Quotesby GlyphDwarkesh Patel quotes on AI, with sources
Host, Dwarkesh Podcast · 16 quotes · latest Oct 30, 2026
- If it allows it, Airlock takes action on behalf of the agent, which means your agents never see your API keys or other credentials.Unverified
- There’s never been as big a shock to the semiconductor industry as the current surge in demand from AI.Unverified
- That way, your software gets tested against scenarios you’d never imagine but that could still arise in the real world.Unverified
- Continual learning will create a clear moat for leading AI labs that they currently lack.
- 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.
- 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.
- 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.
- When deployment becomes part of training, the returns to being ahead will accelerate.
- 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.
- I wish we didn’t live in a world with such strong economies of scale of intelligence (because I’m worried about power concentration).
- 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.
- If you’re spending most of your compute on inference, you’re kind of declaring that AI progress has stalled, because it’s not worth investing more in training, and your business is basically that of a cloud provider.
- 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.
- We see these AIs as a galaxy glittering with capabilities, but at their center, invisible to the naked eye, holding all the constellations together, is an unimaginably massive black hole of data.
- 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.
- 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.
About Dwarkesh Patel
Host, Dwarkesh Podcast. Listed here for public statements only; no personal details.
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Topics: AI agents, Security, Semiconductors, AI, Hardware, Software testing
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