Everyone Is an AI Expert Now. But What Does “AI Expert” Actually Mean?
The technology is advancing at extraordinary speed. The terminology surrounding it is becoming increasingly meaningless.
There is something strange happening around artificial intelligence.
The more powerful AI becomes, the more people seem to describe themselves as “AI experts.”
But what does that actually mean?
Are you a programmer?
Do you design machine-learning algorithms?
Do you train models?
Do you build datasets?
Do you work on GPUs and infrastructure?
Do you develop applications using existing models?
Do you decide which content a model can generate?
Do you work on safety and alignment?
Do you evaluate models?
Do you simply use ChatGPT every day?
These are not the same profession.
And yet they are increasingly placed under the same label.
That distinction matters because the AI economy is becoming much more sophisticated than the public debate suggests.
1. Using AI is not the same as building AI
There are at least four very different levels of participation in the AI economy.
Level 1 — AI User
A professional uses AI to research, write, analyse, code, summarize documents, automate tasks or accelerate decision-making.
This can be extremely valuable.
But it does not make someone an AI engineer or AI researcher.
Level 2 — AI Integrator
This person builds workflows and applications around existing models.
They may use APIs from OpenAI, Anthropic, Google, Meta, DeepSeek or other providers.
They can build sophisticated systems without having designed the underlying model.
Level 3 — AI Engineer
This is a much deeper technical category.
It includes machine-learning engineers, research engineers, data engineers, infrastructure engineers, GPU specialists and software engineers working directly on AI systems.