Meta Shifts the Blame + Do Data Center Bans Work? + The Final HatGPT

Summary

• Meta settled the 47-state attorneys-general child-safety suit for up to $17.1 billion — the largest settlement in its history. Roughly $12 billion is due initially, rising to $17.1 billion if TikTok and YouTube also settle; Casey Newton traces his changed view of the case to the unredacted complaint showing Meta “absolutely knew” millions of under-13s were on its platforms — “the AGs have Meta dead to rights” — plus Meta’s estimate that trial fallout “could be over a trillion dollars,” approaching its total market capitalization.
• The product changes matter more than the money. A default two-hour cumulative daily limit across Facebook and Instagram, a midnight–6:00 a.m. block, push notifications muted from 8:00 a.m.–3:00 p.m. (DMs exempt), and hidden like counts — dropping to a one-hour cap and 10:00 p.m.–7:00 a.m. night mode if TikTok and YouTube adopt the terms. Casey’s read: Meta is “holding America’s teenagers hostage” to force competitors to disarm alongside it, and he expects the rivals will probably comply rather than risk their own $17 billion settlements.
• Casey’s framework is harm reduction, using cigarettes as an analogy. “You cannot actually solve the teen mental health crisis at the level of app design” — but making social media “a little bit harder all the time” may reduce use over time. Kevin adds that, in his view, the real needle-mover in smoking was the cultural “vibe shift,” not any single feature or restriction.
• Arvind Narayanan’s counterintuitive math: data-center bans will not meaningfully slow AI progress. Kevin summarizes Narayanan’s estimate as a one-year statewide moratorium setting back AI efficiency progress by only 5–10 hours; Narayanan’s own calculation is “something like 10 hours.” Software and hardware efficiency gains are “about an order of magnitude greater” than the capacity gains from new physical buildings — yet relative compute remains “a surprisingly big factor in the relative competitive positions of companies,” which is why labs still fight for every site.
• The effective lever, per Narayanan, is bargaining, not blockage. The credible threat of moratoria is “arguably a pretty rational way” to force direct payments and community investment; the better target for those worried about AI’s pace is “premature decision-making” by executives — the “AI psychosis among CEOs” of firing staff on a Claude Code first cut, then rehiring when “there’s nobody to fix the mess.”
• Meta’s “public posture is that it is running away from its own products to build something completely different.” Reuters reports that Zuckerberg considered replacing large teams with AI-native pods and cutting some teams by as much as 60%, before getting cold feet and calling off planning for future cuts; Casey predicts, “I bet they try again next year when the AI systems are better” — even as Facebook and Instagram “just print money.”
• In the final HatGPT, OpenAI’s Mark Chen estimates the company is 80% of the way to AGI, with Sam Altman expecting an internal system he would call AGI by the end of the year. Kevin thinks that “by any pre-2022 definition of AGI, AGI is here,” citing Claude 4 or GPT-5 taken back to 2017, and thinks the official declaration will largely be a marketing decision. The segment also features China’s Tiangong Ultra running 100 meters in 8.86 seconds before bursting into flames, and a UC San Diego study reporting 19% shorter Vision Pro-assisted surgical operating times.

Deep dive

1. Meta pays up to $17.1B — the AGs had Meta “dead to rights”

• The scope: 47 states plus D.C. and U.S. territories, the largest settlement Meta has ever agreed to and, per Casey, “one of the biggest probably in the history of tech” — distinct from the New Mexico case, where a jury ordered $375 million and the judge later added another $567 million.
• Casey’s arc on the case: he initially thought the case filed in 2023 did not look compelling, but the unredacted version changed his mind — “tons and tons of evidence that Meta absolutely knew that millions of kids under 13 were using the platform” without parental permission, violating one of the country’s few actual privacy protections. “The AGs have Meta dead to rights here.”
• The second prong concerned what Meta did to maximize use: round-the-clock push notifications, ranking algorithms surfacing “the absolute most enticing material,” and no real screen-time limits. The AGs’ framing was, “This is actually just addiction that you’re trying to create here,” and Meta “said, well, I guess we’re not gonna fight that one anymore.”
• Around $12 billion is due initially. Kevin says Meta can amortize that over several years and does not have to pay it all at once. The total could rise to roughly $17 billion if TikTok and YouTube also settle and agree to product changes.
• Kevin’s dark accounting joke builds on Jeff Horwitz’s Reuters reporting that Meta expected about $10 billion in scam-ad revenue this year: just “move the scam budget over to the settlement budget… and it basically nets out.”

