The moat isn't the code - it's the causal data. Why I think being early beats being big in AI tools.

Everyone building AI tools right now is scared of the same thing: OpenAI or Anthropic ships your feature as a native capability and you're done.

I've thought about this a lot for Gero OS. My conclusion: the code isn't the moat. Anyone can wire up integrations and prompt a model. The moat is the causal data that accumulates over time - which specific actions actually moved the needle for this specific company. After a few months, the system knows things about your business that can't be exported to a fresh ChatGPT session. It lives in the product or nowhere.

So my bet is that being early - accumulating that company-specific causal data before the big platforms get there - beats being big.

But I genuinely hold this loosely. The counterargument is that a platform with distribution can catch up on data fast once they decide to.

So I'll put it to the people who've thought harder about this than me: in AI tools, does an early data advantage actually compound into a real moat - or is it a comfortable story we tell ourselves? Argue me out of it.