/Tech15h ago

Researcher Claims Network Of Small AI Models Will Beat Large Monolithic Systems

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⿻ Andrew Trask@iamtrask#366inTech

@peterthiel To be more specific. A global network of highly interconnected, neural-network routed, small, specialized models will ultimately deliver: - higher accuracy - faster speed - lower cost

than large, monolithic systems.

If @peterthiel asked me "what does everyone believe that I know to be wrong"... this would be my answer.

(And Aidan is very smart and he's describing a view for which there is plenty of evidence, and near universal agreement. I just disagree.).

9:15 PM · Jun 9, 2026 · 604 Views
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Users are optimistic about the claim that networks of small AI models will outperform large monolithic systems because it encourages breaking free from industry groupthink to enable breakthrough research.

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If none of this makes sense and you just need to read "a completely different way to think about AI progress" and a bunch of launch points for research in AI, a few links: - https://attribution-based-control.ai/ - https://github.com/iamtrask/abcGPT - https://openmined.org/blog/what-is-broad-listening/ - - https://openmined.org/blog/secure-enclaves-for-ai-evaluation/

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@peterthiel They'll also be easier to make safe in many ways, but now I'm going on a tangent. This is enough for now.

@peterthiel To be more specific. A global network of highly interconnected, neural-network routed, small, specialized models will ultimately deliver: - higher accuracy - faster speed - lower cost

than large, monolithic systems.

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One final thing - I think the biggest barrier to breakthrough research is allowing yourself to subscribe to industry groupthink (or to the polar opposite of that groupthink).

Go in a 3rd direction. Follow the scaling laws. Look for bridges across fields (especially deep learning, cryptography, and distributed systems).

It's never been a better time to do research.

@peterthiel They'll also be easier to make safe in many ways, but now I'm going on a tangent. This is enough for now.

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