Perspectives on sovereign AI, silicon-rooted identity, component-level provenance, counterfeit detection, and the trust layer beneath AI infrastructure.
The ML attack surface spans the model, the data, and the infrastructure. Hardware-rooted identity shuts down a whole class of infrastructure and provenance attacks — model theft, substitution, rogue hardware, repudiation — and composes with model-level defenses for the rest.
Read →The FCC just closed the "component part loophole." Verifying where a device was assembled no longer proves it's clean — a logic-bearing part from a Covered List entity can hide inside an otherwise US-built device. Proving genuineness component by component is a harder, and more important, problem.
Read →Border control solved impostor identity with a chip that can't be forged and can be checked anywhere. AI infrastructure faces the same question at every node — and needs the same kind of answer.
Read →A cloned credential and a counterfeit chip look identical to software. When identity comes from the physics of the specific device, a genuine machine and an impostor stop looking the same.
Read →Confidential computing protects what runs, in memory, while it runs. It doesn't prove which enrolled machine ran it, under whose authority, or hold evidence you can check tomorrow. That's a different layer — and UBIQS composes with it.
Read →The portable, tamper-evident record that ties the physical node, the model, and the policy to a result — and turns "trust us" into evidence an auditor can verify long after the job is gone.
Read →Certificates, API keys, and tokens are data — and data gets copied, extracted, and replayed. Why the only machine identity that holds under pressure is one derived from physics, not assigned by an authority.
Read →We're talking with design partners, technical collaborators, and investors working on sovereign AI and verifiable infrastructure.
