TMTPost Edge AI briefing tracks Anthropic chips and Google edge win

This briefing reads like a compressed snapshot of where the frontier is drifting: more agents, more custom silicon, more open science and more edge deployment. The reported Anthropic move to secure HBM supply and pursue custom ASICs is another confirmation that leading labs no longer want to be pure tenants of Nvidia’s roadmap. If that trend sticks, we will see vertically integrated AI stacks where model, compiler and hardware are co‑designed for agent workloads, which tends to favor the labs with the most capital and the most demanding use cases. ([tmtpost.com](https://www.tmtpost.com/8080059.html))

At the other end of the spectrum, the ESP32 demo puts a nearly 29M‑parameter language model on an eight‑dollar chip by treating storage and compute separately, hinting at a future where “smart enough” models live everywhere and call out to the cloud only when needed. Combined with Meta’s UMA open‑sourcing for computational chemistry and rising pressure around open weights, the picture is of a stack that is simultaneously centralizing around a few hyperscalers and decentralizing into cheap, specialized, widely deployed models. That duality is exactly the kind of environment in which AGI‑adjacent behaviors could emerge in unexpected places.