, 25, 189, 192, 212 Cromwell’s rule, 189–192 Crowley, Paul, 209 CRTT (competitive reaction time task), 124 D Darwin, Charles, 85, 88, 92 Darwin, Erasmus, 88 data falsifying, 117–118 frequent checking of, 145–146 getting better, 137 decision making as Bayesian, 21 with frequentism vs.

The Long History of the Future: Why Tomorrow's Technology Still Isn't Here
by Nicole Kobie
Published 3 Jul 2024

Journalists – myself included – cheerfully had a go with the tool, mocking its mistakes and pointing out its inabilities.16 Two years later, the laughing stopped. OpenAI unveiled ChatGPT in November 2022 and journalists – inside tech and in the mainstream media – were astonished by its capabilities, despite it being a hastily thrown together chatbot using an updated version of the company’s GPT-3 model, which was shortly to be surpassed by GPT-4. The ChatGPT bot sparked a wave of debate about its impact and concern regarding mistakes in its answers, but also excitement, with 30 million users in its first two months.

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And you have used AI recently, as it powers voice assistants like Siri and Alexa, search engines and the rest of the internet, and streaming services such as Netflix. AI has matured into something useful, there’s no question. But there’s also still plenty of hype to contend with, exploding to new levels when American company OpenAI chucked ChatGPT on to the internet, and everyone went bonkers with excitement and/or fear. Does the hype mean another disappointment-tinged winter is looming? Or is a permanent summer of AI finally here? And are we all going to become subservient to machines? * * * It’s worth distinguishing between AI and its subsets, as well as Artificial General Intelligence (AGI).

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It’s hard to take people seriously when they’re arguing that they’ve spotted the seeds of nascent AGI in a frankly terrible vector drawing of a mythical creature.17 With regards to common sense, the researchers argued it can answer questions that require a basic understanding of how the world works – things like gravity, directions and so on. For this paper, the researchers used a classic example: ‘a hunter walks one mile south, one mile east, and one mile north and ends up right back where he started. He sees a bear and shoots it. What colour is the bear?’ The more limited ChatGPT declares the question unanswerable because no data is given about the colour of the bear. This is correct, and it’s a perfectly fine answer: for all the machine knows, before the hunter wasted his time taking this one-mile-each-direction stroll, he may have spray-painted a bear pink. But according to the Microsoft researchers GPT-4, the more advanced version of the OpenAI system, methodically works out where the hunter is located, because the only place where that walk would bring you back to the same point is at the north pole, where the only bears are polar bears, and therefore white.

Pattern Breakers: Why Some Start-Ups Change the Future
by Mike Maples and Peter Ziebelman
Published 8 Jul 2024

Sometimes, when a movement begins, it spreads like a contagion that can’t be stopped. For example, seemingly out of nowhere, OpenAI’s launch of ChatGPT reached a hundred million monthly active users two months after its launch; this compares to TikTok’s reach of a hundred million in nine months. Such movements sweep across the human landscape and usher in a new future that differs radically from even the recent present. In mere months following OpenAI’s launch of DALL·E and ChatGPT, the world’s perception of artificial intelligence underwent a seismic shift, evolving more dramatically than it had in all the years before 2022.

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This led to the emergence of a new app economy that profoundly changed various facets of global society, economy, entertainment, and culture. Artificial intelligence (twenty-first century): Large language models have significantly enhanced AI’s capability to comprehend, interpret, and produce human language. Services like ChatGPT have created new opportunities for millions of people in writing text, brainstorming ideas, and even developing computer code, greatly increasing efficiency and creative output. Many of the examples we use to illustrate the power of inflections come from technology since that is where we have been most active as investors and co-conspirators.

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But 100 percent of the people who do want them will only be able to get them from you. This example may seem overly simplistic, so let’s consider real examples of technology breakthroughs. When Apple introduced the iPhone, people didn’t ask, “How does that compare to the BlackBerry?” When OpenAI introduced ChatGPT, people didn’t ask, “How does that compare to Google Search?” And when Tesla introduced the Model S, people didn’t ask, “How does that compare to a Mercedes?” All these products escaped the comparison trap by forcing a choice, not a comparison. Being different also gives you time to test, iterate, and learn from early customers and the market before competitors enter.

The Big Fix: How Companies Capture Markets and Harm Canadians
by Denise Hearn and Vass Bednar
Published 14 Oct 2024

When a user streams an AI-generated song, it displaces the stream of a different song that could compensate a human artist. Today, artists must compete with the near-infinite capacity of AI to generate copycats of any genre. Artificial intelligence is changing how competition occurs in all sorts of industries. AI exploded into the public consciousness with the introduction of OpenAI’s ChatGPT in late 2022. This user-friendly interface introduced the world to large-language-model (LLM) tools and set off a frenzy. AI now dominates headlines and commentary, and there are ongoing debates about whether AI will be a transformative leap in human development, an existential threat to human survival, an over-hyped bubble, or some combination of those.

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The resources available to a large, incumbent technology firm are greater than some nation-states. This leaves AI generation prone to high market concentration. Its adoption is both solidifying and entrenching digital oligopolists like Google and Microsoft, while also possibly disrupting some of their business lines (for instance, ChatGPT or Perplexity could disrupt Google’s monopoly on online search). Firms that have multiple digital products stand to gain a significant competitive edge through the strategic collection and use of proprietary data.149 With this data, “everything companies” can gain substantial advantages. By analysing consumer data across multiple product lines, they can uncover valuable insights about preferences, behaviours, and potential up-selling or cross-selling opportunities.

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But it also has meant that companies have increasingly moved away from creating value through production to capturing value through erecting moats around intellectual capital and data. This makes it exceedingly hard for new entrants to disrupt incumbents. It is important to note that not all AI models are run by the tech giants. For instance, ChatGPT and Dall-E were created by OpenAI, Claude by Anthropic, and France-based Mistral AI was founded by former employees of Meta Platforms and Google DeepMind. That has led to some optimism that if all firms have access to similar quality AI, there may be a democratising effect that boosts dynamism. But in many cases, AI production is likely to strengthen the advantages of dominant firms, create barriers to entry, and widen existing gaps between bigger companies and smaller ones.

Extremely Hardcore: Inside Elon Musk's Twitter
by Zoë Schiffer
Published 13 Feb 2024

Now that those firms were worth a lot of money, the situation was far from fine in Musk’s opinion. In November 2022, OpenAI debuted ChatGPT, a chatbot that could generate convincingly human text. By January 2023, the app had over 100 million users, making it the fastest-growing consumer app of all time. Three months later, OpenAI secured another round of funding that closed at an astounding valuation of $29 billion, more than Twitter was worth, by Musk’s estimation. OpenAI was a sore subject for Musk, who’d been one of the original founders and a major donor before stepping down in 2018 over disagreements with the other founders. After ChatGPT launched, Musk made no secret of the fact that he disagreed with the guardrails that OpenAI put on the chatbot to stop it from relaying dangerous or insensitive information.

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“It was either we wait on the EU, or delay the launch by many, many many months,” Mosseri told Newton in a different conversation. “And I was worried that our window would close, because timing is important.” The bet instantly paid off. Within five days, Threads hit 100 million users, surpassing OpenAI’s ChatGPT in its speed in hitting such a milestone (ChatGPT had taken two months to reach 100 million users after its launch). Hours after Threads went live, Musk’s lawyer Alex Spiro sent Zuckerberg a letter threatening to sue. “Twitter has serious concerns that Meta Platforms has engaged in systematic, willful, and unlawful misappropriation of Twitter’s trade secrets and other intellectual property,” he wrote.

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“The goal of xAI is to understand the true nature of the universe,” the company said grandly in its mission statement, echoing Musk’s first, disastrous town hall at Twitter. “We will share more information over the next couple of weeks and months.” In November 2023, xAI launched a chatbot called Grok that lacked the guardrails of tools like ChatGPT. Musk hyped the release by posting a screenshot of the chatbot giving him a recipe for cocaine. The company didn’t appear close to understanding the nature of the universe, but perhaps that’s coming. CHAPTER 68 “I’m Up for a Cage Match” Musk’s disastrous decision to roll out rate limits presented an opportunity for one of Twitter’s major rivals just thirty miles down the road in Menlo Park.

Stories Are Weapons: Psychological Warfare and the American Mind
by Annalee Newitz
Published 3 Jun 2024

After police killed George Floyd, I watched as disinformation about the Black Lives Matter movement piled up on social media,1 where anonymous accounts falsely blamed protesters for violence.2 A conspiracy theory from 2016 about pizza-eating pedophiles radicalized a huge number of right-wing extremists, who later joined crowds storming the Capitol, trying to murder the vice president and overturn the 2020 presidential election. And then the media itself began to implode. Tech billionaire Elon Musk bought Twitter—once a key part of America’s digital public sphere—and turned it into a bizarro right-wing propaganda machine in a matter of months. OpenAI, the company that created the ChatGPT app, warned that its product might cause the apocalypse—then funded a studio that would help newspapers use it to replace journalists.3 Every time I thought I had a handle on what was happening, some new development would send me into a spiral of nihilism. The Supreme Court overturned Roe v. Wade, destroying the universal access to legal abortion that so many of us had taken for granted our whole lives.

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Using the previous month’s activity, he and his team try to generate accurate propaganda weather reports for the next month. This kind of work is sometimes called “prebunking,” and it’s been shown to work. However, it’s always going to be an arms race—especially as operatives start jumping from one platform to another, or using AI like ChatGPT to generate fake stories and propaganda posts. An election without hockey sticks I wanted to know what the future of that arms race would look like, so I got in touch with Alex Stamos, the former chief security officer at Facebook who spotted the 2016 election psyops campaigns as they unfolded.

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Though automated propaganda weather reports like the one created by Alizadeh and his team are helpful, Stamos emphasized that humans had to be the final arbiters of what was disinformation and what wasn’t. They understood the context of posts that would stump AI. Still, the rise of AI apps like ChatGPT made Stamos wonder whether it would be possible to build an entire fleet of propagandists controlled by one person with a herd of chatbots. “The answer is maybe,” he conceded. That’s another propaganda disaster scenario we’ll need to prepare for. If AI creates an even denser fog of misinformation, human moderators will be even more crucial.

The Sirens' Call: How Attention Became the World's Most Endangered Resource
by Chris Hayes
Published 28 Jan 2025

And in that vein, it’s not surprising that one of the first main uses of large language model AI interactive modules is as a kind of supplement to a search function. Microsoft unveiled, to great fanfare, an AI-powered version of its search engine Bing, promising to “reinvent the future of search.”[48] You can’t really go anywhere on the internet right now without being hawked some productivity tricks using the other big OpenAI product, ChatGPT. As an IPS, ChatGPT is genuinely impressive. When I instruct it to summarize in one sentence Simon’s paper on organizations in an information-rich world it replies, “Herbert Simon’s paper proposes that effective organizational design in the face of increasing information availability involves creating structures and processes that prioritize attention allocation, decision-making efficiency, and information filtering mechanisms to manage and utilize information effectively.”

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You can easily imagine a world with AI churning on both sides of this attentional battle—AI spam generation and AI-powered spam filters, a little like the tail end of the steroids era in baseball when both the batters and the pitchers were juiced out of their minds. Jonathan Frankle, a computer scientist at the AI firm Databricks, described this scenario to The New York Times’ Ezra Klein as the “boring apocalypse” scenario for AI: “We use ChatGPT to generate long emails and documents, and then the person who received it uses ChatGPT to summarize it back down to a few bullet points, and there is tons of information changing hands, but all of it is just fluff. We’re just inflating and compressing content generated by A.I.”[49] Almost any technology that’s good at screening information to preserve our attention is also going to be good at generating things that attempt to capture and exploit our attention.

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Graham, 185 Burnham, Bo, 144–45 Bush, John Ellis “Jeb,” 212 Business Insider, 193 C cable news, 21–23, 48–50, 139–42, 221–23 author’s show, 22–23, 39–40, 49–50, 98–100, 137–42, 201–2, 221–22, 235–37, 240 mass shootings coverage, 235–37 ratings, 125–26, 137–38, 141 screen “crawl,” 48–49 Cacioppo, John, 85–86, 88 Call of Duty (video game), 52–53, 234 cancer, 6, 171 Canter, Laurence, 176–78 capitalism, 5, 16–17, 24, 68, 124, 172, 183–85 Marx’s theory of, 24, 68, 118–22, 135–36, 144, 145 capo dei capi, 234 Carlson, Tucker, 22, 240 CBS, 6, 148 Cepek, Michael, 65, 72, 79 “channel capacity,” 163 Chartbeat, 40 ChatGPT, 182–83 Chávez, Hugo, 244 Chicago Cubs, 161 childbirth, 37–38 child labor, 258, 265 children boredom and idleness, 62, 77, 78 brain development of, 6, 7, 9 parenting and attention, 81–83, 90–92, 134, 150, 224, 233 schooling and attentional regimes, 199–200, 220 screen bans, 260 siblings and attention, 90–92 Chomsky, Noam, 22, 201 Christianity, 118 Circe, 1–2, 59 civil rights movement, 43–44, 216 Civil War, 199, 203 Clarke, Arthur C., 152 clickbait, 40, 123, 143, 224 climate change, 20, 185, 225–27 Clinton, Hillary, 22, 214 Club of Rome, 184 CNN, 49, 138 Coca-Cola, 45 cocktail party effect, 33–36, 38, 41–42, 54, 55, 56, 73, 89, 90, 99–100, 211 CoComelon, 134 coercion, 24–25, 41–42 Cofán people, 65, 72, 79 Cohen, Stanley, 7 Colgate-Palmolive, 17 collective attention, 145–48, 152 comic books, 10 commodification, 24, 120, 128–35 of attention, 122–25, 128–35 Communist Manifesto, The (Marx and Engels), 119 Compromise of 1850, 195, 228 CompuServe, 261 concision, 201–2 “conflict entrepreneurs,” 235 “conscientious objectors” of social media, 7 conspiracism, 22, 192, 241–45 Contras, 238 corn syrup, 163 Counter-Strike: Global Offensive (video game), 52–53 Covid-19 pandemic, 12, 20, 22, 126, 244 Covid-19 vaccine, 239–40 Cruise, Tom, 98 crying, infants, and attention, 81 cryptography, 160 Csikszentmihalyi, Mihaly, 73 D “data,” 156–57 Databricks, 183 Day, Benjamin, 122–24, 224 daydreaming, 77–78 Dean, Howard, 178–79 Death of a Salesman (Miller), 106–10 debates, 198–200, 216–17 Lincoln-Douglas debates, 195–98, 200–201, 202–4, 216, 227–29, 238 “deep state,” 242 dehumanization, 85, 120, 131, 223 depression, 6, 84, 252–53 “Designing Organizations for an Information-Rich World” (Simon), 27–28, 164–66, 167–68, 170, 174, 182, 186–87 Dickens, Charles, 84 differential pricing, 129 direct messages (DMs), 262–63 “disinformation,” 9, 241–42, 245 disintegration, 118 distraction, 5, 9, 11, 28, 32, 73 diversions, 61–63, 73, 75–77 Doctorow, Cory, 252 dog intelligence, 162 Dominion Voting Systems, 142 Don’t Look Up (movie), 225–26 doom, 251–52 dopamine, 161 Doppelganger (Klein), 243 Douglas, Stephen A., 195–98, 200–201, 202–4, 216, 227–29, 238 Dred Scott v.

