I've built products on ideas that people thought were wrong—and watched those same ideas become inevitable.
When I helped rebuild Gmail at Google in 2005 and was head of engineering at Medium, we started with an insight about where behavior, technology, and markets were moving—and built our bets on top of it. But now, the rapid pace of change in AI means that the assumptions and insights underlying a strategy can shift mid-execution, causing those bets to crumble.
Founders need a more flexible way of thinking about which bets to take. Instead of asking "What do we think will happen?", ask yourself two fundamental questions. First: What assumptions are irreversible? If you build deeply on top of one provider, such as one model provider, you need to rewire the product to move away from that provider. Second: What assumptions can we still revise? Assuming tokens will stay expensive is a bet you can hold loosely—if costs fall, you can change your approach without rebuilding anything.
I've developed a framework to help founders understand which bets they're making, and which ones they can reverse when building with AI. The framework moves the conversation away from broad labels like "AI wrapper" and toward the specific futures a company is betting on. The goal is to recognize sooner when you're wrong and move before the market forces you to—not to predict the future more precisely.
