Like, Comment, Subscribe: Inside YouTube's Chaotic Rise to World Domination
by Mark Bergen · 5 Sep 2022 · 642pp · 141,888 words
one goal above others. * * * • • • Cristos Goodrow had sent his email right as YouTube’s leaders convened. For maximum attention, he sent it to all of them and gave it a compelling subject line: “Watch time, and only watch time.” Goodrow came to YouTube after two decades programming software for companies across Silicon Valley and for Google
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gridlock and lack of clear marching orders. In his prognosis he proposed rewiring YouTube’s machines to favor just one outcome: how long people stayed with videos. “All other things being equal, our goal is to increase watch time,” he wrote in the email. Goodrow liked to debate this idea with fellow
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rising nemesis, Facebook, flourished not just by racking up accounts but by keeping people engaged. TV certainly did. So YouTube would keep people engaged by promoting videos that racked up the most watch time. Will it make the boat go faster? Then do it! Now they just needed a big, hairy, audacious goal
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?” he asked. “When could we reasonably do that?” * * * • • • Mehrotra announced the new OKR the following year at YouTube’s annual leadership summit in Los Angeles: YouTube would work to get one billion hours of watch time every day within four years. “Look, I know what you’re all thinking,” Mehrotra began. Impossible. Once trained
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unit plotted trajectories on a chart, which they called their “Break the Internet Graph.” Goodrow’s coders began rewiring YouTube’s search and recommendation system to promote videos that generated the best watch time, not the most views. Only videos that made their boat go faster. One team was not so enthusiastic. Bing
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email in early 2012 from Mehrotra inviting him to a room in San Bruno nicknamed the product den. Before setting its audacious goal, YouTube wanted to test prioritizing watch time in its algorithm. Inside the den, Chen was told he had five days to prepare an explanation of the planned change for creators
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“Your algorithm has a bug.” O’Brien was responsible for YouTube’s search product and neatly fit Google’s product manager mold—a fast talker with software know-how and better people skills than coders. Once YouTube switched its system to favor watch time, some staff were not prepared for the immediate upheaval it
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the clip, which had been selected to run ads, nearly halved. It didn’t help that no one had updated the“analytics” web dashboard for YouTubers to show “watch time” as a figure. They saw “views” and saw those views plummeting but had no idea why. The company had to convince
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the redesign that drove viewers away from hitmakers like him. Months later, when YouTube moved to favor watch time, that trend worsened. Penna’s management company, Big Frame, tracked stats for several YouTubers and watched them all drop practically overnight. YouTube’s blog post introducing the switch had less than helpful advice: How can you
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be this now.” Really? How do you know? No one really did. But in a few months, the main lever became clear: only watch time. When he began on YouTube, Wong carefully studied the science of virality, testing the right ingredients. Now its formula felt painfully simple. “Okay,” he realized. “We’re just
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Disneyland, and Wong saw swarms of attendees all start to hold cameras at arm’s length, pointed back at themselves. * * * • • • No YouTube genre benefited from the switch to favor watch time more than “Let’s Play.” That was the name given to footage of video gamers filming themselves playing popular or deeply bizarre
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had raised $1.5 million in venture capital. But many of Maker’s channels had scripted, costly productions, and YouTube’s ad intake didn’t always cover expenses. Once YouTube switched to watch time, the economic tilt became impossible to ignore. Gamers filmed themselves playing and talking. Maybe they edited a bit, added some
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teed up one video after another. After the Google meeting the Jhos saw even more traffic on their site. YouTube let them into its ads program. A year later YouTube switched to prioritize watch time, and very quickly Mother Goose Club got company. It began with BluCollection, an anonymous account that only posted videos
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for the eyes”—colorful, sugary, craved. But plenty of videos were educational and healthy, too, YouTube thought. (It surely had more of those than TV did, if you added the hours up.) Delicious videos certainly improved watch time, although some staff expressed concern that this type of viewing could be fleeting. After gorging
