The Trust Spectrum

Peter Steinberger stopped reviewing AI-generated code entirely. That works for indie software. In regulated environments, it can't. Here's how to think about where you sit.

Three AI Governance Blind Spots No Framework Covers

Most AI governance frameworks are technically-focused risk checklists. Three structural risks are missing from almost all of them.

AI Vendor Selection Is Now a Values Decision

OpenAI took the Pentagon contract Anthropic refused. Your AI vendor just became a political statement — and enterprise procurement hasn't caught up.

Backtest vs Operational Validation: The Control You Think You Have

We presented a model control to a regulator. It had never actually fired. The gap between validated-by-backtest and validated-in-production is invisible until someone asks.

The Upstream Constraint Pattern

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

Why AI Assistants Make Us Dumber (And What Governance Should Do About It)

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.

Banks Have an AX Problem They Don't Know About Yet

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.

What Surprised Me Studying for the GARP Responsible AI in Finance Exam

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

Three Things AML AI Models Still Get Wrong in 2026

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

The AI Job Title Illusion

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

Skills as Behavioral Nudges: The Lightweight Alternative to Fine-Tuning

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

The Real Reason Mox Won (and What It Means for AI Transformation)

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.

AI Governance Category Error: Routing vs. Compliance

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

What Makes a Great AI Consultant (Beyond Technical Skills)

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

HK/APAC as an AI Hub for Financial Services: The Story Being Missed

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

AI Agent Frameworks for Enterprise FS: What Actually Works vs. Hype

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

RAG for Compliance: The Hard Problem Is Chunking, Not Retrieval

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

Banking DS to AI Consulting: What the Transition Actually Teaches You

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

Most Banks Don't Need an AI Strategy

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

Expansion, Not Speedup

The real ROI of AI coding isn't doing the same work faster. It's doing work that wasn't worth doing before.

Skills as Files

The simplest agent architecture might already be the right one: give the agent a file explaining how to do something, and let it read when needed.

Traces Are the New Debugger

When behaviour emerges from both code and model responses, reading source files isn't enough. You debug by examining execution traces.

Rules Decay, Hooks Don't

The difference between writing down a rule and making the system enforce it — illustrated by a 15-line hook.

Agentic Engineering: Why Less Is More

Tool enthusiasm is often net-negative. Context pollution degrades performance faster than features improve it. The principles that actually work.

CLIs Enforce Structure and Save Tokens — Not Just Discipline

The instinct to add a rule to a skill file is usually the wrong abstraction. A CLI wrapper enforces at the tool level: zero deliberation, zero token cost.

Software Engineering Principles for AI Instruction Files

LLM instruction files are code. They have the same failure modes — with one interesting twist that changes everything.

Agent-First CLI Design: TTY Detection as Philosophy

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

Per-Token Pricing Is the 'Megapixels' of AI

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

The Contract Pattern: Hard Gates for AI Agents

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

AX: Agent Experience Is the New DX

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

Claude Code, Analyze My Spending

When AI coding assistants become workflow orchestrators, the most powerful compiler processes reality, not code.

Intelligence on Tap

When artificial intelligence becomes as mundane as running water, how does thinking itself change?

What and Why Beat How

When implementation becomes automated, human intelligence reallocates to purpose and strategy. The cognitive hierarchy inverts.

Everyone Becomes Middle Management

The automation tool that creates more coordination work

I Made the AI Remind Me of My Own Blind Spots

I kept missing things at the end of AI sessions. So I stopped relying on willpower and systematised the nudge instead.

AI Evals: Why Teams Build Metrics Before They've Read a Trace

Most teams build evaluators before reading a single trace. The sequence that actually works is the opposite: observe, categorise, then measure.

The Kutta Condition of AI: Engineering Ships Before Theory Catches Up

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

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

Building My Own Consulting Toolkit Before Day One

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

Three Crates Before Lunch

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.

When to Build vs. When to Wait: The Recurrence Rule for AI Tooling

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

Don't Ask Your AI to Find Problems

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.

I Don't Read Documentation Anymore

When AI can execute complex setups through conversation, learning shifts from reading documentation to observing execution.

Claude Code Mobile is Better Than Desktop

Walking meetings, voice input, and location changes unlock cognitive advantages desktop workflows can't access.

How Claude Code Helps You Think

AI becomes most powerful when it helps you discover what your ideas actually are. Cognitive partnership over replacement.

Claude Code is Not a Coding Agent

Why I use Claude Code for everything except coding: cognitive compiler for strategy, decisions, and understanding.

Production AI vs Demos: The Intent Classification Reality Check

Building AI systems that work in the real world requires thinking beyond the demo. What actually matters when users depend on your models.

Taste Requires Stakes

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

Five Archetypes of AI-Era Business Defensibility

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.

AI Succeeds, Economy Breaks: The Displacement Loop Nobody Models

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.

The AGI Question Nobody Is Asking Correctly

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."

Why Every Tool Now Needs Two Faces - CLI for Humans, MCP for AI

We're building parallel interfaces for the same functionality because humans and AI agents parse the world through fundamentally different grammars. The future isn't human OR machine interfaces - it's both, simultaneously.

Ambient Agents: When AI Disappears Into Capability

The real revolution isn't making AI smarter - it's making it invisible

The Hidden Violence of Vague Instructions

LLMs aren't just tools we prompt - they're forcing functions for human linguistic evolution

MCP is Not a Glorified API

The protocol that looks like plumbing but acts like philosophy - how MCP fundamentally changes the agent-tool relationship

The Invisible Puppeteer

When algorithms shape decisions we think are ours

AI Agents Need Passports, Not Passwords

The authentication systems we're building assume AI agents are tools. What happens when they become economic actors with their own accounts, credentials, and legal standing?