Nathan Lambert
Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.
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What they believe
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All measurements on open model adoption are incredibly imprecise.
Still, all the measurements we have on open model adoption are incredibly imprecise.
interconnects.aiSaid 4 Nov 2026This on korrents
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Changed their mind OpenAI's o1 does an online search over reasoning steps at test time, unlike a traditional autoregressive language model.
Before Unlike traditional autoregressive language models, it is doing an online search for the user.
Now This is the argument that all of the “search” in OpenAI’s o1 (and some of the replications) is all within the reinforcement learning training behavior.
Said 4 Dec 2024This on korrents
The bold line is korrents’ wording; the quote is theirs. These one: what they wrote before what they said aloud, one per source, newest first.
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As its own page →Hiding
4 November
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Their words
Still, all the measurements we have on open model adoption are incredibly imprecise.
3 November
2 November
1 November
30 October
From one piece Was I wrong about open models & cyber risks? 2 beliefs · interconnects.ai
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Their words
Closed models have played a very heavy part in the recent hacks, at least more than open models have.
Was I wrong about open models & cyber risks?interconnects.ai
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Their words
We’re living through this plot of an exponential rise in vulnerabilities, which will take a long time to level off at the top of the sigmoid.
Was I wrong about open models & cyber risks?interconnects.ai
29 October
28 October
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korrents.com
The era of singular intelligence indices should be phased out soon, but it will take a long time.Their words
Our era of singular intelligence indices should be phased out soon, but it will take a long time.
27 October
26 October
25 October
23 October
21 October
20 October
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korrents.com
The release of DeepSeek's internal inference stack is a significant open-source AI artifact.Their words
While 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.
17 October
From one piece Meta shakes up the frontiers, open and closed 3 beliefs · interconnects.ai
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korrents.com
The technical core of the AI industry lacks a high hit rate for predicting model success in real-world usage.Their words
Despite 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
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Their words
These 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 words
One 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
14 October
9 October
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Their words
I fully agree that the models will be superhuman distributed GPU engineers in a few years.
6 October
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Their words
The 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.
Nothing matches.
Beliefs
Korrents What they believe 59 beliefs, 1 changed mind — each backed by an exact quote.
Each is a — compiled by korrents.com, not by them: the one-line wordings are korrents', the quotes are theirs.
Recent
All measurements on open model adoption are incredibly imprecise.
Still, all the measurements we have on open model adoption are incredibly imprecise.
How big is the open model economy today? Said 4 Nov 2026
The cybersecurity space is experiencing an exponential rise in vulnerabilities that will take a long time to level off.
We’re living through this plot of an exponential rise in vulnerabilities, which will take a long time to level off at the top of the sigmoid.
Was I wrong about open models & cyber risks? Said 30 Oct 2026
Closed models have played a larger part in recent hacks than open models have.
Closed models have played a very heavy part in the recent hacks, at least more than open models have.
Was I wrong about open models & cyber risks? Said 30 Oct 2026
Show 56 more
The era of singular intelligence indices should be phased out soon, but it will take a long time.
Our era of singular intelligence indices should be phased out soon, but it will take a long time.
Artificial Analysis v5 and the benchmarker's dilemma Said 28 Oct 2026
The release of DeepSeek's internal inference stack is a significant open-source AI artifact.
While 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.
Latest open artifacts (#25): DeepSeek V4.1 Flash 1017, American-made models, and new regulatory controls in the US & China Said 20 Oct 2026
Western open-weight models receive significantly more adoption than Chinese models with similar performance-to-size footprints.
One 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 closed Said 17 Oct 2026
The costs of building true frontier models are now measured in billions per major model, meaning only huge companies can sustain them.
These 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 closed Said 17 Oct 2026
The technical core of the AI industry lacks a high hit rate for predicting model success in real-world usage.
Despite 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 closed Said 17 Oct 2026
Models will be superhuman distributed GPU engineers in a few years.
I fully agree that the models will be superhuman distributed GPU engineers in a few years.
I expect rapid progress but not towards general superintelligence Said 9 Oct 2026
The current proponents of open-weight risks are likely to be proven wrong regarding their predictions of substantial new AI harms.
The 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.
The Cyber Risk Discourse is Broken Said 6 Oct 2026
The AI research cycle from idea to experiment will likely soon be 10x faster, but that may not make researchers 10x more productive in the field's net understanding.
