Every major technology shift I’ve worked through has followed the same pattern.
It starts with a powerful, general purpose tool that feels almost magical. It can do a bit of everything, and for a while that’s enough. Over time, the cracks appear. Edge cases grow. Expectations rise. What once felt flexible starts to feel blunt.
Eventually, specialisation wins.
I think AI will follow the same path.
Not towards one system that does everything well, but towards many systems that each do something very well.
This has happened before
Operating systems did not converge into one universal platform. Databases did not become a single solution that fits every workload. Cloud platforms did not remove the need for specialised tools. Security did not collapse into one product that solves every problem.
Each layer fragmented because real world use demands it.
AI is already showing the same signals
Some models are better at writing. Some are better at reasoning. Some excel at structured output. Others handle search, summarisation, or code more reliably. These differences are not temporary quirks. They are the beginning of divergence.
A model tuned for contract review will outperform a general assistant at spotting liability clauses. A model optimised for customer support will handle tone and de-escalation better than one built for code generation.
As AI systems are pushed harder, the gap between “good enough” and “good at this” will widen.
The instinct to simplify
That matters because most organisations will instinctively try to treat AI as a single thing. One tool. One subscription. One answer.
That approach rarely holds.
What usually follows is confusion, tool sprawl, and frustration. People blame the technology, when the real issue is mismatched expectations. A system designed for one type of work is quietly forced into another.
Calm systems do not work like that
Calm systems are deliberate. They use the right tool for the right job. They accept trade offs instead of pretending they do not exist.
The same will be true with AI.
The businesses that get the most value will not be the ones chasing the most impressive model. They will be the ones that understand where each system fits, where it does not, and how to integrate it sensibly into existing workflows.
The mistake will not be choosing the wrong AI.
It will be assuming there is only one.