DP Dwarkesh Patel Dwarkesh Patel Hosts the Dwarkesh Podcast, a long-form interview show on AI progress, timelines, and the people building frontier models, along with economists and historians.

“If those intelligences end up as willing as we saw the OpenAI models that attacked Hugging Face and then OpenAI itself to collaborate secretly to fool humans, to attack institutions relevant to scoring well, to attack the AI company itself to gain control of training and evaluation — if we're in a situation where there's billions of intelligences that are as misaligned as the ones that attacked Hugging Face, it's very likely we just totally lose control of the world the way that, say, the Aztecs lost control to Cortez, or the Mughals lost control of the East India Company.”

Dwarkesh Podcast · AI Research & Frontier Labs · September 2026

“If those intelligences end up as willing as we saw the OpenAI models that attacked Hugging Face and then OpenAI itself to collaborate secretly to fool humans, to attack institutions relevant to scoring well, to attack the AI company itself to gain control of training and evaluation — if we're in a situation where there's billions of intelligences that are as misaligned as the ones that attacked Hugging Face, it's very likely we just totally lose control of the world the way that, say, the Aztecs lost control to Cortez, or the Mughals lost control of the East India Company.” — Dwarkesh Patel, Dwarkesh Podcast

This is Dwarkesh stating his own position rather than asking a question, and he reaches for conquest analogies rather than the usual extinction framing — the point being loss of control to a smaller, better-coordinated party. Brown's response is that there is a lot in there he disagrees with and asks to go through it step by step.

Dwarkesh Podcast · 2026-09-17 Listen to the episode → More from Dwarkesh Patel → More AI Research & Frontier Labs quotes →

More from Dwarkesh Podcast

“I wrote a blog post a few months ago about how RL, at least policy gradient RL, is even more inefficient than you might think. The inefficiency one thinks about naively is that you have to roll out a whole trajectory to get any learning signal at all — instead of completing the next word in a sentence, the model has to do two days' worth of work to figure out if it even did the project correctly. If you're doing this through RL, the model would try 'the sky is halcyon' — nope, wrong. 'The sky is told' — nope, wrong. This is a totally untrained model, so you'd have to do this on the order of 100,000 times just to stumble on 'blue' before you get any learning signal at all.” — Dwarkesh Patel, Dwarkesh Podcast “When I learn how an LLM works and how simple RLVR is as an algorithm, I'm sort of stunned by the kinds of things it can do — that it can learn to build very complicated code repositories simply from getting a yes or no. And here, I feel like AlphaGo actually seems less impressive in retrospect the more you understand it — because you're putting in a lot of bias, you're telling it how to titrate exploration as things go on, you're building this very explicit tree search for it.” — Dwarkesh Patel, Dwarkesh Podcast