Writing

A model control that's never fired in production isn't a control — it's a hypothesis. The gap between backtest and operational validation is invisible until someone asks.

The standard AI economic models assume wage effects and retraining timelines. They don't model the feedback loop where successful AI deployment reduces the customer base that purchases AI-enabled products.

In digital transformation, the bottleneck is almost always upstream of where the pain is felt. Mox is the cleanest case study.

The cognitive offloading problem is real. The governance response mostly isn't. There's a specific mechanism at work, and it has a specific fix.

Aeronautics flew for decades before anyone could explain why wings worked. AI is in the same position. The engineering is ahead of the theory.

The failure mode of AI advice isn't hallucination. It's that it agrees with you. Here's the architecture that fixes it.

The primary user of my CLI tools isn't me anymore. Designing for that changes everything about how output should work.

Most consultants arrive at a new firm and learn their tools from colleagues. I tried something different.

I published three Rust CLI tools to crates.io before noon — none existed at breakfast. The interesting part isn't the speed. It's that the bottleneck moved.

AI can simulate aesthetic judgment with impressive fluency. What it cannot simulate is the consequence of being wrong.

Banks are building AI agents to call their APIs. Those APIs weren't designed for agent callers. The mismatch is subtle, consequential, and almost nobody is talking about it.

Most AI tooling debates are actually recurrence debates. The question isn't whether to build — it's how many times you'll need it.

I expected the hard parts to be the technical sections. They weren't. The governance sections were harder, and more useful.

Sequoia says AGI is here. Dan Shipper says we're not there yet. They're both right — they're measuring different things. The question that actually matters is Sholto Douglas's "nines of reliability."

The models aren't the problem. The operating models are. Three structural failures in AML AI from years building these systems inside a bank.

Two job ads. Same bank. Same week. Same title pattern. Completely different jobs. The AI hiring market has a labelling problem.

When AI models commoditize, the moat isn't the model. It's the infrastructure AI must flow through but can't replace. Five archetypes of what that looks like.

We're optimising for the wrong number — and the history of consumer electronics suggests we'll figure this out eventually.

We fine-tune models with gradient descent. We nudge agents with skill files. Same goal, radically different cost.

AI agents know how to start a task. They don't always know when to stop. The contract pattern is the architectural fix.

Mox didn't win because they hired better designers. They won because they had no legacy to fight. The pattern applies directly to AI transformation.

Your AI governance framework is a routing spreadsheet pretending to be a compliance programme. Regulators will spot the difference.

Developer experience became a competitive moat in the API era. Agent experience is next. Most tools aren't designed for it yet.

Ask for bugs and you'll get bugs — whether they exist or not. Sycophancy is a design feature, and the fix isn't better prompting.

The most dangerous person in an AI consulting engagement knows how the model works but has never sat in a credit committee.

Hong Kong has quietly run one of the most sophisticated GenAI experiments in global banking. Almost no one outside the region is paying attention.

Most enterprise AI agent pilots in financial services fail at the same point: the second tool call. The problem isn't the framework.

Banks are deploying RAG for compliance and discovering the hard problem isn't retrieval. It's the pipeline before it.

The operational instincts built in production banking don't belong in the past. They're exactly what makes a practitioner-turned-consultant useful.

The real project isn't artificial intelligence. It's the data infrastructure that AI exposes as broken.