That, to me, is one of the clearest signals in the enterprise AI market right now.
Companies are allocating money.
Presentations are getting brighter.
Adoption pressure is real.
But confidence is still missing.
And the reason is simple: there is no universal playbook.
Codestrap co-founder Dorian Smiley puts it bluntly — companies still do not really know which AI architectures and use cases actually fit their business. That creates a familiar corporate trap: the market wants a repeatable recipe, so many teams act as if the recipe already exists.
It doesn't.
The second problem is how success gets measured.
A lot of AI effectiveness is still judged through formal output:
- how much code was written,
- how many tasks were closed,
- how fast something was delivered.
But quality is much harder to see.
Code can look correct.
It can pass tests.
And still be wrong.
That is why so many AI discussions sound stronger in slides than in operations.
One example says a lot: in an experiment with rewriting SQLite using generative tools, the result was 3.7x more code and roughly 2,000x slower performance. In business terms, that is not innovation. That is wasted budget wrapped in technical optimism.
There is also a deeper issue: if the system cannot reliably verify its own output, then scale does not remove risk — it multiplies it.
That is where economics stops listening to hype and starts counting:
- quality failures,
- legal exposure,
- pricing pressure,
- and now even insurance friction.
Because when insurers start excluding AI-related losses from coverage, that is not a marketing signal. It is a risk signal.
My view: the biggest enterprise AI problem right now is not adoption speed. It is false confidence created by weak metrics.
What are companies in your market actually measuring today: output, quality, or real business value?