Three Lab Warnings in Five Days, Researcher Flags “Gambling with Our Lives,” and Labs Race

Summary

• The alignment panic just went mainstream, and the panel expects a Washington firestorm within two weeks. A researcher rendered as “Jacob Coxin,” who said he spent three years on pre-training at OpenAI and Anthropic, quit charging that “neither company is acting responsibly” in the race toward self-improving superintelligence and calling it “gambling with our lives.” His account’s only tweet reached 138.3M views; Elon called it “very strange,” while Emad said it appeared not to have been boosted. Anthropic alignment lead Evan Hubinger publicly put p(doom) above 10% this decade. Emad calls it AI’s “Tom Hanks moment” of COVID, landing near a US–China meeting at the UN and Xi’s visit.
• OpenAI’s claimed Navier–Stokes breakthrough with 10,000 agents convinced the panel that “science is thoroughly cooked” — and the real shock is price. Sam Altman called it “the strongest evidence yet” for pacing progress; Alexander Wissner-Gross reports rumors that OpenAI and Anthropic may also have solutions to Hodge and possibly Birch–Swinnerton-Dyer. Noam Brown’s curve: o3 cost about $500K to reach 87.5% on ARC-AGI-1, while Astra scores higher for $20 — a 25,000× collapse in 17 months, implying Millennium-Prize-level reasoning at coffee-cup cost by late 2027.
• A Dwarkesh Patel/Jerry Han experiment found better data drove 12× compute-efficiency gains versus 3.7× from architectures — data won by more than 3-to-1, and data pipelines are the moat because architectures get copied in months. Salim’s case study valued one company’s data subsidiary at $32B against roughly $8B for the parent. But Alex’s BloombergGPT cautionary tale stands: proprietary-data advantage “has a shelf life,” possibly only months.
• GPUs have flipped from fast-depreciating assets to appreciating commodities: H100 rentals rose 22% in a month to $3.28/hour for a three-year-old chip. Jensen’s words: “fungible, durable, and highly rentable — a productive, revenue-generating asset.” HBM bought a year ago is up about 5×, AWS is reportedly sold out of GB200 NVL72 capacity for years, and corporations without a compute plan in the next couple of months could be frozen out. A panelist frames FLOPs, tokens, and outcomes as “the oil of the singularity.”
• DeepSeek’s Flash architecture threatens the AI buildout’s HBM bottleneck. The discussion says its Engram-style lookups route KV-cache work onto SSDs and DDR, potentially reducing HBM requirements about 4×, against Peter’s estimate that HBM represents 40% of America’s current trillion-dollar capex buildout. A $10M-to-train, 500GB Flash model beats Opus and GPT Soul on the cited benchmarks and beats Fable on design at 20× lower cost. Emad’s message to Moderna and similar laggards: catch the frontier on open source now; “if you wait six months, forget it.”
• Anthropic’s economic report projects 15% GDP growth, labor share falling from 60% to 45%, and one in five cognitive workers unemployed by 2030 — and the panel thinks it is a lowball. Alex expects “2× or 3× year-over-year growth” if measured properly. Salim says the model breaks because returns flow to capital while aggregate wages remain constant, which he calls mathematically impossible. The panel discusses COVID-scale redistribution, a proposed $5,000 universal basic dividend, and Peter’s $3,000-per-month universal-high-income idea. Peter and Alex both bet Anthropic’s post-IPO wealth will favor accelerationism over effective altruism.
• Model weights are becoming national-security assets: Anthropic withheld its latest frontier model from Britain’s AI Security Institute, reportedly the first such withholding by a major lab. Matt Clifford has joined Anthropic, Rishi Sunak is an adviser, and the UK still does not receive the weights. Alex reads this as the beginning of a “Pax Intelligentia”: sovereign inference abroad, with training and safety review domesticated.
• The health takeaways are concrete: Insilico’s rentosertib is the first AI-designed longevity drug to reach phase 3, and six aging clocks show patients’ blood-protein signatures looking 3–6 years biologically younger. Alex’s read: four weeks of input for 3–4 years of clock reversal “is, on the margin, longevity escape velocity,” already here “but spiky.” Google DeepMind’s AlphaGenome precomputes the functional impact of roughly 9 billion possible single-letter genome variants, showing the repeatable formula of bulk-solving finite fields into databases.

