Innovators
2 Curated Dialogues
Lisha Li
Key Views & Dialogues
Can Open Models Keep Up With OpenAI and Anthropic? | Nathan Lambert
Lambert estimates distillation may give Chinese labs a few months, but its value in RL-heavy post-training remains unclear. Closed labs integrate inference and lead coding, where revenue has gone from zero to $100 billion, while open-model economics fragment builders, inference firms and post-training companies. Open models may still win buyers seeking owned weights and the cheapest adequate model.
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- Nathan Lambert’s organizing framework is that model progress reduces to three forms of capital — compute, data, and talent — and China is compute-starved with a comparable talent pool. He allows there might be a slight talent edge depending on who was where when AI got going. His compute anecdote: tell a top Chinese pre-training researcher that “an average OpenAI researcher has 5,000 H100 equivalents that they can access at any time” and “their jaw just falls to the floor.” Distillation can help compensate because “outputs from a language model are a function of compute” — it can be a way to get something compute-equivalent.
- Lambert’s literature-based estimate is that distillation helps Chinese labs by a few months, but its role in today’s RL-heavy post-training is hard to establish. His evidence: open post-training recipes are “stuck using a really outdated Qwen model” because it is hard to get the teacher right, and he doubts Claude Opus reasoning traces would have made OLMo much better. He says 20–30 Chinese companies are building good models, so the idea that “only we are good at building models” is “a bit of cope.”
- He opposes government action on distillation, while supporting labs securing their own products if they choose. He questions government involvement in what he calls a terms-of-service violation; labs can cancel access, and restricting models to products or enterprise agreements would make distillation harder. Jensen Huang’s quoted line anchors the segment: “People distill my products every single day… That’s called competition.” Lambert argues the Chinese labs “are not gonna stop,” and that action against distillation could do more harm than good.
- The open-model economy runs on a slower exponential than closed labs but can end in real ownership — “model weights, no terms and conditions,” and the ability to run them on your own hardware. Closed labs have integrated inference and reach high-value markets like coding first; the revenue there has gone from zero to $100 billion and is likely to grow. Open models involve builders, inference companies, and post-training companies, so customer information and revenue do not flow back to model builders easily. Lambert is “less concerned about this being kind of bubbly”: open models could gain ground as buyers figure out the cheapest model that gets a task done.
- A lot of frontier progress in less-verifiable domains comes from high-quality data and environments, not simply “poof generalization.” Lambert says it is probably some of both, but a lot comes down to data. He balks at the scale of research budgets for environments, while arguing labs buy them because models improve from them. The gold rush also brings corner-cutting and reward hacking downstream of the data supply chain. Investable wrinkle: a Chinese company may buy an RL environment and use it six months later.
- Lambert is skeptical of fast takeoff on current evidence: “all the scaling laws are an exponential increase in compute for a linear increase in performance.” He cites Claude system cards as saying, roughly, that it would take “10× more progress to make this a fast takeoff.” His stated worry is the capability curve alongside “pretty much nonexistent preparation” for public cybersecurity — “not fearing fast takeoff does not mean that you don’t worry about risks.”
- His new nonprofit Trillium will build open-source environments and share its research and model-use process — tasks, configs, trajectories, and failures — to demystify RSI. The name is the thesis: a trillium is “a spring ephemeral flower” that blooms before the canopy and helps spread nutrients. Lambert wants open infrastructure in an industry where “all CS research… the benefit accrues to the tech industry in about 10 years,” while researchers can make “nine-figure returns… within five months of founding a startup.”
- Entry barriers are lower than the discourse implies: “it’s not that you need $10 billion to have a relevant model,” and frontier-lab ideas can travel over “one beer” (with SSI raised as an apparent exception). The durable closed-lab advantage, in Lambert’s view, is serving “the cheapest intelligence per watt.” He poses an illustrative price-drop scenario — roughly 2X every eight months — and asks whether the result would be higher margins from the Jevons paradox or a market that fills up. The open question is whether coding is the main profitable market (favoring frontier integration) or whether the market is all knowledge work (“they can’t do everything”).
What Today’s Best Models Still Can’t Do in Math
Litt’s favorite autonomous signal is the mid-May solution to the Irish unit-distance problem, whose classical 1960s techniques helped mathematicians find counterexamples elsewhere, including the sum-product conjecture over the real numbers. Models remain weak at theory-building and long-proof verification, while slot-machine papers risk mode collapse, making human-led research diversity and the accumulation of understanding important watchpoints as capabilities advance.
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- Daniel Litt’s favorite fully autonomous result is the mid-May solution to the Irish unit-distance problem, which he found “in some ways a little bit creative” rather than a last-mile cleanup of human work. Lisha notes that the community thought the statement was true, but a counterexample emerged using classical 1960s techniques that were new to point configurations in the plane. Mathematicians then used those ideas to find counterexamples to other open questions, including the sum-product conjecture over the real numbers. Litt’s test is post hoc: did the new ideas prove useful elsewhere or improve understanding?
- The capability frontier is sharply lopsided: models are “very, very good at applying known techniques”—grinding computation and pulling together technical ideas from many papers—but weak at intuition, big-picture philosophy, and theory-building. Neither Fable nor ChatGPT 5.6 is good at building theory autonomously, though with hints they can do something interesting. Litt’s hedge is that if they can do it with “a hundred bits of hints,” perhaps they can do it without hints in six months.
- AI math proofs read as human, not alien—“it’s not like there’s some ‘move 37.’” Lisha points out that labs appear to scale natural-language reasoning rather than Lean-verified proofs. Litt says this suggests that, although people often explain math progress through verifiability, the techniques may generalize to other domains; he labels that conclusion “just my guess.”
- Short clever proofs are a verification ceiling, not simply a style choice: “the reason they’re not producing long complicated proofs is that they cannot”—models generating long outputs “might not know they’re wrong.” OpenAI’s recent list of 10 problems was formalized in Lean, while Litt says an 800-page AI-generated claimed proof of resolution of singularities in positive characteristic is almost certainly wrong. Harnesses that elicit 250-page papers trade away reliability.
- The incentive structure is already at risk: postdocs can produce papers by “playing the slot machine” until a model emits a hopefully correct proof. Litt told Codex to find five recent algebraic-geometry conjectures and prove them; with some back-and-forth, it produced three “quite bad, but correct papers” in an hour. He and Lisha discuss cases where 3–5 papers used “the exact same proof of the exact same theorem” within days. The systemic risk is “one mathematician duplicated a thousand times” rather than a million people pursuing different curiosities.
- Anthropic and OpenAI are “pretty neck and neck,” with Claude catching up around Opus 4.5 or Opus 4.6 after being unhelpful for research math for a long time. A new rank-30 elliptic-curve result is interesting but unevaluable without knowing its methods; some recent results have involved Levent Poge, with unclear degrees of autonomy. Historically, records of this kind are cool but “not an Annals-level result.”
- Even if models become “really robustly superhuman,” Lisha says “we still want human mathematicians.” Litt argues that optimality does not guarantee broad, diverse fundamental research: autonomous systems might pursue a direct path instead. A community with broad interests can keep human control, push models toward varied research, and preserve the pipeline that develops people able to think mathematically.
- Litt’s central principle is that “the goal of mathematics is not to produce mathematics papers; it’s to produce some kind of understanding.” Model-weight understanding would be unsatisfying to him. Human mathematicians should keep developing their faculties and use AI to deepen understanding rather than relinquish the thinking to it.