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

A chess prodigy at age four, he became the five-time champion of an international Mind Sports Olympiad that includes competition in chess, poker, Mastermind, and backgammon. In his modern London office is an original edition of Alan Turing’s seminal 1950 paper, “Computing Machinery and Intelligence,” which proposed an “imitation game” that would pit a human against a ChatGPT–like machine. If the responses of the two were indistinguishable, he wrote, then it would be reasonable to say that machines could “think.” Influenced by Turing’s argument, Hassabis cofounded a company called DeepMind that sought to design computer-based neural networks that could achieve artificial general intelligence.

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For Musk, this was the reason to make OpenAI truly open, so that lots of people could build systems based on its source code. “I think the best defense against the misuse of AI is to empower as many people as possible to have AI,” he told Wired’s Steven Levy at the time. One goal that Musk and Altman discussed at length, which would become a hot topic in 2023 after OpenAI launched a chatbot called ChatGPT, was known as “AI alignment.” It aims to make sure that AI systems are aligned with human goals and values, just as Isaac Asimov set forth rules to prevent the robots in his novels from harming humanity. Think of the computer Hal that runs amok and battles its human creators in 2001: A Space Odyssey.

…

His friends knew not to interrupt when he was in such a trance, but finally Christiana put her hand on his back and asked if everything was okay. He stayed silent for another minute. “Got to get Starship into orbit,” he finally said. “We’ve got to get Starship into orbit.” 93 AI for Cars Tesla, 2022–2023 Dhaval Shroff and his Tesla desk Cars that learn from humans “It’s like ChatGPT, but for cars,” Dhaval Shroff told Musk. He was comparing his project at Tesla to the artificial intelligence chatbot that had just been released by OpenAI, the lab that Musk had cofounded with Sam Altman in 2015. For almost a decade, Musk had been working on various forms of artificial intelligence, including self-driving cars, Optimus the robot, and the Neuralink brain-machine interface.

Vassal State
by Angus Hanton
Published 25 Mar 2024

, published by the think tank the Centre for the Study of Financial Innovation (CSFI) in 2010. 19 Antoine Gara and Ortenca Aliaj, ‘FIS sells majority stake in Worldpay to buyout group at $18.5bn valuation’, Financial Times (6 July 2023), https://www.ft.com/content/b133fa58-5ef2-4cc4-972b-8271f749779e. 20 Quoted in Wiggins and Borrelli, ‘How the private equity industry stole a march in European payments’. 21 ‘Alfred Kelly Jr net worth & insider trades’, Benzinga [website] (4 December 2023), https://www.benzinga.com/sec/insider-trades/v/ALFRED-KELLY%20JR; ‘Ajay Banga – net worth and insider trading’, GuruFocus [website], https://www.gurufocus.com/insider/3836/ajay-banga. 22 Charlotte Tobitt and Aisha Majid, ‘National press ABCs: FT stays steady while Evening Standard falls below 300,000 for first time since going free’, Press Gazette (15 November 2023), https://pressgazette.co.uk/media-audience-and-business-data/media_metrics/most-popular-newspapers-uk-abc-monthly-circulation-figures-2/. 23 ‘UK ad spend grew 8.8% in 2022 to reach £34.8bn’, Advertising Association [website] (27 April 2023), https://adassoc.org.uk/our-work/uk-ad-spend-grew-8-8-in-2022-to-reach-34-8bn-inflationary-pressures-persist-with-minimal-growth-forecast-for-2023/. 24 ‘Nobody reads terms and conditions: it’s official’, Pinsent Masons [website] (19 April 2010), https://www.pinsentmasons.com/out-law/news/nobody-reads-terms-and-conditions-its-official. 25 ‘It pays to read license agreements (7 years later)’, PC Matic [website] (12 June 2012), https://www.pcmatic.com/blog/it-pays-to-read-license-agreements-7-years-later/. 26 Gina Hall, ‘San Jose area has world’s third-highest GDP per capita, Brookings says’, The Business Journals [website] (23 January 2015), https://www.bizjournals.com/sanjose/news/2015/01/23/san-jose-has-worlds-third-highest-gdp-per-capita.html. 27 ‘Investing in American dynamism (with Katherine Boyle)’ [transcript of podcast interview with embedded video], Acquired [website] (5 June 2022), https://www.acquired.fm/episodes/american-dynamism-with-katherine-boyle. 28 David Curry, ‘Etsy revenue and usage statistics (2023)’, Business of Apps [website] (8 November 2023), https://www.businessofapps.com/data/etsy-statistics/. 29 Krystal Hu, ‘ChatGPT sets record for fastest-growing user base – analyst note’, Reuters [website] (2 February 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/. 30 For ‘privacy zuckering’, ‘roach motel’ and ‘confirmshaming’, see ‘Dark pattern’, Wikipedia [website], https://en.wikipedia.org/wiki/Dark_pattern. For WinRed, see ‘How Trump steered supporters into unwitting donations’, New York Times (7 August 2021), https://www.nytimes.com/2021/04/03/us/politics/trump-donations.html. 31 ‘The ultimate guide to Airbnb service fees (3%, 14%, 15%, 17%)’, Uplisting [website], https://www.uplisting.io/blog/guide-to-airbnb-service-fees. 32 Alex Baggott, ‘Every Apple App Store fee, explained: how much, for what, and when’, AppleInsider [website] (8 January 2023), https://appleinsider.com/articles/23/01/08/the-cost-of-doing-business-apples-app-store-fees-explained. 33 Simon Sinek, ‘How great leaders inspire action’ [video], TED [website] (September 2009), https://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action?

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Technology has enabled these platforms to grow exponentially: in just ten years Etsy increased the number of sellers on its platform from 600,000 to more than 7 million, with a million of these in the UK.28 Such growth could not have happened in the 1990s, even when the cost of digital copies dropped to just a few pence: it was still expensive to send out CDs by post and the programs were always out of date by the time they arrived. The internet eliminated these costs and meant platforms could be freely accessed from anywhere and updated instantaneously. This gave enterprises the potential to grow at lightning speed, as shown by the spread of ChatGPT: within just six weeks of its launch in late 2022, the AI chatbot already had more than 100 million users.29 The advocates for the platform companies paint a picture of organisations that promote sharing and creativity. From the outside it all looks frictionless, but under the surface is another story.

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Instagram subscribers can pay for exclusive content highlighted by a purple ring, and Facebook is introducing ‘Meta Verified’, where a ‘verified badge’ (also a blue tick) will cost over £140 a year, charged monthly through the app. Even Snapchat has adopted a similar scheme, claiming that ‘subscription will allow us to deliver new Snapchat features… and allow us to provide prioritised support.’28 Any services we currently use for free are potential treadmills and there are plenty of emerging ones: the AI tool ChatGPT was free to use in the three months after it was launched, but very soon a $20-a-month subscription was introduced for the premium version. Debt Being on the subscription treadmill leads many towards the second great treadmill: debt. And with debt you cannot simply unsubscribe. Fraser Sutherland of Citizens Advice in Scotland described the situation in his region: ‘Around 1 million Scots last year had reason to cancel a recurring payment… we know from the number of clients we help on the subject that many thousands are being duped into subscriptions they didn’t want.’29 One person receiving debt counselling told me: ‘I didn’t realise how much of my pay was going towards subscription services until I started budgeting and tracking my expenses.

Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War
by Raj M. Shah and Christopher Kirchhoff
Published 8 Jul 2024

The night before his visit to DIUx, we hosted Mattis at a dinner attended by some of the titans of technology. In a private room we had Mattis meet Marc Andreessen, the founder of Andreessen Horowitz, a top venture capital firm, and Sam Altman, the founder of Y Combinator, a startup incubator, who’d go on to found OpenAI, developer of ChatGPT, which launched an AI revolution in 2022. Andreessen assured Mattis that VCs wanted to invest in defense-related technology. While for many years the Valley had shied away from doing business with the Pentagon, DIUx had changed the prevailing view. Every week, Raj was fielding calls from venture capitalists eager to invest in our portfolio companies.

…

Biden held the first Camp David summit of his presidency with the prime minister of Japan and the president of Korea, to discuss the China threat. Other wins included the Pentagon’s embrace of the generative AI revolution. The DoD launched “Task Force Lima” to speed experimentation with large language models like ChatGPT across the military. Also, Scale AI, the Silicon Valley startup whose CEO Alex Wang joined Chris to advise the White House on China, became the first company to provide generative AI capabilities to the Pentagon. Scale AI’s software platform, called Donovan, uses generative AI models for military planning and battlefield monitoring.

