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Applied AI·October 17, 2026·1 min read

Sergey Brin says Gemini now helps build the next Gemini

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A major lab openly talking about “getting onto that RSI treadmill” — using Gemini to help build Gemini 4 Argon and successors — normalizes self-bootstrapping in model development. For teams building on these systems, it raises both the upside of faster capability cycles and the need to track how your dependency is evolving without purely human oversight.

Applied AI

Microsoft's Maia 300 swaps raw compute for a 2.5x memory bandwidth gain as it moves to make in-house silicon its main AI engine while claiming a 40% efficiency bump — could going the Google route be the pin that pops the Nvidia AI bubble?

If Maia 300’s 2.5x memory bandwidth and claimed 40% efficiency bump hold up at scale, the constraint in AI infra planning shifts from “get H100s” to “optimize for your own stack.” For operators, this is a warning to avoid hard-coding Nvidia assumptions into long-term architecture and contracts — multi-silicon optionality is now a strategic requirement.