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broccoli”). Some drafted broccoli OKRs. The Torso division, which managed its ever-sprawling creator class, drew plans to get 30 percent of watch time from Nutritious videos. Coders working on YouTube search and ads all discussed the effort. Then, in a fateful twist, these discussions petered out. No company-wide objectives and key
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movies, a foil to Apple’s iTunes. Rubin’s coders also controlled YouTube’s app on Android phones. Several directors at YouTube felt that it should run Google’s music service instead—music videos, after the watch-time transition, were exploding—and that YouTube should control its own app. They pushed Kamangar, who usually avoided
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to practice, fail and laugh at yourself,” a fashion observer explained in The New York Times the year Nilsen debuted. After the watch-time transition, beauty gurus shot up in YouTube’s charts. Nilsen could post ten- or fifteen-minute-long videos with relatively little editing. She expanded beyond makeup tips and skin
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the concept certainly did. More viewers moved from one recommended clip to the next, bringing in more watch time and, just as critically, more data. At San Bruno, YouTube staff rarely watched videos, but they watched video data constantly. In particular they paid attention to the seesaw of data on ads and viewership
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told his coders he was willing to take a 1 percent drop in watch time for a 2 percent increase in ads, but nothing more. Engineers ran tests for Dallas, tweaking experiences for certain viewers without telling them. At YouTube Stats, a meeting Mehrotra held every Friday, they presented the befuddling results:
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the machines found a way to show more ads and improve watch time. “How can it possibly be positive on both?” Mehrotra asked. “No idea,” an
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met with Google’s networking staff, which invited her to do so: they were freaked out about the strain YouTube’s hefty watch-time goal was placing on company servers and wanted to curb the plans to relieve stress on bandwidth. There’s no evidence anyone warned her about
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2005, mixing regular lefty jabs at the press and politicians with clickable tabloid fare. Uygur saw few conservative shock jocks on YouTube, until around the watch-time transition, when they “started popping up all over the place.” Many popped up with videos mocking Uygur’s show or his name; tagging footage with “
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careful not to overuse ugly slurs or call for outright violence, the kind of invective YouTube removed. If the Brain network was set to maximize watch time, which it was, those sorts of videos might perform very well. YouTube had begun to filter videos promoting Islamist terror, restricting certain clips by age or deleting
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produce twenty videos a week across her channels, a new kind of juggling. And then YouTube’s second generation and all the demanding social apps arrived. Kay adapted to YouTube’s watch-time change, uploading gaming and makeup videos. At YouTube events she learned that her fans were mostly teenage girls. Kay, then in her
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, the company converted a 40,000-square-foot airplane hangar into a state-of-the-art production studio for select creators called YouTube Space. But YouTube’s algorithm still wanted the opposite. It desired watch time and daily views; videos that delivered that were usually made cheap. But they rose to the top. One
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tracked videos about particular world or historical events. The French engineer intentionally noted how such a thing could improve YouTube watch time. Chaslot won praise for it from peers but couldn’t find any interested YouTube managers, and he soon received a negative performance review (a “ding”). Google let him go. He had more
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the channels that uncritically cheered Trump, such as Alex Jones, under the guise of commentary or punditry. Bundled together, they had more watch time than legitimate news outlets on YouTube. This is a crisis, the staffer pleaded. If YouTube brass agreed, they didn’t say so. But a certifiable crisis came soon enough, and
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using these surveys and thumbs next to videos to gauge satisfaction. To fix its quality crisis years before, YouTube had switched its gears from views to watch time, but that didn’t cut it anymore. (YouTube never specified the precise equation for its ranking system to outsiders.) When videos suggested the earth was flat
