Many companies call simple automation or classical algorithms “AI”. Real artificial intelligence used in large projects requires heavy and innovative research.
For two years, theartificial intelligence is everywhere: on websites, in software, in sales presentations and even in political speeches. Everything has become “AI”.
And yet, much of what we are presented as artificial intelligence simply isn’t. This is not a technological revolution. This is marketing inflation.
Ten years ago, these same technologies were called algorithms. Sometimes sophisticated, often useful, but perfectly deterministic: we knew how they worked and why they produced their results. Today, the same code is renamed “artificial intelligence.” The word has changed. Not the technology.
Meanwhile, true artificial intelligence is advancing. It no longer just applies rules. She learns. She explores. She discovers. She sometimes invents new strategies that the best human players had never imagined, like in the game of chess. It identifies molecules unknown in pharmacy. It offers industrial architectures that are impossible for engineers to design intuitively.
Modern AI does not follow rules: it learns representations of the world. But this type of artificial intelligence is rare. And above all, it is difficult to produce. Creating real AI requires years of research, specialized teams, heavy infrastructure and considerable investments. This is not a feature that is added to existing software. This is not a module that can be installed in a few days. Nor is it the work of an isolated expert.
No one wants to build an airplane engine in their garage. However, today, everyone seems to be able to “do AI”. The problem is not just technical. It is cultural. By calling everything artificial intelligence, we end up making intelligence invisible.
This confusion clouds public understanding. It sometimes misleads decision-makers. It weakens the credibility of real scientific advances. Above all, it maintains a dangerous illusion: that of a revolution already accomplished, even though it is barely beginning. Because adding a so-called “AI” layer to existing software does not transform a company into a research laboratory.
Today, part of the market sells as artificial intelligence what is in reality automated scripts, statistical rules or tools configured on existing models. These solutions can be useful, effective, sometimes even efficient. But they do not involve the same scientific effort, nor the same level of innovation. Concretely, today it is enough to add a layer of automation or connect software to an existing model to claim “AI”. A rules engine becomes an “AI
predictive”. A simple sequence of scripts becomes “decision-making AI”. An interface connected to a third-party model becomes “proprietary AI”.
Confusing the two is not trivial. In the short term, this creates confusion. In the long run, this destroys trust. This confusion now goes beyond simple technological marketing, it is becoming a strategic issue for Europe.
Because while we call “AI” tools that are not really tools, other powers are investing massively in fundamental research, computing infrastructures and teams capable of producing the next technological breakthroughs. The risk is clear: financing “fake AI” rather than real deeptech programs means weakening our capacity for innovation in the long term.
The danger is not only commercial. It is strategic. Artificial intelligence is already structuring global economic competition. It redefines industrial, military, scientific and energy balances. It conditions the capacity of a continent to produce its medicines, organize its logistics, secure its infrastructure or optimize its ecological transition.
In this context, confusing intelligent automation with artificial intelligence amounts to diverting valuable resources at the very moment when they should be supporting real technological disruptions.
All digital solutions have their uses. But not all of them are artificial intelligence. True AI leaves traces: scientific publications, peer-reviewed work, identified research teams, structuring datasets, dedicated computing infrastructures, measurable and reproducible results.
This level of requirement is not a technical detail. It is a condition of collective credibility. Because by promising intelligence everywhere, we especially risk provoking general distrust. And that day, it will not be false artificial intelligences that will pay the price. These will be the real ones.
