/AI3h ago

E/acc co-founder bayes predicts rapid robotics breakthroughs, while Herbie Bradley argues severe data bottlenecks block a GPT-3 equivalent

Bayes predicts two major robotics milestones within three years.

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bayes@bayeslord#1291inAI

Robotics

There will be a ChatGPT style November 2022 moment, and then an Opus 4.5 style November 2025 moment for robotics. Neither has happened yet, but they’re coming and it will happen faster than people think as a function of fast AI progress, including AI-accelerated physical systems engineering. Seems likely that the gap between these two moments for robotics will not be three years.

bayes@bayeslord

Open source might further languish as capital rushes into the labs. There is a coordination problem here where no one wants a token monopoly except the labs, but if that can get solved and the regulatory environment is favorable maybe things work out.

11:39 AM · Jun 4, 2026 · 366 Views
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bayes@bayeslord

To physically scale up the count of robots in the world, however, might take until 2030 or later. Although we do build ~100M cars per year and humanoids are much smaller than cars. Given that we also build 1B smartphones per year it seems reasonable to expect order of 100M robots/yr by 2030 if capital and algorithms move quickly. Definitely 10M/yr is achievable as we already do that for the drone market. Good software proving that humanoids are worth it at small scale can drive infinite capital, proportional to the quality of the proof.

bayes@bayeslord

Robotics

There will be a ChatGPT style November 2022 moment, and then an Opus 4.5 style November 2025 moment for robotics. Neither has happened yet, but they’re coming and it will happen faster than people think as a function of fast AI progress, including AI-accelerated physical systems engineering. Seems likely that the gap between these two moments for robotics will not be three years.

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bayes@bayeslord

Things that might look like hard limits today for robotics will disappear, including e.g. poor sample efficiency, relative data scarcity, expensive and or challenging hardware designs for hands and motors, fractal complexity of the physical world, and hidden unrecorded knowledge about how we do things in the world (like plumbing). World models seem useful but the particular thing doesn't matter. The research scaling laws will be ground out until utility diminishes.

bayes@bayeslord

To physically scale up the count of robots in the world, however, might take until 2030 or later. Although we do build ~100M cars per year and humanoids are much smaller than cars. Given that we also build 1B smartphones per year it seems reasonable to expect order of 100M robots/yr by 2030 if capital and algorithms move quickly. Definitely 10M/yr is achievable as we already do that for the drone market. Good software proving that humanoids are worth it at small scale can drive infinite capital, proportional to the quality of the proof.

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