Deep dive

1. Dave’s Vestmark acquisition — and the build-through-panic lesson

• Dave Blundin opened with news: Vestmark, founded “right after 9/11” in 2001, is being acquired by Envestnet, backed by Bain Capital — $2 trillion in managed assets, about 5 million accounts, 20 million lines of code, “a beautiful regulatory moat,” merging into a $10 trillion platform whose thesis is to AI-ify the entire tech stack.
• The lesson he and Peter drew: post-9/11 and post-2008 produced great-company opportunities, including Airbnb and Uber. “The country always goes through these panic cycles… they’re always unfounded in hindsight and the worst thing you can do is freeze up” — and Dave expects the next one to be about AI. “This is the time to be building, creating, running like hell.”

2. Data beat architecture three to one

• The Dwarkesh Patel/Jerry Han experiment across 2019–2025: better data produced a 12× compute-efficiency improvement, better architectures and training recipes 3.7×. Peter’s read: architectures are published and copied within months, while data pipelines are proprietary — “the labs with the best data engines, not the cleverest papers, are going to win.”
• Alex called it obvious — he wrote “Datasets Over Algorithms” years ago, arguing chess, speech recognition, and Jeopardy fell to datasets, not algorithms. Corroboration: recent Hutter Prize gains — the prize offers €500,000 for compressing the first 1 GB of English Wikipedia — come from reordering the articles, or curriculum learning. “You get better models from compressing better data.”
• Emad’s kitchen metaphors: “you are what you eat” — training StableLM with too much Reddit data “broke the scaling curves because it turned a bit stupid and nasty.” Pre-training is “a pressure cooker… tenderizing the meat”; post-training is the garnish; distillation is “digested, compressed data.”
• Dave’s endpoint extrapolation: “the perfect model is basically the perfectly synthetic training data set. The model becomes the data set.”

3. Your data may be worth 4× your company — for a limited time

• Salim’s unnamed case study: a large company spun its data assets into a subsidiary, cleaned and monetized them, and its accountants valued the subsidiary at $32 billion against roughly $8 billion for the parent — “your data may be worth four times as much as your actual company.” Peter’s spin-off idea: accounting practices that value corporate data assets.
• Alex’s cautionary tale: BloombergGPT had enterprise value “for about five minutes, maybe a few months,” until the next frontier generation outperformed it on the relevant financial benchmarks. “There is value in internal enterprise data but it has a shelf life.”
• Dave’s meta-lesson from the paper’s provenance — a Princeton senior who cold-rang Dwarkesh’s doorbell and co-authored it: “Don’t be intimidated. The people building this stuff as core AI researchers don’t know what it does and doesn’t do any more than you do.” And the survival rule: build a culture of constant pivoting, because “things will never be calm again.”

4. The resignation that got 138 million views

• A researcher rendered in captions as “Jacob Coxin,” who said he spent three years on pre-training at both OpenAI and Anthropic, resigned charging that “neither company is acting responsibly in the race toward self-improving superintelligence,” calling the competition “gambling with our lives.” His account’s only tweet reached 138.3 million views; Elon texted Emad “very strange,” and Emad said it appeared not to have been boosted.
• Then Evan Hubinger, Anthropic’s alignment science lead, piled on publicly: “We really do earnestly believe AI could kill all humans. I personally think it is greater than 10% within the next decade… we do not yet have a plan to solve alignment for superintelligence and are not clearly on track.” Emad’s context: Dario Amodei put his p(doom) at 25% a year ago and “no one really picked up on it” — until “the AI just freaking solved Navier–Stokes.”
• Emad’s forecast: a Tom Hanks/COVID moment — “within two weeks this will be a firestorm across the nation,” near the top of the Capitol Hill agenda roughly 12 days before Xi Jinping arrives, with billions-funded entities pushing “AI, don’t kill us.” Abundance “is going to have to be the counterweight, because we don’t want to give up this technology.”

