MechanismAudio · 39:47 — 48:07
For the world to be radically transformed, AIs only need to be superhuman at verifiable R&D (chips, fabs, robotics, AI research itself); being great at intangible domains like politics and negotiation is not required.
Ryan argues that even without good transfer to hard-to-verify domains like politics, AIs superhuman at hardware, robotics, and AI R&D could drive an 'industrial explosion' — the 18th-century analogy of having steamships and Maxim guns even if you can't navigate parliament. ✦ AI generated
Ryan Greenblatt · Dwarkesh Podcast · 2026-08-11 · original ↗
plays this moment only · 39:47 — 48:07
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“So you're suggesting that at this time, AI companies are also working on robotics progress, which is very commingled with AI research progress.”
My perspective is that if AIs are sufficiently good at R&D, including hardware R&D, robots, whatever, then they can radically transform the world, even if they're not that good at playing politics. Also, we're in a pretty dangerous situation, because the AIs might be doing huge amounts of really hard-to-understand R&D, building out basically the whole economy of the future, and we may not understand what's going on in there.
verbatim transcript · starts at 39:47
Transcript · around this moment
39:47– Skills AI can’t train on: does it even need them?
48:07– Aligned to whom?
39:47– Skills AI can’t train on: does it even need them?
48:07– Aligned to whom?
Around this claim
In practice · 2
Wafer-scale engineering was the right architectural bet because radical improvement requires radical architectural difference, not incremental modification of the GPU.Andrew Feldman · No Priors · conf 78%Once AIs roughly match top human experts in AI R&D, could kick off a feedback loop where AIs doing AI research produce smarter AIs, yielding four or five years of AI progress in a single year.Ryan Greenblatt · Dwarkesh Podcast · conf 72%