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The gap between AI hype and AI execution is where most companies will disappear.
There are currently thousands of startups with "AI" in their pitch deck. Most of them will fail, and not for the reasons you might think. The failure will not be technical. It will be architectural.
The most common pattern we see is what we call "AI wrapping" -- taking an existing workflow, adding a ChatGPT API call in the middle, and calling it an AI product. This is the equivalent of putting a horse in a car-shaped shell and calling it an automobile. The shape is right. The engine is wrong. These products deliver marginal improvements at best because they are constrained by the human-designed processes they wrap around.
The startups that will succeed are the ones rebuilding workflows from scratch with AI as the foundation. Not "how do we add AI to customer support?" but "what does customer support look like when AI handles 95% of interactions natively?" Not "how do we use AI to write code faster?" but "what does software development look like when the AI is the primary builder?"
The second failure mode is what we call "demo-driven development." It is remarkably easy to build an impressive AI demo. A chatbot that sounds smart. A code generator that produces working snippets. An image creator that makes beautiful outputs. But demos are not products. The gap between "works in a demo" and "works reliably at scale with real users and edge cases" is enormous. Most startups never cross that gap.
The third and perhaps most insidious failure mode is building AI products without understanding AI limitations. Every model hallucinates. Every agent makes mistakes. Every system has failure modes. The companies that survive will be the ones that design for these realities -- with fallbacks, guardrails, human-in-the-loop checkpoints, and graceful degradation. The ones that pretend AI is infallible will ship products that break spectacularly in production.
The winners will not be the companies with the most advanced models. They will be the companies with the deepest understanding of the problems they are solving and the wisdom to know where AI helps and where it does not.