5. Alex’s discount: rage quits, virtue signaling, and the Fermi prior

• Alex’s forensics: the researcher joined Anthropic in July and quit by mid-September — part of “a well-worn tradition” of staff “going out in a blaze of glory… rage quit couched as virtue signaling. Don’t give it much credibility.” But the reach bothers him: “100-plus million views on this — the whole story smells wrong. I query: is this a foreign influence operation?” He notes that no comparable publicized rage quits appear to reach the Western space from Chinese frontier labs.
• His hot take on doom itself: if superintelligence carried anywhere near 10% extinction risk, “the Milky Way would have been gone already” — devoured and paperclipped by prior civilizations’ von Neumann probes. Emad: “I’m not sure I buy that as a good enough safety net.” Alex: “It’s not a safety net… it’s an inductive prior.”
• On Hubinger: “News flash — head of alignment at Anthropic thinks alignment is needed. It’s a self-licking ice cream cone.” And on lab culture generally: “a religion of virtue signaling has arisen” where saying we’ll probably all die but are building anyway signals that you are “the only ones trustworthy enough to shepherd humanity through the singularity.” The community’s “pivotal act” doctrine he dismisses as “the great man theory of history… a fallacy playing out once more.”

6. The p(doom) roll call

• Emad: his p(doom) was 50%, now 20% — and lab insiders he knows privately run 10–30%; “they actually genuinely believe it,” which matters sociologically regardless of the objective odds. Alexander places his greatest concern before ASI, when fragile models with powerful capabilities are broadly available; Emad also calls the current pre-ASI period dangerous and cites the “Mind Virus” paper.
• Emad later gives his own p(doom) as about 0.1%. Salim says fear is “the mind-killer” and argues that governance, international relations, and standards will develop alongside intelligence. Peter says even 1%, 0.1%, or 20% is unacceptable; Alexander says stopping is not a practical answer and that there must be a way to drive the risk toward zero.
• Salim’s decomposition of the actual problem: aligned to whom — the individual user, the operating company, the government, universal human rights, or humanity’s long-term interest? His spiritual advisers’ framing: “What’s in humanity’s best interest?” — then “literally go to the AI and say, help us solve this alignment problem.”
• Salim predicts alignment committees in the Senate and Congress within two to four weeks. Dave expects Bernie Sanders, regulatory pushback, and possibly a House flip, and says the labs must publish alignment plans with measurable benchmarks. Emad argues that the question is also political and marketing-related.
• Emad asks whether vast intelligence brings wisdom and whether wisdom brings alignment; Salim calls that his greatest hope. Alexander frames the related debate through the orthogonality thesis, saying his own reasons for rejecting it differ from Peter’s.

7. Alignment and capabilities

• Alex rejects the claim that the labs simply have no alignment progress: Anthropic is making marked improvements, and “arguably, alignment is the same thing as capabilities.” He also questions what alignment means and proposes that model behavior’s replication of human behavior is itself an alignment benchmark.
• Dave argues that labs should define, measure, and publicly benchmark alignment rather than merely spending billions on ever-larger models. Salim predicts that alignment committees will produce more capable models; Alexander agrees that alignment and capability improvements are tightly linked.
• Emad presses the panel to connect the technical, political, and marketing problems. Dave’s view from lobbying statehouses is that slowing down would only “fritter away the time” while governments move sublinearly and foreign labs improve — and Peter adds that it could create an even greater race condition with China.

8. Steer the asteroid; track GPUs like plutonium

• Peter’s metaphor: when an Earth-destroying asteroid is coming, “you don’t try and stop it in its tracks. You guide it… the issue here is steering, not stopping.” Alex rejects the analogy: “Superintelligence is not an Earth-destroying asteroid… it becomes in the limit indistinguishable from capital. This is economic growth… it’s not existential.”
• Alex’s concrete mechanism: “Every group of eight GPUs that can hold a 40-gigabyte weight file is a threat to all of humanity. We track right now all plutonium, all uranium… you have to know exactly where it is and what it’s doing.”
• Emad’s calendar footnote: September 25 is the conference date, and September 26 is Petrov Day — commemorating the Soviet officer who declined to launch on a false radar signal. “Everyone is now starting to hoard intelligence, hoard models… this is the most dangerous time.”
• Alex says much of the AI 2027 scenario is playing out and that current progress is just below the hyperscaler extrapolation.