2. The product changes are the real story, not the check

• The mandated defaults: a two-hour daily limit cumulative across Facebook and Instagram, an app block from midnight to 6:00 a.m., and — for the first time — muted push notifications from 8:00 a.m. to 3:00 p.m., “because for the past decade plus, they have been continuously interrupting children at school.” Direct messages are exempted; like counts get hidden by default and “extreme makeup filters” disabled.
• The escalation clause: if TikTok and YouTube agree to Meta’s terms, the daily cap drops to one hour and night mode expands to 10:00 p.m.–7:00 a.m. Casey’s translation: “It would be a real shame if something happened to the children of this country. But you have a way out.”
• Kevin’s assessment is that these are not cosmetic tweaks: “it will make for a much different experience for a teenage user of Instagram or Facebook,” making them a bigger deal than the financial piece.

3. Hostage game theory: dragging TikTok and YouTube into the deal

• Meta is preparing a full-page open letter in major newspapers urging rivals to “join us in supporting teens,” arguing that “when teens are restricted on one app, they simply move to another.” Casey grants that is “narrowly true,” but says: “Please do not wait until truly the last possible second to do the bare minimum, effectively at gunpoint from 47 attorneys general… the depth of cynicism in this approach is breathtaking to me.”
• Kevin sketches the rivals’ likely defenses — YouTube insisting “we are not social media, we are more like TV,” with classroom use; TikTok presumably offering its own differentiation — but Casey expects both will probably adopt the standards anyway, “because we now know that if they don’t, they might have to go sign their own $17 billion settlement,” allowing Meta later to claim, “we led the industry.”

4. Why Meta folded: a tough judge and trillion-dollar exposure

• Adam Mosseri had already testified and Zuckerberg was expected next when lawyers struck the deal. Casey’s read: with Judge Yvonne Gonzalez Rogers — who has “a reputation for being really tough” — presiding, and recent similar lawsuits Meta had lost, the company estimated that fallout could exceed $1 trillion, approaching its total market capitalization. “This case truly was too dangerous for Meta to pursue all the way to the end.”
• The looming alternative, per Casey: “more and more democracies around the world are just banning these apps for teenagers, period.” Meta is willing to go “pretty far by its limited standards” to avoid that fate.
• Both hosts land on a rare pro-regulator note. Kevin says the AGs “got the goods on one of the most important companies in the world… a case of democracy working.” Casey calls it “state attorneys general doing what Congress tried and failed to do.”

5. Harm reduction, not a cure — the cigarette parallel

• Casey’s analogy: smoking declined as it became “a little harder and a little worse” over a long period — through rising prices, fewer places to smoke, and information about negative health effects permeating the environment. He thinks social media may follow a similar slope. Kevin extends the comparison to the 1998 multistate tobacco settlement but says the bigger needle-mover was the cultural “vibe shift,” not any single restriction.
• The honest limit, from Casey: “You cannot actually solve the teen mental health crisis at the level of app design… but harm reduction is a very effective strategy to have in your toolkit.” On short-form video specifically: “I don’t yet see an off-ramp from that particular phenomenon.”

6. A company “running away from its own products”

• Kevin’s Microsoft-antitrust parallel: the litigation did not break Microsoft up, but was “so distracting” — with lawyers in every meeting — that the company missed mobile and search. Could this legal siege do the same to Meta?
• Casey’s answer: Meta is famously paranoid and has shifted resources to AI — “when you look at what this company says it’s doing, it’s building superintelligence… running away from its own products to build something completely different.” That tells him a lot about what the company thinks of what it has already built.
• The tension he flags: Facebook and Instagram “just print money… you could argue that these lawsuits are essentially targeting the fact that they are too popular.” On Katie Paul’s Reuters report that Zuckerberg considered cutting teams by up to 60% into AI-native pods before calling off planning for future cuts, Casey predicts: “Here’s a prediction — I bet they try again next year when the AI systems are better.”

