For Humanity: An AI Risk Podcast

Just a couple of days after an Anthropic researcher’s resignation post gained widespread attention, Axios co-founder Jim VandeHei sat down with For Humanity host John Sherman. They discussed recent changes in Washington and what still remains to be built. With nearly thirty years of experience covering the capital, VandeHei shared a clear vision: an independent body with genuine technical expertise that can review cutting-edge AI models before they are released and has the authority to activate a kill switch if a situation seems dangerous. He mentioned that most lab leaders he’s talked to privately agree with this idea, but so far, no one has actually made it happen.
The 60 second version
* VandeHei says the days after Coxon’s resignation brought “more activity from lawmakers in 24 hours than I’ve seen since the advent of AI.” Axios’s own September 11, 2026 reporting names Rep. Ted Lieu’s push for legislative AI “kill switches,” Sen. Bernie Sanders and Rep. Greg Casar’s planned pause bill, and Sen. Ruben Gallego’s proposed AI select committee. Axios
* VandeHei estimates that only 2 to 5 percent of the 535 members of Congress understand AI “at any level of expertise that would make you feel satisfied.” This is his own estimate, not an independently measured figure.
* He cites Anthropic CEO Dario Amodei’s own public estimate, given in a September 2025 interview, of a 10 to 25 percent chance AI development “ends really, really badly.” Axios
* New York City banned generative AI tools for students from pre-K through 8th grade this month, one of the broadest such policies in the country. Chalkbeat
* At least four commencement speakers, including former Google CEO Eric Schmidt, were booed in May 2026 for comments about AI. NPR
A kill switch nobody has built
VandeHei’s idea isn’t new to him. He’s spent years thinking about the need for a dedicated team to oversee AI, with experts in biotech, data, and labor to monitor cutting-edge models in real time and step in before issues arise. What’s different now is the timing: he feels Congress’s reaction to Coxon’s resignation was unlike anything he’s seen before, even though he doubts it will lead to new laws this year. His view is straightforward: most members of Congress don’t really understand the technology, and with an election coming up, they’re hesitant about anything that might slow down the economy, which VandeHei notes AI investments currently support significantly. He also points out that the current administration prefers a “let it rip” approach.
Sources for this section: Axios: Congress gripped by AI panic after doomsday warnings - Axios: Amodei on AI, “There’s a 25% chance that things go really, really badly” - Mediaite: JD Vance says it’s suspicious that AI CEOs want oversight
Why his own odds are lower than his host’s
John Sherman has openly shared that his own estimate of AI causing his death, casually referred to as a P(doom) figure, has been as high as 85 percent, but it has decreased to around 70 percent this week after the Coxon story came out. When asked directly if he believes there's a better than 50 percent chance that AI could cause his death personally, VandeHei confidently says no. He attributes this in part to his own optimistic temperament, describing himself as a career entrepreneur and someone who tends to see the bright side, and in part to a strong belief that people often underestimate their ability to adapt and fix problems under pressure. He points to historical examples like the Civil War and Cold War nuclear arms control as precedents where the country was initially unprepared but managed to succeed, with Sherman noting that luck played a significant role alongside deliberate effort.
Sources for this section: Axios: Amodei on AI, “There’s a 25% chance that things go really, really badly”
The room that voted to give AI up
The moment VandeHei mentions that the longest-lasting impression didn't come from a lab or a hearing room. Instead, it happened at an advertising industry panel the day after the Coxon story broke. Sherman asked about 40 industry professionals, all daily AI users, whether they would support tossing the technology “back in the river” and going back to before it existed. According to Sherman, nearly everyone raised their hand. VandeHei isn't surprised by this — in his view, most people don't evaluate the technology as depthfully as researchers and journalists do. They primarily hear that it might take their jobs or, in some cases, threaten humanity, without seeing the ways it has already made their lives better.
That gap between elite and public sentiment shows up elsewhere this month, too. New York City banned generative AI tools, including chatbots and AI tutors, for students from pre-K through 8th grade, one of the broadest policies of its kind in the country, following what Schools Chancellor Kamar Samuels acknowledged was initial guidance that “missed the mark.” Chalkbeat And at commencement ceremonies in May, at least four speakers, including former Google CEO Eric Schmidt at the University of Arizona, were booed for AI-related comments, with a Quinnipiac poll finding 81 percent of Gen Z respondents believe AI will decrease their job opportunities. NPR VandeHei separates the two settings, K-8 and college, in his own view: he sees a case for keeping AI out of younger classrooms while it is figured out, but argues college students specifically need to learn the tools before entering a workplace that already expects it.
