Field-tested guidance for founders and product teams making decisions about AI implementation, product architecture, reliable delivery, debugging, rapid MVPs, and technical leadership.
Peter Diamandis predicted a future of technological abundance. The harder question is whether our politics, institutions, and appetite for power will let us live in it.
Most of what we read about AI in software comes from clean demos and tidy lab examples. The real story starts when AI code meets thousands of users, messy data, and production pressure. Here is what actually happens, and where engineering still matters.
ChatGPT, Google AI Overviews, and Perplexity do not cite the web in the same way. Before trusting an AI visibility percentage, inspect its denominator, query set, time window, and citation unit.
A demo request that looked like it came from the Philippines turned out to be a family-owned hat business in the United States. When I asked how they found us, the answer was one word: Gemini. Here is what that taught me about the future of product discovery.
To get scalable code from ChatGPT, Claude, or Gemini, you need to understand how these models actually think. Here is the math behind why they write beautiful but non-performant code, and how to prompt your way out of it.