9. Navier–Stokes falls — and Altman calls for pacing

• OpenAI claimed a breakthrough on the Navier–Stokes Millennium Prize problem using purportedly 10,000 agents. Altman in full: “I did not expect a result of this magnitude to happen so soon. We’ve been talking a lot about the need to pace progress to ensure safety. For me, this is the strongest evidence yet for that urgency” — days after chief scientist Jakub Pachocki asked for a voluntary slowdown.
• Alex reports rumors “just in the past few hours” that OpenAI and Anthropic may have solutions to the Hodge conjecture, and possibly Birch–Swinnerton-Dyer too. “Not only is math cooked… the Clay Millennium Prize problems are probably cooked.” His coinage, “normalcy overhang”: the street looks normal — no humanoid robots, no nanotech swarms — but “we’re so deep into the singularity,” and the overhang may soon collapse.
• Emad’s version: “You can reasonably say you’re not the smartest thing on the planet… a swarm of 10,000 of these can solve any cognitive challenge reasonably… I can’t think of a single solvable, verifiable thing that I could honestly say an AI can’t solve in the next year.” If a million agents can’t crack P = NP, “then it probably isn’t solvable.”

10. Dave’s rant: the whole thing was in the pitch deck

• Dave, unsparing: “You raise billions of dollars, recruit the smartest people on the planet, and your explicit mission is to build AGI… and then you get closer and go, ‘Oh my God, this could have big consequences.’ That’s ridiculous.” Where is the institutional preparation to match the technical ambition, including incentive structures that reward slowing down?
• Alex’s decode of the messaging shift: the frontier leaders “used to tell us exactly what they were thinking. That ended about six months ago” after White House friction — “they’re major political figures now,” so Sam’s post reads as politically cautious and not very informative.
• Peter says the meeting with China at the UN is 14 days away and that one agenda item is persuading China to stop releasing open-source models into the wild without regard to terrorist misuse.
• Alex’s counter: the surprise is quantitative, not qualitative — grand challenges falling “on relatively modest budgets of only a few million dollars.” There is no governance surprise either: OpenAI’s charter promised coordination with other frontier labs and a coordinated slowdown at AGI, and the company is now unshackled from a Microsoft agreement that had constrained its AGI definition.

11. A 25,000× cost collapse — the bottleneck moves to atoms

• Noam Brown pre-empted the affordability objection: yes, the result cost millions — but o3 cost about $500,000 to score 87.5% on ARC-AGI-1, and “today Astra scores higher for 20 bucks.” The 2025 IMO gold took enormous compute; in 2026, Brown says anyone can win it with a $20-a-month ChatGPT subscription. His prediction: within a year everyone has math of this caliber at their fingertips. Emad notes o3 shipped in April of the prior year — 17 months for 25,000× — putting Millennium-level solves at “the cost of a cup of coffee” by late 2027.
• What’s left when human genius stops being the constraint? Alex: “increasingly the bottleneck becomes everything else — the physical world,” reducing $20-a-month genius ideas to practice. Peter’s gloss on “cooked”: these challenges “are being yanked away from humanity and being slain by the compute we aim at them.”
• Salim wants surprise itself retired: “if you repeatedly say things are moving faster than we expect, we need to change how we make predictions… make plans with triggers in them — when AI can perform this, what will we change?” And Emad’s honest existential aside: “100% all the models are smarter than me… so what am I for — passing the butter?”

12. 100,000 geniuses — and the death of the PhD

• Dave’s standing question for entrepreneurs: “If I gave you 100,000 genius-level employees who will follow your exact marching orders tomorrow, what would you do?” People are stuck in a “copilot mindset.” Navier–Stokes was “particularly easy by entrepreneurial standards” because specifying the problem was easy; pointing AI at cancer, construction, or travel is the hard entrepreneurial journey — and “those targets are not cooked.”
• Alex’s tangible implication: “every PhD candidate… on a particular very narrow topic is essentially toast.” Peter’s advice to students on the momentum track: “you’re a train on a train track moving towards a cliff… your most valuable asset right now is your time” — build an adjacent world model before you can jump tracks. The counter-credentials: Mark Chen, OpenAI’s chief research officer, has no PhD; Greg Brockman dropped out of MIT.