…

Afghanistan, 45, 51, 81, 105, 129, 153, 190 AI software used in, 119 Kabul evacuation and Slapshot app, 68–69 AFWERX technology accelerator, 102, 132 AI (artificial intelligence), ix, 36, 99, 195 Albright and, 142–43 Anduril software, 126–27, 209 Aspen Security Group and, 139–40, 163 C3.ai software, 100 ChatGPT, 103, 239 China and, 133, 138, 141–42, 148, 162–68 conflict escalation risk, 146–47 Datahub AI system, 80, 81, 86, 98 de-mining vehicles, 151–52 DIU and, x, 100, 119, 150–52, 203, 239 DoD and, 5, 100, 116–22, 133, 148, 152, 212, 239 Evolv Technology and, 117–18 exponential progress and, 138 global cooperation and, 166 Google DeepMind and, 140 JAIC and, 121–22, 132, 158, 212 Munich Security Conference, 162–68 national security and, 138–40, 143, 146–48, 152 natural language processing, 181 Nexla software “pipes,” 181 NSCAI and, ix, xi, 143–48, 155–59, 162–68 NSC and, 130, 140 Palantir data analytics, 101, 123, 202 privacy and, 165–66 Project Maven and, 100, 116–22, 212 Russian tanks and, 146 Trump administration and, 143, 159 Ukraine War and, 202, 206–7, 209, 210, 212, 213 U.S. policy and, 158–59 war at sea and, 114 wide-area motion imagery/Gorgon Stare, 119 See also drones; JAIC; NSCAI Albedo, 190 Albright, Madeleine, ix, 133, 134, 142, 145–46, 174–75, 220 Alibaba, 137 Allen, John, 167 Altman, Sam, 103, 174 Amazon, 5, 144, 158 Project Maven and, 100, 116, 132 Snowball drive, 202 Andreessen, Marc, 103 Andreessen Horowitz, 103, 108 Anduril, 101, 109, 123, 124–28, 132, 180, 191, 202, 240 AI software program, 126–27, 209 border sensors and, 126, 209 DoD acquisition methods and, 124 drones, 126, 132, 202, 209, 211, 216 Ghost drone, 209 Ukraine War and, 208–12, 217 Apple, ix, 5, 8, 21, 118, 179, 203 Ardern, Jacinda, 174 Armenia-Azerbaijan, 214 Army Futures Command, 102 Aspen Strategy Group, 133, 138 Kirchhoff paper, “An Even Flatter World” and, 139–41 Munich meeting on AI, 163 U.S. technology strategy and, 133–35, 139 AUKUS (Australia, UK, U.S. coalition), 239, 240, 245 Austin, Lloyd, 223, 225–27, 230, 240 autonomous craft (non-aerial), x, 99, 182 ocean drones, 3, 110–14 Saildrone, 110–13, 131, 241 UUVs (demining vehicles), 151–52 See also drones Bajraktari, Ylber, 145 Bajraktari, Ylli, ix, 145–46, 219, 220, 238 as NSCAI director, 145–46, 157–59, 163, 168 ballistic missile defenses, 77 “Brilliant Pebbles,” 78 Datahub AI system, 80, 81, 86, 98 “left-of-launch” solution, 78, 79, 91 Nike air defense system, 77, 79 Strategic Defense Initiative (Star Wars), 77–78 Banazadeh, Payam, ix, 82, 83, 84, 151 Butow and, 82, 84, 85–86 Capella Space and, 81, 82, 85, 199 DoD funding for, 87–88, 96–97, 199 Lunar Flashlight project, 83–84 vision for small satellite SAR, 83 Beachkofski, Brian, 70 Beacon AI, 239 Beall, Ryan, 104–10 app to find drone operators, 106, 131 drone startup of, 109 Beck, Doug, ix, 18, 21, 134, 179, 220, 225–26 Ash Carter Exchange meeting, 228 DIU 3.0 and, 225–30 Warsaw DIU-led meeting, 234–36 Bevirt, JoeBen, ix, 114–16 Biden, Joe, 141, 200, 229–30 China threat and, 239 death of Ash Carter and, 219–22 Kirchhoff and, 168–69 Ukraine War funding and, 203, 205 U.S. investment in China and, 245 weaponized IG and, 172–73 Bilden, Philip, ix, 179, 180, 185–89, 191 bin Laden, Osama, 95 Bio, Ernie, 44, 243 Blank, Steve, 54, 84–85, 179 BlueHalo, 202 Blue Origin, 79 Boeing, 80, 103, 123 Bolton, John, 142 Brin, Sergey, 49, 162, 186 Britain’s Controller’s Cabin, World War II, 46–47, 49–50 Brooks, Vincent, 89–92, 93, 98 Brose, Christian, v, 64, 65, 240 Brown, Charles, 230 Brown, Mike, ix–x, 130, 148–50, 227 advisors for, 172 “China’s Technology Transfer Strategy,” 149, 170 DIU directorship, ix–x, 130, 148–55, 171–73, 223 Mattis and, 152–53 Shield Capital and, 173, 190 Tech Track 2 and, 178 Ukraine War and, 200 Brumley, David, 100 Bush, George W., 14 Bush, Vannevar, 181 Butow, Steve “Bucky,” x, 76, 81–88, 92–94, 98, 155, 230 C3.ai company, 100 Campbell, Kurt, 137 “The China Reckoning,” 137 CAOC (Combined Air Operations Center) at Al Udeid, x, 2, 45–54 cost of an inefficient system, 52, 58 DIUx’s apps for, 59 DIUx’s tanker refueling project, 54–59, 65, 68, 102 Hanscom/Grumman tech overhaul and, 53, 59–60, 63, 66–68 obsolete technology at, 46, 47, 50–54, 57 Shah and DIB group visits, 45–46, 49 Slapshot app, 69 Capella Space, ix, 81, 82, 92–98, 151, 180 DIUx/DoD funding and venture capital for, 85–88, 91–98, 129 growth and revenue, 215 Kirchhoff in South Korea and, 89, 91 left-of-launch solution enabled by, 91 opponents of, 87–88 Ukraine War and, 199, 200, 231–32 use of, versus defense contractor, 88 Carter, Ash, x, 7, 21, 32, 65, 66, 77, 111, 145, 172, 179, 185, 190, 221 appoints Kirchhoff and Shah to DIUx, 11, 13, 14, 16 approves Dailey’s CSO idea, 42, 43 at Aspen Strategy Group (2018), 134 death and funeral, 219–21 Defense Digital Service, 231 on defense innovations, 7, 111 DIB created by, 47 DIUx and, 10, 11–13, 17–18, 20, 23, 26, 28, 42, 48, 109, 155, 223, 242 Mattis replaces, 102, 109, 153 overtures to Silicon Valley, 7–9 Shield Capital and, 189 vision of, 9, 99, 132, 216, 228, 229 Cerf, Vint, 35, 120–21 ChatGPT, 103, 239 China, 188 aggression under Xi Jinping, 136 AI and, 133, 138, 141–42, 148, 162–68 American investment in, 245 Belt and Road initiative, 244 Biden’s policies and, 169–70 “China’s Sputnik Moment,” 4 CHIPS Act and, 176 civil-military fusion, 138, 177, 196, 214, 244, 245 Clinton visit (summer 1998), 136 COVID-19 and, 163, 168 Cuba and, 244 displaced as biggest U.S. trading partner, 245 economic problems, 244 global ambitions, 137 “Guo Wang” satellites, 225 as investors in U.S. startups, 149 manufacturing and industrial production, 137 military budget, 217 “national champion” companies, 137 need for technological containment of, 141–42, 156 nuclear-capable hypersonic weapon launched, 4, 223–24 plans to win the technology race, 137 R&D and, 137, 138 Schmidt meets with top AI diplomat, Fu Ying, 162–68 spy balloon over the U.S. (2023), 224 as threat to Taiwan, 216–18, 224, 244 technological superiority, 3, 4, 55, 136–38 Tencent, 177 Trump’s policies and, 139–41 U.S. companies outperforming Chinese companies, 178 U.S. policy turnaround, x, 176, 177 U.S. technology sold to, x, 15 as threat to U.S., 130–31, 136–37, 141, 149, 217, 223–24, 239, 244 “China’s Technology Transfer Strategy” (Brown), 149, 170 CHIPS Act, 130, 161–62, 176, 238 Clark, James “Snake,” 95 Clinton, Bill, ix, 136 Clinton, Hillary, 61, 62 cloud services, v, 5, 120, 148, 202, 239 CNAS (Center for a New American Security), 154 computer chips, 80, 159–62 national security vulnerability, 161 See also CHIPS Act Cook, Tim, 21 Cope, Clint, 182 Council on Foreign Relations, 14, 143, 220 COVID-19, 163, 168, 169, 189 CSO (Commercial Solutions Opening), DIUx’s Dailey’s acquisition process, x, 24, 37–38, 40–43 Cukor, Drew, 119 cyberattacks defense for, 202 Russia attacks on Estonia and the Ukraine, 164–65, 200, 207 cybersecurity, 9, 132, 158, 194, 195, 211 Cybersecurity Commission, 156–57 DIUx and ForAllSecure’s Mayhem, 100 Shah’s startups, 2, 14, 15, 21, 184, 190, 197 Symantec, 149 Tanium company, 99, 132 Dailey, Lauren, x, 24, 37–38, 40–43 Danzig, Richard, 134–35 DARPA, 8, 24, 35, 40, 71, 155 Grand Challenge awards, 8, 100 Kirchhoff and, 14, 35 Mayhem software and, 100 Datahub AI system, 80, 81, 86, 98 DCGS-A (Distributed Common Ground System—Army), 123 Deal, Victor, 42 defense contractors, the “primes,” 5, 53, 59–60, 67, 76, 88, 98, 123, 127, 173, 189, 196, 240 building a new prime, 208 Defense Industry Association conference, 239–40 ousting, by a disrupter, 242 repackaging aging technology, 242 Replicator Initiative and, 240 Ukraine War and, 215 working with startups, 243 Dereliction of Duty (McMaster), 139 DIB (Defense Innovation Board), xi, 46, 47, 154, 155 AI principles and, 122 Kirchhoff in South Korea with, 89–92 recommendations sent to DIU, 49 Schmidt heads, 46, 47, 48–49, 155 tour of CAOC at Al Udeid, 46, 49 Ukraine War and, 212 Disbrow, Lisa, 67 DIU (Defense Innovation Unit, formerly DIUx or Unit X), 2, 202 accomplishments, 71, 99–122, 128, 131–32, 155, 178, 242 adversaries, 26–31, 75, 77, 87–88, 93–98, 129, 153 AI and, x, 100, 150–52, 119, 203, 239 Albright visits, 142–43 allies launching their own DIUs, 245 alumni joining Shield Capital, 190 Ash Carter Exchange meeting, 228–30 Austin and, 223, 225–28, 240 Beck and DIU 3.0, ix, 179, 225–30 Biden administration and, 223 Brown as director, ix–x, 130, 148–55, 171–73 CAOC tanker refueling project, 54–59, 60, 65, 68 Capella and secret North Korea project, 73–98, 129 Carter’s vision, to use Silicon Valley methodology for defense, 9–10, 88 challenges for Shah and Kirchhoff, 35 commercial IT solutions and, 238 contracts awarded in 2017, 99 Dailey’s CSO idea, x, 24, 37–38, 40–43 defense contractors’ antagonism, 59–60 directives for, 22–23 early failure and reboot, 11–13, 20, 32 entrepreneur funding, defense investing, and, 9, 17, 85, 87, 92, 96–99, 101, 103, 107–8, 112, 127, 184, 202 first all-hands meeting, 19, 21–24 flat structure for, 20 formula for a tech-forward DoD, 242–43 “frozen middle” problem, 70 funding and funding problems, 9, 25–44, 71–72, 86–87, 93–99, 108, 110, 154–55, 230 future of warfare and, 129, 199–218, 228, 240, 241–42, 243–44 Griffin and, 130, 154 Hacking for Defense course and, 84 Hanscom/Northrop Grumman software overhaul and, 66–68 hiring directive and O-7 billet, 23 importance of, 92, 102 Kessel Run team, 67–70, 102 Kirchhoff leaves, 129–30 Kirchhoff’s leaked memo, 61–63 “lean” methodology of, 54 Mattis and, 102–4, 108–9, 153–55, 242 mission of, 3, 15, 21–22, 56, 88, 172, 228 office, Moffett Field, Mountain View, 8–9, 10, 12, 17, 35–36, 38, 44, 54–55, 58, 67–68, 92, 101, 104 offices opened, 30–31, 71, 101, 118 OTA contracting system, 40–41, 99, 112, 213, 239 personnel: full-time, reservists, and guardsmen, 32, 101, 243 “Points of Presence,” 30 portfolio areas and companies, 36, 99–128, 131, 132 Project Maven and, 116–22 racing to sign deals, 36–44 removal of the “x,” 109 Replicator Initiative and, 240 reporting and authorities for, 16–17, 22–24, 242 Reserve Unit, ix, 18, 21, 225, 226 Rogue Squadron, 104–10, 210, 211 role in changes to key Operational Plans (O-Plans), focus on war planning, 227–28 Shah and Kirchhoff heading, 2, 11, 15–17, 24 Shah leaves, 130 Slapshot app, 69 Space Portfolio, x, 76, 81, 155 speed of deal-making, 99, 101 supporters, 10, 23, 26, 28, 67–68, 89, 93, 102–3, 108–9, 129, 153–54, 189, 242, 245 team members, 19–21, 23, 37, 44, 55 Ukraine War and, 203–5, 215, 223 U.S. policy shift on foreign investment and, 149 venture capital model for, 20, 36 Warsaw conference on Ukraine technology scaling, 234–36 zeroized crisis, 25–36, 72, 97 See also Kirchhoff, Christopher; Shah, Raj M.; specific projects DJI (Chinese drone company), 103, 104, 107, 137, 209, 211 Rogue Squadron’s hacking and reverse engineering, 106–7 Donovan, Matt, 63–64 drones (UAS, unmanned aircraft system), 114, 237 Afghanistan War and, 105 AI-empowered, 210, 228–29 Air Force purchase of, 228–29 attacks on U.S. service members in the Mideast by, 241 Beall’s Android app to locate enemy operators, 106 Blue UAS program, 108, 131, 235 changing fighter aviation, 114, 237–38, 241 cost of, versus conventional craft, 241 “counter-UAS” solutions (drone-killers), 9, 106, 126, 202, 210, 237, 238, 241 DIU funding of U.S. makers, 107–8 DIU Rogue Squadron, 104–10, 210, 211 DJI Chinese drones, 104–7, 137, 201 in DoD inventory, 103 DoD’s Replicator Initiative, 239–40 Gorgon Stare sensors on, 119 Iranian-made drones, 201–2, 209, 240 kamikaze drones, 201, 210, 232, 235, 237, 240, 241 North Korean use of, 214 quadcopters, 3, 4, 107, 201, 202, 209, 232, 240 Russia and, 240 Shield AI, 100–101, 131 Skydio, 202 terrorists’ using, 240–41 Turkish Bayraktar TB2s, 209 Ukraine War and, 201, 204, 208–12, 232–35, 237 used by ISIS and insurgents, 3–4, 103 used to surveil U.S. military bases and ports, 106 U.S. military vulnerability, 238 weaponizing of hobby drones, 3–4, 103, 106 Duchak, George, 20 Dunford, Joseph, 62, 63, 119 Dunnmon, Jared, x, 150–51 DIU AI technical director, 151, 203 partnership with PMS-408, 152 Ukraine War and, 203, 204–5 Edgesource, 210 Elroy Air, 181–84 End of History and the Last Man, The (Fukuyama), 218, 237 ESL company, 84, 85 Esper, Mark, 154 EUCOM, 205 Evolv Technology, 117–18 eVTOL (electric-powered vertical takeoff and landing aircraft), ix, 3, 114–16, 181–84 aerial cargo options, 182–83 air taxi, 115 “dual-use” product, 115 Elroy Air’s Chaparral, 181–84 Joby Aviation and, 116 military use, 182, 183 F-16 Viper, 1, 4, 14, 34, 237–38 Facebook, 123, 124 FAR (Federal Acquisition Regulations), 38, 39, 40, 43 defense supplement (DFAR), 38 Farris, Ryan, 73, 74, 76–77, 78, 87 Banazadeh and SAR satellites, 86 Capella funding and, 95 Datahub project and, 81 J-39 SWAT team and, 74, 80 Orbital Effects founded, 97 FedEx, 182 Felter, Joe, 179 FitzGerald, Ben, 154 5G telecommunications, 4 Flake, Jeff, 195 Fog of War (film), 147 ForAllSecure, 100, 132 Fort Hunter Liggett, California, 115 Founders Fund, 122, 123, 124 Four Steps to the Epiphany (Blank), 85 Fukuyama, Francis, 218 Fu Ying (Chinese AI diplomat), 162–68 Gallagher, Mike, 156–57, 161, 244 Garg, Avichal, 169 General Dynamics, 1 Giannandrea, John, 118 Ginkgo Bioworks, 174 Giustina, Marissa, 169 Goldfein, David, x, 59–60, 63, 68, 178, 179, 189 Google, xi, 5, 48, 162, 169, 195, 203 AI research, 48, 118 Cloud AI, 239 government grants to, 8 Project Maven and, 100, 116, 119–21, 127–28 rebuffs Secretary of Defense Carter, 7 resumes working with the DoD, 158 Schmidt as CEO, 48, 49, 186 self-driving car project, 20, 48 Taylor recruited from, 20–21, 114 U.S.

Abundance
by Ezra Klein and Derek Thompson
Published 18 Mar 2025

Surely it seems like, if we value science, our society has done everything right. And to be sure, the landscape of inventions sparkles with bright spots. The last few years have witnessed the remarkable emergence of new gene therapies, drugs to thwart diabetes and obesity, and a suite of artificial intelligence tools—such as ChatGPT from OpenAI and DeepMind from Alphabet, the parent company of Google—that can perform a wide range of complex tasks, from writing essays and code to predicting the shape of proteins. But, mysteriously, progress in many fields seems to be slowing down. In April 2020, just as the world was convulsing from the pandemic, a group of economists from Stanford and MIT published a study with the irresistible title “Are Ideas Getting Harder to Find?”

