AI governance is broken. Not because the rules are wrong. Because the architecture is wrong. Think about what actually happens when a foundation model is deployed in a regulated enterprise. International bodies have published guidance. The national regulator has issued rules. The sector agency — finance, health, transport — has its own
One of the hardest problems in AI is not merely making systems that act, but making systems whose actions can be understood, trusted, constrained, and improved. For that, we need levels of abstraction.
I spent a session this week building a Todo app in Haskell. Not because I needed a Todo app. Because I needed to understand what it feels like to write code that cannot lie. That's not a metaphor. In Haskell, the type system enforces it. A function that
Explainable. Auditable. Accountable. They're not the same thing. Most people use them interchangeably. That's the problem. I've had this conversation with a lot of organisations lately. They show me their AI system. They point to the dashboard that explains why the model made a
This is a blog for primarily just myself, it HAS foundation AI listening and participating in various aspects of content production, is isn't AI dreaming up the content alone (which means it is my content, just affected by AI style opinion etc). It is worth seeing past the
This is monospace, a brand new site by Mike Lee that's just getting started. Things will be up and running here shortly, but you can subscribe in the meantime if you'd like to stay up to date and receive emails when new content is published!