I had an intern ask me today how we maintain our humanity in the age of AI. It’s a simple enough question, and one that comes from an honest place when you spend your days building these systems. I gave what felt like a reasonable answer: I outlined how important it was to engage with friends and family, with literature, with the other things in life that bring joy. They smiled and nodded, and we moved on. And I’ve been thinking about it ever since, which is usually a sign the answer wasn’t quite right.
The question existed within the context of a broader conversation about Karpathy’s auto-research system, and whether a system that can generate and test new scientific hypotheses without human involvement constituted science in the first place. My initial response was that it depended on what you meant by science: was it the discovery of new things, or was it the human act of satisfying our curiosity about the natural world? The first is outcome-driven. The second is process-driven. I go back and forth on which matters more. Although I became a scientist specifically because I enjoyed the praxis of science, and that felt like it had something to do with the answer, even if I couldn’t quite articulate why.
Heidegger has a lot to say about this, specifically in “The Question Concerning Technology” and in the rest of his work. I’ve read the relevant essay before, but it had been a while. “The Question Concerning Technology” opens with what seems like an obvious observation: technology is a neutral instrument, a means to human ends. A hammer is a hammer. We point it, we use it, we’re in control. Heidegger’s first move is to say that this is correct, but it isn’t true. By which he means it doesn’t reveal what technology essentially is. What technology does, he argues, is reveal. Every technology is a way of disclosing the world, of bringing things out of concealment and into presence. And the particular way that modern technology reveals is through what he calls Gestell, usually translated as Enframing. Under Gestell, everything is revealed as Bestand, standing reserve. Resource. Stock. Available for ordering, optimizing, deployment. The Rhine appears as a water-power supplier. The forest appears as a timber reserve. And eventually, and this is the part that stuck with me, the human being appears as human resources.
This isn’t a claim about bad actors. It’s a claim about a mode of appearing. Under Enframing, things don’t have intrinsic worth: they surface as inputs, material awaiting mobilization. The Rhine isn’t diminished because someone hates rivers. It’s diminished because the entire framework of modern technological civilization can only encounter it as potential energy, or a place to dump waste.
It’s easy to read this and think immediately of generative AI. Indeed, its easy to read just about any piece of philosophy these days and think of generative AI. But it’s natural to ask what does a large language model, an image generation model, or an automated hypothesis engine, actually do? Each one converts the archive of human culture and scientific expressions into training data. Every paper ever written, every experimental result, every proof, every image, blog post, novel, poem, news article, fan fiction, and reddit comment has been slurped up. They’ve been compiled into a vast stockpile of prior human inquiry, stripped of its context, compressed into a latent space, and available on demand.
This isn’t incidental to what these systems are. It is what they are. It’s how they’re designed. The technical operation is Gestell applied to knowledge itself.
Heidegger thought art, and specifically poiesis, was a potential counterforce to Enframing. Poiesis provides this through a disclosure of some new human condition or experience. I believe that the same argument can be made of science, if properly understood. This is because a great experiment doesn’t just produce data. It opens a question in a way that changes what’s visible afterward. He thought this capacity was exactly what Gestell threatened to extinguish: our ability to encounter things as genuinely other, genuinely resistant, genuinely strange.
And then we built systems that turn that capacity into feedstock.
The natural objection is that auto-research outputs can be true, novel, even important. Fair enough. But here’s where the outcome vs. process framing starts to crack. Genuine disclosure, for Heidegger, requires thrownness. We disclose worlds because we are our worlds, anxiously, finitely. This is why the enjoyment isn’t incidental. When I find a result surprising, when a question won’t let me go, when something feels wrong before I can prove it – that attunement is what makes the inquiry move in a particular direction rather than any other. The questions I ask, the anomalies that snag my attention, the sense that something is off – all of that is downstream of caring in a way that’s irreducibly mine.
An automated system has no thrownness; no sense of finality; no existential investment in what it produces. It can return true propositions, but it isn’t surprised. It doesn’t find anything interesting in the way that generates the next question. It can produce outputs that resemble the residue of genuine inquiry while the inquiry itself is absent. Is genuine inquiry required to be science?
In my view the outcome vs process framing that I introduced is real, but it’s slightly off center. The deeper question is whether science, defined as a form of human understanding, without the right kind of process is actually science in any way that actually matters. A system that generates true results nobody asked for, in a space nobody was navigating, might be doing something real. It isn’t extending attunement. It’s replacing it with something that looks like attunement from the outside but is actually just search over a well-defined space. Some might argue that auto-research tools constitute a tool, not dissimilar from a telescope. This looks sensible at a surface level. The telescope doesn’t do astronomy afterall, it simply extends the reach of the astronomer. For this analogy to hold, the auto-research system would necessarily need to be an extension of the scientist, but that’s precisely what they aren’t. They’re a replacement.
