Custom chips do not solve the AI landlord problem

Custom silicon announcements get treated as an immediate escape hatch from NVIDIA, but swapping out who fabricates the accelerator does little when the software ecosystem stays put. As Anurag Gurtu observes in The Robot Report, frontier labs developing in-house chips are optimizing their own cost of goods. That does nothing for the broader tier of engineering teams that rely on standard marketplaces and frameworks to find, adapt, and run their models.

The friction is sharper in physical AI. Autonomous systems require local inference, bounded latency, and predictable edge execution. On a factory floor or in a warehouse, edge autonomy is a strict operational requirement rather than a governance talking point. Yet deploying on alternative silicon provides little independence if the simulation pipelines, runtimes, and upstream model distributions remain tied to a single vendor's commercial strategy.

Technology sovereignty cannot be achieved simply by taping out an ASIC. If the tools used to simulate physics, distribute weights, and optimize runtimes are held by one company, hardware diversification just changes the supplier while keeping the lease.