consulting

Citing a vendor defender product in a paper that argues the threat surface is moving faster than controls undercuts the case it is supposed to support.

Persona-based AI reviews predict how stakeholders will react to a paper. They cannot tell you whether to act on those predictions, because the commissioning history is invisible to the lens.

One paper for two audiences reads like leverage. It is actually a trap. The director commissions; the board governs a portfolio. They cannot read the same document.

Five rounds of the same question produced diminishing returns by round three. Then I changed the question — same document, different reviewer.

Risk teams know risk. The open problem is designing controls for systems that are non-deterministic, probabilistic, and attackable in natural language.

Shopify's pi-autoresearch got 300x test speedups. But the real insight isn't performance — it's that the pattern works on anything with a number.

I scraped 16,181 of Simon Willison's posts and analysed the 395 from 2026. An inflection in November 2025, GLM-5 closing the gap, and why the harness — not the model — is the competitive moat.

A method for finding gaps in AI systems that architecture reviews miss. Force a naming constraint, and the breaks reveal what's missing.

The governance patterns for autonomous AI agents are the same ones good managers already use: cadence reviews for normal flow, escalation channels for urgent anomalies, and human judgment only where it has maximum information value.

AI-prepared domain research creates false readiness. The vault says you know five regulatory jurisdictions. You can't name three.

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.

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

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

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

What does the Frontier Alliance actually mean for advice quality?

Every AI fairness debate is secretly a values debate disguised as a technical question.

Interaction, inference, orchestration, tooling. The boundaries between them must be enforcement points, not design principles.

Adding detail to a deliverable doesn't fix credibility — it creates new interrogation targets.

Most technology decisions can be reasoned through. AI solution design can't — the domain is too empirical, too fast-moving, and too non-linear for theory alone.

Getting ahead of AI best practices is a treadmill. The durable skill is testing assumptions faster than they expire.

The companies that win with AI agents aren't deploying the most agents — they're redesigning their organisations to work with them.

LangGraph is the SAP of agent orchestration — powerful at scale, overkill for most. Here's the line.

Most AI workflows are parallel-then-aggregate, not agent graphs. Knowing the difference saves you from framework theatre.

Enterprise AI keeps promising to capture institutional knowledge. The most valuable kind resists capture by design.

I built an AI landscape intelligence pipeline for zero marginal cost. Here's what it does and what it can't.

The scarce AI skill isn't building — it's knowing if what you built actually works.

The gap between AI assistants and AI agents isn't about reasoning capability — it's about whether the thing can survive your laptop closing.

Evaluating LLM systems requires judgment, not statistics. That shifts who's qualified to do it — and where the gap is in most organisations.

Clients hire consultants to solve problems. What they're actually paying for is the reduction of a particular feeling. The distinction matters.