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popular but what kind of videos were made. Also, when the company wanted to, it went in and turned the dials. Consider Minecraft. After the watch-time transition, YouTube’s audience clearly loved Minecraft, heaving the niche game into the mainstream. At one point, in May 2015, fourteen slots on
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. (Wikipedia, after Wojcicki spoke, said it had not been informed of this plan.) Wojcicki also introduced a term she had begun using frequently at YouTube. Its algorithms favored watch time, daily viewers, and satisfaction, but they had added a fourth metric. “We’re starting to build in that concept of responsibility,” she told
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website she documented this corporate crackdown, which she viewed as retaliation for her outspoken challenge to the meat industry. She posted three screenshots of her YouTube dashboard, showing watch time, views, and subscribers on her videos and how they kept falling. One post listed 307,658 minutes of
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watch time and 366,591 views. “Your estimated revenue,” the YouTube dashboard read, “$0.10.” This she circled in red pixels. “There is no equal growth opportunity on YouTube,” her website blared in bright, frantic text. “Your channel will grow if
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their videos and use an overall pool of ad money instead, doling out checks based on engagement—the likes, comments, and watch time videos got. This felt fairer and more sustainable. YouTube briefed a few creators on its ambitious plan. In March, Wojcicki presented it to her staff, telling them, “Please don’t
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. Nearly a decade after tilting its system toward longer videos, YouTube was now paying for shorter ones. Of course, the main algorithmic metric for Shorts, like that for all of YouTube, remained watch time. Most signs indicated that TikTok did chip away at YouTube’s dominance. A 2021 report revealed that for the first time
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2020, 388 and quality content, 175 responsibility metric, 328 screeners’ role in training, 320 and skeptics of YouTube, 223 skin-detection by, 255–56 titles of content chosen for, 172 watch time favored in, 156–60 and YouTube Kids app, 238, 244–45 Allen & Company (investment bank), 49 Alphabet, 257 alt-right, 263, 269
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viewers, 252, 254 emphasis on growth of, 91 and initiative to recruit female viewers, 369 and length of viewing sessions, 252 (see also watch time of audience) loyalty to YouTube, 394 number of videos watched daily, 49, 140 satisfaction ratings of, 296–97 See also engagement of users Auletta, Ken, 97 authoritative sources
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of, 323–24 video responses between, 39 volume of uploaded material, 6, 49, 140, 215–16, 389 and watch time of users, 157, 158–60 and Wojcicki, 261, 373 women’s experience as, 303 and YouTube Creator Summit, 250–51, 253, 262, 289–90 See also partner program; payment and income of creators
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employees, 317–20, 327, 349 diversity hiring in, 301 as parents, 174 and perks at YouTube offices, 148 poached from Yahoo, 52 and Wojcicki, 211–13 engagement of users emphasis placed on, 154, 158–59 (see also watch time of audience) and machine learning applied to advertising, 191 payments based on (Moneyball proposal
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, 388 neural networks in, 233–35 as new feature, 23 Reinforce program behind, 298 and right-wing content, 223, 224, 227 and watch time, 154, 155 See also algorithms of YouTube re-creation aesthetic, 27 Reddit, 218, 270 Redstone, Sumner, 60, 62, 76, 253–54 refugees, 264 Reinforce program, 298 related videos sidebar
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and word of funded channels, 133 Walmart, 286 war crimes, archives of, 296 Warner, Mark, 341 Warren, Elizabeth, 365 watch time of audience and billion-hours goal of YouTube, 228, 270 and COVID-19, 376 and “Delicious”/ “Nutritious” content, 174 and engagement-based payments, 337 and machine learning, 191–92, 233 of pro
Walled Culture: How Big Content Uses Technology and the Law to Lock Down Culture and Keep Creators Poor
by Glyn Moody · 26 Sep 2022 · 295pp · 66,912 words
YouTube’s chief business officer, Robert Kyncl, told a conference: ‘We are roughly neck-and-neck with Netflix on revenue, actually we are slightly larger and growing faster.’597 Kyncl also revealed that video represents 25% of YouTube ‘watch time’, 50% is YouTube creators and 25% is music
Culture Warlords: My Journey Into the Dark Web of White Supremacy
by Talia Lavin · 14 Jul 2020 · 231pp · 71,299 words
explained racial disparities, and that feminism was a dangerous ideology.” Underlying this push toward radicalization was not just YouTube’s algorithm, which has a documented propensity for recommending extreme content to increase engagement and watch time. There’s a consistent pattern of cross-promotion, collaboration, and high production value that builds audiences for
Leadership by Algorithm: Who Leads and Who Follows in the AI Era?