And 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 AI Said 22 Sept 2026
Chinese AI companies are the clear leaders in open-weight models.
Chinese AI companies are the clear leaders in open weight models.
The current balance of power in open models Said 21 Sept 2026
The capabilities gap between open models and the closed models available to users has been decreasing over the last three years.
The 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 models Said 21 Sept 2026
Open-weight models are becoming an essential tool for AI diffusion.
Open-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 models Said 21 Sept 2026
AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence.
all 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 RSI Said 19 Sept 2026
Recursive self-improvement in AI will mainly make existing model capability cheaper rather than raise peak intelligence.
RSI 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 RSI Said 19 Sept 2026
The rising level of public concern about AI extinction risk is overblown relative to how progress will actually unfold.
lossy 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 RSI Said 19 Sept 2026
The performance gap between open and closed AI models has narrowed to roughly 4-6 months.
The open-closed model gap has reduced in recent years, and is now at roughly 4-6 months.
Open-Source AI & Open Models Reading List Said 11 Sept 2026
Since 2024, the leading open-weight AI models have come from Chinese labs rather than American ones.
The leading open models have all come from Chinese labs since ~2024.
Open-Source AI & Open Models Reading List Said 11 Sept 2026
Distillation is the most significant debate surrounding open AI models in 2026.
Distillation - 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 List Said 11 Sept 2026
AI-caused catastrophic extinction risk is negligible, but disasters like critical-infrastructure cyberattacks and bio-risks from AI are real and worth debating.
I 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 wildfire Said 10 Sept 2026
AI progress is jagged: models are becoming superhuman at math and software engineering while remaining far weaker at intuition, creativity, and other forms of human reasoning.
AI 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 wildfire Said 10 Sept 2026
Employees at frontier AI labs, particularly Anthropic, are out of touch in ways that distort their forecasts and accounts of AI events.
Many 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 wildfire Said 10 Sept 2026
A powerful new technology that benefits only part of society is inherently politically destabilizing.
It's highly destabilizing to have such a transformative, productive tool only bring half of society along.
When will average people feel AI’s impact? Said 9 Sept 2026
AI is becoming as fundamental to knowledge work as electricity was to industry.
For 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? Said 9 Sept 2026
Even after decades of AI-driven progress, most people's daily material lives will look largely unchanged.
It 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? Said 9 Sept 2026
Frontier Chinese AI labs are moving toward more restrictive licensing terms for their open models.
Chinese model makers at the frontier, however, are becoming more restrictive
Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses Said 8 Sept 2026
Hybrid and sparse-attention model architectures will become more widely adopted as the ecosystem catches up.
we 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 licenses Said 8 Sept 2026
Open-source AI's future is uncertain because building frontier models is extremely capital-intensive.
Open-source AI has a tricky future, as building the best models is extremely capital intensive.
Teaching Everyone to Fish for Tokens Said 17 Aug 2026
Releasing free access to AI intelligence is one of the strongest available business strategies.
releasing access to intelligence is one of the strongest business strategies available.
Teaching Everyone to Fish for Tokens Said 17 Aug 2026
Open models will end up serving a long-tail of specialized uses while closed models retain the most valuable applications.
open 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 Tokens Said 17 Aug 2026
OpenAI and Anthropic's internal, unreleased models are likely far more capable than Z.ai and Moonshot AI's public models.
It 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 frontier Said 14 Aug 2026
The pace at which dangerous AI capabilities spread is set by the least cautious developer, not the most careful one.
The capability diffusion is determined by the lowest common denominator.
GLM-5.3: How Chinese labs keep stride with the frontier Said 14 Aug 2026
Staged, controlled rollout of a powerful AI model provides little safety benefit once open-weight versions of similar capability are inevitably released.
At 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 frontier Said 14 Aug 2026
Progress toward AI autonomously solving major open scientific problems requires first mastering the organization and presentation of established knowledge in long-form writing.
Models being stagnant in long-form, non-fiction writing should be alarming to those reliant on models autonomously solving grand, open science problems in the near future.
I wrote an AI textbook — how long until AI can do it better? Said 12 Aug 2026
Using AI agents to do a task does not by itself increase a person's own pace of understanding, intuition, or taste for that work.
