If building a startup is like playing a tough video game, building a startup in generative AI is like playing that video game at 2x speed.
If your startup uses an AI model provided by companies like OpenAI and Anthropic, you're relying on technology that is improving at an unpredictable and unprecedented rate. If you're not careful, you might spend weeks on a feature, only to find that the next AI model release automates it. And because everyone has access to great APIs and frontier large language models, your incredible product idea can be built by anyone.
The traditional laws of "startup physics"—like solving the biggest pain points first or that supporting users gets cheaper at scale—don't apply anymore. And if your intuitions were trained on regular startup physics, you'll need to develop some new ones in AI.
The solution? Don't solve problems that won't be problems soon. LLMs are undergoing one of the fastest technical developments in history. Three years ago, ChatGPT couldn't process images, handle complex math, or generate sophisticated code—tasks that are easy for today's LLMs. And two years from now, this picture will look very different.
If you're building at the app layer, it's easy to spend time on the wrong problems—those that will go away when the next version of GPT comes out. Don't spend any time working on problems that will go away. It sounds simple, but doing this is hard because it feels wrong.
Predicting the future is now part of your job (uncomfortable, right?). To know what problems will stick around, you'll need to predict what GPT-X-plus-one will be capable of, and that can feel like staring into a crystal ball. But once you have your predictions, you can base your product roadmap and strategy on them.