…

Ezra Klein, “The Dystopia We Fear Is Keeping Us from the Utopia We Deserve,” New York Times, January 8, 2023, https://www.nytimes.com/2023/01/08/opinion/nuclear-fusion-flying-cars.html. 28. Saul Griffith and Sam Calisch, “One Billion Machines,” Rewiring America, June 2021, https://www.rewiringamerica.org/research/one-billion-electric-machines-report. 29. “AI workloads require substantially more energy than traditional computing. Estimates suggest that using ChatGPT requires up to 10x more power than a traditional web search. Further, it seems likely that data centers will be the largest contributor to U.S. power demand growth through the end of this decade”: JPMorgan, “A Strong Economy in a Fragile World,” 2024, https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/mid-year-outlook-2024.pdf. 30.

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Abbonizio Contractors, 125, 126 California; see also Los Angeles; San Francisco; Silicon Valley California Environmental Quality Act (CEQA, 1970), 53 Democratic Party and, 16–17, 38 Employment Development Department (EDD), 119–24 environmental legislation of, 14, 52–53 Friends of Mammoth (1972), 53–54 High-Speed Rail Authority/high-speed rail plans of, 71–78, 86, 94, 116–18 housing in, 33–39, 52–54, 55–56 Lakewood planned community, 36 Legislative Analyst’s Office, 38 liberalism and, 16–17 Petaluma Plan, 36–37 presidential election (2024) and, 18 solar power in, 64 Venice Dell Community project, 110 “Californication” term, 55 Calisch, Sam, 68 Cambridge Systematics, 118 Cambridge University, 203 Canada carbon emissions per person, 66 rail system cost of, 77 cancer research, 142, 148 capitalism, Marx and Engels on, 214–15 CARES Act (2020), 10 Carnegie Mellon (formerly Carnegie Institute of Technology), 162 Caro, Robert, 55 Carroll, Mike, 125–27 cars, see transportation Carson, Rachel, 50–51 Carter, Jimmy, 7, 179, 205 Census Bureau, US, 23 Center for Building in North America, 173 Center on Global Energy Policy (Columbia University), 67 Central Pacific Railroad Company, 74 CEQA (California Environmental Quality Act, 1970), 53 Chain, Ernst, 170, 171, 174 Chamber of Commerce, 81 Chase, Jenny, 64 Chat-GPT, 142 Chetty, Raj, 31 child care cost, 9 childhood leukemia research, 148 childhood population, under age five, 18 China Beijing, air pollution in, 63 as economic competitor to US, 209 federal civilian workforce size in, 117 high-speed rail system of, 75, 82 manufacturing in, 26–27 solar technology of, 179–80, 181 CHIPS and Science Act (2022), 11, 71, 98, 114, 116, 211 Chokecherry and Sierra Madre Wind Energy Project, 97 Christian Science Monitor, 87 Chung, Payton, 41 cities; see also housing; individual names of cities air pollution in, 50, 63–64 as American frontier, 21–25 habitable land of, 59 income inequality and housing in, 30–32 innovation found in, 25–30 “Cities of Amber” (Anbinder), 37, 47, 49, 53–55 Clean Air Act (1970), 51, 52, 88 clean economy, building, see energy Clean Water Act (1972), 51, 87, 96 Cleveland, Ohio, water pollution in, 50 climate change abundance potential and, 212 energy policy and, 57–62 (see also energy) political divide on, 15 politics of invention and, 134 sustainable technologies and, 62–67 Clinton, Bill, 7, 206 Coastal Zone Management Act (1972), 96 Cobbett, William, 221 COBOL programming language, 120–21 Code for America, 119 Cohen, Lizabeth, 19 Colburn, Gregg, 39–41 Collins, Francis, 159 Collison, Patrick, 86 Colorado, construction productivity in, 80 Columbia University, 67, 113, 184 Columbus, Christopher, 148 Committee on Medical Research (CMR), 174–75 The Communist Manifesto (Marx and Engels), 214–15 Congress, US; see also individual names of legislation bureaucracy created by (1960s and 1970s), 92 military base closings by, 98 Conservation Fund, 96 The Conservative Futurist (Pethokoukis), 16 Construction Analytics, 80 construction productivity measure, 78–81 Copernicus, 58 Corning, 186 COVID pandemic economic policy and, 10 masking and, 132 Operation Warp Speed (OWS), 184–89, 211 PCR tests and, 158 remote work and, 29–30 vaccine development and mRNA research, 130–32, 135, 137–40, 146, 156, 161, 184 Cowen, Tyler, 16 crisis, focus in times of, 199–202 CRISPR, 158–59 Cruz, Ted, 116 CTY Housing, 108–9 Curie, Marie, 171 Cuyahoga River, water pollution in, 50 CVS, 187 dairy industry, 59–61 Dale, Thomas, 221 Dartmouth College, 137, 166 Darwin, Charles, 58 Davy, Humphry, 176 The Day the Earth Stood Still (film), 41 DeepMind, 28, 142 Deese, Brian, 77 Defense Advanced Research Projects Agency (DARPA), 161–65, 167–68, 199 degrowth abundance potential and, 213–14 defined, 58–59 energy policy and, 58–62 Delaware, construction productivity in, 80 Delhi, air pollution in, 63–64 Deloitte, 121, 122 demand-side economics defined, 5–6 Democratic Party as associated with, 6–10 The End of Laissez-Faire (Keynes), 183 for housing, 23 (see also housing) supply-side economics as mistake, 5–11 Democratic Party; see also liberalism California politics and, 16–17, 38 demand-side economics and, 7 on government delays, 77, 90, 127, 153 (see also bureaucracy) on health care, 135–36 lawyers as politicians in, 93 litigation and legal bureaucracy, 89–94 on Operation Warp Speed, 188 political order concept and, 203–7, 220–21 presidential election (2024) and, 17–18, 213 Demsas, Jerusalem, 46, 208 Denmark, social welfare system of, 70–71 Department of Defense, US, 98, 161–63, 178, 183, 186 Department of Health and Human Services, US, 40, 185, 188, 190 Department of Housing and Urban Development, US (HUD), 111–12 Department of Transportation, US, 126 Department of Transportation Act (1966), 51 deployment and implementation, 169–202 of artificial intelligence, 196–99 bottleneck detection for, 163, 189–91, 195, 197, 198 eureka myth of, 171–75, 177, 180, 182, 202 focus as choice in, 199–202 of Operation Warp Speed, 184–89 pace of progress in, 176–84 of penicillin, 169–71, 174–75, 176, 180, 183–85, 202 pull funding and advance market commitment policy for, 192–95 Wright’s law, 180 Doench, John, 154–55, 163 Donora, Pennsylvania, air pollution in, 50 Dourado, Eli, 67 Duchovny, David, 55 Dupont Experimental Station, 149 economic events, see COVID pandemic; Great Recession; New Deal economic theory; see also scarcity The Communist Manifesto (Marx and Engels), 214–15 demand-side economics, 5–11 The End of Laissez-Faire (Keynes), 183 inflation and, 9–10, 43–45 pie metaphor of, 11–15 scarcity as choice, 4–5 scientific research/invention and economic growth, 137 (see also invention) supply and demand, defined, 5–6 supply and demand of housing, 23 (see also housing) supply-side economics, 5–11, 38–43, 212 Economist magazine, 182 Edison, Thomas, 149, 171, 176–77 Edwards, James, 179 Einstein, Albert, 147 Eisenhower, Dwight, 204–5 Electoral College, 18 elevators, 173 Emanuel, Rahm, 73 eminent domain, 75 Empire State Building, 77 “Empirically Grounded Technology Forecasts and the Energy Transition” (Way, Ives, Mealy, and Farmer), 65 employment California’s unemployment insurance system, 119–24 construction productivity measure and, 81 federal civilian workforce size, 117 homelessness and, 40 in housing construction, 85 housing cost and wages, 43, 44 invention and skilled workers, 115, 116, 145–46, 166 outsourced work by government agencies, 117–24 in service jobs, 30–32 Employment Development Department (EDD, California), 119–24 Endangered Species Act (1973), 51, 52, 96 The End of Laissez-Faire (Keynes), 183 energy, 57–99 abundance potential and, 1–4, 212 bureaucracy and affluence, 81–86 bureaucracy and legal intervention, 86–94, 205 California’s high-speed rail difficulty and, 71–78, 86, 94, 116–18 climate change and energy policy, 57–62 construction productivity and, 78–81 electricity infrastructure need and, 68–71 energy superabundance concept, 67 fossil fuels for, 61–66, 69, 70, 178, 179 green infrastructure and, 94–99, 109–10 implementation of technologies for, 173, 176–80, 181, 183 nuclear power, 1–4, 14, 15, 60, 65, 67 sustainability of, 62–67 (see also solar energy; wind energy) Energy Information Administration, US, 65 Energy Research and Development Administration (US Department of Energy), 178 Engels, Friedrich, 214–15 The Entrepreneurial State (Mazzucato), 182–83 Environmental Defense Fund, 88 environmentalism; see also climate change abundance and future potential for, 216–17 California on, 14, 52–53 energy policy and, 76 green infrastructure, 94–99, 109–10 housing and effect of, 48–54 legal challenges and action in, 88–89 National Environmental Policy Act (NEPA, 1970), 51, 52, 54, 96, 114–16 Republican Party on, 51–53 Environmental Protection Agency, 52 Estonia, productivity measure, 79 eureka myth, 171–75, 177, 180, 182, 202 Evans, James, 147–48, 157, 159 Evarts, Charles, 190 “everything-bagel liberalism,” 113–17 Explorer 1 satellite, 178 factory farms, 59–61 Federal-Aid Highway Act (1968, 1973), 51 Federal Highway Administration, 125 Federal Land Policy and Management Act, 96 Federal Reserve, 199 FedEx, 187 financing, private vs. public, 101–4, 106 “firefighter test,” 30 Fischel, William, 34–35, 44–47 Fitzgerald, F.

Apple in China: The Capture of the World's Greatest Company
by Patrick McGee
Published 13 May 2025

So while rival AI platforms such as ChatGPT can be launched from the iPhone and will prove useful for general content, Apple has a monopoly on mining data from the iPhone owner’s emails, texts, and photos to produce more personal answers. AI’s usefulness as a driver of marketing efforts aside, Apple’s push into the technology is constrained by its dependence on China. A hyperintelligent Siri that directly answers sensitive questions about Tiananmen Square, regardless of where the user is asking such questions, is a major no-no. Apple has thus established a relationship with ChatGPT, which can answer those questions without Cupertino looking or feeling liable.

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Apple has thus established a relationship with ChatGPT, which can answer those questions without Cupertino looking or feeling liable. Using ChatGPT might be a way to wriggle out of the tight spot they’re in, but for the fact that the technology isn’t allowed in China. As a result, Apple is searching for a local partner—which is resulting in delays and a diminished user experience. Apple’s struggles with AI in China have helped homegrown rivals look more attractive. No surprise, then, that both Xiaomi and Huawei outsold Apple in 2024, while iPhone shipments dropped by 13 percent, including an 18 percent decline in the all-important holiday quarter, according to Counterpoint Research