7. Arvind’s math: a one-year state moratorium buys only hours

• The setup Kevin flags: Narayanan co-wrote “AI as Normal Technology” and is “widely considered a serious critic of AI hype” — yet he argues that a typical state’s one-year data-center moratorium would only slow AI efficiency progress by 5–10 hours. Narayanan’s own calculation is “something like 10 hours.”
• His mechanism: training happens in specialized clusters, so “stopping a few data centers here and there is not going to slow down training at all” — you would need a national or global moratorium. On inference, the gains come from squeezing more out of existing hardware plus newer power-efficient GPUs going into existing buildings: efficiency gains are “about an order of magnitude greater” than the capacity gains from literally new physical buildings.
• Kevin’s pushback is worth keeping: efficiency gains themselves depend on compute, and frontier labs are in a capacity crunch, “selling as much AI as they can make.” Arvind’s answer is that efficiency progress is shared industry-wide and “cancels out between companies,” so “while the total amount of compute is not a big factor in the aggregate rate of AI progress, the relative amount of compute is a surprisingly big factor in the relative competitive positions of companies” — labs seek capacity “if only to stop your competitors from getting their hands on it.”

8. Nuclear-style blockage will not repeat — the real levers are elsewhere

• Kevin’s nuclear analogy: post–Three Mile Island opposition did not stop the technology; it shifted construction to France and other places around the world.
• Arvind is “very skeptical” that data-center opposition will meaningfully change even the distribution. He says one reason stopping nuclear plants in the U.S. was successful, if he understands correctly, is that it raised regulatory costs. Today’s backlash is not nationwide regulation forcing every AI developer through costly additional review; it mainly “moves it from one location to another,” which “locally might seem like a big win, but at a national level, I don’t see it doing much.”
• What the backlash can accomplish: it is “arguably a pretty rational way to go about it, because you have to have the credible threat of bans and moratoria… to force companies to come to the negotiating table” for direct payments and community investment. Kevin’s frame via Eric Hobsbawm’s Luddites — “collective bargaining by riot” — draws Arvind’s “100% agree,” while Casey cites Korean workers’ strike threats and Hollywood unions as examples of traditional collective bargaining working.
• The better target, per Arvind: “premature decision-making” — the “colloquially named AI psychosis among CEOs” who use Claude Code to produce a first-cut version that superficially seems to do the job, fire people, then recognize, “Oh, shit… there’s nobody to fix the mess,” and hire people back.
• His individual-agency coda: as a heavy AI user, he prompts agents “to do the grunt work for me, not to do my thinking for me” — configuring and personalizing the tools rather than accepting the developer’s defaults.

9. The final HatGPT: 80% to AGI, flaming sprint robots, meat proxies

• The lead item: Alex Heath’s Time cover story on OpenAI has Chief Research Officer Mark Chen estimating that the company is 80% of the way to AGI, and Altman saying that by the end of the year OpenAI will have an internal system he would call AGI. Kevin’s considered view: “by any pre-2022 definition of AGI, AGI is here” — take Claude 4 or GPT-5 back to 2017 and researchers “would’ve said, well, yes, this is obviously AGI.” Kevin thinks the official claim will largely be a marketing decision about when to say they have gotten there, and doubts there will ever be consensus.
• From Beijing’s World Humanoid Robot Games, where more than 2,000 robots from 16 countries competed: Tiangong Ultra eventually ran 100 meters in 8.86 seconds, beating Usain Bolt’s 9.58-second record after two robots had already beaten it in the preliminaries — then “had trouble stopping and ran straight into a padded wall and burst into flames.” Kevin’s dissent: “a car can also go faster than Usain Bolt… who cares?” It is the crashes that transfix him.
• Two workplace-AI notes: Business Insider’s “meat proxy” — Niklas Grun’s term for people who blindly relay AI output — which Casey ties to “creeping human disempowerment… that I think is actually bad”; and The Wall Street Journal on AI-startup employees, including founders, waking at odd hours to babysit agent fleets (“it’s like a drug”).
• The redemption arc for the Vision Pro: a UC San Diego study of 32 tear-duct procedures found 19% shorter operating times, 100% functional success, no postoperative complications, and a significantly lower surgeon-reported workload. Kevin: “the people most in need of the Vision Pro were the vision pros.”