by David de Cremer · 25 May 2020 · 241pp · 70,307 words
/customer-service-trends.html 34 Hoffman, P. (1986). ‘The Unity of Descartes’ Man,’ The Philosophical Review 95, 339-369. 35 Google Duplex (2018). https://www.youtube.com/watch?v=D5VN 56jQMWM Chapter 2: The Leadership Challenge in the Algorithm Age The machine age arrived a long time ago, but today’s
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example of how utilitarian companies really employ their algorithms is the discussion surrounding how the YouTube algorithm makes recommendations to viewers. The metric that YouTube uses to decide on their recommendations for you as a customer (i.e. watch time) is not aimed at helping customers to get what they want, but rather to
Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World With OKRs
by John Doerr · 23 Apr 2018 · 280pp · 71,268 words
“ the true scarce commodity is increasingly human attention.” When users spend more of their valuable time watching YouTube videos, they must perforce be happier with those videos. It’s a virtuous circle: More satisfied viewership (watch time) begets more advertising, which incentivizes more content creators, which draws more viewership. Our true currency wasn
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’t views or clicks—it was watch time. The logic was undeniable. YouTube needed a new core metric. Watch Time, and Only Watch Time In September 2011, I sent a provocative email to
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my boss and the YouTube leadership team. Subject line: “Watch time, and only watch time.” It was a call to rethink how we measured success
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: “All other things being equal, our goal is to increase [video] watch time.” For many folks at
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be negative for views, the critical metric for both users and creators. Last (but not least), to optimize for watch time would incur a significant money hit, at least at the start. Since YouTube ads were shown exclusively before videos started, fewer starts meant fewer ads. Fewer ads meant less revenue. * My argument
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six months, but I won the argument. On the Ides of March, 2012, we launched a watch-time-optimized version of our recommendation algorithm aimed at improving user engagement and satisfaction. Our new focus would make YouTube a more user-friendly platform, particularly for music, how-to videos, and entertainment and late-night
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Round Number In November 2012, at our annual YouTube Leadership Summit in Los Angeles, Shishir gathered a few of us together. He said he was about to announce a big stretch goal to kick off the coming year: one billion hours in daily user watch time. (There is power in simplicity, and round
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day [by 2016], with growth driven by: KEY RESULTS Search team + Main App (+XX%), Living Room (+XX%). Grow kids’ engagement and gaming watch time (X watch hours per day). Launch YouTube VR experience and grow VR catalog from X to Y videos. Principled Stretching Stretch goals can be crushing if people don’t
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down to size. While one billion daily hours sounded like an awful lot, it represented less than 20 percent of the world’s total television watch time. Introducing that context was helpful and clarifying, at least for me. We weren’t gunning to be arbitrarily big. Rather: There was another thing
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scale up to it. In pursuing our mission over the next four years, we weren’t 10x absolutists. In fact, we’d commit to some watch-time-negative decisions for the benefit of our users. For example, we made it a policy to stop recommending trashy, tabloid-style videos—like “World’s
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Worst Parents,” where the thumbnail showed a baby in a pot on the stove. Three weeks in, the move proved negative for watch time by half a percent. We stood by our decision because it was better for the viewer experience, cut down on click bait, and reflected our
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principle of growing responsibly. Three months in, watch time in this group had bounced back and actually increased. Once the gruesome stuff became less accessible, people sought out more satisfying content. Once the billion
