The graveyard of AI demos is enormous. Impressive benchmarks, slick interfaces, and… nobody uses them after the first week.
I've shipped AI features at scale and consulted on dozens of AI product bets. The pattern is consistent: teams optimize for capability, not for behavior change.
The Wrong Question
Most teams ask: "What can our model do?"
The right question is: "What behavior do we want to change, and why hasn't existing tooling changed it?"
AI is a technology primitive, not a product. A hammer doesn't create the need for nails — the nails were always there, the users just didn't have a good way to hit them.
Habit Anchoring
The most durable AI products attach to existing habits. They don't create new workflows — they compress existing ones.
GitHub Copilot works because developers were already writing code. The model fits inside the groove that decades of muscle memory carved. You don't need to teach the user a new mental model; you augment the existing one.
The mistake: building standalone AI apps that require users to remember when to open them.
Lesson: Find where users already have the intent, and compress the gap between intent and outcome.
The Blank Slate Problem
An empty chat interface is an empty box. Users don't know what to put in it.
ChatGPT solved this with extreme discoverability (suggestions, examples, share links) and massive brand awareness that primed users with expectations before they ever opened the product.
Most AI startups don't have that. They put a text box on the page and expect users to discover the value proposition themselves.
Lesson: Don't make users discover what your product is for. Make the first interaction so specific that the value is undeniable in 30 seconds.
Latency Kills
The psychological research is clear: perceived wait time above ~400ms breaks the flow of thought. In a text editor, even 200ms feels sluggish.
Most AI products are built with the assumption that users will tolerate latency because the output is good. This is wrong. Users tolerate latency for asynchronous tasks (generate a report, draft this email). They abandon synchronous flows (autocomplete, search, inline suggestion) the moment latency becomes perceptible.
Lesson: Design your product around your actual latency profile, not your aspirational one. Streaming is not optional.
The Trust Ladder
AI makes mistakes. This is a feature when the mistake surface is controlled — spell-checkers get away with false suggestions because the undo cost is one keystroke. It's a bug when the mistake surface is opaque — AI-generated code that compiles but does the wrong thing in production.
Successful AI products calibrate the trust ladder deliberately:
- Start with low-stakes, reversible actions where wrong outputs are obvious
- Build user trust through consistent accuracy in that narrow domain
- Expand scope as trust accumulates
Lesson: Ship in the domain where a bad output is annoying, not catastrophic. Expand from there.
What to Build
The best AI products I've seen share three traits:
- They have a clear escape hatch — the user can always override or ignore the AI
- They make the AI's reasoning visible — not just the output, but why
- They are embarrassingly narrow at launch — one job, done extremely well
Build the version that does one thing so well that users feel cheated by every alternative. The scope will expand naturally as trust grows.
The AI companies that win won't be the ones with the best models. They'll be the ones with the deepest understanding of why their users show up every day.