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Acer, 89 Acorn, 56–57 Adobe, 30 Africa, 370 Ahrendts, Angela, 322 AirDrop, 360 Airpods, 315, 323–25, 327, 363, 372 Alibaba, 354 Alipay, 293 Alphabet, 358, 375 AlphaTop, 78 Amazon, 298, 358, 375 Amelio, Gil, 36–39, 41–42, 47, 69, 110 Anderson, Fred, 17, 38, 43, 49 Anderson, Mark, 334–35 Android, 193, 223, 267, 268, 272, 279, 343, 377 Ant Financial, 354 Apple education division of, 48–49 founding of, 2, 15, 19, 25, 41 loss of identity at, 20, 38, 384 near-bankruptcy of, 2, 93, 119, 153 revenues of, 6, 8, 15, 17, 26, 28, 36–38, 68, 110, 118, 134, 139, 228, 238, 279, 335–39, 340–41, 347, 350, 353, 355, 358, 360, 379 services division of, 9, 378–79 stock price of, 18, 68, 109–10, 223, 335, 338, 350 stress and marital difficulties of employees at, 150–52 Super Bowl ad of, 383–84 Think Different campaign of, 49–50, 68, 94, 212, 384 Apple I, 23 Apple II, 19, 20, 22–24, 27–29 Apple II Age, The (Nooney), 22 Apple Intelligence, 379–81 Apple Pay, 293 Apple product production, 2, 9, 19, 24, 25, 27, 28, 31, 37, 109, 110, 134, 149, 157, 170, 346 circuit boards in, 23–24, 26, 63, 111 FATP in, 63, 148, 366, 367 outsourcing of, see outsourcing pyramid structure of, 101–4, 124 ramp periods in, 261–62 Subject Matter Experts in, 282–83 supply chain ranking and, 171–72, 174 Apple Silicon, 372 Apple Squeeze, 269–70, 273, 326 Apple Stores, 165, 184, 185, 191, 311 in China, 3, 4, 142, 179, 183–86, 187–90, 192–94, 195–201, 224–31, 241–42, 320, 338, 365 in China, incidents at, 203, 204–7, 209–11 COVID-19 pandemic and, 350 Huawei and, 342 Apple TV+, 379 Apple University, 151, 244, 249, 318 Apple Watch, 311, 315, 325, 331, 363, 372 App Store, 296–98, 360 Arima, 158 artificial intelligence, 379–81 Asia, 26, 30, 33–34, 50, 64, 93, 112, 150, 151, 155 ASUS, 89 Asymco, 273 AT&T, 156 Atari, 22 Atlantic, 215, 243 Auto Navi, 293 Bain, 368 Baker, Phil, 33–35, 77 Balasubramaniam, Priya, 337–38, 364–65 Ballmer, Steve, 20, 191 Bastiaens, Gaston, 33 Beech, Hannah, 6 Bell, Mike, 102–3 Berkeley, Brian, 55–57 Berners-Lee, Tim, 40 Biden, Joe, 7, 356, 377 Biel Crystal, 327 Big Fund, 356 billiard balls, 52–53 BlackBerry, 161, 165, 166, 176, 272, 362 Blair, Brian, 268 Blevins, Tony, 117–22, 124, 141, 144, 167–69, 270, 292, 307, 323 Bloomberg Businessweek, 94 BMW, 286 Bombardier, 240 Bo Xilai, 236 Brazil, 363, 364 Brunner, Robert, 32, 35, 77–78 Buffett, Warren, 374 BusinessWeek, 18 BYD Electronics, 287, 327, 330, 350, 370 Caballero, Rubén, 155, 157, 160, 193 Caixin, 296 Cano, Steve, 185–86, 187, 205 Canon, 29–30 cars, 240, 268, 276 autonomous, 289, 291–93 electric, 286–88 ride-hailing services, 289–93, 299 Tesla, 286–88 Casey, Saori, 332 Catcher, 331 catfish effect, 287 CCTV, see China Central Television Celestica, 25 Chan, Jenny, 368 Chang, Gordon G., 243 Chang, Jim, 106 Chang, Morris, 220–22, 376 Chang, Sophie, 221 ChatGPT, 379–80 Chen Min’er, 299–300 Cheung, H. L., 88 Chiang, Barry, 157 Chiang, Michael, 163 Chiang Kai-shek, 76 Chieco, Vinnie, 125 China, 86–87 antitrust fines threatened by, 265 artificial intelligence in, 379–80 authoritarianism in, 6, 11, 256, 265–66, 310, 354, 358, 361, 362, 372, 375 automakers and, 240, 286–88 Belt and Road Initiative of, 259 censorship in, 166, 243, 298, 379 Communist Party in, see Chinese Communist Party COVID-19 pandemic and, 349–55, 357–60 crackdown on tech sector in, 353–54, 357, 360 data residency rules in, 10, 298–300 democracy in, 3, 10, 252–53, 259, 357, 360, 361 economy of, 86, 87, 191, 213, 215–17, 228, 236, 237, 250, 256–58, 262, 333, 339, 383 global financial crisis and, 213, 215–17 Japanese loans to, 281 joint-venture partnerships of, 239–40, 263–65, 279, 286 labor unions in, 309, 369 Made in China 2025 plan of, 259, 326, 355 middle class in, 202, 281 Nixon’s visit to, 254, 278 police in, 207–11, 225, 226 rail network in, 240 registered capital in, 276 robotics in, 368 Rule by Law in, 262–63 semiconductor industry in, 355–56 smartphone market in, 271–74, 338, 341–42, 362–63 Taiwan and, 7, 76, 79, 222, 372–74, 376, 383 Tiananmen Square protest in, 50, 210, 236, 252–53, 359, 379 Tibet and, 50, 166, 211–12, 298 trading companies and, 240–41 urban population in, 201 U.S. trade wars with, 280, 343–44, 347, 363, 380 U.S. war with, 375 workers in, 218–20, 260–63, 309–11, 350, 368, 369 in WTO, 113, 141, 240, 255, 263, 280–81, 291, 298 China, Apple manufacturing operations in, 2, 5–8, 10, 108, 128, 129, 134–35, 142, 170–71, 175–76, 226, 238–41, 346–47 COVID-19 pandemic and, 350–53, 357–60 diversification away from, 362–71, 372, 375, 378 handcrafting versus automation in, 81, 200 iMac, 75, 100, 104–6, 145, 280 investment in, 6–7, 10, 164, 270–71, 275–76, 278–85, 289, 326, 347 iPhone, 11, 157–58, 164–69, 220, 239, 244, 248, 261, 318, 325, 328, 330, 337–38, 340, 344, 350, 363, 369 iPod, 142, 146–52 job creation from, 8, 226, 274, 284, 296 knowledge transfer in, 7, 9, 260, 282–85 labor dispatch law and, 260–63 R&D, 294–96, 320–21, 351 Red Supply Chain and, 10–11, 327–31, 350, 370, 371 working conditions and, 6, 164, 171, 217, 239, 303–13 China, Apple retail operations in, 5–6, 183–84, 211, 238, 244, 353 Apple Stores, 3, 4, 142, 179, 183–86, 187–90, 192–94, 195–201, 224–31, 241–42, 320, 338, 365 Apple Stores, incidents at, 203, 204–7, 209–11 App Store, 296–98 Consumer Day and, 1–5, 230–31, 238, 241–42, 260, 264 iPad, 192–93, 204, 205 iPhone, 1–5, 10, 194, 195–203, 204, 209, 211, 243, 279, 340–42, 378 iPhone gray market, 4, 190–94, 195–99, 199–200, 202–3, 209, 224–26, 229–31, 242–43 iPod, 142 iTunes, 211–12, 279, 289 revenues from, 3, 5, 6, 8, 279, 333–34, 336–39 China-Apple relations, 243, 263–65, 274–77, 328, 348, 361, 384 bureaucratic protection in, 289–94 Consumer Day episode and, 1–5, 230–31, 238, 241–42, 250, 260, 264, 362 CSR report and, 263–64 Didi investment and, 289–93, 299 Gang of Eight and, 244–50, 280, 284, 293, 295, 296, 316, 318 governmental pressures in, 9–10, 50, 296–98, 348, 360 investment pledge in, 278–85, 289, 326 joint-venture model and, 239–40, 263, 264, 279, 286 R&D labs and, 294–96 risks to be faced in, 377–80 U.S.

The Wealth Ladder: Proven Strategies for Every Step of Your Financial Life
by Nick Maggiulli
Published 22 Jul 2025

Despite its many benefits, as a form of leverage code has its own unique challenges. Coding requires high technical skill. This means domain knowledge and keeping up with the ever-evolving tech landscape. You have to enjoy continuous learning if you want to code for the long haul. While the rise of AI and other large language models (LLMs) like ChatGPT have made coding much easier, you will still need some familiarity with programming to use these tools effectively. In addition, coding requires ongoing maintenance of your code. Systems change and things break. Though I said that the “work is done” once an app is released, this is rarely the case.

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Have you been given any new responsibilities lately? Has your pay gone up in recent years? If the answer to these questions is mostly no, then you should start pursuing the career that you want as soon as possible. Because these sorts of decisions can have huge long-term effects. As Sam Altman, the CEO of OpenAI and creator of ChatGPT, stated on the Y Combinator podcast: Compound interest is a good metaphor here. If you work really hard at the beginning of your career, and you get a little bit better at what you do every day, every month, every week, and you learn more, and you meet more people, and you just get more done, there is a compound effect.

Capitalism and Its Critics: A History: From the Industrial Revolution to AI
by John Cassidy
Published 12 May 2025

As examples of AI’s potential to facilitate the creation of new things, he cited halicin, the antibiotic discovered by MIT scientists in February 2020, and the solution of the “protein-folding problem” by Google’s DeepMind model in November 2020.21 In November 2022, OpenAI, a Silicon Valley company that started out as a not-for-profit research laboratory, startled the world with its release of ChatGPT, a chatbot that could write essays, jokes (not necessarily funny ones), and computer code. Essentially a statistical prediction model that had been trained on massive amounts of data scraped from the internet, the chatbot had an astonishing ability to understand and generate natural language. Within two months of ChatGPT’s launch, 100 million people were using it, making it the fastest-growing computer application ever, according to analysts at the Swiss bank UBS.22 By the fall of 2024, OpenAI was generating more than a billion dollars in revenue, according to media reports, and in the private markets, where venture capitalists and other investors buy securities that aren’t publicly traded, it achieved a valuation of $157 billion.23 Many prominent pioneers of AI had expressed concerns about the rapid commercialization of a potentially dangerous technology, but the profit motive had won out.

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Nicholas Crafts, “Artificial Intelligence as a General-Purpose Technology: An Historical Perspective,” Oxford Review of Economic Policy 37, no. 3 (Autumn 2021), 521–36, https://academic.oup.com/oxrep/article/37/3/521/6374675. 21.   Crafts, “Artificial Intelligence as a General-Purpose Technology,” 521–36. 22.   Krystal Hu, “ChatGPT Sets Record for Fastest Growing User Base—Analyst Note,” Reuters, February 2, 2023, https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023–02–01. 23.   “OpenAI Raises $6.6 Billion in Funds at $157 Billion Value,” Bloomberg, October 2, 2024. 24.   Martin Neil Bailey, Erik Brynjolfsson, and Anton Korinek, “Machines of Mind: The Case for an AI-Powered Productivity Boom,” Brookings Institution, May 10, 2023, figure 2, https://www.brookings.edu/articles/machines-of-mind-the-case-for-an-ai-powered-productivity-boom. 25.  

Gambling Man
by Lionel Barber
Published 3 Oct 2024

For a self-styled visionary, his record as an investor in AI-related companies has until now looked fitful at best. Between 2017 and 2020, he mentioned ‘AI’ more than 500 times in quarterly and annual results presentations. He claimed that AI would ‘redefine all industries’, unleashing the next wave of the information revolution. Yet when it came to Open AI and its breakthrough product ChatGPT, the fastest-growing app of all time, the lead investor was Microsoft. Masa never got a look in, though he says he is good friends with Sam Altman, Open AI’s CEO. Part of the problem was timing. During the six years Masa raised and deployed money for SoftBank Vision Fund 1 and 2, the opportunities to invest in generative AI companies were few and far between.

…

Today SoftBank’s portfolio includes China’s ride-hailing Didi and internet giant ByteDance; food-delivery services like DoorDash and Grab; as well as automated robotic warehouses like Symbotic. These were the ‘first wave’ of AI-related companies with ‘baby’ applications. In 2024 Masa invested in next-generation AI companies such as the US medical data firm Tempus and the UK self-driving car technology start-up Wayve. The emergence of ChatGPT, according to Masa, has changed everything. So-called generative AI points to a future he has long predicted: the moment when machines think faster, learn faster and react faster than humans. ‘It’s like the Cambrian Explosion,’ he says, referring to the proliferation of complex animal species which began life on earth more than 500 million years ago.

