Nathan Lambert
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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.
19 October
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
16 October
15 October
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.
22 September
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Their words
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.
21 September
From one piece The current balance of power in open models 3 beliefs · interconnects.ai
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Their words
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.
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Their words
The capabilities gap from open to closed models available to users has been decreasing over the last 3 years.
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Their words
Chinese AI companies are the clear leaders in open weight models.
19 September
From one piece Why I still haven’t bought into true RSI 3 beliefs, in the piece's order there
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Their words
lossy self-improvement remains my baseline on the trajectory of progress, and the increased discussion of extinction risk seems very misplaced
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LLMsrecursive self-improvement
Their words
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.
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korrents.com
AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence.Their words
all of our scaling laws show that you need exponential compute and resources to make linear improvements in intelligence.
11 September
From one piece Open-Source AI & Open Models Reading List 3 beliefs, in the piece's order there
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Their words
Distillation - the process of training on output tokens from another model - is the single most eventful debate around open models in 2026.
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korrents.com
Since 2024, the leading open-weight AI models have come from Chinese labs rather than American ones.Their words
The leading open models have all come from Chinese labs since ~2024.
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korrents.com
The performance gap between open and closed AI models has narrowed to roughly 4-6 months.Their words
The open-closed model gap has reduced in recent years, and is now at roughly 4-6 months.
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Recommendsrcmnd.app
A Safe Path to Open WeightsTheir words
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).
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Recommendsrcmnd.app
Are Open Models Catching Up?Their words
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)
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Recommendsrcmnd.app
How far behind are open models?Their words
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)
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Recommendsrcmnd.app
On the Societal Impact of Open Foundation ModelsTheir words
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).
10 September
From one piece One resignation turned the embers of AI fear into a wildfire 3 beliefs, in the piece's order there
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Their words
Many frontier lab employees, especially at Anthropic, are out of touch and this will impact their forecasting and/or descriptions of current AI events.
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Their words
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.
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Their words
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.
9 September
From one piece When will average people feel AI’s impact? 3 beliefs, in the piece's order there
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korrents.com
Even after decades of AI-driven progress, most people's daily material lives will look largely unchanged.Their words
It feels very likely in 50 years that the average American's day to day life looks very similar.
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Their words
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).
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korrents.com
A powerful new technology that benefits only part of society is inherently politically destabilizing.Their words
It's highly destabilizing to have such a transformative, productive tool only bring half of society along.
8 September
From one piece Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses 2 beliefs, in the piece's order there
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korrents.com
Hybrid and sparse-attention model architectures will become more widely adopted as the ecosystem catches up.Their words
we expect similar architectures to become more popular and the ecosystem to fix integrations by the time Qwen4 drops.
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korrents.com
Frontier Chinese AI labs are moving toward more restrictive licensing terms for their open models.Their words
Chinese model makers at the frontier, however, are becoming more restrictive
2 September
17 August
From one piece Teaching Everyone to Fish for Tokens 3 beliefs, in the piece's order there
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Their words
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.
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korrents.com
Releasing free access to AI intelligence is one of the strongest available business strategies.Their words
releasing access to intelligence is one of the strongest business strategies available.
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korrents.com
Open-source AI's future is uncertain because building frontier models is extremely capital-intensive.Their words
Open-source AI has a tricky future, as building the best models is extremely capital intensive.
14 August
From one piece GLM-5.3: How Chinese labs keep stride with the frontier 3 beliefs, in the piece's order there
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Their words
At the end of the day, this type of safety barely matters when true open-weights are coming.
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korrents.com
The pace at which dangerous AI capabilities spread is set by the least cautious developer, not the most careful one.Their words
The capability diffusion is determined by the lowest common denominator.
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Their words
It is very, very likely that OpenAI and Anthropic have far better internal models than Z.ai and Moonshot AI.
12 August
From one piece I wrote an AI textbook — how long until AI can do it better? 3 beliefs, in the piece's order there
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Their words
So, in 2-5 years I still expect the best textbooks to be heavily crafted by the human hand.
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Their words
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.
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Their words
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.
10 August
9 August
26 February
1 February
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Recommendsrcmnd.app
Season of the WitchTheir words
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.
3 February 2025
From one piece DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 22 beliefs, in the piece's order there
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Their words
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.
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LLMsopen sourceAmericamotivation
Their words
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?
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
I almost think it's practically impossible because you effectively have to remove them from the internet.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
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Their words
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.
4 December 2024
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LLMsOpenAIreinforcement learning
Their words, before
Unlike traditional autoregressive language models, it is doing an online search for the user.
Their words 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.
4 September 2024
24 August 2022
26 July 2022
8 July 2022
5 February 2021
21 December 2020
15 July 2020
30 June 2020
30 January 2020
17 September 2019
Nothing matches.
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