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-hour BHAG was set, however, we never did anything without measuring impact on watch time. If a change might slow our progress, we’d be scrupulous about estimating just how much. Then we’d build internal consensus before going through
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leading AdWords for a decade, I was used to complex ecosystems. I was eager to take on the challenge of unifying YouTube. When YouTube leadership set the one-billion-hour daily watch time goal, most of our people judged it impossible. They thought it would break the internet! But it seemed to me that
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of the way through the four-year, mega-stretch OKR. But while the objective was well planted, it wasn’t quite on pace. Watch-time growth had dropped significantly below what we needed to make our deadline
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to get to 100 percent, especially once an objective seems within reach. It’s safe to say that no one at YouTube would have been satisfied to reach 700 million daily watch-time hours. In all honesty, though, I wasn’t certain we’d reach the billion hours on time. I thought it
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when you’re making decisions about what to prioritize and where to lean, please keep in mind that we are not going to meet this watch-time OKR if we don’t do something about it.” Susan: I had some pressing concerns. One was a rearguard action with Google’s machine
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keep an eye on the gray, the nuances that might get overlooked. Daily watch time is driven by two factors: the average number of daily active viewers (or DAVs) and the average amount of time those viewers spend watching. YouTube was doing a good job on the second variable—but that was lower
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took over YouTube, she wasn’t obligated to get behind the billion-hour OKR. That was the previous administration’s goal. She could have reverted to a views goal, or one more oriented toward revenue. Or she could have kept the watch-time OKR but added three others of
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video search and recommendations. We were the tip of an OKR spear that had raised YouTube’s profile and stature throughout Google. The company’s morale had never been higher. I’d hear marketing people discussing watch time with real fervor, something I never would have expected. Even so, this OKR was
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growth rate was lagging our year’s-end goal. I was nervous enough to ask my team to think about reordering their projects to reaccelerate watch time. In September, folks returned from their summer travels. As old viewers resumed their habits and new ones tuned in, all of our search and
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. Reaching one billion hours was a game of inches; our engineers were hunting for changes that might yield as little as 0.2 percent more watch time. In 2016 alone, they would find around 150 of those tiny advances. We’d need nearly all of them to reach our objective. By
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growing well beyond our target rate. That’s when I knew we were going to make it. Still, I kept checking our watch time graph every day, seven days a week. When I was on vacation. When I was sick. And then, one glorious Monday that fall, I checked
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I did not check the graph. * * * — Our landmark OKR had some unanticipated consequences. Through the four-year push to reach the billion hours of daily watch time, our daily views soared in parallel. Stretch OKRs tend to set powerful forces into motion, and you can never be sure where they’ll lead
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catch is to find the right one. The billion hours of daily watch time gave our tech people a North Star. But nothing stays the same. In 2013, the watch-time metric was the best way to gauge the quality of the YouTube experience. Now we’re looking at other variables, from web-added videos