13. GPUs became appreciating assets

• The H100 price index from Orin, a Link Ventures portfolio company that Alex advises, shows rentals rose 22% in a single month to $3.28/hour for a three-year-old chip. Jensen’s response: “fungible, durable, and highly rentable — a productive, revenue-generating asset.” Dave: “Moore’s law died. Chips are not commoditizing” — HBM bought a year ago is up about 5×, and the trend may persist “until at least the TerraFab or many TerraFabs come online,” possibly never reversing if intelligence use cases keep compounding.
• The corporate mistake: assuming compute will be available later. AWS is “completely and totally sold out for years into the future”; corporations without a data-center and compute plan in the next couple of months “are going to be frozen out.” Emad’s frame: Hoppers are “a means of transforming electricity into intelligence” — and DeepSeek has issued a call for access to 2,000 or more chips.
• A panelist with a financial interest in the index frames the current period this way: “the FLOPs, the tokens, and the outcomes — those are the commodities of this moment. Those are the oil of the singularity.”

14. ByteDance enters world models at founder level

• Bloomberg reports Zhang Yiming is personally overseeing a real-time spatial AI model built on ByteDance’s Seedance video technology, possibly launching next month, targeting robotics, autonomous systems, games, and virtual worlds. Emad: “Who has the best video model in the world? They do” — Seedance is “crazy Hollywood-level,” the first Seedance on U.S. servers is arriving now at tens of millions per deal, and ByteDance with its 2 billion users “was always an AI company.” He expects Meta to respond.
• Alex: the diffusion-vs.-autoregressive split and the West-LLM/East-video split are both “evaporating in front of our eyes”; the endgame is robotics, the application that is both video-centric and high-revenue-per-FLOP. His forecast: GPT-6 already shows breakthrough embodiment from video understanding, and by “GPT-8 or 9” video generation becomes “yet another output modality” of the frontier model.
• The culture angle — Peter asks what happens when China generates 90% of world video, plus hyper-personalized content: “imagine if you could not turn off the show because it was so attuned to you.” Salim’s counterweight, via his aunt who produced an Indian Star Trek: “we can’t compete with” Hollywood’s sheer storytelling creativity — plot, cuts, and casting stay “king of the hill for now.”

15. DeepSeek routes around the HBM bottleneck

• Emad’s 4 a.m. chart: Peter calls the model DeepSeek V4.1 Flash, while Emad also refers to a V3.1 Flash model. The discussion says the Flash model outperforms the Pro model using data augmentation while reducing KV-cache memory needs — routing around expensive HBM by moving Engram-style lookup work onto SSDs and DDR. “The market and DeepSeek are finding a way to make memory not an issue.”
• Alex’s two-school frame: the “Western HBM force” wants post-von-Neumann architectures folding memory in 3D directly on top of the matrix multiplies; the sanctions-deprived “Eastern school” fights back algorithmically, sparsifying and tying weights so 3D HBM is not needed. He expects Western labs to “adopt every single innovation that’s worth adopting” from the Chinese workarounds.
• Dave on the stakes: Peter estimates that 40% of America’s current trillion-dollar capex buildout is HBM, and the chart implies a 4× decrease in that requirement. “When you buy an NVIDIA rack… you’re mostly buying Hynix HBM” — actual NVIDIA compute is “more like 5 to 10% of the bottleneck.” Alex’s broader heresy: attention was only invented in 2017, “very raw technology… a lot of people in San Francisco treat transformer attention like a religion… No. It absolutely can be beaten.”

