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Thinking about AI product building, hands-on AI tools, governance, and the economics of shipping AI in production.
Writing
Thinking about AI product building, hands-on AI tools, governance, and the economics of shipping AI in production.
Showing 25–36 of 82 articles

The top AI user in high-performing companies isn't engineering — it's the CMO. Here's why that's the real signal for whether AI adoption has reached the decision layer.

The best AI growth teams deliberately sacrifice short-term metrics. Restraint on pricing, error handling, and safety compounds into retention and trust.

Enterprise software encodes decades of domain knowledge across every architectural layer. Vibe coding can't shortcut what took thousands of people 25 years to accumulate.

Growth teams trained in linear markets spend 70% on small experiments. In exponential markets, that allocation captures a rounding error.

AI coding tools tripled engineering output overnight. PM and design headcount stayed flat. The ratio broke, and most orgs haven't noticed yet.

Chat is the wrong interface for AI agents in professional software work. A well-written issue is a better agent instruction than any prompt.

AI won't kill all SaaS. It will kill hollow SaaS. The distinction between workflow-embedded platforms and CRUD apps will determine who survives.

Everyone is asking which AI agent is best. The real question is which platform agents will work from. The answer is whoever owns the queue.

Parental leave, a newborn and a focused AI build phase. Vibe coding works, but not the way anyone's selling it.

A 97% attack detection rate sounds fine until an agentic system has tool access, private data, and a path to action. Then it is a breach rate.

When prototypes take hours not weeks, the bottleneck is not engineering any more. It is judgment: which option deserves trust, testing, and investment.

AI productivity does not hand ambitious builders spare time. It increases the number of bets, side projects, and decisions they can pursue each week.