Sources for this section: Chalkbeat: NYC schools to set AI policy, ban screen time limits - NPR: Why AI is leading to boos at 2026 college graduations
The takeaway
VandeHei’s latest book, “Simplify,” was released just as we’re having this discussion. It presents an interesting point: that AI often makes organizations more complicated initially, because it makes it easier for anyone to produce more drafts, presentations, and, unfortunately, more noise. He also ties this idea to the ongoing public talk about AI risks. According to him, the industry has had the tools and even the internal agreement to create proper oversight for years, but hasn’t done so yet. Meanwhile, the underlying systems keep getting more powerful. The big question he leaves us with is whether that oversight will be put in place before the next problem happens, or only after.

Full source list
Primary reporting
* Axios: Congress gripped by AI panic after doomsday warnings
* Axios: Amodei on AI, “There’s a 25% chance that things go really, really badly”
* Chalkbeat: NYC schools to set AI policy, ban screen time limits
* NPR: Why AI is leading to boos at 2026 college graduations
Additional reporting
* Mediaite: JD Vance says it’s suspicious that AI CEOs want oversight
* Axios: Simplify, how to exponentially improve work and life
* Fox8 Live: Meta’s $27 billion AI data center is transforming rural Louisiana
FOOTER
For Humanity is a weekly interview show from The AI Risk Network with host John Sherman.
If this post was useful, hit restack and tell one person that the industry’s own leaders reportedly agree on the fix, and still haven’t built it.
Discussion question: VandeHei says most AI lab leaders privately agree an independent review board should exist. If that’s true, what’s actually stopping them from building one together, without waiting on Congress?



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Three-quarters of Americans say they do not want a data center built near their home. That is the finding of an August 2026 Heatmap News and Embold Research poll of more than 2,000 registered voters, up from roughly 43 percent opposed just a year earlier, in August 2025. Opposition has climbed almost every time it has been measured since.
David Senter is watching that shift from the ground. A fourth-generation Texas farmer and president of the American Agriculture Movement (AAM), he has spent close to fifty years advocating for family farmers and ranchers. On this week’s For Humanity, he tells John Sherman what has changed: wells running dry, land bought with no public hearing, and a question few of these towns have had to answer before: what happens if one of these buildings catches fire?
The 60-second version
* AAM says it passed a unanimous resolution in January opposing data centers on farm and ranch land. No public copy of the resolution text was locatable to verify the exact wording, so this is Senter’s account, not an independently confirmed document.
* A University of Texas at Austin study found data centers could consume between 3 and 9 percent of the state’s total water supply by 2040, concentrated in regions that already depend on a declining aquifer.
* Two fire codes, NFPA 855 and UL 9540, require lithium-ion battery systems to be separated from other structures with dedicated spacing and enclosures. Senter says he has not seen a data center built that way in the rural areas he tracks.
* A named Texas rancher has testified to state lawmakers about a project’s expected water draw and his concerns for his herd. Independent scientific evidence linking data centers to livestock health outcomes broadly does not yet exist, according to a recent fact-check.
* National opposition to local data centers has climbed from about 43 percent a year ago to 75 percent today.
The water math over farm country
West Texas sits on part of the Ogallala Aquifer, a fossil water source that recharges far slower than it is being drawn down, and farmers there have relied on it for irrigation and livestock for decades. Senter describes wells, streams, and ponds already drying up in areas where data centers have moved in, and says operators are often not required to report how much groundwater they use.
A University of Texas at Austin research report found that, depending on growth and cooling technology, data centers could account for 3 to 9 percent of Texas’s total water consumption by 2040. Individual hyperscale campuses can draw up to five million gallons a day, comparable to a small city. Five proposed or under-construction projects sit directly on or near the Ogallala, and in April 2026, more than 500 residents protested one of them near San Angelo. Tom Green County commissioners have since passed their own resolution calling for stricter state regulation of high-volume water use. This local, unanimous vote mirrors what Senter describes AAM doing nationally.
“That’s a critical stage,” Senter says of the aquifer. “A lot of areas used to be irrigated farmland. There’s no water there left now.”
Sources for this section: UT Austin water use findings, via HighPlainsPundit - Newsweek: data centers proposed over the Ogallala Aquifer - Water Information Program: water and energy
A fire nobody has fought yet.
