Plain-English books on AI, risk, and governance.

A durable way to think about any AI system, across data types and tasks, so a new model or paper stops being a mystery. Plain English, no hype.

One durable way to think about any AI system: three questions - what data, what task, which method - and the math under every model, from a line to an agent. Plain English, no hype, no code.

Three moves to collapse twenty AI governance frameworks into one backbone you can actually scale.

Governance that scales is not a longer checklist. Three moves: govern to a common spine, govern with the unit that scales, govern as a system.

A short, board-facing guide to AI risk: five questions to take into the next board meeting, through the financial sector's risk lens.

AI risk management as 3 questions, not an ethics poster: what do you have, how are you managing it, who's accountable. From ML to agents, in plain English.

You cannot review every path an AI agent might take. Govern it like a bonsai: cut the branches that must never happen, wire the rest, check with your own hands.

A curated, no-noise starting path for AI risk and governance: six short reading lists by area, each reading with a note on why it's worth reading.

Four agent patterns, with runnable code, to build AI investing tools grounded in real data and auditable calculations - not hallucinations.

Why an LLM on its own makes up your portfolio numbers, and the architecture that fixes it: buns not meat, the complexity ladder, four agent patterns. No code.

Why an LLM cannot forecast on its own, what the real forecasting tools are, and why the rational way to use AI here is an agent that drives those tools. No code.