This is not the first time in history that reasoning has been used to serve almost any goal.
In classical Athens, the Sophists taught rhetoric (often for a fee) and promised something very attractive: you could learn to win arguments. Socrates pushed back with a different standard: reasoning should be tied to truth, not just persuasion.
That tension didn’t disappear.
It scaled.
Today, the “Sophist” can be a machine.
Generative AI can produce fluent explanations, confident arguments, and clean narratives on demand. The risk is also familiar: reasoning that sounds correct without being grounded in reality.
The machine Sophist
If I try to describe GenAI in one sentence, I keep returning to this: we have built a high-throughput argument generator.
It is not that the output is always wrong. Often it is helpful, and sometimes it is excellent. The uncomfortable part is structural:
- It can produce plausible answers faster than we can verify.
- It can speak with confidence without carrying accountability.
- It can optimize for coherence even when reality is messy.
In other words, it does not come with truth guarantees.
Socrates argued against the Sophists not because rhetoric is useless, but because persuasion without orientation to truth becomes dangerous. It trains the listener to confuse clarity with correctness.
GenAI, at scale, risks doing the same thing, except it can do it for millions of people simultaneously and differently.
A systems lens
I read this less as a moral panic and more as a systems problem. We have introduced a component into our knowledge ecosystem that is:
- High output (it can generate endless text)
- Low friction (it feels easier than thinking)
- Low cost per unit (the marginal effort approaches zero, for now)
- Weakly grounded (it may or may not be tethered to verifiable sources)
Any system with those properties will change the equilibrium.
It changes how people search, how they decide, how they justify, and how they debate. It doesn’t just answer questions; it shapes what counts as an answer.
If you are building software, you know this pattern: when you add a tool that makes an action effortless, people will do more of that action, even when it shouldn’t be done.
If the effortless action is producing convincing reasoning, then the system will generate convincing reasoning everywhere.
A Hegelian lens
Hegel’s language helps frame why this feels like more than “just another tool.”
If history expresses a spirit (Geist) through institutions and shared practices, then major technologies don’t only change what we do, they change how we understand knowledge, agency, and freedom.
Printing didn’t merely make books cheaper; it changed the social structure of authority.
The internet didn’t merely connect us; it changed what it means to “know” something.
GenAI, similarly, doesn’t merely speed up writing. It changes the surface area of speech, and therefore the conditions under which public reason operates.
A pessimistic thought experiment
Let me attempt a pessimistic thought, even from within a broadly Hegelian frame.
A central assumption in the Hegelian story is that Reason realizes itself through human freedom and consciousness. History advances dialectically because there is a subject, collective humanity, capable of experiencing contradictions, reflecting on them, and transforming institutions and norms through that reflection.
But GenAI can generate outputs that mimic human thought without the underlying rational intentionality.
Its products arise from iterative calculation and optimization, not from a self-conscious subject striving toward understanding.
If those products begin to substitute for human deliberation, shaping what people believe, repeat, and act upon, then we should ask whether this introduces not merely a new contradiction for Spirit to sublate, but something out of phase with the dialectical logic.
To put it differently:
- The dialectic presupposes participants who can mean what they say.
- GenAI can produce speech without meaning.
If the “content layer” of society becomes saturated with outputs that do not originate in lived intention, then public discourse may fill with signals that resist integration. Not fruitful antagonisms that trigger higher self-awareness, but a fog of persuasive fragments.
In that scenario, the risk is not simply that GenAI makes errors.
The risk is that it changes the conditions of sense-making:
- It scatters attention.
- It fragments shared reference points.
- It dilutes the meaningful tensions that drive genuine learning.
Instead of catalyzing a higher synthesis, GenAI could stall the movement toward clarity by overwhelming the rational subject with an endless stream of articulate noise.
What would “truth orientation” look like now?
If the Sophist problem has returned in machine form, then the Socratic response is not nostalgia, it is design.
Truth orientation, in practice, looks less like a vow and more like constraints:
- Grounding: answers that can point to sources, evidence, or reproducible steps.
- Provenance: knowing where a claim came from, and what incentives shaped it.
- Verification loops: institutional habits that reward checking more than sounding right.
- Friction in the right places: making it slightly harder to publish confident claims that cannot be defended.
This does not eliminate persuasion. It makes persuasion pay rent.
Closing
The Sophists were not defeated by a better speaking style. They were challenged by a higher demand: that reasoning must answer to truth.
GenAI revives the old temptation at an entirely new scale: argument without responsibility, fluency without grounding, coherence without contact.
Whether this becomes a new stage in the unfolding of Reason, or a distortion that delays it, may depend less on the models and more on the institutions we build around them.
The dialectic, if it continues, will need something like a Socratic upgrade: not to silence the machines, but to ensure that human freedom and understanding remain the authors of what counts as knowledge.