Something intertwined with this story, which I stumbled upon when thinking about agents, is how your pace of understanding won't increase by using agents.
I wrote an AI textbook — how long until AI can do it better? Said 12 Aug 2026
The best textbooks will continue to be predominantly written by human hand rather than AI for at least the next several years.
So, in 2-5 years I still expect the best textbooks to be heavily crafted by the human hand.
I wrote an AI textbook — how long until AI can do it better? Said 12 Aug 2026
Data processing, filtering and quality, not architecture, is the number one determinant of how good a language model turns out.
And we'll get into the details of the models and again and again as we try to get deeper into how the models were trained, we will say things like the data processing, data filtering data quality is the number one determinant of the model quality.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one.
The scale word gets a lot of attention in this. The interpretation that I use is effectively to avoid adding the human priors to your learning process. And if you read the original essay, this is what it talks about is how researchers will try to come up with clever solutions to their specific problem that might get them small gains in the short term while simply enabling these deep learning systems to work efficiently, and for these bigger problems in the long term might be more likely to scale and continue to drive success.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Post-training is the best place to work in AI right now, because cheap runs mean a far higher share of your experiments can be all-or-nothing bets.
This is why you want to work in post-training because the GPU cost for training is lower. So you can make a higher percentage of your training runs YOLO runs.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
A published training-run cost always understates the truth, because any notable model takes two to four times that run's compute in experiments alone.
Accepted practice is that for any given model that is a notable advancement, you're going to do two to 4x compute of the full training run in experiments alone.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Export controls cannot stop China from training frontier models; what they can do is cap how much AI China gets to run.
There's not many worlds where China cannot train AI models. I think export controls are decapping the amount of compute or the density of compute that China can have.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence.
I think my personal definition of AGI is much simpler. I think language models are a form of AGI and all of this super powerful stuff is a next step that's great if we get these tools. But a language model has so much value in so many domains that it's a general intelligence to me.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Language models have not made misinformation meaningfully worse, because distribution, not the cost of writing convincing text, was always the limiting factor.
There's some research that shows that the distribution is actually the limiting factor. So language models haven't yet made misinformation particularly change the equation there.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Making safety your number one goal costs real release speed, and on a steep progress curve that shows up directly as a worse-looking model.
We know that a lot of the American companies are very invested in safety, and that is the central culture of a place like Anthropic. And I think Anthropic sounds like a wonderful place to work, but if safety is your number one goal, it takes way longer to get artifacts out.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Open frontier models will keep being released whether or not anyone wants to stop them, and stopping them would only leave the world less prepared.
these open models are probably going to keep coming for the time being, whether or not we want to stop them, and stopping them might make it even worse and harder to prepare.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Censoring a fact out of a language model is practically impossible, because you would first have to remove that fact from the internet.
I almost think it's practically impossible because you effectively have to remove them from the internet.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
RLHF is not a tax on capability: preference tuning also raises maths and code scores, which is why the labs keep reaching for it.
And the important thing to say is that no matter how you want the model to behave, these RLHF and preference-tuning techniques also improve performance. So, on things like math evals and code evals, there is something innate to these, what is called contrastive loss functions.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Reasoning is not taught to a model by people: it emerges from reinforcement learning on questions with checkable answers, with no human preference data at all.
And these reasoning behaviors emerge naturally. So these things like, "Wait, let me see. Wait, let me check this. Oh, that might be a mistake." And they emerge from only having questions and answers.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Distilling from another lab's model is standard industry practice, not cheating; who you distill from just depends on whether you have products to protect.
Distillation is standard practice in industry. Whether or not, if you're at a closed lab where you care about terms of service and IP closely, you distill from your own models.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
OpenAI's claim that DeepSeek trained on its outputs is narrative management: other startups bootstrapped exactly that way and were never banned for it.
I think that they're trying to shift the narrative. They're trying to protect themselves. We saw this years ago when ByteDance was actually banned from some OpenAI APIs for training on outputs. There's other AI startups that most people, if you're in the AI culture, were like they just told us they trained on OpenAI outputs and they never got banned.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Stealing AI code and data is hard and stealing ideas is easy, because Silicon Valley already moves ideas legally, inside the heads of the people it hires.