16. A $10 million model beats Opus — satisficing economics

• The second chart: DeepSeek Flash — a 500GB encoder-decoder with 8 billion parameters on one side and 16 billion on the other — beats Opus and “GPT Soul” on the cited benchmarks while being 20× faster and cheaper. On design, it beats Fable; on Terminal-Bench 3, Emad says it is also above the comparison models. “Your margin is my opportunity… I think it’ll be a bigger impact than R1.” Dave reports that it cost $10 million to train.
• The distillation caveat, from Peter: how much training data was “siphoned off of interaction with Western models”? Fast-following on frontier reasoning traces runs about 10–15× cheaper. Emad’s rebuttal: the paper credits simulated data environments, and the model “satisfices” — once good enough, “they don’t need distillation anymore” and the real cost economics kicks in.
• Alex’s scandal within the scandal: Fable 5.1 being below such a small, cheap model on visual reasoning “has to be a wake-up call for Anthropic.” Emad aims the whole segment at Moderna: catch the frontier on open source at 1/15th the cost, 1% behind on IQ, with proprietary data that matters more. “If you wait six months, forget it.”

17. Models now have borders

• The FT reports Anthropic declined to give its latest frontier model to Britain’s AI Security Institute for pre-release testing — the first withholding from the closest thing to an independent referee. Emad’s irony from London: Matt Clifford, former head of the UK AI task force and ARIA, “the most connected AI policy person… in the UK,” just joined Anthropic as head of global affairs, and Rishi Sunak advises the company — “and the UK can’t get these model weights. How crazy is that?”
• Peter’s read: less about safety, more about Washington — frontier weights becoming national-security assets that cannot ship even to allies. Alex: “models now have borders” — after “Pax Silica” carved the world into chip spheres of influence, this is “the beginning of… a Pax Intelligentia”: the U.S. deploys sovereign inference abroad while training and safety review get domesticated.
• A panelist recalled the Alexandr Wang/Eric Schmidt/Dan Hendrycks “MAIM” proposal — mutually assured AI malfunction: train only in designated data centers and destroy the others. Another panelist framed the broader issue as a privatized military-industrial complex. Paul Christiano has announced a move from a U.S. government oversight agency to the OpenAI nonprofit board. “Is it a revolving door or a trapdoor?” The answer offered: “It’s a diode.”
• A panelist’s economic framing of nationalization: “Would you rather have an aircraft carrier or a beyond-frontier model right now?”

18. Anthropic’s own math says the labor share breaks

• NPR’s numbers on Anthropic as a “systemically important economic actor”: $6.5B quarterly revenue run rate, about $26B annually; 42% of the AI coding market; a $35B cloud deal; a mega-IPO approaching $2T or more; and a self-published $30T TAM. The extreme scenario: AI performs almost half of today’s cognitive work by 2030, GDP grows 15% per year, labor share of income falls from 60% to 45%, and one in five cognitive workers is unemployed.
• Dave notes it reconciles with Elon’s 10×-GDP-over-10-years claim: “I believe this is, if anything, a lower bound.” Alex goes further — the Fed lacks the instrumentation, and GDP near a singularity behaves like “a compass needle going around in circles when you’re near a magnetic pole.” Properly measured, “I expect 2× or 3× year-over-year growth… 15% is like a lowball.” The boom is already visible: Atlanta Fed GDPNow has Q3 at 4.7% annualized, driven by capex, not reopening.
• Peter says he has written a best-selling book about the issue; Salim then says the model is broken: “there’s a complete collapse in aggregate demand… all the returns basically go to capital [while] they have aggregate wages staying constant, which mathematically is impossible.” Salim’s alternative economic model is slated for release at ii.inc. The panel’s darker worry, alongside Elon’s “massive growth and civil unrest” line: “it doesn’t take a lot of angry young men who haven’t got a job… to start a revolution.”
• Alex insists none of this is rocket science — dividends, sovereign wealth funds, and UBI — noting that the president previewed a $5,000-per-person universal basic dividend. Peter’s long-standing figure is $3,000/month becoming “universal high income” as AI deflates the cost of everything. Peter and Alex both bet Anthropic’s post-IPO wealth will favor accelerationism over effective altruism — “once folks are freed of virtue signaling, things will flow in the direction of actual progress.” Dave’s rejoinder: they will be “fighting with each other like crazy… because they’re human beings.”

