The most valuable part of AI is the applied layer
In applied AI, the moat often sits between the model and the person: workflow, trust, data access, evaluation and change management.
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Human-Centered AIAI strategy and adoption grounded in human needs, organizational reality, and responsible design.
The most valuable part of AI right now is not the model itself, but everything standing between the model and the person who has to trust its output.
Case after case is showing that the work happens in what founders call the applied layer, and it rarely looks the same twice. An agent embedded in a bank’s workflow needs a completely different shape than one running in a law firm, because the data is different, the compliance context is different, and the way people are willing to hand off a decision is different. A model that reads financial filings needs a different harness than one reading medical records, and building one system tuned for both is close to impossible given how long that tail of use cases actually is.
None of this shows up in a benchmark. Evaluation only means something once it is domain-specific, and pricing only makes sense once it reflects how a given industry actually consumes the product, not a flat rate on tokens. Founders who treat the model as the product tend to underestimate how much of the real moat sits in this unglamorous layer: the change management, the data access, the harness tuned to one specific workflow that nobody else bothered to tune.
This is also where a smaller company with real domain depth can still win against a much larger one with a stronger model. The frontier lab is not going to hand-tune your firm’s contract review workflow. Somebody has to.
Where in your product does the real value sit between the model and the person using it? What’s your customers says on that? Do you have an answer to that?