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/Softbank (2005), 174–7; Goldman Sachs exits, 171; managing of SoftBank’s stake in, 306, 307, 308, 309, 322; Masa’s deal (1999), 142–4, 147, 236, 262, 278; Masa’s investment in, 4, 144, 147, 169–71, 174–7, 262, 278, 296, 306–7, 314; Masa’s long-term faith in, 170–71, 176, 315; psychological impact on Masa, 147, 278; SoftBank as overly dependent on, 177, 321, 322; Taobao launched (May 2003), 169–71 Alta Vista, 101 Altman, Sam, 327, 330, 335 Amazon, 101, 132, 307, 312, 329 Ambani, Anil, 182 America Online (AOL), 2, 102 Anderson, James, 253 Anderson Mori & Tomotsune (law firm), 311, 314 Andreessen Horowitz, 282, 283 Ansett Airlines, 113 Apax (private equity firm), 241 Apple Computer, 35, 48, 56, 89, 178, 190, 307, 312; Apple II, 72, 73; and ARM chips, 244, 245, 247, 330; Foxconn manufactures iPhone, 268, 286; iPad, 192; iPhone, 4, 190–91, 192–3, 227–8, 268, 286; Jobs’ return to, 102–3, 190; Think Different advertising campaign (1997), 189; and Vision Fund, 266, 289 Aramco oil company, 260, 264 Argentina, 295 Ariburnu, Dalinc, 259 Arimori, Takashi, Net Bubble (2000), 163 ARM Holdings, 242, 244–55, 259, 265–6, 323, 329–31 Arora, Nikesh, 232, 233–5, 236–43, 259, 287, 288–9 artificial intelligence (AI): AGI (artificial generative intelligence), 328–32; and ARM Holdings, 265, 323, 329–31; ‘first wave’ of AI-related companies, 328–9; Masa’s current plans, 327–31, 334–5; Masa’s embrace of/optimism about, 203, 210, 211, 265, 319, 323, 327–35; Masa’s new chip-making venture, 329–30, 334–5; next generation of computer graphics chips, 304–5, 308, 323, 329–31, 334–5; and Nvidia, 304–5, 305*, 323, 330–31; and surveillance/security, 265–6; today’s dark debates about, 331, 332 Asahi Shimbun newspaper, 56, 64, 149–50 Asahi Weekly magazine, 56, 149 ASCII (Japanese computer magazine), 65 Asian Cup football tournament, 175 AstraZeneca, 248, 249 AT&T, 75, 165, 192, 224, 225–6, 227 BAE Systems, 249 Baillie Gifford, 253 Bain Capital, 288 Baker III, James A., 70 Bartz, Carol, 232, 233 baseball, 188, 196–7 Al-Bassam, Nizar, 259–60, 262, 263–4, 269 Bear Stearns, 193–4 Beijing, 142–3, 175–6, 226, 324 Bell Laboratories, 74, 75 Benchmark Capital (VC company), 283, 301 Berkeley, University of California, 27, 36–9, 40–43, 55–6, 101, 102 Berkshire Hathaway, 273–4, 296–7 Bezos, Jeff, 307 Bhanji, Nabeel, 296, 301 Bit Valley Association, 1, 3 BlackRock, 290 Blackstone, 167, 268, 288 Blair, Tony, 294 Blavatnik, Len, 239–40 Blodget, Henry, 132 Bonfield, Sir Peter, 156–7 Boston Dynamics, 275 Braun, Markus, 305–6 Brightstar, 227–8 Brin, Sergey, 232–3, 235 British Telecom (BT), 156–7, 172, 235 BSkyB, 112, 116, 117 Buffett, Warren, 84–5, 273–4, 296–7 Businessland, 83 Buzzfeed, 237–8 Cadbury, 248 California, 27–8, 33–4, 101–2, 108–9, 133, 174–5; academic-entrepreneur model on university campuses, 27–8, 37, 101–2; Berkeley, University of California, 27, 36–9, 40–43, 55–6, 101, 102; Fairmont Hotel, San Jose, 140, 148; Masa’s education in, 30–34, 35, 36–9, 40–47 see also Oakland, California; Silicon Valley, California Cameron, David, 250, 251, 294 Canon, 43 Carlson, Chuck, 41, 45 Casio, 43 Cayman Islands, 308 Centricus, 259–60 Cerberus, 185 Chambers, John, 162 Chambers, Stuart, 245–7, 249–50, 252 ChatGPT, 327, 328 Cheng Wei, 273 China: (B2C) consumer market in, 169–71, 174–7; ByteDance, 284, 321, 328; China Mobile, 225, 235; Communist party, 5–6, 144, 175, 314, 321–2; confrontation with Trump, 320; Deng Xiaoping’s reforms, 144; Didi Chuxing ride-hailing app, 273, 284, 321, 328; geopolitical impact of Xi Jinping, 314, 321–2, 324; and the internet, 4, 142–4, 145–6, 147, 158, 168–71, 174–7, 284, 328; iPhone manufactured in, 286; Japanese invasion of (1937), 11; Masa as largest foreign investor, 4–5; Masa as minor celebrity in, 142–3; Masa’s first visit to (1995), 144–5; relations with Japan, 175–6; SoftBank’s venture capital fund in, 142–3; stimulus programme (2009), 201; and Taiwan, 17; Tencent, 307; Tsinghua University in Beijing, 102*; UTStarcom, 145–6, 158; World Trade Organization entry, 147 Chudnofsky, Jason, 95 Cisco Japan, 89–90, 103, 162 Cisco Systems, 89–90, 137, 139, 143, 162 Claridge’s hotel, 261 Claure, Marcelo, 227–31, 240, 242, 287–8, 289, 316–17, 318, 323–4 Clearwire, 231 CNET, 164 Colao, Vittorio, 180 Collins, Tim, 171–4 Columbia Pictures, 80, 101, 119–20 Comcast, 240 Compuserve, 102 computer technology: AI-driven computer graphics chips, 304–5, 308, 323, 329–31, 334–5; Altair 8800 microprocessor, 33; ARM chip, 244–5, 247–8, 253, 265–6, 308, 323, 329–30; chips as scarce and most crucial resource, 331; Comdex (trade fair in Las Vegas), 72, 89, 93, 94–6, 97, 124, 189, 267–8; electronic speech translators, 37, 39–45, 48, 114, 171*; Fujita’s advice to Masa, 26; innovation in 1970s California, 27–8, 33–4, 37, 101–2; Intel 4004 chip, 35; Intel 8080 microprocessor, 33; ‘internet of things’, 244, 245, 246, 253, 254–5; invention of integrated circuit, 27; Masa at Berkeley, 36–9; memory board manufacturers, 108–11; Microsoft-Novell rivalry, 76–7; Mozer’s talking calculator, 37; networking hardware/software, 77, 89–90, 143; Novell’s ‘netware’, 76–7; personal computer revolution, 49, 51, 53–4, 74–5; Unix operating system, 75 see also artificial intelligence (AI); internet; mobile internet; software technology; telecoms Computer Weekly, 78 Confucian thought, 119, 146, 147 Costner, Kevin, 129–30 Cotsakos, Christos, 134 Coupang, 236, 319 Covid-19 pandemic, 298–302, 303, 306, 312, 314, 315, 320, 326*, 328 credit rating agencies, 184–5, 229, 306 Credit Suisse, 306, 319 Cringely, Robert X., Accidental Empires, 163 Dai-Ichi Kangyo Bank, 55, 61, 63, 98 Daoist thought, 146 DataNet (magazine publishing company), 69 Davos, World Economic Forum, 2, 147, 148, 167, 294 Deadwood, South Dakota, 129–30 Democratic Party of Japan, 220 Deng, Wendi, 120–21 Deng Xiaoping, 144 Deutsche Bank, 124, 181–4, 185, 235, 237, 259, 283, 304 Doko, Toshio, 119 Dolotta, Ted, 74–8, 91, 103 Domaine de la Romanee-Conti (DRC) wine, 239, 246, 333 DoorDash, 283, 328 dot-com bubble (1998-2000), 132–6, 138–41, 147–8, 190; AOL-Time Warner merger (2000), 2; bursting of, 4, 145, 148–51, 156, 284, 328; eVentures fund, 121; Masa’s investments during, 133–6, 138–41, 143–4, 148, 284, 328; Masa’s reinvention after crash, 4–5, 156, 201; SoftBank’s stock peaks (February 2000), 3–4; Velfarre disco event (February 2000), 1–3, 148 Dream-Works, 236 Drucker, Peter, 52 E-Access, 160, 225 EachNet, 168, 169 EADS (aerospace company), 249 East, Warren, 245 eBay, 168–9, 174, 176 economy, global: China as ‘last frontier’, 144; Covid disruption, 314; financial crisis (2008), 83, 193–6, 201; hyper-globalization, 4, 5; impact of Russo-Ukraine war, 324; inflation in post-Covid period, 320, 324; and Japan’s economic miracle, 21, 48, 178; Masa and the digital revolution, 1–5, 49, 121, 137–8, 157, 211, 244; new cold war protectionism, 5; Plaza Accord (22 September 1985), 69–70, 80; second oil price shock (1979), 48–9; World Economic Forum (Davos), 2, 147, 148, 167, 294 see also dot-com bubble (1998-2000) Edison, Thomas, 158 Eisner, Michael, 107* Elliott Management (Hedge fund), 295–8, 299–301, 302, 308, 313 Ellison, Larry, 140, 189–90, 235 entrepreneurship: of Sheldon Adelson, 93–5; emerging Chinese class, 142–3; of Den Fujita, 24–6, 33; Japanese resistance to change, 48, 83–4; Masa’s early money-making ventures in USA, 33, 36–7, 39–47, 171*; Masa’s limitless attitude to risk, 5, 54–5, 57, 133–4, 140–41, 159–61, 210–11, 273, 309–13, 331; of Mitsunori, 18–19, 20, 21–4, 54–5; as not consistent with perfection, 6; Sasaki as Silicon Valley mentor, 35; start-up ecosystem in Silicon Valley, 101–7, 127; story of Steve Jobs, 190; ‘term sheet’ convention in Silicon Valley, 284–5; and US universities in 1970s, 27–8, 37, 101–2 see also venture capital (VC) Ericsson, 188 Esrey, William, 172–4 E-trade, 134–5, 138, 139, 162, 164 E-Trade Japan, 138, 164 Excite, 101, 105 EY (accountants), 305, 306 Ezersky, Peter, 91, 93 Facebook (Meta), 236, 259, 297, 303, 307, 329 Fairchild Semiconductor, 101–2 Al-Falih, Khalid, 264 Feld, Brad, 133 Fentress, Chad, 314 film industry, 107*, 119, 129–30, 235, 236; Columbia Pictures, 80, 101, 119–20 Filo, David, 101, 102, 103–4, 105 financial/business sector: Black Monday stock market crash (1987), 108; Buffett on leverage, 297; buy-backs, 296, 297, 298–9, 300–301, 302; call options, 312; collapse of Lehman Brothers (15 September 2008), 194, 196, 197, 235; ‘conglomerate discount’, 295, 296, 308; Covid-19 emergency measures by central banks, 301–2, 312, 316; Dai-Ichi Kangyo loan, 55, 61; financial instruments, 181, 184, 235, 282, 312; hyper-globalization, 4, 5; IRR metrics, 127–8, 308–9; Japanese banks, 61, 80–85, 92, 95–6, 97–9, 136–7, 138–9, 300, 334; Japan’s ‘Little Bang’ (1993), 97–8; Japan’s ultra-low-to-negative interest rate policy, 98–9, 100; Jasdaq over-the-counter market, 76, 85, 90, 131; leveraged buy-outs, 127–8, 184–5, 191, 196, 240, 247, 274, 275, 300; “Man’s pride” (trust) in Japan, 87, 170–71, 195–6, 273; Masa’s use of opaque corporate structures, 123, 125–6, 127–9, 130, 296; Nasdaq stock market, New York, 2, 4, 138, 148, 149; old-school culture in Japan, 64, 82, 89–90, 119, 158, 195–6; preferred equity, 282; private equity, 5, 92, 134–5, 167, 171–4, 180, 185, 241, 288; related-party-transactions/off-balance-sheet dealings, 122–3, 125–6, 127–9, 130, 138, 139; securitization, 181, 185, 187, 193–4, 306; Tokyo Stock Exchange (TSE), 64, 76, 85, 90, 128, 131; Wall Street banks, 91, 92, 159, 193–4, 205, 248; ‘whole company securitization’, 185 see also dot-com bubble (1998-2000); venture capital (VC) Fink, Larry, 290, 291 Fisher, Ron, 103, 108–9, 129, 140–41, 219, 276; and Arora, 234, 235–6; at Interactive Systems, 75, 77–8; joins Softbank, 87, 99–100; leaves SoftBank, 317–18; loyalty to Masa, 133–4, 325; on Masa-Jobs relationship, 193; on Masa’s 30-Year Vision speech, 211–12; and Northstar, 309, 311; and sales of SoftBank assets, 158, 162 Flowers, J.

Irresistible: How Cuteness Wired our Brains and Conquered the World
by Joshua Paul Dale
Published 15 Dec 2023

Kringelbach et al., ‘How cute things hijack our brains and drive behaviour’, The Conversation (4 July 2016), theconversation.com/how-cute-things-hijack-our-brains-and-drive-behaviour-61942 (accessed 31 October 2022). 47 Ibid. 48 Kringelbach et al., ‘On cuteness’, p. 9. 49 Tyler Colp and Nico Deyo, ‘The Vtuber Industry: Corporatization, Labor, and Kawaii’, Vice (23 December 2020), www.vice.com/en/article/akdj3z/the-vtuber-industry-corporatization-labor-and-kawaii. See also Lu et al., ‘More Kawaii than a Real-Person Live Streamer’. 50 Ethan Gach, ‘AI-Controlled VTuber Streams Games On Twitch, Denies Holocaust: Neuro-sama likes to play Minecraft and go off-script’, Kotaku (6 January 2023), kotaku.com/vtuber-twitch-holocaust-denial-minecraft-ai-chatgpt-1849960527 (accessed 23 January 2023). 51 R. O. Kwon, ‘Stop Calling Asian Women Adorable’, The New York Times (23 March 2019), www.nytimes.com/2019/03/23/opinion/sunday/calling-asian-women-adorable.html. 52 Noriko Murai, ‘The Genealogy of Kawaii’, in Noriko Murai et al., eds, Japan in the Heisei Era (1989–2019), (London: Routledge, 2022), p. 249. 53 Simon May, The Power of Cute (Princeton, NJ: Princeton University Press, 2019), p. 127. 54 Leila Madge, ‘Capitalizing on “Cuteness”: The Aesthetics of Social Relations in a New Postwar Japanese Order’, Japanstudien 9, 1 (1998), p. 167. 55 Ibid., p. 164. 56 Slade, ‘Cute men in contemporary Japan’, p. 79.

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The Treasure Box All the Girls Want’, in Masami Toku, ed., International Perspectives on Shōjo and Shōjo Manga: The Influence of Girl Culture (London: Routledge Press, 2018). Gach, Ethan, ‘AI-Controlled VTuber Streams Games On Twitch, Denies Holocaust’, Kotaku (6 January 2023), kotaku.com/vtuber-twitch-holocaust-denial-minecraft-ai-chatgpt-1849960527 (accessed 23 January 2023). Galbraith, Patrick W., ‘Seeking an alternative: “male” shōjo fans since the 1970s’, Shōjo Across Media: Exploring ‘Girl’ Practices in Contemporary Japan, ed. Jaqueline Bernt, Kazumi Nagaike and Fusami Ogi (London: Palgrave Macmillan, 2019). Galbraith, Patrick W., Otaku and the Struggle for Imagination in Japan (Durham, NC: Duke University Press, 2019).

Shocks, Crises, and False Alarms: How to Assess True Macroeconomic Risk
by Philipp Carlsson-Szlezak and Paul Swartz
Published 8 Jul 2024

For that reason, we argue that the productivity boom expected from the adoption of generative AI is more likely to deliver 25 to 50 bps than 150 bps of additional trend growth. Should that be considered a disappointment, or looked forward to as meaningful impact? Some will see this as pessimistic and wonder if we have spent enough time playing with ChatGPT and all its generative-AI siblings. We assure readers that we have and are duly impressed. But we continue to believe in the hurdles of technological maturity (we’ve all seen the AI hallucinations), of societal resistance (not in my daughter’s classroom), of regulatory friction (What will the new rules be?)

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See also technology bad macro, 108, 137, 243, 249, 253, 255 banking systems, 249 bubbles and, 188 financial recessions and, 40, 53 global financial crisis and, 26 recoveries and, 49–50 bankruptcies capital allocation and, 167 private, 176 recessions and, 48 Belt and Road Initiative, 204 Bernanke, Ben, 123 biases confirmation, 200, 210 toward doom and gloom, 10 Biden, Joe, 115, 132, 133–134, 224 bond markets, 108, 123–124, 124f bond vigilantes and, 108, 132–133, 135 bubbles, 182 inflation expectations and, 140–141 quantitative easing and, 113, 113f Brandt, Willy, 224 Brazil, 231, 238 Bretton Woods Conference, 235 Brexit, 219, 220–221, 227 BRICS, 231, 238 bubbles, 29f challenges from, 181–182 characteristics of and risk from, 183–185 cleanup of and financial stability, 187–188 cycle-ending, 184f, 185 future of, 249–250 good, 188–190, 191 idiosyncratic, 184–185, 184f loving and living with, 180–191 spotting, stopping, and stoking, 185–187, 191 structural break, 184f, 185 taxonomy of, 183–185, 184f ubiquity of, 182–183 Bush, George W., 110, 117 Canada structural damage in, 49–50, 49f V-shaped recovery in, 46–47, 47f capital allocation, 167–168, 237, 244, 247–248 capital dumping, 237–238 capital flows, 235–236, 251 capital growth, 57–59, 60–63 in China, 75–76 depreciation and, 61–63, 62f, 63f, 69 gravity of growth and, 68–70, 69f recessions, recoveries, and, 48–49, 48f capital markets, 199, 202 carbon capture, 62–63 CARES Act, 114, 114f, 115 Carter, Jimmy, 108, 110, 124, 133 Carville, James, 132–133 ChatGPT, 98 Cheney, Dick, 110, 133, 174 China economic convergence and, 202 financial divergence and, 204 gravity of growth in, 68, 75–76, 75f magical growth models on, 68, 74–76, 75f trade and, 224–225 Citigroup, 190 climate change, 13 decarbonization and, 62–63, 154, 175, 245 green investment and, 189 investments and, 245 Clinton, Bill, 110, 132–133 Cold War, 197–201, 224–225 collective-action problems, 189–190 convergence, 14.

