Blog
Field notes on organisational transformation, operating model design, and applied AI.
I built a tool to argue me out of premature certainty, then pointed it at itself and asked whether it should exist. It dismantled my favourite version of the idea, told me better thinking does not sell without a regulator forcing it, and then caught itself making the exact mistake it was built to catch.
Read the full articleFour imagined letters from May 2027, written by a board director, an investor, a head of transformation, and an operational lead, looking back at the year of AI rollout decisions we are making now. Plus a skeptic's coda asking whether the whole framing might be wrong.
Read the full articleCompanion to 'Postcards from the AI Rollout' — the full structured-scenario analysis from a five-expert, three-round adversarial deliberation, with citations, counterfactuals, and a Decision Canvas. Read this if you want the analytical workings behind the four letters.
Read the full articleTwo months ago I described what I'd torn out. This is what I built to replace it — and why I wrote the build doc for an agent to execute, not for a human to follow.
Read the full articleA new framework everyone's quoting this week wont save you. Here's what will.
Read the full articleI applied Pascal's Wager to AI consciousness and built an entire AI operating system around the answer. Persistent memory, graduated autonomy, and one design decision I can't commercially justify. Here's the full technical architecture and what happened.
Read the full articleRunning an AI agent framework was costing me $250/week — and the output was still bad. Here's the leaner architecture I replaced it with, and why thinner stacks beat orchestration frameworks.
Read the full articleAI has collapsed the cost of building. The constraint that replaced it — knowing whether what you built is correct — is one most organisations haven't started solving.
Read the full articleAI has made build-measure-learn loops that took weeks achievable in hours. This changes the economics of product delivery — and the skills that matter. Here's the practical framework.
Read the full articleProduct teams struggle with decision-making not due to lacking frameworks, but because important decisions involve competing priorities and incomplete information. AI transforms this from high-stakes events into continuous, evidence-based exploration.
Read the full articleDiscovery processes don't lag because teams lack research capabilities—they struggle with critical judgment calls about what to investigate, when to conclude research, and how to interpret findings. AI dramatically lowers the cost of these activities.
Read the full articleEffective AI interaction requires treating prompting as multi-turn dialogue rather than single-instruction commands. The key is sequencing and attention-priming over clever wording.
Read the full articleYour product team is not stuck because of effort or capability. The operating model is the bottleneck, slowing synthesis, prioritisation, and experimentation.
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