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and photos to viewer satisfaction and a focus on social responsibility. If you watch two videos for ten minutes apiece, the watch time is the same—but which one makes you
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time this book is published, we may have found a whole new metric to grow by. As early as 2015, we began to advance beyond watch time by factoring user satisfaction into our recommended videos. By asking users about the content they found most satisfying, and measuring “likes” and “dislikes,” we
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, 256 , 264 Under Armour, 91 , 97 –98 University of Maryland, 9 Upson, Linus, 152 n Venter, J. Craig, 134 Voltaire, 54 Wall Street Journal, 123 watch time, 161 –63, 164 , 167 , 169 waterfall model, 200 –201 Weekly Active Teachers (WAT), 64 Weiner, Jeff, 50 Wells Fargo, 53 West Coast Computer Faire, 29
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attention”), 118 , 130 , 168 , 231 YouTube, 14 , 154 –71 better metrics, 161 Big Rocks Theory, 160 billion-hour BHAG, 163 , 164 , 165 –69 getting up to speed, 165 –67 mutual support, 168 –70 principled stretching, 164 thinking bigger, 170 –71 top-line goals, 48 –49 watch time, 161 –63 Zendesk, 114 Zilog, 36
The People vs Tech: How the Internet Is Killing Democracy (And How We Save It)
by Jamie Bartlett · 4 Apr 2018 · 170pp · 49,193 words
to Guillaume Chaslot, an AI specialist who worked on the recommendation engine for YouTube, the algorithms aren’t there to optimise what is truthful or honest – but to optimise watch-time. ‘Everything else was considered a distraction,’ he recently told the Guardian.17 These non-decision decisions have
Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US
by Rana Foroohar · 5 Nov 2019 · 380pp · 109,724 words
(often correctly) that this was what would keep them coming back and watching more—thus allowing YouTube to make more money from the advertising sold against that content. But because the subtler algorithms resulted in lower “watch time” than the original ones, the project was dropped. Chaslot was gutted; he believed that these
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-popping content that pays off in shorter—albeit more immediately profitable—bursts. But the powers that be disagreed. Their mentality, according to Chaslot, was that “watch time was an easy metric, and that if users want racist content, ‘well, what can you do?’ ” This was a culture in which the metrics were
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undermining the fabric of democracy.3 A spokesperson at YouTube, which doesn’t contradict the basic facts of Chaslot’s account, told me in 2018 that the company’s recommendation system has “changed substantially over time” and now includes other metrics beyond watch time, including consumer surveys and the number of shares and
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‘Cult of Travis,’ ” Financial Times, March 9, 2017. 2. Video of Kalanick arguing with an Uber driver over fares can be accessed here: https://www.youtube.com/watch?v=gTEDYCkNqns. 3. Katy Steinmetz and Matt Vella, “Uber Fail: Upheaval at the World’s Most Valuable Startup Is a Wake-Up Call
Off the Edge: Flat Earthers, Conspiracy Culture, and Why People Will Believe Anything
by Kelly Weill · 22 Feb 2022
that time, YouTube veered away from recommending videos based on their relevance to someone’s previous viewing habits and started recommending videos that viewers were likely to spend more time watching. “It’s not trying to optimize for relevance,” Chaslot told me. “It’s trying to optimize for watch time, or at
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was working there.” Which videos kept people on the website the longest? “Extreme videos are extremely good for watch time,” he said. The bizarre, the fringe, and the impossible lured in the most viewers. So YouTube’s recommendation algorithm, at least before a major overhaul in 2019, prioritized the strange. The recommendations often
Modern Monopolies: What It Takes to Dominate the 21st Century Economy
by Alex Moazed and Nicholas L. Johnson · 30 May 2016 · 324pp · 89,875 words
important) the latest Justin Bieber videos. There’s more content on YouTube than you could ever watch in your lifetime, but only some of it is relevant. “We believe that for every human being on earth, there