Code and data is hard, but ideas is easy. Silicon Valley operates on the way that top employees get bought out by other companies for a pay raise, and a large reason why these companies do this is to bring ideas with them.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
The faster AI progresses, the better NVIDIA does, so a shock like DeepSeek should expand its market rather than shrink it.
The more progress that AI makes or the higher the derivative of AI progress is, especially because NVIDIA's in the best place, the higher the derivative is, the sooner the market's going to be bigger and expanding and NVIDIA's the only one that does everything reliably right now.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
The biggest untapped business in AI is advertising inside model outputs, and nobody has yet worked out technically how to place an ad there.
The short-term that company that could make the most money is the one that figures out what advertising targeting method works for language model generations.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Reasoning will generalize the way instruction tuning did: add enough verifiable domains and, at some point nobody can yet locate, the rest start working on their own.
And the history of NLP and language processing instruction, tuning and tasks per language model used to be like one language model did one task, and then in the instruction tuning literature, there's this point where you start adding more and more tasks together where it just starts to generalize to every task. And we don't know where on this curve we are.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
AI will not push software engineers off a cliff; it will flatten their growth curve, the way Meta's stories flattened Snapchat.
The big picture is that I don't think it's going to be a cliff. I think a really good example of how growth changes is when Meta added stories. So Snapchat was on an exponential, they added stories, it flatlined.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one.
And humans are actually very good at reading or judging between two things versus... This goes back to the core of what RLHF and preference tuning is that it's hard to generate a good answer for a lot of problems, but it's easy to see which one is better.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
Open-source AI is still an ideological mission rather than an ecosystem, because it has none of the feedback loops that make open-source software compound.
until there are feedback loops of open source AI, it seems like mostly an ideological mission. People like Mark Zuckerberg, which is like America needs this and I agree with him, but in the time where the motivation ideologically is high, we need to capitalize and build this ecosystem around, what benefits do you get from seeing the language model data?
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
AI takeover is not the thing to worry about: physical constraints bound recursive self-improvement, and humans reliably act once a risk becomes immediate.
And for that reason, there's physical constraints to things like AGI, like recursive improvement to kill us all type stuff. For the physical reasons and for how humans have figured things out before, I'm not too worried about AI takeover.
DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 Said 3 Feb 2025
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Recommends rcmnd
rcmnd What they recommend 5 things — each backed by an exact quote.
Compiled by rcmnd.app, not by them: the quotes are theirs, and each links to the post it was copied from.
Recent
Recommends On the Societal Impact of Open Foundation Models
Early paper on marginal risks that showed text-focused LLMs very marginally increased documented potential risks of models — On the Societal Impact of Open Foundation Models , Sayash Kapoor, Rishi Bommasani et al. (Feb. 2024).
interconnects.ai 11 Sept 2026
What others say about On the Societal Impact of Open Foundation Models →
Recommends How far behind are open models?
An independent analysis of the open-closed gap across a mix of public and private evaluations — How far behind are open models? , Håvard Tveit Ihle (May 2026)
interconnects.ai 11 Sept 2026
Recommends Are Open Models Catching Up?
SemiAnalysis article which ran independent evaluations, concluding that open models have been getting closer to the closer frontier of performance over time — Are Open Models Catching Up? , SemiAnalysis (Aug. 2026)
interconnects.ai 11 Sept 2026
Show 2 more
Recommends A Safe Path to Open Weights
A clear articulation on how to balance releasing powerful open-weight models while taking safety seriously — A Safe Path to Open Weights , Thinking Machines Lab (Jul. 2026).
interconnects.ai 11 Sept 2026
Recommends Season of the Witch
I need to get him a copy of Season of the Witch. It’s a history of SF from 1960 to 1985 that goes through the hippie revolution, the culture emerging in the city, the HIV/AIDS crisis, and other things. That is so recent, with so much turmoil and hurt, but also love in SF. No one knows about this. It’s a great book, Season of the Witch; I recommend it.
lexfridman.com 1 Feb 2026
Where they publish
Site natolambert.com Personal site: papers, talks, and what he is working on now.
Link verified 20 Sept 2026.
Newsletter Interconnects His newsletter on the frontier labs and open models. Has a feed.
Posts several times a month on RLHF and post-training, open-weight model releases, and what the frontier labs are actually doing, usually working from the papers and the model cards rather than the announcements.
Recent
- How big is the open model economy today? 4 Nov 2026 Our first open model economy research note for paid subscribers.
