Home Speakers Nathan Lambert Quote It is important to not confuse massive steps in inference-time scaling, a dynamic which should be fairly predictable, with being the outputs of RSI, which is highly uncertain.
Words found on the source page on Nov 6, 2026.
More from Nathan Lambert Blog The current balance of power in open models Sep 21, 2026
Since about April 2025, Chinese AI companies have been the clear leader in open-weight models. Blog The current balance of power in open models Sep 21, 2026
The simple metric showing this is Hugging Face Downloads, where China took the lead in July of 2025 primarily through the success of Alibaba’s Qwen models. Blog Why I still haven't bought into true RSI Sep 19, 2026
I have my doubts that the labs can afford to spend a constant portion of this compute on internal R&D as the total volume goes up, especially with plans to IPO, as they face increased scrutiny on basic economics. Blog Why I still haven't bought into true RSI Sep 19, 2026
The step from this anxiety, and incidents like OpenAI-HuggingFace, to extinction risks feels very religious. Blog When will average people feel AI's impact? Sep 9, 2026
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). Blog When will average people feel AI's impact? Sep 9, 2026
If given 100 years to diffuse into society, its impacts will certainly become much more obvious, like the industrial revolutions of centuries past. Blog 6 months to live for open models Jul 12, 2026
Open models lack the central economic champion to represent the potential downside of action against them. Blog 6 months to live for open models Jul 12, 2026
The most serious test to date of open source AI’s viability is happening right now. Blog 6 months to live for open models Jul 12, 2026
The primary driver motivating regulation today is the inevitable truth that an open-weights model will soon reach the capabilities of Claude’s Mythos model.
On the same topics Blog The Specter Of Neuralese Sep 24, 2026
An AI with more real layers can actually be “smarter” in the conventional sense of the term. Blog The Specter Of Neuralese Sep 24, 2026
For 100 steps in a row, the AI can shoot delicate subtle ideas from layer to layer at the speed of light. Then there’s one step where it has to encode them into twenty-six glyphs invented by Phoenician turquoise miners in 1800 BC. Then it has to re-encode the Phoenician glyphs into delicate subtle lightspeed ideas before it can do anything else. Blog The Specter Of Neuralese Sep 24, 2026
The science of reading these thoughts is a subfield of AI interpretability, which is still in its infancy. Blog Mysteries Of AI Generalization Sep 23, 2026
This trains the AIs to be focused on task success, which naturally risks including things like reward-hacking, cheating, and single-minded pursuit of stated goals at the expense of ethical injunctions. Blog Banning self-recursive improvement in AI models? Sep 22, 2026
If the top U.S. labs have to give up on what is supposed to be really important, they will look very “catchable.” The policy creates a big incentive for some other parts of the world to invest a lot more in compute. Blog Frontier Overhangs Sep 21, 2026
A world with sufficient compute is one where intelligence is a commodity, and in commodity markets margin comes from a superior cost structure, which I would expect the leading model providers to have, in part because they have a meaningful lead in scaling and can apply superior AI to their infrastructure. About this quote 
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