AI and Work: The Economy of Discernment Redefines Productivity

As artificial intelligence integrates into organizations, it is profoundly transforming the nature of work. This change is noticeable from the very start of a typical day: reviewing a presentation, validating a document, processing a few pending messages. These tasks seem to require a bit more effort than before, not to produce them, but to understand and verify them.

For several months, a discreet yet significant transformation has been taking place in companies adopting AI, whether officially or unofficially. AI has not only accelerated the pace of work but has also shifted its center of gravity. Once, writing, structuring, and formulating took time. Today, these tasks are almost instantaneous. However, this time gain comes with a hidden cost: that of sorting, evaluating, and doubting.

We are entering what could be called a discernment economy. Faced with rapidly generated, well-written, and seemingly coherent content, a new skill becomes essential: the ability to detect what is missing, what seems right but is not true, what is fluid but not thoughtful. This skill relies on two precious resources: attention and critical thinking.

Researchers refer to "cognitive overload" to describe this mental pressure. It is not due to the sheer volume of work but to the constant effort of filtering information. Too much plausible information, too few reliable benchmarks. The mind no longer rests on production but exhausts itself in verification.

In this context, everyone’s role evolves almost despite themselves. It is no longer just about evaluating results but about reconstructing reasoning. Reintroducing intention where there is sometimes only a jumble of sentences. Part of the work becomes invisible but decisive: giving meaning back to what appears to have it.

The paradox is clear. The more tools promise to simplify, the more they demand fine vigilance. Not technical vigilance, but intellectual vigilance. A form of critical attention that cannot, at least for now, be automated.

This shift is not trivial. It questions how we define the value of work. Is it about producing quickly, or understanding deeply? Is it about delivering content, or ensuring it holds up? Perhaps the challenge is not to slow down the use of AI, nor to accelerate it. But to learn to inhabit this new space it creates: a space where the essential no longer lies in the act of writing, but in the act of judging.

And where, paradoxically, thinking becomes an effort again. As Hartmut Rosa points out, "acceleration does not necessarily free up time; it transforms our relationship with the world."