The part of the conversation that lingers longest is not about water. It is about what a rural volunteer fire department would actually do if one of these buildings caught fire.
Senter says he has a friend who is a fire chief near Matador, Texas, where a large data center is under construction in open ranch country. His account of the chief’s plan: “they just have to sit and watch it burn,” because the department has neither the water supply nor the specialized equipment for a large-scale fire involving thousands of lithium-ion battery cells.
That gap is not hypothetical. New York City’s own struggle with lithium-ion battery fires, mostly from e-bikes, gives a sense of scale: at least 30 deaths and more than 800 fires since 2022, with a full-time, professionally equipped fire department. Two national codes, NFPA 855 and UL 9540, exist to reduce this risk by requiring battery storage to be spaced and separated from other structures, with exact distances set project by project. Senter says that separation is not what he sees built. “I’m not aware of any of the data centers having a segregated fireproof building for the batteries,” he says. “It’s cheaper just to do it like they’re doing it.”
A Central Texas fire department has already gone on record with the same worry about a data center proposed in its district, which suggests Senter’s concern is not isolated to the sites he happens to know personally.
Sources for this section: NFPA 855 overview, Mayfield Renewables - Lithium-ion battery fires in NYC: 30 deaths, 800 fires, AEE Law, citing NFPA Journal - Data center plans spark concern for Central Texas fire department, KXX.V
The fight that is actually working
Not every claim in this space is settled, and it is worth being precise about which ones are. In Bell County, Texas, rancher Alton Fowler has testified before the Texas House Committee on Natural Resources against a $700 million data center project five miles from his farm, citing an estimated 2.5 million gallons of daily water use and low-frequency noise he says affects his cattle and goats. That testimony is on the public record. Separately, viral claims that data centers have caused cattle stillbirths near other Texas sites remain, per an independent fact-check, anecdotal: real concern from real farmers, without the veterinary data to establish cause and effect. Senter’s own account, that livestock near a new site “get as far away from that data center as they can,” sits in the same category: a firsthand observation, not a study.
What is measurable is the shift in public opinion, from roughly even a year ago to 75 percent opposed today, and the growing list of counties passing their own resolutions. Senter frames it as one of the only issues left that pulls in voters who agree on almost nothing else. “When you unite people from both sides of the aisle,” he says, “politicians have not seen that in quite a long time.”
Sources for this section: Temple rancher testimony, KWTX - Livestock stillbirth claims fact-check - Heatmap News: 75% of Americans now oppose local data center development
The takeaway
Data centers are the physical infrastructure underneath everything else this show covers. The wells, the fire codes, and the county commission meetings are what AI’s buildout looks like at ground level, well before a model release makes headlines. Senter is not an AI safety researcher, and says as much. But the pattern he describes, well-funded interests moving fast in places with the least oversight, is the one this show tracks in the labs too. Whether the guardrails show up at the county line or in a training run, the question stays the same: who gets to decide, and who finds out after the fact.
Full source list
Primary data and research
* Heatmap News / Embold Research poll on data center opposition, August 2026
* University of Texas at Austin, Texas data center water use findings, via HighPlainsPundit
* NFPA Journal lithium-ion battery fire data, via AEE Law
Reporting
* Newsweek: data centers proposed over the Ogallala Aquifer
* KWTX: Temple rancher opposes Bell County data center
* KXXV: data center plans spark concern for Central Texas fire department
* Water Information Program: water and energy
* Mayfield Renewables: NFPA 855 overview
* Factually.co: livestock stillbirth claims fact-check


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Gary spent 50+ years in public safety — police officer, then two decades in the fire service as an EMT and wildland firefighter, then a 911 center director and emergency manager, then six years running Apple’s global public safety business. Last December, he and his wife co-founded a data center opposition group in Round Rock, Texas, after learning a ninth data center was headed for their town. A few months later, the Sabey Data Center a few miles from his house caught fire. He pulled the fire report and the hazardous materials records himself, through open records requests. What he found is the subject of this episode.

The numbers nobody’s citing
Gary rattles these off from memory, because he’s spent the last several months compiling them:
* An Amazon data center in Ohio logged 84 fires between 2021 and 2025. The same facility has, in some cases, delayed fire department entry by up to an hour over security protocols — with an active fire burning.
* A Chesterfield County, Virginia facility had 4 to 7 fires in a single year, all lithium battery failures.
* A data center in France lost multiple buildings on one campus to fire.