- Was I wrong about open models & cyber risks? 30 Oct 2026 Last week coined the term “Black October“ after an onslaught of critical vulnerability disclosures, including PHP2Shell and a long-standing GitLab bug, signified the moment when the cyber risks of AI models started to m…
- Artificial Analysis v5 and the benchmarker's dilemma 28 Oct 2026 A sort of review of the benchmarking industry and what's missing.
Show 15 more
- Podcast: Kimi K3.1 Pro, the new cyber reality, and Muse musings 25 Oct 2026
- Meta shakes up the frontiers, open and closed 17 Oct 2026 US open weights are back!
- I expect rapid progress but not towards general superintelligence 9 Oct 2026 I’ve often been surprised when I hear from top researchers in industry that they think AI will be better than them at their job in a few years, and I didn’t really know why I doubted it.
- The Cyber Risk Discourse is Broken 6 Oct 2026 Open-weights, ideology, and acknowledging trade-offs.
- Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AI 22 Sept 2026
- The current balance of power in open models 21 Sept 2026 The expanded form of a testimony I prepared for Congress.
- Why I still haven’t bought into true RSI 19 Sept 2026 An “AI moderate’s” view on recent events and the trajectory of frontier models.
- Open-Source AI & Open Models Reading List 11 Sept 2026 How to get up to speed on open models and their implications.
- One resignation turned the embers of AI fear into a wildfire 10 Sept 2026 Some quick notes on a truly weird week.
- When will average people feel AI’s impact? 9 Sept 2026 We’re <5 years into a compounding revolution which could take a century, and how the AI industry should manage this.
- Teaching Everyone to Fish for Tokens 17 Aug 2026 Nvidia wants you building your own model, not buying from Anthropic/OpenAI.
- GLM-5.3: How Chinese labs keep stride with the frontier 14 Aug 2026 Hint: It’s really not a distillation story.
- I wrote an AI textbook — how long until AI can do it better? 12 Aug 2026 Reflections on AI's writing ability and how AI models get more capable.
- 5 useful things you'll learn in my new post-training textbook (shipping now!) 10 Aug 2026 After a few long years of finding time to document my lessons from training open models, my post-training book is done!
- Lessons from the hacks 9 Aug 2026 Musings on model alignment, what determines safety, and where we go from here.
Link verified 20 Sept 2026. Recent items update automatically from the channel.
x.com @natolambert Posts.
Recent
- Love this scrappy project. Hoping more US labs follow suit, showing they can also make solid models with small focused teams. This also intellectually follows on the olmo lineage, so super cool to see what our baby looks like scaled up. 4 Nov 2026
- I’ve been obsessing collecting all the public data around the open model economy, organizing it in our public & free dashboard dot interconnects dot ai We’re starting a new series where I explain my interpretation of said data. This will be a paid product on Interconnects, while the data stays free. The first post is around how we can measure the inference growth around open models and what it means. We’re very interested to hear what other questions (or data) people have in this space! 4 Nov 2026
- Very interesting how the open model labs in China always have delivered on their promises of when weights come out. Will be telling when one of them finally backs out of a certain model timeline (feels inevitable). 4 Nov 2026
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- Rule one of "nothing ever happens" is the reactions on twitter to the implications of an election night are overblown (especially economic). 4 Nov 2026
- Fireworks serves >40T tokens a day of "specialized models." I'd define this as any post-trained model for a customer. 3 Nov 2026
- New talk on the open model economy. I wanted to break down the case of where open model builders should steer the economics of the industry, if we want to avoid being swallowed by OpenAI and Anthropic in due time. This rests fully on being able to drive the cost of inference down dramatically on specialized, repetitive tasks, rather than trying to compete on cost with the frontier labs in any general agent. A core piece of it is how the open model economy is very fragmented, leaving an interesting puzzle we need to solve to get revenue from applications to feed back into the next model cycle.… 2 Nov 2026
- It's unbelievable that many hotels have worse wifi than United Starlink 2 Nov 2026
- This is a good post. The reality is that the AI build is pricing in more markets to emerge, and AI has continued to deliver them over the last few years. Timing is everything -- the markets could come well after the compute comes online. 2 Nov 2026
