Role-Specific AI Beats General Chatbots
Samiur Rahman, CEO of Heyday, on building a production-grade AI stack
The real moat in AI productivity is not a broad chat box, it is a workflow that already knows what good work looks like for one job. Heyday learned that a general assistant for everyone is easy to try but hard to trust, because users still have to decide what context to give it, what output is good enough, and when to rely on it. Focusing on coaches and consultants turns the product from a neat demo into software trained around recurring client work.
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Heyday started as a broad assistant that pulled from notes, documents, conversations, browsing history, and email. The shift was toward executive coaches and consultants, where users repeat similar research and client prep tasks, so the system can be tuned around a narrower pattern of work.
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This is the same split showing up across AI software. Horizontal chat tools can answer almost anything, but vertical products win when they package the right inputs, steps, and outputs for one profession. In venture, for example, the emerging view is that most firms will buy a purpose built tool rather than assemble a general stack themselves.
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The practical problem with general products is that they create delight before they create habits. In the productivity panel, founders repeatedly described AI as strong at drafting, classification, and organizing text, but weak at open ended task completion unless the company has already defined the task, the data, and the success criteria very tightly.
The next wave of winners in AI productivity will look less like universal copilots and more like software for one role, with built in context, preferred data sources, and repeatable outputs. As foundation models improve, that shift should make specialized tools more valuable, not less, because better models increase the payoff from owning the workflow layer on top.