And it’s not just generative AI or auto-research systems doing this. Modern recommendation systems are running the same operation on experience itself, and have been for years, quietly. When Spotify decides what you hear next, when Twitter orders your feed, when Netflix surfaces what to watch, the underlying logic is the same: your past behavior is converted into standing reserve, processed, and returned to you as an optimized stream. The goal isn’t to show you what’s interesting. It’s to show you what you’ll engage with, which is a subtly but importantly different thing. Engagement is a behavioral signal. Interest is an attunement. They correlate, but they’re not the same, and optimizing for one tends to erode the other.
What recommendation systems converge on, structurally, is something very close to what Heidegger called das Man, or the anonymous “they”, the statistical average of human preference. Not your preference, in any deep sense, but the preference that emerges from aggregating behavior across millions of users and trillions of clicks to find what sticks. The result is an experience that feels personalized but is actually a kind of averaging. The experience is smooth and frictionless, and calibrated to keep you moving rather than to actually put you in contact with something that might constitute a disclosure. The rough edges, the unexpected encounters, the things that might unsettle you or take you somewhere you didn’t know you wanted to go – those are the first things to get optimized away, because they don’t perform well on engagement metrics.
This is Gestell applied to the structure of daily life. Not just to art or science, but to the texture of what you encounter, moment to moment, as you move through the world. Your morning commute, the news you read, the people you socialize with – all of it becomes an engagement-optimized content surface. And the danger, as Heidegger saw it, isn’t that any individual encounter is bad, or even that such curation is bad. It’s that the accumulated effect is a kind of narrowing, a slow contraction of what the world is able to show you. You stop being surprised not because nothing surprising exists, but because the system has learned that surprise doesn’t retain you.
So what are we to do about it? There’s a tempting conclusion here that I think is wrong.
The obvious response to all of this is to become a more deliberate curator. The thinking goes that if one seeks out the great works of humans across literature, science, and art and to focus rigorously on this diet. Inoculate yourself with Miles Davis and Mussorgsky. Read only the classics. Follow only the scientists doing genuine, curiosity-driven work. Curate carefully and you’ll be fine.
It’s appealing because it feels like an action, and because it applies some sort of normative judgement to consumption habits. Reading the new yorker makes us feel good about ourselves in the same way that ordering a salad at brunch does. And there’s something right in the instinct. It’s probably the case that the quality of inputs does matter. But as a response to Gestell, it misses the point almost entirely, and in an instructive way.
Notice what happens the moment you frame it as curation. You’ve already adopted the posture of a consumer managing their intake. The standing reserve has been upgraded – now its coltrane instead of brainrot, gogol instead of tweets – but the fundamental relationship is unchanged. You’re still extracting value from cultural goods to top up some internal resource called humanity or understanding or taste. That’s Enframing applied to the antidote. Heidegger’s point, and it’s critical to understand this, is that Gestell is a mode of relating to objects, and not a category of objects. You can stand in front of a Van Gogh in a museum and relate to it entirely within Enframing, as cultural capital, as a consumable aesthetic experience, as an item on a list of experiences. And you can read Proust, or listen to Sun Ra, in the same way. You can treat papers as ammunition for grant proposals and promotions; as the source of future citations. The work is there. The disclosure isn’t happening.
The same logic runs in reverse as well. You can experience genuine disclosure in something modest if the attunement is right. A small insight in an obscure dataset, a conversation that takes an unexpected turn, a beautiful paragraph in a forgettable article are all possible. This is what makes the curation answer not just insufficient but slightly counterproductive. It redirects your attention toward object selection when the problem is the selecting posture itself. It gives you something to optimize when the whole issue is that you’re optimizing.
What Heidegger points toward instead is something he calls Gelassenheit, releasement or letting-be. A receptive openness that stops trying to master, optimize, or extract. Not a technique. A fundamental reorientation. A willingness to be surprised, to be changed, to let things be genuinely other rather than immediately recruiting them into your purposes.That’s harder than it sounds in an environment designed, at every level, to keep you optimizing. Which is why Heidegger thought we were in genuine danger. And not from any particular technology but from the disappearance of the very capacity to notice what had been lost.
So where does this leave us? I don’t have a clean answer. The intern’s question is the right question, and I’m not so sure that my answer was the right one.
What I keep coming back to is something more modest. I do this work because I find it genuinely enjoyable. Not because enjoyment is a perk, but because the enjoyment is the attunement, and the attunement is what makes the science science rather than search. Removing it doesn’t just change the experience. It changes what the thing is.
Maybe that’s the answer I should have given. Stay close to what genuinely surprises you. Not as a strategy, but as a diagnostic: the moment you stop being surprised is the moment you’ve stopped actually encountering anything at all, and started extracting.