- I give an incredible amount of alpha away for free at this website with my open models data 1 Nov 2026 dashboard.interconnects.ai
- Was reading the score-centering RL paper to add some notes on it the my book, and they have a great table in the appendix on the different importance sampling based train-inference mismatch methods in RL. These have been the biggest focus of RL algorithm research since the GRPO takeoff, imo. 30 Oct 2026
- Open models will be used for cyber attacks, just like closed models have been. At the same time, open models aren’t the super high leverage tool magically increasing cyber crime by 100x — those bottlenecks lay elsewhere. 30 Oct 2026
- Was I wrong about open models & cyber risks? I don't think so. The AI community is focusing on the wrong lens - which type of model is causing harm - rather than how cybersecurity maintains an equilibrium, who enacts harm, and how they use new tools. 30 Oct 2026 interconnects.ai
- Glad to see more institutions exploring the ideas here. I don’t think it’s cut and dry that distillation is bad for the US’s AI competitiveness. 29 Oct 2026
- This is very needed. Talking to models like Claude about anything that has an overlap with an area like legal, healthcare, etc is mind numbing. They’re so desperate to hedge and steer away from grey area that they often are really avoiding the real question. 29 Oct 2026
- I feel a little bad ranting at Gemini about the unreleased model, as going through the unofficial channels to get a frontier model out today I hear is a good bit of a mess and definitely not easy. That's why they say patience is a virtue. 28 Oct 2026
- Gdmi whenever I want to try a Gemini model it’s not publicly available. Don’t bother announcing your scores if we can’t use it. 28 Oct 2026
- I honestly don’t know how to try Gemini in the agentic era, maybe I finally need to fix that. Pretty wild they just have been out of it since December 2025 but look maybe back. 28 Oct 2026
- Private benchmarks often show open models are further behind, but the problem with private benchmarks is you don’t have the community to squash the inevitable bugs for you. I'm 50/50 on if I think private or open-source is the best approach. 28 Oct 2026
- Open models' demise has been greatly exaggerated, but the previous AA index did flatter them. As an industry we need more specific intelligence indices, as LLMs are used in vastly different tasks. My meta-review on the benchmarking industry. 28 Oct 2026 interconnects.ai
- I feel a need to defend the AAv5 index and open models at the same time. Benchmarking is messier, more opinionated, and trickier than most people give credit to. 28 Oct 2026
Link verified 20 Sept 2026. Recent items update automatically from the channel.
Bluesky @natolambert.bsky.social Posts.
Recent
- I’ve been obsessing collecting all the public data around the open model economy, organizing it in our public & free dashboard.interconnects.ai We’re starting a new series where I explain my interpretation of said data. This will be a paid product on Interconnects, while the data stays free. 4 Nov 2026
- New talk on the open model economy. I wanted to break down the case of where open model builders should steer the economics of the industry, if we want to avoid being swallowed by OpenAI and Anthropic in due time. Watch it here: 2 Nov 2026 youtu.be
- I give an incredible amount of alpha away for free at this website with my open models data: 1 Nov 2026 dashboard.interconnects.ai
Show 17 more
- Was reading the score-centering RL paper to add some notes on it the my book, and they have a great table in the appendix on the different importance sampling based train-inference mismatch methods in RL. These have been the biggest focus of RL algorithm research since the GRPO takeoff, imo. 30 Oct 2026
- Was I wrong about open models & cyber risks? I don't think so. Much of the AI community focuses on the wrong lens - which models are causing harm - rather than how the cybersecurity industry maintains an equilibrium, who enacts harm, and how they use new tools. 30 Oct 2026 interconnects.ai
- Before you get excited for Google with the Gemini Carbon scores, it’s actually not publicly available, they’re teasing us with private benchmarks. Cool cool, what an L. 28 Oct 2026
- Private benchmarks often show open models are further behind, but the problem with private benchmarks is you don’t have the community to squash the inevitable bugs for you. I'm 50/50 on if I think private or open-source is the best approach. 28 Oct 2026
- With the release of Artificial Analysis’s v5 index, this plot has been going around. Open models' demise has been exaggerated, but the previous AA index did flatter them. 28 Oct 2026 interconnects.ai
