Nathan Lambert
Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.
Nathan Lambert did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.
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Our reading
The release of DeepSeek's internal inference stack is a significant open-source AI artifact.
Their wordsWhile not an open model or dataset, we consider this release to be one of the bigger open-source AI artifacts in the last few months.
- 3 days earlier
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Their wordsThese models still can get substantial adoption, enough to start growing a business, but the costs of building true frontier models are now measured in billions per major model and only huge companies can sustain that.
↗Meta shakes up the frontiers, open and closedinterconnects.ai
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Their wordsOne thing we see again and again at Interconnects through our open model analysis is that Western open-weight models get astonishingly more adoption than Chinese models with the same performance-to-size footprint (even though the Chinese models usually come out first)!
↗Meta shakes up the frontiers, open and closedinterconnects.ai
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Their wordsDespite this massive scaling in resources, the technical core of the industry still doesn’t have a high hit rate on predicting how successful a model will be in real-world usage.
↗Meta shakes up the frontiers, open and closedinterconnects.ai
- 8 days earlier
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Their wordsI fully agree that the models will be superhuman distributed GPU engineers in a few years.
↗I expect rapid progress but not towards general superintelligenceinterconnects.ai
- 3 days earlier
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Their wordsThe leading proponents of this new wave of open-weight fear mongering have effectively been making a falsifiable prediction — that the current open-weight models are going to cause substantial, not seen before AI harms by crippling our cyber infrastructure. Based on all the evidence I have, which I’ve unpacked above, they’re set up to be wrong.
- 2 weeks earlier
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Their wordsAnd my kind of statement is that I think the cycle from of having an idea and doing the experimentation to test it, I agree will be 10X faster very soon. But I don’t necessarily agree that I would say that AI researchers will be 10X more productive in net, which I would describe as the pace of the field’s complete understanding. And understanding is a different axis from just continuing to scale models. I think that’s one of my core confusions on the AI research side.
↗Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AIinterconnects.ai
- 1 day earlier
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Their wordsOpen-weight models are becoming an essential tool for AI diffusion, and the best path to get ahead of these risks and unbalanced relationships where American companies rely on models built in China is to continue to enable investment in open models in the US.
↗The current balance of power in open modelsinterconnects.ai
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Their wordsChinese AI companies are the clear leaders in open weight models.
↗The current balance of power in open modelsinterconnects.ai
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Their wordsThe capabilities gap from open to closed models available to users has been decreasing over the last 3 years.
↗The current balance of power in open modelsinterconnects.ai
- 2 days earlier
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Their wordsall of our scaling laws show that you need exponential compute and resources to make linear improvements in intelligence.
↗Why I still haven’t bought into true RSIinterconnects.ai 1st of 3 in this piece
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LLMsrecursive self-improvement
Their wordsRSI is poised to make modern LLMs vastly cheaper. Trends that have shown LLMs get exponentially cheaper at a given intelligence are likely to accelerate.
↗Why I still haven’t bought into true RSIinterconnects.ai 2nd of 3 in this piece
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Their wordslossy self-improvement remains my baseline on the trajectory of progress, and the increased discussion of extinction risk seems very misplaced
↗Why I still haven’t bought into true RSIinterconnects.ai 3rd of 3 in this piece
- 8 days earlier
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Our reading
The performance gap between open and closed AI models has narrowed to roughly 4-6 months.
Their wordsThe open-closed model gap has reduced in recent years, and is now at roughly 4-6 months.
↗Open-Source AI & Open Models Reading Listinterconnects.ai 1st of 3 in this piece
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Our reading
Since 2024, the leading open-weight AI models have come from Chinese labs rather than American ones.
Their wordsThe leading open models have all come from Chinese labs since ~2024.
↗Open-Source AI & Open Models Reading Listinterconnects.ai 2nd of 3 in this piece
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Their wordsDistillation - the process of training on output tokens from another model - is the single most eventful debate around open models in 2026.
↗Open-Source AI & Open Models Reading Listinterconnects.ai 3rd of 3 in this piece
- 1 day earlier
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Their wordsI put the probability of complete extinction as being so low it isn't worth discussing, but the probabilities of AI caused disasters - e.g. cyber attacks on critical infrastructure or bio-risks - as being worth debating.
↗One resignation turned the embers of AI fear into a wildfireinterconnects.ai 1st of 3 in this piece
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Their wordsMany frontier lab employees, especially at Anthropic, are out of touch and this will impact their forecasting and/or descriptions of current AI events.
↗One resignation turned the embers of AI fear into a wildfireinterconnects.ai 2nd of 3 in this piece
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Their wordsAI has always been very jagged, and we are making models which are superhuman goal-seekers at math and software engineering, but they have massive limitations on intuitions, creativity, and other types of reasoning that humans are strong at.
↗One resignation turned the embers of AI fear into a wildfireinterconnects.ai 3rd of 3 in this piece
- 1 day earlier
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Their wordsIt feels very likely in 50 years that the average American's day to day life looks very similar.
↗When will average people feel AI’s impact?interconnects.ai 1st of 3 in this piece
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Their wordsFor knowledge work, which is roughly half of the U.S. economy, AI is as fundamental as electricity (or quickly will be so, with rapid improvements to agents in the next 18 months).
↗When will average people feel AI’s impact?interconnects.ai 2nd of 3 in this piece
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Their wordsIt's highly destabilizing to have such a transformative, productive tool only bring half of society along.
↗When will average people feel AI’s impact?interconnects.ai 3rd of 3 in this piece
- 1 day earlier
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Our reading
Frontier Chinese AI labs are moving toward more restrictive licensing terms for their open models.
Their wordsChinese model makers at the frontier, however, are becoming more restrictive
↗Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licensesinterconnects.ai 1st of 2 in this piece
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Their wordswe expect similar architectures to become more popular and the ecosystem to fix integrations by the time Qwen4 drops.
↗Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licensesinterconnects.ai 2nd of 2 in this piece
- 3 weeks earlier
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Their wordsOpen-source AI has a tricky future, as building the best models is extremely capital intensive.
↗Teaching Everyone to Fish for Tokensinterconnects.ai 1st of 3 in this piece
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Their wordsopen models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc.
↗Teaching Everyone to Fish for Tokensinterconnects.ai 2nd of 3 in this piece
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Our reading
Releasing free access to AI intelligence is one of the strongest available business strategies.
Their wordsreleasing access to intelligence is one of the strongest business strategies available.
↗Teaching Everyone to Fish for Tokensinterconnects.ai 3rd of 3 in this piece
- 3 days earlier
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Their wordsIt is very, very likely that OpenAI and Anthropic have far better internal models than Z.ai and Moonshot AI.
↗GLM-5.3: How Chinese labs keep stride with the frontierinterconnects.ai 1st of 3 in this piece
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open sourceAI alignmentinnovation
Their wordsThe capability diffusion is determined by the lowest common denominator.
↗GLM-5.3: How Chinese labs keep stride with the frontierinterconnects.ai 2nd of 3 in this piece
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Their wordsAt the end of the day, this type of safety barely matters when true open-weights are coming.
↗GLM-5.3: How Chinese labs keep stride with the frontierinterconnects.ai 3rd of 3 in this piece