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Thinking about AI product leadership, building with AI tools, governance, and the economics of shipping AI in production.
Writing
Thinking about AI product leadership, building with AI tools, governance, and the economics of shipping AI in production.
Showing 49–60 of 77 articles

The window for AI-enabled builders to capture outsized value is open but narrowing fast. Vertical SaaS is being built by individuals, not large teams.

AI features that work in demos fail in deployment because adoption is a product problem, not a training problem. A playbook from rolling out AI to Tier 1 banks.

Weekend build to 145K GitHub stars to acquisition in weeks. The pattern: agents that execute locally instead of chatting in a browser window win on adoption.

Your AI product market fit depends on a model that has not shipped yet. Build your product architecture for the capability curve, not today's snapshot.

I built AI voice receptionists that handle real phone calls for real businesses. Latency, conversation flow, graceful handoff. Here's what actually matters.

AI's biggest obstacles are not technical. They are structural: professional guilds, regulatory capture, procurement inertia, and incumbents profiting from it.

Chunking, retrieval, and grounding are not engineering details. They are product decisions that determine whether your AI feature helps or hallucinates.

The best AI products aren't imagined. They're discovered by watching how people misuse your existing ones. A framework for finding what to build next.

The 6-week discovery sprint is a relic. When you can build a working prototype in a weekend, the fastest path to insight is shipping, not researching.

For decades, companies like CoreLogic built massive moats by accumulating proprietary structured property data. Visual intelligence just evaporated that advantage.

Most AI tools are deployed but unused. The friction isn't capability. AI lives in a separate tab instead of where work happens. Build inline, not destination.

4 engineers, 10 days, a new product line. AI coding agents collapsed build economics. If code is a commodity, your moat is data, integrations, and trust.