AI does not create every inefficiency. It surfaces and amplifies the ones that already exist. A search that returns thin or contradictory answers, a workflow that still requires manual lookup, a category where the useful detail lives only in scattered comments—these are places where a clear page, a small tool, or a better data source can capture attention and revenue.
The practical path is to watch what the systems fail to answer cleanly. If multiple results hedge, contradict, or bury the fact, that is a signal. Write the direct version. If a task still requires copying between tools or re-entering the same facts, that is a signal. Build the small automation or the structured page that removes the step. The economic return sits in the reduction of that friction, not in the novelty of the model itself.
This is the same logic that applies to SEO and AEO. Answer engines cite the page that states the answer. Search still ranks the page that matches the query with supporting depth. AI tools that help with research or drafting make it cheaper to produce those pages, which increases the supply of average content and raises the relative value of the pages that are actually clear and accurate. The inefficiency to target is the remaining set of questions and workflows that are still poorly served.
None of this requires predicting the next model. It requires noticing where current tools and current results still leave work on the table, then doing that work in a form the systems can use. The opportunity is specific and observable.