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that the viewer will spend watching videos on YouTube, not only on the next view, but also successive views thereafter.”9 So on March 15, 2012, YouTube flipped the switch. Watch time—not just views—was now the determining factor for its matching system. Not surprisingly, the
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-stars-uber/all/. 7. “Statistics,” YouTube, https://www.youtube.com/yt/press/statistics.html, accessed June 2015. 8. Quotes from Cristos Goodrow are from Jillian D’Onfro, “The ‘Terrifying’ Moment in 2012 when YouTube Changed Its Entire Philosophy,” Business Insider, July 3, 2015, http://www.businessinsider.com/youtube-watch-time-vs-views-2015-7. 9. Eric
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Meyerson, “YouTube Now: Why We Focus on Watch Time,” YouTube Creator Blog, August 10, 2012, http://youtubecreator.blogspot.com/2012/08
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/youtube-now-why-we-focus-on-watch-time.html. 10. “Twitter Usage Statistics,” Internet Live Stats, http://www.internetlivestats
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was incredibly fast,” he said. “I think having that time to baby was really helpful for us.” “Mark Zuckerberg at Startup School 2012,” YouTube, October 25, 2013, https://www.youtube.com/watch?v=5bJi7k-y1Lo. 25. Luz Lazo, “Uber Turns 5, Reaches 1 Million Drivers and 300 Cities Worldwide. Now What?” Washington
The Key Man: The True Story of How the Global Elite Was Duped by a Capitalist Fairy Tale
by Simon Clark and Will Louch · 14 Jul 2021 · 403pp · 105,550 words
/McGuire_Testimony.pdf Arif’s turn to speak: Milken Institute, “Framework for Investing in the Long Term,” published on June 26, 2017, YouTube video, 1:01:45, www.youtube.com/watch?time_continue=3364&v=JTrHX7RjmKQ&feature=emb_title the supreme court removed: Salman Masood, “Nawaz Sharif, Pakistan’s Prime Minister, Is Toppled by
The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds and Our World
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Behave: The Biology of Humans at Our Best and Worst
by Robert M. Sapolsky · 1 May 2017 · 1,261pp · 294,715 words
The Space Barons: Elon Musk, Jeff Bezos, and the Quest to Colonize the Cosmos
by Christian Davenport · 20 Mar 2018 · 390pp · 108,171 words
The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War (The Princeton Economic History of the Western World)
by Robert J. Gordon · 12 Jan 2016 · 1,104pp · 302,176 words
Transport for Humans: Are We Nearly There Yet?
by Pete Dyson and Rory Sutherland · 15 Jan 2021 · 342pp · 72,927 words
Grand Transitions: How the Modern World Was Made
by Vaclav Smil · 2 Mar 2021 · 1,324pp · 159,290 words
Hiding in Plain Sight: The Invention of Donald Trump and the Erosion of America
by Sarah Kendzior · 6 Apr 2020
Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else
by Jordan Ellenberg · 14 May 2021 · 665pp · 159,350 words
Nomads: The Wanderers Who Shaped Our World
by Anthony Sattin · 25 May 2022 · 412pp · 121,164 words
Survival of the Friendliest: Understanding Our Origins and Rediscovering Our Common Humanity
by Brian Hare and Vanessa Woods · 13 Jul 2020
The Dream of Europe: Travels in the Twenty-First Century
by Geert Mak · 27 Oct 2021 · 722pp · 223,701 words
The Story of Crossrail
by Christian Wolmar · 5 Sep 2018 · 292pp · 85,381 words
Gnomon
by Nick Harkaway · 18 Oct 2017 · 778pp · 239,744 words
Radiant Rest
by Tracee Stanley · 9 Mar 2021
I, Partridge: We Need to Talk About Alan
by Steve Coogan · 1 Sep 2011
Strong Towns: A Bottom-Up Revolution to Rebuild American Prosperity
by Charles L. Marohn, Jr. · 24 Sep 2019 · 242pp · 71,943 words
Our 50-State Border Crisis: How the Mexican Border Fuels the Drug Epidemic Across America
by Howard G. Buffett · 2 Apr 2018 · 350pp · 109,521 words
The Fourth Revolution: The Global Race to Reinvent the State
by John Micklethwait and Adrian Wooldridge · 14 May 2014 · 372pp · 92,477 words
The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness
by Morgan Housel · 7 Sep 2020 · 209pp · 53,175 words
Don't Trust, Don't Fear, Don't Beg: The Extraordinary Story of the Arctic 30
by Ben Stewart · 4 May 2015 · 347pp · 94,701 words
On the Clock: What Low-Wage Work Did to Me and How It Drives America Insane
by Emily Guendelsberger · 15 Jul 2019 · 382pp · 114,537 words
The Highly Sensitive Person: How to Thrive When the World Overwhelms You
by Elaine N. Aron · 1 Dec 2013 · 323pp · 94,683 words