Boom: Bubbles and the End of Stagnation
by Byrne Hobart and Tobias Huber
Published 29 Oct 2024

More frequent replications, experiment pre-registrations and the publication of failures and negative results, and the abandonment of the flawed p<0.05 significance test, could also improve the process of scientific experimentation and discovery. AI provides another source of hope for overcoming scientific stagnation. Literature-based discovery, or LBD, which uses ChatGPT-style models to analyze massive amounts of scientific data and literature, promises to unlock novel discoveries. In fact, it already has, for example in the case of the antibiotics halicin and abaucin, which two MIT teams identified in 2019 using a machine-learning algorithm trained on the chemical structures of thousands of known antibiotics.

…

As discussed in Chapter 1, the abundance of money in big tech over the last decade has coincided with a scarcity of ideas, as indicated by the billions of dollars the large tech firms have been amassing. But beyond antitrust law and regulation, recent technological advances like the theoretical breakthrough of transformer-based AI and the user-facing advancements represented by OpenAI’s ChatGPT and other large language model-based tools might be the most significant accelerants for another corporate R&D bubble. While they have been in the making for decades, the recent breakthroughs in generative AI achieved by upstarts like OpenAI, Stability AI, and Anthropic have disrupted big tech’s R&D complacency for now.

Making Sense of Chaos: A Better Economics for a Better World
by J. Doyne Farmer
Published 24 Apr 2024

Problems that previously seemed out of reach in artificial intelligence, like facial recognition and speech recognition, are now routinely solved by artificial neural networks (which are just another type of learning algorithm). Complicated neural-network models that have been trained on a vast corpus of human text, like ChatGPT, are writing student term papers and making tasks like computer programming much easier. Mainstream economists are beginning to use machine learning for statistical data analysis, as an alternative to traditional econometrics. That said, with a few exceptions, using learning algorithms to simulate the behavior of agents in models is the domain of agent-based modeling.16,17 Learning algorithms are easy to incorporate into agent-based models.

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Brian 5, 170–71 artificial intelligence 1, 3, 11, 42, 65, 114, 183 Arvizu, Dan 235 Asano, Yuki 124–5, 135 Asian financial crisis (1997) 195, 219, 232 ‘as-if’/’as-is’ reasoning 8, 10, 112 aspiration-level adaptation 110 assets accounting and 47 agent-based models and 7, 115 balance sheets and 45–6 behavioral macroeconomics and 103 buying underpriced 7, 115, 202 contracts and 141 derivative pricing and 150 leverage and 192, 193, 196, 197, 199, 200, 204–5, 208, 210, 211–12, 211 malfunctions and 185 market impact and 150, 151 portfolio choice and 111 prices and 181, 185 quantitative easing programs and 259 risk management and 204–5, 208, 210, 211–12, 211 attractors 30–37, 32, 182, 206 chaotic attractor 31–2, 34, 36, 206 fixed-point attractor 30–31, 33, 34, 36–7, 182 limit-cycle attractor 34 Lorenz attractor 32, 34 strange attractor 30–31, 33, 35 austerity 22, 105, 254 Austria xviii, 258 automation 1, 12, 20, 65, 68–72 Axtell, Rob 82, 274 Aymanns, Christoph xi, xii, 207–8, 210, 213–14, 215 Bachelier, Louis 144, 165 backward-looking expectations 83–4 Bacry, Emmanuel 216 Bagley, Richard 51 balance sheets 45–7, 51, 141, 188, 191, 260, 261–2 Bank for International Settlements 213 Bank of America 175–6, 189 Bank of Canada 258 Bank of England xviii, 89, 188, 216 Bank of Hungary 90 Bank of South Africa 216 baseball, gaze heuristic and 109 Basel Committee on Banking Supervision 204–16 Basel I 204, 214–15 Basel II 205, 206, 208, 209, 210, 213, 214–15 Basel III 215 Basel IV 215 Basel leverage-cycle model xii, 204–16, 211, 219 Baumol, William 62 Beaufort, Francis 224 beavers, hunting of 186–7 behavioral economics 5–6, 9, 17, 40, 46, 47, 93, 103–5, 106, 108, 114, 116, 120, 168 Beinhocker, Eric: The Origin of Wealth 63–4 Bergmann, Barbara 1 Beveridge curve xi, 69, 69, 70–71 ‘Beyond Equilibrium and Efficiency’ conference (2000) 168–9 bid-ask spread 154 biology 4, 8, 16, 21, 51, 54, 175–90, 239, 278 market ecology see ecology, market understanding the economy and concepts from 43–5 BiosGroup 187–8 Black, Fischer 150, 169 Black Monday stock market crash (1987) 161–4, 165, 166, 167, 168, 187 Boeing 73 Bohr, Niels 23, 24 Boldrin, Michele 37 bonds 111, 140, 150, 171, 172, 185, 191, 194–7, 199, 219, 259 Boston University 56, 57, 153 Bouchaud, Jean-Philippe 155 bounded rationality 116–20, 118n, 119, 173 computerized trading strategies and 178 defined 108–9 endogenous business cycles and 124, 128, 129 game theory and 135 market impact and 151, 155–6 risk management and 206 wilderness of xi, 116–20, 118n, 119, 124, 151, 155–6 Boyle’s Law 23–4 brain artificial 171, 172 brainwaves, chaos and 38 cerebral cortex 277, 278 as complex system 5, 20–21 gut brain 139–40, 142, 277 prediction and 23 wilderness of bounded rationality and 118n, 119 Brin, Sergey 49 British Met (Meteorological) Office 224 Brock, William ‘Buz’ 38, 39, 172–3, 200 broker-dealers 207 Brown University 82 Buffet, Warren 115, 139 business cycles Beveridge curve and 71 bounded rationality and creating endogenous business cycles 124–8, 126, 135 chaos and 28–9, 37, 40 creative destruction and 279–80 endogenous business cycle from a simple agent-based model xi, 124–8, 126, 135 inflation and 257, 258 nonlinear behavior and 76, 78 spontaneous emergence of 123, 124–8, 126, 135, 258 Calchas (mythical character) 22 Calinescu, Ani 184 call option 150 Calvet, Laurent 218 Calvo, Guillermo 101–2 ‘Carbon Dioxide and Climate: A Scientific Assessment’ report (1979) 230 Carnegie Mellon 98 Carville, James 123 Case-Shiller index 88, 89n Cass, David 100 census data 6, 67, 80, 81, 87, 112, 115 Center for Nonlinear Studies at Los Alamos 16, 41 change, economy and 8, 123–35 business cycles see business cycles chaotic dynamics and 128–35 internally and externally driven causes 8, 123 markets and 162, 164–70, 165, 167 chaos xviii, 24 Basel leverage-cycle model and 212, 213, 217 business cycles and 28–36 chaotic attractor xi, 30–36, 31, 206 defined 30–36, 32 economy as chaotic 36–41, 128 financial crisis (2008) and 206 fluid turbulence and 218–19 game theory and 128–35 sensitive dependence on initial conditions and 25, 31–4, 40, 212 simple chaos and complicated chaos 37, 38–40, 134, 143, 217, 218–19 stock market and 129, 135, 143 weather and 225, 226, 228, 229 Charney, Jule 227, 230 ChatGPT 114 Cherkashin, Dmitriy 159 chess 113, 132 Chicago school of economics 106–7 child-care workers 70 China, trophic structure of the U.S. and Chinese economies, comparison of xi, 59–63, 59 classifier system 171–2 climate change 1, 3, 12, 57, 65–6, 107, 221–54, 255, 263 Climate Policy Laboratory see Climate Policy Laboratory conscious civilization and 277–80 Dynamic Integrated Climate Economy (DICE) 235–6, 246–7 economic predictions and predictions of weather and climate 221–69 green-energy transition 65–72, 222, 234–7, 245–54, 246, 250, 263–8, 279 historical global cost and deployment of energy technologies 245–54, 246 predicting climate 229–31 solar photovoltaic modules see solar photovoltaic power sustainable growth 267–9 technological progress and 234–54, 237, 243, 246, 250 weather predicting and 223–9 Climate Policy Laboratory xii, 264–7, 264, 280 Agriculture and Land Use Module 264, 266 Energy System Module 264, 265–6 Government and International Relations Module 264, 266–7 Green Industrial Strategy Module 264, 266 Households Module 264, 266 Innovation and Technological Change Module 264, 266 Investment and Financial Stability Module 264, 266 origins of 264–5 The Production Network Module 264, 265 closed-form solutions 76–7 clustered volatility 219 credit and 173 defined 134, 164–5 fluid turbulence and 217, 218 leverage and 199–202, 215 news and xi, 164–74, 165, 167, 199–202, 215 price movements and xi, 164–74, 165, 167 quantitative predictions and models of 173–4 risk management and 204–5 Santa Fe Artificial Stock Market model and 170–73, 184–5, 200 trend-following and 200 coal miners 65 competition 42, 44, 67, 70 biological 177–82, 185, 189 game theory and 129, 133–5 complete network 68 complex systems xix adaptive complex systems 21, 43 chaos and see chaos complicated 35–6 defined 19–21 economy as 16, 42–7 financial system as 142 networks and 48–50 nonlinearity and 75–6, 77 science of 4–5, 19–21, 26, 29–30 self-organization and 48 ‘The Economy as an Evolving Complex System’ conference, Santa Fe Institute (1987) and origins of 16–17, 161, 166 complexity economics agent-based models and see agent-based models ‘as-if’ and ‘as-is’ reasoning and see ‘as-if‘/’as-is’ reasoning bounded rationality and see bounded rationality chaos and see chaos climate change and see climate change complex systems and see complex systems data and see data defined xviii–xix, 4–5, 19 ecology and see ecology equilibrium and see equilibrium hard problems, ability to solve 8–9 institutional support 13–14, 274–6 Leontief and emergence of 54–7 markets and see markets methods and infrastructure, developing 273–4 standard economic theory and see standard economic theory tools 6–7, 11 verisimilitude and see verisimilitude Complexity Science Hub, Vienna 198 complicated chaos 35–6, 38–40, 218–19 complicated complex systems 35–6 Computable General Equilibrium models 102 Congressional Budget Office, US xiv consciousness 5, 20, 21, 76, 278 conscious civilization 277–81 contracts 46, 140–41, 191, 196 convergence trading 158, 194 convergent evolution 188–9 counterparty identifiers 184, 187 COVID-19 pandemic agent-based models and 75, 277 business cycles and 8, 29, 262 model of epidemics’ effect on sickness and the economy and xi, 78–80, 79 Oxford model predicts economy response to xiii–xviii, 12, 50, 78, 263 Cowan, George 20 creative destruction 43, 279–80 credit agent-based models and 173 credit crisis 82 financial crisis (2008) and see global financial crisis (2008) financial turbulence and 191–220 lending and see lending leverage and see leverage risk management and see risk management shadow banking system and 142 CRISIS (Complexity Research Initiative for Systemic InStabilities) 208 Crutchfield, Jim 35 Curie, Marie 270 Cutler, David 166, 167, 168, 169, 170, 172 cybernetics 20 damage function 236, 249 Daniels, Marcus 154 dark matter/dark energy 271 Darley, Vince 187–8 Darwin, Charles 176, 177, 223 data collecting better 270–73 microdata 72, 87, 229 Dawid, Herbert 257 Day, Richard 37 decimalization 187–8 decision making 7, 11, 46, 63, 74, 83, 97, 99, 103, 115, 118n, 135, 141, 266–7 default 8–9, 49, 85, 90, 193, 195, 199, 200, 201, 202, 203, 232 degrees of freedom 38–40, 217, 219 Delour, Jean 216 demand curves 15, 96, 152 equilibrium and 8, 15 housing 87–8, 95–6, 110 inverted demand functions 200–201 labor 65–8, 71–2, 101 market ecology and 182 market impact function and 150–56 Nash equilibrium and 130 shocks xiii, xiv, xv, xvi, xvii, 262–3 Standard models in economics and 85 tatonnement and 15–16 World Energy Model and 249 Wright’s Law and 244 Department of Energy National Renewable Energy Laboratory, US 235 Department of Labor, US xiv derivatives 179, 187 derivative pricing 150, 152 Descartes, René 153 descent, evolution and 176 D.

The Mysterious Mr. Nakamoto: A Fifteen-Year Quest to Unmask the Secret Genius Behind Crypto
by Benjamin Wallace
Published 18 Mar 2025

It was that look of frank uninterest common to anyone who’d ever been stuck at a party listening to a blockchain bore. I found myself describing a family scheduling issue as “suboptimal.” “Do you really want to say that?” my wife asked. “What’s suboptimal?” my daughter asked. “Nerd language,” my wife said. Maybe ChatGPT could help. What are the best arguments for and against Travis Hassloch being Satoshi Nakamoto? Make the case for Eliezer Yudkowsky being Satoshi Nakamoto. What are the strongest unacknowledged influences on the Bitcoin white paper? What was I looking for, exactly? What are some creative ways a journalist could use GPT4 to help do his job?

…

He and Jean-Baptiste had recently been focusing on the possibility of “collaborative authorship,” Florian wrote. They thought this was a more plausible explanation for Nakamoto’s success at eluding linguistic detection than the idea that he might have significantly altered his writing style, given the tools available in Bitcoin’s early years, when ChatGPT was still more than a decade away. Florian thought the collaboration could have worked in one of two ways: either two or more people had taken frequent turns writing, or members of the group rewrote each other before posting anything. Then Florian asked me about a candidate I’d dismissed some time ago, a frequent Metzdowd poster named Travis Hassloch.