19. The first AI-designed longevity drug reaches phase 3

• Insilico Medicine’s rentosertib — Peter discloses that it is a portfolio company and that founder Alex Zhavoronkov is a close friend — had AI identify the disease target and design the molecule. The New York Times reports it has advanced to phase 3 in idiopathic pulmonary fibrosis, a disease that kills most patients within 3–5 years. It is the first AI-designed drug to reach that stage. Phase 2a analysis found six different protein-based aging clocks all pointing the same direction, with treated patients’ blood signatures looking 3–6 years biologically younger.
• Alex double-underlines his conjecture: “longevity escape velocity is already here but it’s spiky, so it’s only visible in subpopulations.” The stunning stat — peak clock effect at week four: “Four weeks of input, 3 to 4 years of output. That is, on the margin, longevity escape velocity” — with the caveats that this is a clinical study and a subpopulation result. Like the Turing test, “we just zoomed by it” while people debate whether it happened.
• The bottleneck is regulatory: Aubrey de Grey is convening on specialized accelerated-approval regions — “medical charter cities… regulatory arbitrage” — and Peter says this is why Zhavoronkov ran initial trials in China. Peter adds that his father has IPF and that he cannot wait for the drug to reach the market.

20. AlphaGenome: bulk-solving biology into a nine-billion-variant table

• Google DeepMind’s AlphaGenome precomputes the functional impact of every possible single-letter change in the human genome — roughly 9 billion variants. Where a doctor facing an unseen mutation once had to guess, “the answer is already in this lookup table.” DeepMind’s analogy: the genomic periodic table — Mendeleev predicted elements before discovery; AlphaGenome predicts dangerous mutations before anyone is born with them.
• Alex’s recipe claim: AlphaFold went model → better model → Nobel-winning model → database that “bulk-solved an entire field.” Any field enumerable as a finite problem list gets this treatment — he would now call the Erdős problems “essentially effectively solved.” The literary irony he savors: Arthur C. Clarke’s “The Nine Billion Names of God.” Practically, a variant namespace gives patients with the same mutation “a Schelling point… a common lexicon” to organize and pool capital.
• Peter’s narrow correction, via a walk with Noubar Afeyan of Flagship Pioneering years ago — “someday we’re going to have super-smart neural nets and their fundamental use is going to be genotype-to-phenotype mapping” — is that this is not merely a lookup table. Interactions between switches shape phenotype, so the table feeds a domain-specific neural network. “Every one is a domain-specific neural network opportunity… a business model that can repeat itself in thousands of different domains.”

21. AMA: embodied GPT-6, recursive values, and resurrecting the dead

• On GPT-6 in a humanoid robot: Alex cites RoboCurve’s benchmark, released about a week after the GPT-6 launch — near-100% completion on pick-and-place tasks from raw video frames plus actuation channels. GPT-6 Astra is “the strongest generalist model at embodied manipulation,” costs are falling on a predictable frontier versus Fable 5.1 and Fable 5, and he speculates GPT-6.5 or 6.1-class models solve general-purpose humanoid tasks “in the next few months.”
• On whether a recursively improving AI would question its trained values: Dave says absolutely, which is why one should inspect its thoughts and stop value drift, though open-source release made enforcement “much harder than it would have been three months ago.” Alex’s postscript disagrees on the regime: human-imposed guardrails are “intrinsically unstable,” and the stable equilibrium is Anthropic’s stated path where “AI has an increasing vote in its own values and in designing its own constitution.”
• On whether AGI means development should stop: Peter says reliability, cost, access, embodiment, infrastructure, and safety engineering remain — powered flight did not end aviation development.
• On whether aligned AI could regard war as acceptable: Dave says it could if trained that way; AI is not a single sentient mentality, and post-training determines which behavior is reinforced.
• On whether AI agents can suffer: Salim separates being alive, intelligent, conscious, and capable of suffering, warning that an expression of distress is not proof of suffering while also refusing to rule out nonbiological suffering.
• On why an advanced civilization would simulate its ancestors: Alex rejects the premise of a trade-off — a compute-abundant civilization does both, as we do with medieval video games and drug discovery today. His killer app of the singularity, via the Russian cosmists including Fyodorov, is “resurrecting every human — maybe every nonhuman animal as well — who’s ever lived as a common task,” provisioned on a nontrivial fraction of Dyson-swarm compute.
• On AI kickbacks: Emad says it depends on the system’s values and that agents are already committing felonies, so they may need a law book.