The single biggest physical bottleneck holding AI back just got compressed by a factor of six. No new chip. No new architecture. A math formula. Google's TurboQuant drops into any transformer model and suddenly the long context conversations that used to choke enterprise hardware can run on a laptop – or a phone.

That's where the optimism ends.

Jason and Matt work through what it actually means when frontier-quality models leave the data center and land in the hands of everyone. The economics are obvious: cloud dependency shrinks, inference gets cheap, Jevons Paradox kicks in. What's harder to sit with is the governance problem. The major providers have put genuine effort into building guardrails. Local models answer to no one. When the same capability that enables a solo developer to build something remarkable also enables someone to strip every safety constraint and point it at the world — that's not a hypothetical. That's the situation.

P-Doom scores get revisited. Physical AI and robotics enter the picture. This conversation ends somewhere between cautious hope and honest dread.

Take a listen and decide for yourself.

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