Analysis and findings from building and operating AI systems in production.
Most products built with AI are designed the same way as products built without it. That's the mistake. When the underlying capability changes this dramatically, the design principles have to change too.
Customers are experts at describing their pain. They are not experts at designing solutions. The best product builders know the difference — and know when to listen carefully and when to stop listening.
The startup world spent a decade celebrating growth at all costs. The companies that survive the next decade will be the ones that understood the difference between scale and profitability — and chose both.
After deploying over $1B in technology investments, I saw the same pattern repeat. The best outcomes didn't come from the best advice. They came from founders who were too close to the problem to see any other option but to solve it.
Every AI system that removes human oversight in pursuit of efficiency eventually creates a failure mode that no one anticipated. The companies building durable AI products know this. Human control isn't a constraint — it's the architecture.
There are two kinds of companies right now: those that are AI-enabled, and those that are AI-native. The difference isn't about technology stack. It's about whether AI is core to how the business creates value, or whether it's a feature layered on top.
Twenty people. Ten ventures in production. Most companies that size are building one product and fighting to keep it alive. The operating model that makes this possible isn't a hack — it's a philosophy about where leverage actually lives.