AI moves from moat to commodity
How AI is transforming productivity apps
The strategic shift was that AI moved from a product moat to a commodity input. Once OpenAI, Anthropic, and Google exposed strong language models through APIs, startups no longer needed years of ML work to ship decent summarization, drafting, or classification. That compressed the advantage of early AI builders like Heyday, and pushed competition up the stack, toward workflow design, proprietary context, trust, and distribution.
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Heyday had already built its own search, embeddings, and ML workflows before foundation model APIs arrived. Samiur describes the new API era as giving many builders roughly 80th percentile capability from day one, which made raw model access far less defensible than solving a specific user workflow well.
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That is why these companies narrowed from broad AI assistants to concrete jobs. Heyday focused on coaches, where the product prepares for sessions and pulls key takeaways from prior conversations. In the panel, all three founders converge on the same point, generic chat is easy, reliable task completion inside a real workflow is hard.
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The better analogy is AWS for intelligence. Just as cloud infrastructure made servers easy to rent, model APIs made language intelligence easy to rent. The value then shifts to the application layer, where teams decide what data to feed the model, how to structure prompts, how humans review outputs, and how the product fits into daily work.
Going forward, winning productivity apps are less likely to be the ones with the fanciest base model, and more likely to be the ones that own a narrow workflow deeply enough that users trust the result. As APIs keep improving, the app layer should consolidate around products with better context, better interfaces, and better judgment about when AI should act and when a human should.