There's a recurring argument in AI product teams that I've started calling the "model gap" trap.

It goes like this: the product isn't working because the model isn't good enough yet. When the model improves, the product will improve. Until then, we wait.

This argument is almost always wrong.

The Real Problem

Most AI products that fail don't fail because the model was bad. They fail because the team spent 80% of their time optimizing the model and 20% of their time understanding the market.

The model gets better. The product never gets clearer.

I've seen this pattern in enterprise AI tools, consumer apps, and developer products. The technical team is obsessed with benchmark scores, latency metrics, and accuracy rates. The market team is saying "users don't understand what this does." These two conversations happen in parallel and never intersect.

The result: a technically impressive product that nobody buys twice.

What "Good Enough" Actually Means

Here's the thing about AI model quality: "good enough" is defined by the workflow, not by the benchmark.

A model that's 85% accurate on a general text benchmark might be completely useless for a specific legal document review task — not because 85% is low, but because in legal review, the 15% failure rate creates more work than the tool saves.

The same model might be perfect for drafting first-pass marketing copy, where 85% accuracy means the writer gets a useful starting point 85% of the time and ignores the rest.

"Good enough" is a function of:

  • What happens when the model is wrong
  • How easy it is to catch and correct errors
  • What the alternative (no AI) looks like
  • The emotional state of the user when errors occur

None of these are in the benchmark.

The Market Question Nobody Asks

When I join an AI product team, I always ask: "Who is currently losing the most because this problem isn't solved?"

Not "who would use this if it existed." Not "what's the TAM." Who is actively suffering right now because the problem you're solving isn't solved?

That question almost always produces a more useful answer than any amount of model benchmarking.

Because the people who are suffering the most will:

  • Tell you exactly what "good enough" means in their context
  • Forgive early roughness if the core value is there
  • Give you the signal you need to know which model improvements actually matter

The model matters. But the market defines what "good" means.

A Simple Reframe

Instead of "how do we make the model better," the more useful question is often:

"Given our current model quality, what is the smallest, most specific problem we can solve completely?"

Complete solutions to small problems beat partial solutions to big problems. Every time.

The AI teams that win aren't the ones with the best models. They're the ones who found the workflow where their model is already good enough — and then made that workflow so smooth that users never want to go back.

The model is a tool. The market is the map. Most AI PMs are spending all their time sharpening the tool and not enough time reading the map.