- I think it's vastly underrated how important org chart and team functionality is to building a good LLM. Over 18 months old, and still one of my favorite all time Interconnects posts. It's why we still often see orgs crumble and need to get rebuilt (e.g. Llama/xAI) & why underdogs can still catch up 27 Oct 2026
- My first open model lecture, covering all the many pieces that build the ecosystem, and the open questions/debates determining where it goes next. How does distillation fit in? How do we manage risks? How do we see access to models changing in the future? Video: 26 Oct 2026 youtube.com
- We got the Interconnects team together to debate hard questions and messy realities after a crazy, somewhat intimidating, week in the AI world. Interconnects Podcast #20: Kimi K3.1 and the new cyber reality YouTube: youtu.be/0xiq7RHVGRE Transcript / Podcast: 25 Oct 2026 interconnects.ai
- TLDR on the kimi k3.1 pro benchmarks (just released) is that it looks like they figured out their RL, where the initial release was a very impressive base model with lighter post-training. China is very much keeping pace with capability gains. 23 Oct 2026
- Kimi K3.1 is announced as 3.1 Pro. The benchmark scores look to be clearly the strongest current open weight model, if the weights are released as they claim by November 4th. They are very impressive scores. 23 Oct 2026 kimi.ai
- Finally got to put together a complete deck on the state of open models. What they are, licenses, how competitive they are, signs people are using them, distillation's impact, and how we balance the risks and benefits of openness! 21 Oct 2026 natolambert.com
- Seems like cyber risk for AI may be more of an ecosystem issue than an open-weight models specific issue. If we can figure out the right balance here, I think there are a lot of people out there who want to support more openness in light of RSI and AI dynamics coming soon. 20 Oct 2026
- The latest open models and some analysis of recent regulatory action from MOFCOM on model exports / DHS on DeepSeek. Great to see a bunch of strong US open weight models from Reflection, Meta, and Nvidia in this one! 20 Oct 2026 interconnects.ai
- Trying to use the models for things outside of coding / ml, like making slides or coherent plans or longish content design, makes me feel the AGI-is-going-to-take-a-while. 19 Oct 2026
- Reflecting on Meta’s big week, where US open-weight models now stand, and the outlook for any meaningful competition to Anthropic & OpenAI’s models. Meta feels like they’re going from strength to strength right now, but can they close the last bit of the gap? 17 Oct 2026 interconnects.ai
- A safe assumption when people are all commenting on a short Chinese policy document is that they don't know what it means and tried to parse a partial translation from claude, without reading all the way through. There are maybe 5-10 people who i trust to comment. I'm normally not one. 16 Oct 2026
- Guys this is like my superbowl I waited so long for US open weight models to be relevant again. Before today the US models were still behind Chinese models from as early as Feb. 2026. Still more work to do! This cut the gap by at least half, in time (say 3 months) 15 Oct 2026
Link verified 20 Sept 2026. Recent items update automatically from the channel.
GitHub @natolambert Code, mostly around RLHF and open models.
Recent
- rlhf-book — Textbook on reinforcement learning from human feedback 3 Nov 2026
- colloquium — A markdown native slides tool for academics building with agents. 1 Nov 2026
- dynamicslearn — Working directory for dynamics learning for experimental robots. 14 Oct 2026
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- dotfiles 2 Sept 2026
- dgx-spark-setup — Setup guide for ML training on NVIDIA DGX Spark (GB10 Blackwell, CUDA 13, aarch64) 26 Feb 2026
- blogcaster — Python tools for easily translating your blog content to podcasts & YouTube 4 Sept 2024
- continuousprediction — Formulating Model-based RL Dynamics as a continuous rather then one step prediction 24 Aug 2022
- mbrl-lib — Library for Model Based RL 26 Jul 2022
- job-search-viz — A tool for visualization of complex job searches. 8 Jul 2022
- plotting-basics — Collection of lines of code for basics of clean plots in Plotly and Matplotlib 5 Feb 2021
- mems-bo — Bayesian Optimization of MEMs design 21 Dec 2020
- robot-ethics-books — A collection of readings for one interested in robot ethics. 15 Jul 2020
- model-learning-control — Studying papers and how they use learned forward dynamics models for control 30 Jun 2020
- si-rl-samples — Investigate the sample efficiency of classical system identification with control verses model-free RL. 30 Jan 2020
- ros-crazyflie-mbrl — Working ROS Directory for MBRL Crazyflie Project 17 Sept 2019
Link verified 20 Sept 2026. Recent items update automatically from the channel.
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