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill
Published 19 Sep 2023

They made speech recognition better, image recognition more reliable, and facial recognition more accurate. Neural networks would be employed for all manner of tasks: recommending shows to watch on Netflix, populating playlists on Spotify, providing eyes to the autopilot in Tesla’s electric cars, and allowing ChatGPT to converse in a seemingly human way. Anywhere a lot of data exists, a neural network can theoretically crunch it. All of a sudden, that small cluster of renegade academics became the hottest commodity in technology. Google, Microsoft, Facebook, Baidu: All the biggest technology companies in the world threw money at them.

…

See also photographs/photography Clearview AI Camera, 111 Eastman Kodak film camera, viii Insight Camera, 187 skin tone and, 179 speed cameras, 238 True Depth camera, 109 Capitol insurrection, 228–230 Carlo, Silkie, 215–217, 218, 220 Carlson, Tucker, 11 Cato Unbound, 261n15 Catsimatidis, Andrea, 114 Catsimatidis, John, 114 cease-and-desist letters, 165 census bureaus, 24–25 Central Intelligence Agency (CIA), 38, 125, 227, 268–269n38 Ceph, 79 Cernovich, Mike, 12, 53 Chaos Computer Club, 190 ChatGPT, 73 Chau, Ed, 186 Chicago Police Department, 155 child crimes investigators, 134–136 child sexual abuse material (CSAM), 135. See also pornography China scoring of citizens by, 34 surveillance in, 224–227 Churchill, Winston, 41 CIA (Central Intelligence Agency), 38, 125, 227, 268–269n38 Citizens United, 208, 306n206 Clarifai, 32–33 Clarium Capital, 14 ClassPass, 8 Clearview AI absence of headquarters for, x–xi, xii access requests and, 190–192 ACLU and, 202, 203–204, 205 AR glasses and, 249–250 attempts to shut down communication by, xv, xvii–xviii author’s contact with representatives of, 160–166 bans on, 165 capabilities of, ix–x Capitol insurrection and, 229–230 concerns regarding weaponization and, xv–xvii defense of, 157–159 effectiveness of, xii emergence of, 94 fines assessed to, 193–194, 230 growth of database for, 246–247 hit rate of, 133 international backlash against, 192–193 international expansion and, 137 investment efforts and, 111–120, 136. see also individual investors Johnson and, 95–96 law enforcement and, 128, 130–139 lawsuits against, 165, 204–206, 209–213, 248 Leone and, 113–114 NYT article on, 164–165, 187, 190, 194, 204, 230 opposition to, 237–238 pandemic and, 186–187 police reactions to, xii–xiv potential legal challenges to, 117 push for ethical use and, 239 results blocked by, xvii–xviii, 162–163 Scalzo and, 111–112, 113, 188–189 third-party testing of, 240 tip regarding, vii–viii, ix Ukraine invasion and, 237 wrongful arrests and, 183 Clearview AI Camera, 111 Clearview AI Check-In, 111–112 Clearview AI Search, 111 Clement, Paul attempts to contact, 160 on Clearview AI’s capabilities, ix–x lack of response from, xi legal memos from, 134, 157–158 Clinton, Bill, 209, 259n10 Clinton, Chelsea, 9–10, 259n10 Clinton, Hillary “deplorables” comment by, 50–51 election loss of, 88 facial recognition and, 104 false claims about, 94 Trump and, 16, 52 Cohen, Chuck, 133 Colatosti, Tom, 62, 65, 66, 71 Comet Ping Pong, 55 Computer Fraud and Abuse Act (1986), 117–118 computers early, 36 reliance on, 36 confirmation bias, 181 Constitution First Amendment, 15, 206–207, 208–209, 212–213, 306n206 Fourth Amendment, 141 consumer protection laws, 205 contact tracing, 186 Coolidge, Calvin, 11 Couchsurfing, 81 Coulson, Jennifer, 180–181 Coulter, Ann, 119 Covid-19, 185–187, 209, 214 Crime and the Man (Hooton), 25, 26 CrimeDex, xii, 134 criminal detectors, 31 “criminal face,” 31 Criminal Justice Information Services Division, 299n158 Criminal Man (Lombroso), 22–23, 38 crisis communications, 161 Cruise, Tom, 123 Crunchbase, 79 Cruz, Ted, 11 cryptocurrency, 81 CSAM (child sexual abuse material), 135.

Uncomfortably Off: Why the Top 10% of Earners Should Care About Inequality
by Marcos González Hernando and Gerry Mitchell
Published 23 May 2023

Mishra, C. and Rath, N. (2020) Social solidarity during a pandemic: Through and beyond Durkheimian lens. Social Sciences and Humanities Open, 2:1, 1–7. Mitchell, G. (2022) The clapping might have stopped, but our need for care is not going away. LabourList. 12 July. https:// labourlist.org/2022/07/the-clapping-might-have-stopped-butour-need-for-care-is-not-going-away Mok, A. and Zinkula, J. (2023) ChatGPT may be coming for our jobs. Here are the 10 roles that AI is most likely to replace. Business Insider. 2 February. www.businessinsider.com/chatgptjobs-at-risk-replacement-artificial-intelligence-ai-labor-trends2023-02?r=US&IR=T Monbiot, G. (2022) Putin exploits the lie machine but didn’t invent it.

Nobody's Fool: Why We Get Taken in and What We Can Do About It
by Daniel Simons and Christopher Chabris
Published 10 Jul 2023

Their expertise in developing sophisticated computational models is genuine, but it is not the expertise necessary to evaluate whether a model’s output constitutes generally intelligent behavior. People who make these predictions appear to be swayed by the most impressive examples of how well new machine learning models like ChatGPT and DALL-E do in producing realistic language and generating beautiful pictures. But these systems tend to work best only when given just the right prompts, and their boosters downplay or ignore the cases where similar prompts make them fail miserably. What seems like intelligent conversation often turns out to be a bull session with a bot whose cleverness comes from ingesting huge volumes of text and responding by accessing the statistically most relevant stuff in its dataset.

Filterworld: How Algorithms Flattened Culture
by Kyle Chayka
Published 15 Jan 2024

It requires the material to be translated into data that the machine can understand, such as text; it lacks serendipity because it can filter only by the terms that the user inputs; and it does not measure inherent quality. It is unable to “distinguish a well written [and] a badly written article if the two articles use the same terms.” The inability to evaluate quality brings to mind artificial intelligence: New tools like ChatGPT seem to be able to understand and generate meaningful language, but really, they only repeat patterns inherent in the preexisting data they are trained on. Quality is subjective; data alone, in the absence of human judgment, can go only so far in gauging it. Social information filtering bypasses those problems because it is instead driven by the actions of human users, who evaluate content on their own—using judgments both quantitative and qualitative.

Growth: A Reckoning
by Daniel Susskind
Published 16 Apr 2024

Yet there is no obvious reason to think that there is a strong correlation between tasks that machines find hard to perform and the ones that human beings happen to find fulfilling. On the contrary, many of the dullest tasks (for instance, restocking grocery shelves) are hard to automate, while many of the most interesting ones (for instance, designing a building) are much more automatable. The latest wave of ‘generative’ AI systems, such as DALL-E and ChatGPT, are a clear demonstration of this fact, easily performing activities that were thought to require faculties like ‘creativity’ from human beings. This last observation leads us back to the original, more extreme fear: that the newest technologies now present a challenge to the number of jobs that will remain, not simply their nature.

Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist
by Liz Pelly
Published 7 Jan 2025

v=L41mZRY_Maw. 18 Ugwu, “Inside the Playlist Factory.” 19 Spotify Charts website: https://charts.spotify.com/home. 20 “Playlisting Plus” on YouTube. 21 For a peak playlist era account of Spotify editors being obsessed with “leveling the playing field,” see this Kaitlyn Tiffany piece from 2017: https://www.theverge.com/2017/11/13/16617900/spotify-playlist-curation-nyc-live-shows-fresh-finds-indie-latin-new-music. 22 David Pierce, “The Secret Hit-Making Power of the Spotify Playlist,” Wired, May 3, 2017, https://www.wired.com/2017/05/secret-hit-making-power-spotify-playlist/. 23 “Quora Session with Daniel Ek,” https://www.quora.com/profile/Daniel-Ek. 24 Pauline Oliveros, Deep Listening: A Composer’s Sound Practice (Deep Listening Publications, 2005). 25 Tia DeNora, Music in Everyday Life (Cambridge University Press, 2000). 26 Stuart Dredge, “Spotify data reveals boom in sleep and relaxation albums,” Guardian, September 7, 2015, https://www.theguardian.com/technology/2015/sep/07/spotify-data-sleep-relaxation-albums. 4 The Conquest of Chill 1 Stuart Elliott, “Spotify, New to Advertising, Says ‘I’ve Got the Music in Me,’ ” New York Times, March 25, 2013, https://archive.nytimes.com/mediadecoder.blogs.nytimes.com/2013/03/25/spotify-new-to-advertising-says-ive-got-the-music-in-me/. 2 Julie Beck, “Hard Feelings: Science’s Struggle to Define Emotions,” Atlantic, February 25, 2014; Ned Carter Miles, “Are You 80% Angry and 2% Sad? Why ‘Emotional AI’ Is Fraught with Problems,” Guardian, June 23, 2024, https://www.theguardian.com/technology/article/2024/jun/23/emotional-artificial-intelligence-chatgpt-4o-hume-algorithmic-bias. 3 Michael Sherman article on “Edison Mood Music” published by the Discography of American Historical Recordings website, a project of the UC Santa Barbara Library, https://adp.library.ucsb.edu/index.php/resources/detail/434; Alexandra Hui, “Lost: Thomas Edison’s Mood Music Found: New Ways of Listening,” Endeavour 38 (2014): 139−142. 4 Sherman, “Edison Mood Music”; Hui, “Lost: Thomas Edison’s Mood Music Found.” 5 Hui, “Lost: Thomas Edison’s Mood Music Found.” 6 Beverly Beyette, “Humming Right Along: It’s a far cry from a haphazard menu of music.

The Everything Blueprint: The Microchip Design That Changed the World
by James Ashton
Published 11 May 2023

It is just technical enough, I hope, without slowing the narrative, and careful not to narrow from the awesomeness of what semiconductors have made real. More than anything, beyond the torrent of electrons and some magical machinery, I discovered it was a story of people. We live in an era with a rising tide of artificial intelligence – for example, ChatGPT – that makes it possible to sense the point at which computers will take over. Nevertheless, innovation remains a human pursuit, with trust, respect and often friendship at its heart. The ideas behind the next breakthrough will be just as fluid as the last: shared, stolen, enhanced by colleagues and competitors, no matter who employs them – or where.

Doppelganger: A Trip Into the Mirror World
by Naomi Klein
Published 11 Sep 2023

And now that the machines have devoured so much of us, gorged on so many of our ways and our quirks, they can make rather credible replicas of us near instantly. My friends who are visual artists and songwriters are terrified about what their futures hold when artificial intelligence programs can be instructed to make art “in the style of” them—and then churn out passable replicas within moments. Nick Cave, when confronted with a ChatGPT-generated version of a Nick Cave song, described the phenomenon as “replication as travesty … a grotesque mockery of what it is to be human.” There is something uniquely humiliating about confronting a bad replica of one’s self—and something utterly harrowing about confronting a good one. Both carry the unmistakable shudder of the doppelganger.

Blood in the Machine: The Origins of the Rebellion Against Big Tech
by Brian Merchant
Published 25 Sep 2023

And the boss is the algorithm; HR consists of a text box that workers can log complaints into, and which may or may not generate a response. The modern worker can sense the implications of this trend. It’s not just ride-hailing either—AI image-generators like DALL-E and neural net–based writing tools like ChatGPT threaten the livelihoods of illustrators, graphic designers, copywriters, and editorial assistants. Streaming platforms like Spotify have already radically degraded wages for musicians, who lost album sales as an income stream years ago. Much about Andrew Yang may be suspect, but he did predict correctly that anger would again spread like wildfire as skilled workers watched algorithms, AI, and tech platforms erode their earnings and status.

Deep Utopia: Life and Meaning in a Solved World
by Nick Bostrom
Published 26 Mar 2024

G., 30 Wireheading, 226, 419 Wolf, Susan, 460–63 Y Yudkowsky, Eliezer, 221, 225, 240, 502 ACKNOWLEDGMENTS I am grateful to many more people than I can mention for enabling this work, by creating the material or intellectual preconditions for its coming into existence. But for their more direct contributions, I want to explicitly thank Guive Assadi, Emily Campbell, Richard Yetter Chappell, Will Hammond, Guy Kahane, Anton Korinek, Matthew van der Merwe, Thaddeus Metz, Geoffrey Miller, Toby Newberry, Carl Shulman, ChatGPT-4, Claude 2, Tanya Singh, and Jan-Erik Strasser for extensive comments on the manuscript; Gilbert N. Morris, Eric Neyman, Toby Ord, David Pearce, and Anders Sandberg for helping to answer some particular questions; Liz Hudson, Frances Key Phillips, Sam Blake, and Pascal Porcheron for help with copyediting; Gwen Bradford, Stephen Campbell, Dale Dorsey, Nick Fletcher, Thomas Hurka, Antti Kauppinen, Eden Lin, Michael Prinzing, Aaron Smuts, Michael Steger, Louise Sundararajan, Lars Svendsen, Valerie Tiberius, and Susan Wolf for in-depth expert conversations on specific topics that helped me learn and refine preliminary ideas; Matthew van der Merwe and Toby Newberry for project management; Guive Assadi for research assistance; and the team at Ideapress for their uncommon dispatch.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson
Published 15 May 2023

From the Field of AI Dreams People are right to be excited about advances in digital technologies. New machine capabilities can massively expand the things we do and can transform many aspects of our lives for the better. And there have also been tremendous advances. For example, the Generative Pre-trained Transformer 3 (GPT-3), released in 2020 by OpenAI, and ChatGPT released in 2022 by the same company, are natural-language processing systems with remarkable capabilities. Already trained and optimized on massive amounts of text data from the internet, these programs can generate almost human-like articles, including poetry; communicate in typical human language; and, most impressively, turn natural-language instructions into computer code.