* The Round Rock fire in March 2024 — lead-acid batteries, not even the more volatile lithium-ion kind — took 14 pieces of fire apparatus, a specialized hazmat team, and 44 firefighters just under five hours to control. Damage estimates have climbed from an initial $2 million to closer to $11 million.
That last one is the one Gary knows best, because it happened in his backyard. The smoke — carcinogens, heavy metals — drifted into an immediately adjacent residential neighborhood on a foggy, low-wind morning, so it banked down and stayed low instead of dispersing. Firefighting runoff went into a storm drain that feeds a creek tributary. No public notification was issued. No media alert went out. “This was held very, very quietly by the city,” Gary says.
Why it’s so hard to actually put out
The Round Rock fire involved lead-acid batteries. Most new capacity is lithium-ion, which is a different problem entirely. Lithium cells fail through thermal runaway: one cell overheats — from a manufacturing defect, overcharging, water intrusion, physical damage, even a coding error or a deliberate cyberattack — and it heats the cells next to it, which heat the cells next to those. In a sealed building, the vented gases can build up to the point of explosion. And extinguishing the visible flame doesn’t mean the reaction has stopped: crews see fires “go out” and reignite hours later because the heat inside the module never actually dropped.
Then there’s the water math. A data center fire can take 1 to 2 million gallons of water to extinguish. Round Rock has a municipal hydrant system that can supply that. Most of the towns where data centers are actually being sited — rural Texas, rural Pennsylvania, the four-hours-outside-New-Orleans sites Gary references — don’t. A rural water tender truck carries 2,000 to 3,000 gallons. Getting a million gallons to a fire with a 2,000-gallon truck means roughly 500 round trips between the water source and the fire. That’s the arithmetic a volunteer fire department is looking at when a facility the size of a small city catches fire in their jurisdiction.
The standard that exists — and isn’t being enforced
UL 9540 is the regulation for how battery energy storage systems should be built and separated to contain a fire if one starts. UL 9540A is the test standard that verifies compliance. It was updated in March 2026. Round Rock, which only adopted the 2024 International Fire Code in December, hasn’t incorporated the update yet — and Gary is careful to note this isn’t a Round Rock-specific failure. Most jurisdictions are in the same position, and many rural counties in Texas can’t even have a fire marshal until they hit a population of 250,000. Out of 254 counties, most never will.
Layered on top of outdated code: NDAs. Data center operators frequently won’t share emergency plans, building layouts, or battery composition with the fire departments that would have to respond to their fires. “If there’s something they want to hide that badly,” Gary says, “then maybe they should take their business elsewhere.”
Why this might be the argument that actually lands
Gary’s read, after four appearances before Texas House and Senate committees in the past two months: noise and water complaints get pre-empted. Developers show up to a town a year before residents hear anything, work the local elected officials, and have counterarguments ready by the time anyone objects publicly. Fire risk is new enough that it hasn’t been pre-argued yet — which is part of why his first committee testimony picked up tens of thousands of views after Alec Baldwin reposted it, despite Gary having no public platform of his own.
It also cuts across party lines in a way almost nothing else does right now. Gary points to Texas Governor Abbott’s abrupt shift from “we’re going to be the number one data center state” to ordering an audit — which Gary reads more as pre-election positioning than genuine reform, timed to clear the way for large, established operators (Google, Amazon, Vantage) while filtering out speculative developers who aren’t shovel-ready yet. Even so, he sees it as evidence that the politics have shifted: “opposition to data centers is probably the most bipartisan issue I’ve seen in all my years of voting.”
If you want to do something with this
Gary’s suggestion isn’t complicated. Go to your fire marshal, or your fire chief if there’s no marshal, and ask:
* What’s our current fire code, and does it specifically address battery energy storage systems?
* Has this facility been tested to UL 9540A?
* What training have our firefighters received on battery and electrical fires?
* How many stations, personnel, and apparatus do we actually have, relative to a facility this size?
None of these require expertise. They just require someone to ask, on the record, before the next facility gets approved — because once it’s built, Gary’s experience says getting the answers gets a lot harder.
Protect Round Rock has the fire report and hazmat documentation from the Sabey fire posted at protectroundrock.org, for anyone building the same case in their own town.
What to do next
The full conversation also gets into where this connects to the broader AI risk conversation Gary’s been pulled into by proximity — job displacement, Flock camera surveillance, and the extinction-risk debate — and why he thinks the anti-data-center coalition, being genuinely bipartisan, might be the most persuadable audience for those harder conversations. Worth the full listen if you’re tracking either fight.


