Build for the AI mess, not the consensus
AI deployment is still fragmented. For startups, that heterogeneity is an opportunity to define categories before standards harden.
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Human-Centered AIAI strategy and adoption grounded in human needs, organizational reality, and responsible design.
Cloud had maybe two or three real deployment patterns once it settled, mostly because there were only a few infrastructure vendors to choose from. AI has none of that convergence yet. Some companies standardize every employee on one model. Others let people pick freely. Others build their own orchestration layer just so nobody has to choose. On data access, some let agents act as the user outright, others give agents their own identity and a leash. There is no consensus on any of it, this early.
I keep meeting founders who read this fragmentation as a threat - as if the market needs to settle before their product makes sense. It is closer to the opposite. Heterogeneity this early usually means years before anything hardens into a standard, and years is exactly the window where a smaller, sharper player can define how a category gets built, not just fit into it.
Anyone telling you with confidence how this settles is guessing. Worth building for the mess instead of waiting for it to resolve.
What does your read on this fragmentation say about where to place your bets?