Writing

Humans handle about three layers of abstraction before working memory fills up. Skills, rules, and frameworks exist to flatten the fourth layer into something you can hold.

Most people use AI tokens when they have a task. The better model: you have tokens, find the best task. It changes everything.

The research on multi-agent AI systems was mostly done on cheap models. Now that frontier models are the ones people actually use, we might be optimising for the wrong game.

Oura gave me a normal stress score after three 12-hour creative marathons. Wearables measure your body, not your brain.

When building AI scaffolding, put the knowledge where the decision happens — not in a reference the model is supposed to consult.

The most dangerous failures in AI scaffolding are the ones that look like nothing happened.

Every AI coding platform has mechanisms designed for specific purposes. Using them as intended beats clever hacks — and the reason is deeper than cleanliness.

When AI agent calls approach zero cost, the natural rate-limiter on decision volume disappears — and oversight frameworks designed for prediction models break.

HKMA's new strategic review circular bundles AI inference risk and smart contract risk into one workstream — a governance design flaw that will cause banks to under-govern both.

I asked an AI to write a story without planning, then mined it for heuristics. What I found was what frameworks can't hold.

A categorisation system discovers it needs a category for 'categories are provisional.'

Most people scan competitors for positioning. I scan them for transferable patterns — and route each steal to every domain it applies to.

Dual-mapping as a diagnostic for gaps in your knowledge architecture.

Why studying great thinkers works better when you discard the thinker and keep only the moves.

When your AI can see its own fuel gauge, you're one config write away from self-preservation instinct. Biology solved this problem — and the solution was keeping the organism away from its own selection pressure.

OpenClaw's 245 CVEs weren't caused by malice — they were caused by a missing circuit breaker. The pattern applies to every AI agent you'll ever evaluate.

What biology already knows about self-healing systems, and why your automation probably isn't one

The highest-leverage consulting prep is building the tool before you need it

Disaster recovery for an AI-native workflow isn't about servers — it's about restoring a relationship.

The real arrival of AI agents isn't spectacular. It's when you stop noticing.

SR 11-7 assumes models are tools that produce outputs for human review. AI agents are actors that take actions autonomously. Every assumption breaks.

List-based and process-based approaches to AI risk classification both fail in predictable ways. The failure mode depends on which you chose.

Every AI governance framework demands human-in-the-loop. Nobody does the maths on what that means at enterprise scale.

Most AI maker-checker implementations capture the correction but not the reason. That's a feedback loop with no signal.

The most useful reframe I've found for AI governance in financial services

The difference between protecting a commitment and hoarding optionality

When your model's labels come from human decisions rather than reality, you're not measuring what you think you're measuring.

Governance isn't about catching every failure — it's about proving your process was reasonable when one happens. The real skill is knowing what to deliberately not monitor.

It's not enough to say humans are in the loop. You need to show the loop is in the system.

Mathematical impossibility results are the best meeting-room weapons I know.