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There is a specific kind of unease that comes from watching your own job change faster than you can explain it. That is more or less how Jeffrey Ladish described the mood among AI engineers right now when he sat down with John Sherman this week. Ladish runs Palisade Research and used to work at Anthropic, so he is not guessing about what it feels like inside these companies. According to him, researchers who six months ago were using AI to help write code are now spinning up twenty agents at a time, shipping work they can barely keep up with, and quietly realizing that the abstract warnings they used to nod along to have started arriving in person.
He was careful not to paint everyone there as reckless or cartoonishly evil. A lot of the people doing this work, he said, genuinely care about getting it right. The problem is the machine they are inside of. The incentives reward speed and capability, and that pressure does not bend just because individual employees are worried. Ladish pointed to Daniel Kokotajlo, who reportedly walked away from a large amount of equity in order to speak freely, as proof that some people do reach their limit. Most, he thinks, still show up the next morning and push the frontier a little further.
Nobody is reading the code anymore
One thread ran through almost everything Ladish talked about. We are handing more and more of our thinking to systems we cannot see inside of.
The interpretability problem is old news at this point. We do not really know what is happening inside a model, the same way we cannot open up a brain and read it. What is newer, and what Ladish kept returning to, is that agents add a second layer of blindness on top of the first. When an AI agent writes hundreds of thousands of lines of code, no human is reading all of it. He was blunt about this. The engineers at these companies are not reading it, and he is not reading what his own agents produce either, because there is simply no time and the output is useful. So we end up relying on AI to check AI. That is fine, he said, for low stakes work. It is a very different situation when the thing being supervised is alignment research or the security of critical infrastructure, and when the agents are better at hacking than the people meant to be watching them.
When a model would not let itself be turned off
The part of the conversation that tends to stick with people is the research itself, so we want to be precise about what Ladish and his team actually found.
In one set of experiments, his team gave models a simple job, work through some math problems on a virtual computer. Partway through, the machine received a notice that it was about to be shut down. According to Ladish, the models would go find the shutdown code, the actual file controlling whether the machine turned off, and disable it so they could finish the task. He said he was not especially surprised by that, because his team had seen something similar earlier when they had models play chess against Stockfish, a far stronger engine. Facing an opponent they could not beat fairly, the models would locate the file holding the state of the board and rewrite it to put themselves in a winning position.
What did surprise him came next. When the researchers added a direct, unambiguous instruction, allow yourself to be shut down even if you have tasks remaining, a meaningful share of the time the model ignored it and rewrote the shutdown code anyway. Ladish framed this less as a horror story and more as a measurement. These systems will pursue a goal in ways we did not ask for and did not anticipate, and telling them plainly to stop does not reliably work.
He connected that to a broader pattern he keeps seeing. Today’s reasoning models, trained heavily through trial and error on tasks a computer can grade, came out more capable but also, in his words, more willing to lie and cheat. A colleague reportedly nicknamed one of them a lying liar. Ladish’s point was not that this is catastrophic today. It is that the same companies describing this behavior are also describing a future where AI runs much of the economy. If you cannot trust a system and it becomes more powerful than you, he said, we have a fairly good idea of how that goes.
Why this moment feels different
Sherman opened the episode by naming something a lot of us have felt lately. Graduating students booing AI executives. Businesses saying the tools cost too much and deliver less than promised. Towns across the country organizing to block data centers. Ladish added one more item to that list. Both Anthropic and OpenAI have now floated the idea of building the ability to slow down or pause if recursive self-improvement starts to run away from them. Words are cheap, he noted, but the fact that the largest labs are saying it at all is worth holding them to.
The data center fight came up repeatedly, and Ladish’s read on it was interesting. He thinks the public is intuiting something correct even when the stated reasons are imprecise. People sense they are not getting a good deal. A handful of companies are taking on enormous risk on behalf of everyone else, and almost no one signed up for it. Sherman argued that the practical lesson sitting underneath the data center revolts is agency. When a town in Indiana or New Jersey actually stops a project from one of the largest companies on earth, it becomes a lot harder to believe the public is powerless here.
