Home Speakers Dwarkesh Patel Quote I think the reason it is relatively easy for open source and previous laggards to catch up to within months of the frontier is that data is the real driver of progress.
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Continual learning will create a clear moat for leading AI labs that they currently lack. Essay 8 Predictions for the Era of Continual Learning Aug 7, 2026
I don’t think you can have AIs that perform whole jobs as competently as humans if they’re forced to just write Markdown files from session to session. Essay 8 Predictions for the Era of Continual Learning Aug 7, 2026
If you want to change what AI you are using, you basically have to fire an employee that has months of context on your organization and replace them with a fresh one that you have to retrain from scratch. Essay 8 Predictions for the Era of Continual Learning Aug 7, 2026
This is one of many reasons why I think it’s unwise to lock in some kind of regulatory safety regime right now. We simply don’t know what kind of technology we’re going to be looking at even in a year, let alone in five years or ten years, and we’d be entrenching an archaic and potentially counterproductive approach to dealing with the threats from AI. Essay 8 Predictions for the Era of Continual Learning Aug 7, 2026
When deployment becomes part of training, the returns to being ahead will accelerate. Essay Why compute might get 10x+ more expensive in coming years Jul 29, 2026
I want to emphasize the key conclusion here: as AI models become smarter, they’ll better monetize the same amount of compute. If a true human-level software engineer that could run on an H100 equivalent, at current market rates for software engineers, that H100 should rent for over $250k a year. Essay Why compute might get 10x+ more expensive in coming years Jul 29, 2026
I wish we didn’t live in a world with such strong economies of scale of intelligence (because I’m worried about power concentration). Essay Why compute might get 10x+ more expensive in coming years Jul 29, 2026
If by 2028 we’ve automated software engineering and the price of compute is 15x higher than it is right now, then it’s going to be much more difficult for you, with no revenue, to compete for compute against the frontier labs. Essay Why compute might get 10x+ more expensive in coming years Jul 29, 2026
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But in the real world, power is more the product of having the authority and trust to get lots of people to collaborate with you, rather than some galaxy brain scheming capability. Essay The mistake of conflating intelligence and power May 16, 2026
I’m simply pointing out that the way AI systems are currently becoming smarter (by getting trained to be to be really good at specific economically valuable tasks like coding) is not that strongly correlated with power.
On the same topics 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. Podcast Dwarkesh Podcast: AI researchers debate how close we are to recursive self-improvement Sep 11, 2026
Basically anything that can be learned through RL can be distilled very easily, because it’s a small number of bits. Blog Arguments in Favor of AI Fair Use Sep 7, 2026
As LLMs continue to scale up, the value of any particular source continues to diminish. Blog Arguments in Favor of AI Fair Use Sep 7, 2026
Much, if not most, of the value of LLMs derives from their ability to generate new benefits beyond what the source material alone can yield. Blog The Pulse: tech companies move to open AI models Sep 3, 2026
Cost-saving efforts reveal that moving simpler workloads to open AI models is the easiest way to save ~50% on AI bills. About this quote 
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