When Facebook launched its relationship status feature, I was a young adult navigating the particular social anxiety of having to declare, publicly, whether you were single or not. “It’s Complicated” was a gift. It said everything and committed to nothing. You were neither in nor out, but that was apparently a legitimate place to be.
The feeling is a bit similar to how we use AI. The real complication here isn’t our relationship to it, but rather what AI is doing to our minds and especially to the way we think, learn, and work. Multifaceted enough to resist easy answers and probably more exposing than most of us want to admit.
This is why we hosted this dinner.
For Tandem’s fourth edition, we wanted to go somewhere harder than the usual conversations about AI productivity or job displacement. We wanted to sit with what AI is doing, concretely and physiologically, to the way we think. To the cognitive processes we tend to assume are stable: synthesis, judgment, formulation, the capacity to hold a difficult thought long enough to work through it. We spent time before the evening mapping the profiles and tensions of the people we’d invited: researchers, founders, a physician, a philosopher, a neuroscientist, practitioners who build with AI every day.
The recomposition of cognitive labor
The first thing we questioned together was the dominant narrative: that AI takes work away. The more accurate picture that emerged was recomposition. When you delegate formulation, structuring, and exploration to a model, the cognitive effort doesn’t disappear. It migrates. It moves toward selection, verification, and supervision. A different kind of work: one that feels lighter but may not be.
This matters because the shift is easy to misread as relief. The real risk surfaces later: if you stop formulating long enough, you may gradually lose the capacity to formulate. Not dramatically. Quietly. Without noticing.
Which raises a question that kept returning throughout the evening in different forms: who decides on delegation? Not in theory, but in practice, in the moment. The choice of what to hand over to AI and what to keep is rarely deliberate. It happens at the speed of convenience, shaped by defaults, by what the tool makes easy, by deadline pressure. The people at the table who seemed most intentional in their AI use had made this choice explicit. They had decided, in advance, what they would not delegate: the first draft, the initial framing, the discomfort of sitting with a problem before reaching for an answer. Not because AI couldn’t help with those things, but because the struggle itself was part of what they were trying to preserve.
The table also pushed back on a comfortable analogy. We’ve absorbed other cognitive tools before, the calculator, the GPS, and survived. But this is different, and the difference is worth naming precisely. The calculator handled arithmetic. The GPS handled navigation. AI handles synthesis, judgment, and writing. It operates at the level of higher-order thinking, which makes cognitive offloading a fundamentally new phenomenon, not a faster version of an old one.
Where minds, biaises and models collide
This part of the conversation was less comfortable and more interesting for it.
Two cognitive biases came into focus. The first: a sense of authorship over ideas that emerged from a brainstorming session with AI. The model generates, you select, and somewhere in that transaction, the selected idea begins to feel like your own. A bias of paternity. The second: the tendency to stop reasoning when the model’s output is sufficiently convincing, not because you’ve been persuaded, but because it looks persuasive. A bias of authority.
What struck me is that intellectual rigor doesn’t protect against either of these. They are design problems, not competence problems. The interaction itself produces the distortion, regardless of how careful the user believes themselves to be.
Then something more confrontational was put on the table: in certain contexts, notably medical diagnosis, AI alone outperforms the combination of AI and the best human specialist. Not because the human adds noise, but because the way human cognition and algorithmic analysis get combined tends to degrade both. Ego, status, cognitive biases, the pressure of the room: they interfere. In some categories of judgment, human involvement is not neutral. It is a liability.
In a conversation that had been broadly protective of human thought, this landed like a useful bomb. The question it opens is not whether AI is smarter. It is whether we are honest about the conditions under which human judgment actually improves outcomes, and the conditions under which it doesn’t.
Fatigue: the underdiscussed dimension
One thread surprised me more than I expected: the conversation about fatigue.
AI does not reduce workload. It compresses timelines, inflates the volume of what is addressable, and multiplies the number of subjects one can work on in parallel. The promise of less effort turns, in practice, into an intensification of cognitive labor. You cover more ground in the same time. The ground isn’t lighter.
New patterns are emerging: a frenzy of question-and-answer exchanges that reproduces the addictive feedback loops of social media, shortened nights among people who cannot disconnect from a model that is always available (AI fatigue). The cognitive fatigue this produces is not metaphorical. It is biologically measurable.
The analogy that came up and stayed: the AI practitioner as a high-performance athlete. Alternating peak performance and structured recovery is not a luxury. It is a sustainability condition.
What should remain human and why
The final territory was the most contested, and productively so.
One guest had lots of context on the question of decision-making in an age of AI, and it gave this part of the conversation a useful anchor. The question “who decides” is not only personal. It is political. When institutions, governments, employers, and platform designers determine how AI is deployed at scale, individual choices about delegation happen inside a frame that was set without you. The cognitive autonomy we were discussing throughout the evening is partly a function of literacy, partly a function of power. Not everyone has equal standing to decide what they hand over.
Even in domains where AI demonstrably decides better, performance is not the only relevant dimension. There is also legitimacy. Certain domains must remain human not because humans are better at them, but because something essential is at stake in the fact of human judgment. A human should judge a human, even fallibly, because shared fallibility is constitutive of the justice contract. We accept imperfect verdicts differently when they come from someone who could, in principle, be wrong for the same reasons we are.
A different angle on preservation emerged toward the end of the evening. The capacity to be in genuine dialogue, to be destabilized by another person’s thinking, to hold space for a thought that doesn’t fit your frame, is itself a cognitive competence. One that requires stakes to function. Relational intelligence, the ability to read a room, to sense what is unspoken, to make someone feel genuinely seen: these may be the next genuinely differentiating competence, not in spite of AI, but because of what it cannot do.
What we walked away with
We left with more questions than answers. That was the intention.
Does AI extend thought, or gently short-circuit it, and can we tell the difference in the moment? Are individual protocols sufficient, or does the real intervention need to happen at the level of tool and experience design? Is the emerging divide between those who orchestrate AI and those who depend on it a technical fracture or a social one?
And underneath all of it, a question that felt both abstract and very urgent: what are we passing on? The traditional French educational model was built on the top-down transfer of knowledge. The teacher holds it, the student receives it and mastery is defined as the ability to reproduce and build on what was transmitted. AI doesn’t just disrupt that model. It makes the underlying premise feel slightly obsolete. If knowledge is retrievable in seconds, what exactly are we transmitting? What does it mean to educate a child, or a generation, in a world where the bottleneck is no longer access to knowledge but the judgment to use it well? We didn’t resolve that question. But it may be the one that matters most.
“It’s complicated“ felt like the right title before the dinner. It still does. But the nature of the complication is sharper now. We’re not in between because the answer isn’t clear. We’re in between because the right questions are only just beginning to form.
The questions the evening raised are available here: questions/4
Which brings us to what comes next…
If this evening left us sharper on what AI does to cognition, a question kept surfacing that we couldn’t fully address here: what is it doing to our relationships? To the way we confide, listen, show up for each other?
We’ve spent years worrying about cognitive atrophy. We’re starting to wonder whether relational atrophy is the quieter crisis running underneath it. That’s what Marine and I are bringing to the next Tandem dinner in Paris, at the intersection of AI and intimacy.
If that question unsettles you too, you’re probably the right person to be in the room.
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Tandem Dinners is a series of private gatherings in Paris, organized by Marine et Margaux around AI and a specific theme. By invitation only.
We think, explore and work along the same lines it seems. We should talk. :)