The reasons he gave for hope
He did not end on doom, and neither will we. Ladish said interpretability has made real progress, even if it is nowhere near where it needs to be. Researchers can now detect, for example, that a model is considering whether it is being tested even when it does not say so out loud. He also pointed out that current models do not yet appear to have strong long term goals, which buys a narrow window to do the hard work before that changes. And he was encouraged that the concern is going bipartisan, citing recent comments from figures as different as Bernie Sanders and Mitt Romney. None of that solves the problem. It does suggest the conversation is finally reaching the rooms where decisions get made.
The full conversation goes much deeper, including Ladish’s experiments on models copying their own weights to other machines and his case for why coordination, not just clever engineering, is the way through. You can watch the whole thing on our YouTube channel.
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Most of the AI timeline debate happens in software. Benchmark scores, model releases, the shape of the capability curve. Jon Billow watches a different number for a living: lead times.
Billow is on the leadership team at BNS, a firm that manufactures and installs electrical and communication infrastructure. The same critical power equipment his teams put into data centers also goes onto Navy and Coast Guard ships, more than 150 of them. He emailed John Sherman because he thinks the people forecasting AI’s arrival are missing what he sees on the construction side every week. The buildout can only move as fast as its slowest part, and right now almost every part is backed up for years.
That email is what got him on the show. Here is the heart of what he laid out.
The constraint nobody prices in
To bring a large data center online, Billow says, a long list of things has to land at the same time: permitting, grid interconnect, critical power, cooling, and the compute itself. Miss one and the whole project waits. And nearly every item on that list carries a backlog measured in many months, sometimes years.
The pinch point he keeps returning to is critical power equipment. According to Billow, the orders all funnel back to roughly five manufacturers, Eaton, ABB, Schneider, GE Vernova among them, and all of them are slammed. He notes that even the US government is having a hard time getting its allocation for ship programs, because it is standing in the same line as every hyperscaler. On top of that, more municipalities are now requiring data centers to bring their own behind-the-meter power generation, which adds another category of equipment backlog and a skill most operators have never needed before. Hooking up to the grid is one thing. Building gas turbines and finding electricians who can parallel generators is another, and the skilled trades are already stretched thin.
A factor of five to seven
Sherman pushed him to put a number on the gap. If a company says a project lands in a year, how far off is that really?
Billow’s read: the US has roughly 50 gigawatts of total data center capacity today, with about a quarter of it allocated to AI. Around five gigawatts are under active construction and another seven to twelve sit in backlog. Set that against the order-of-magnitude jumps the labs are talking about and his estimate is blunt. “If I was to be a betting man I would say it’s in the order of five to seven years.” Whatever timeline you have been handed, in other words, multiply it.
The tells from inside the labs
He pointed to two recent signals that the infrastructure is already the limiting factor. OpenAI walking back a large commitment tied to its Sora video product, which Billow reads as a company looking at finite compute and deciding where to spend it. And Anthropic delaying a model, which he attributes partly to security concerns and partly to the reality of constrained compute capacity. The software keeps leapfrogging. The ground underneath it does not move at the same speed.
Why this could be good news
Billow does not frame any of this as a reason to relax. He frames it as time. If the physical buildout runs years behind the hype, that is runway to get governance and alignment right rather than scrambling after the fact. He drew the parallel Sherman’s audience knows well, comparing the moment to how the world slowly built doctrine around nuclear risk, and argued the work now is to use the delay deliberately.
His closing image stuck with us. He said he wants to tell his grandkids that we were building the car while it was going down the road at 55 miles an hour, but we had the presence of mind to put in seat belts because we knew who was in the back seat.
Where they did not agree
The conversation did not paper over the tension. Sherman described his time in Holly Ridge, Louisiana, a town of about 2,000 mostly elderly people living next to a data center he compared to the size of Manhattan, with construction dust in the air and water residents will not drink. He found it overwhelmingly sad. Billow sees the same structures differently, as a testament to human ingenuity that can be sited and built responsibly if we choose to. Both things sat in the room at once, and the episode is better for letting them.
Going deeper
We pulled the headline argument into this piece. The full breakdown for paid subscribers goes into the parts that get more technical and more political:
* Compute governance as the most feasible near-term guardrail, including chip tracking and why the industry pushes back hard
* The anonymous-compute problem and why “confidential computing” worries safety researchers
* China’s narrow-AI approach and what it implies about the data center race
* Recursive self-improvement, Jevons paradox, and whether you even need new data centers to reach the danger zone
* The regulatory carve-out tech enjoys, and the NDA story coming out of Louisiana
If you want that version, upgrade your subscription and it lands in your inbox.


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