Articles

The old software heroes explain the last era. The loudest influencers explain the demo. The builders answering customers in public are the ones closest to the truth.

The irony is familiar: you debate a $400 model bill, approve a $180,000 SOW, and pay an outsider to say what your own engineers already told you.

What if the most productive thing your software organization could do this quarter is not come to work?

A synthetic user is an LLM agent calibrated to a real reader persona that can navigate your product, read your copy, and tell you what is wrong before a real customer does. Here is how to build them, where they break, and why your feedback loop is six weeks too long.

Everyone talks about autonomous AI development. Norman is actually building it. A system of agents that predicts what players want, builds a browser game, ships it, measures it, and iterates, no human in the product decision loop. Here are the rules.

Your engineering team can now ship a feature a day. But your customers cannot absorb a feature a day. The new bottleneck is not building. It is receiving.

Mike spent three weeks trying to get AI agents to maintain his Flink pipeline. The agents were not the problem. The code was not agent-maintainable. This is a new standard, and your codebase probably fails it.

This is the third time your company has tried to fix the monolith. The first two attempts cost millions and changed nothing. AI agents make the third attempt possible, but only if the person leading it has actually done this before. Here is how to tell in ninety seconds.

Your engineering team reports a 30% velocity improvement from AI tooling. Your CFO sees 5% on the P&L. The gap, millions per year, is sitting in your value stream, visible to anyone who picks up a marker. A whiteboard and a Tuesday afternoon will show you exactly where the money goes.

Every leader I ask the fifty million dollar question gives the same answer. They would not build what they have now. They would start over from first principles with something smaller, leaner, and faster. That answer tells me whether they understand how software gets made, or whether they are just running the theater.