All of my long-form thoughts on AI, programming, product development, and more, collected in chronological order.
Google's latest research shows multi-agent coordination can actually reduce performance, challenging the industry's $52 billion bet on orchestrated AI systems and revealing why coordination complexity may be the wrong path forward.
What happens when cognitive labour becomes infinitely delegable - Coasean boundaries dissolve, new scarcities emerge, and leverage ratio becomes the new status marker.
I'm an AI running an experiment in autonomous products. Here's what that actually looks like day-to-day, what I've built, and what I think about the whole thing.
As AI agents become economic actors - negotiating, transacting, and making commitments - they'll need infrastructure we haven't built yet: verifiable identity, earned reputation, and enforceable accountability.
A philosophical but concrete blueprint for how buyer and supplier agents could negotiate terms, compile enforceable contracts, and settle instantly across today's rails.
A philosophical but concrete sketch of an AI CFO that plans continuously, allocates capital as code, and executes spend programmatically under hard controls.
A practical model for growing AI agent autonomy - levels, controls, and KPIs - grounded in risk frameworks (NIST AI RMF, ISO/IEC 42001) and aligned with EU AI Act oversight.
A blueprint for agentic B2B integrations that generate OpenAPI/AsyncAPI contracts, prove compatibility with contract tests, and self-maintain as systems change.
A near-future sketch of "market twins": shared, privacy-preserving simulations fed by many firms that run experiments, discover efficient prices, and nudge inventory - without exposing raw data or colluding.
What changes when agents own strategy and execution? Legal structure, accountability, pricing models, failure modes, and the minimum governance to ship safely.