#AI.

AI isn't Skynet (yet). It's a powerful tool, and the real danger is who's using it. Why the way we talk about AI has us afraid of the wrong thing.

The tried and true methods of root cause analysis assume a failure has one cause and will happen again. AI failures break both assumptions. What people smarter than me are saying about it, a test from my own series that needs revising, and why the useful question after an incident is which change to make, not which cause to name.

I went looking for how to do evals better and found an argument about whether they work at all. What I've read, what I've heard, and why the most important discipline in AI product management has quietly turned into a performance.

Enterprise transformations rarely fail because they picked the wrong framework. They fail because they can't work the way any framework requires. Six constraints that bind every product organisation in the AI era, and a worked example of how I'd optimise against them.

Removing friction is a reliable product instinct, but it can quietly harden into an end in itself. What the psychology of effort, and a book about voluntary discomfort, say about when frictionless is the wrong target.

Layer 4 of AI Fluency: Why safety and governance determine whether customers stay after an incident

Layer 3 of the AI Fluency framework, Part 2: the diagnostic discipline. When your AI product breaks, how to figure out whether the model or your application is at fault, route the fix to the right team, and give stakeholders a credible timeline.

Layer 3 of the AI Fluency framework, Part 1: designing the application layer. The quality ceiling of your AI product is set by what reaches the model, not how you phrase the question. Learn context engineering: the five types of context, retrieval techniques, and assembly decisions that are product decisions, not engineering details.

Why evaluation is the biggest genuine gap most product managers have. Teams ship AI features without knowing whether they work, discover regressions from customer complaints, and can't measure impact. This is Layer 2 of the AI Fluency framework.

Layer 1 of the AI Fluency framework: enough technical knowledge to hold your own in conversations with engineers, without becoming an ML researcher.

A structured framework for understanding and diagnosing AI product failures. Learn the four layers of technical literacy PMs need, and how real incidents cross all of them at once.

The EU AI Act is not a future concern. It is actively reshaping products today. But treating compliance as a burden misses the point. The best Product Managers will treat it the same way they treat any product constraint: as a forcing function for better decisions.

Agentic engineering is about to give Product Managers superpowers we never had before. Vibe coding has its place too, but only if we are honest about what it is and what it is not.

Everyone agrees AI is transforming product management. Nobody agrees on which skills survive. The debate over strategy, taste, and the 'editing function' reveals a profession in the middle of an identity crisis.

We often talk about AI writing code, but its real power lies in managing the flow of value. The catch? You can't apply a Ferrari engine to a horse-drawn cart. Here is why organisations must restructure to unlock the AI advantage.

Agile won the war on delivery, but we are losing the peace on value. It is time for a new set of values that prioritises outcomes over outputs and learning over logistics.

AI is reshaping not just the products we build but the very nature of work itself. For Product Managers, this turning point demands a shift in mindset, moving beyond execution to strategy, ethics, and orchestration.