AI capability is growing exponentially. The foundational layers on which that capability operates are not. Models get smarter every quarter. The intelligence infrastructure underneath - how organisations know what their AI is doing, where it's making decisions, whether the knowledge it acts on is still true - is barely being built. In regulated industries, that gap is becoming a governance, accountability, and auditability problem that no amount of policy documentation will solve.

I work on governed intelligence - the architecture that sits between raw AI capability and the environments where failure has legal, financial, or human consequences. Not governance bolted on after the fact. Governance as a structural property of how the intelligence is built. In practice: claims with provenance and decay, decision ownership models, evidence chains a regulator can use, and a circuit breaker that halts agent action when the supporting knowledge falls below the epistemic quality the consequence demands.

The body of work spans five papers on SSRN, five public manifestos, and a maturity model for AI governance across financial services, biotech, and aviation. Built openly. Co-authored with Arnaud Gelas, who leads the engineering side of the programme. Before this: twenty years across Europe's largest financial institutions, and before that, founding a European certification company that defined a methodology and accredited 200+ practitioners. The pattern is the same work at different scales - define the standard, build the architecture, certify the people.

The path here started on concert stages and in a deep-future research lab - environments where complex systems only succeed when architecture and performance are exactly aligned.