The Un-Augmented Anything¶
Status: Pending Review | Centaur Security Labs | 2026
Author: Jay Hawkins, Centaur Security Labs
The views expressed in this publication are those of the author and do not reflect the official policy or position of NORAD, USNORTHCOM, USCYBERCOM, the Department of the Army, the Department of War, or the United States Government.
In a November 2025 interview, Palantir CEO Alex Karp delivered a line that traveled: "If you are the kind of person that would've gone to Yale, classically high IQ, and you have generalized knowledge, but it's not specific, you're effed." AI, he argued, will "destroy humanities jobs," and the only reliable futures belong to two groups — those with vocational training, or the neurodivergent. There is a real observation underneath the provocation, and I think it's mostly correct. But Karp has named the wrong axis. The line that AI is drawing through the labor market does not run between the generalist and the specialist, or between the humanities and the trades. It runs between work that can be commoditized — generic synthesis, the most probable competent output — and judgment that can only be augmented, never replaced. Karp's own life is the cleanest evidence for the better version of his claim. The smart generalist is not obsolete. The un-augmented anything is.
I. The claim, stated fairly¶
It would be easy, and cheap, to treat Karp's remark as a billionaire telling liberal-arts graduates to learn to weld. It deserves better, because the core of it is true.
The argument, reconstructed charitably, is an economic one about substitution. For a century, a high-IQ generalist with a prestigious credential could enter almost any white-collar role, learn it quickly, and command a premium for raw cognitive horsepower applied to general problems: synthesize this, draft that, reason through the other thing. Karp's claim is that a large language model now does exactly that — broad synthesis, fluent drafting, competent reasoning over general information — instantly and at near-zero marginal cost. When the distinctive thing you sold was the ability to be quickly competent at general knowledge work, and a machine is now quickly competent at general knowledge work, your premium evaporates. The reward, he says, shifts to people with "specific" knowledge: the vocational, the deeply technical, the person who knows exactly how to fix the proprietary machine the model has never seen.
I build AI systems for security operations, and at the level of mechanism, this is simply what I observe. The model is a superb generalist and a poor specialist, for reasons that are not incidental but structural — and understanding why is what reveals where Karp's framing goes wrong.
II. What he gets right, and why¶
A language model is, in the most precise sense, a machine for producing the probable. Trained on a vast corpus of human text, it predicts the most likely continuation — the competent, conventional, middle-of-the-distribution response. This is its genius and its boundary at once. Ask it to synthesize the general state of a field and it will give you something better than most generalists could on short notice, because the average of an enormous amount of human writing is genuinely good. The generic synthesis is exactly the thing it was built to commoditize.
But it has never seen the world it writes about. It has seen descriptions of the world — the shadows on the wall of Plato's cave. It has read millions of accounts of network scans and never run one; read about a thousand proprietary machines and touched none. This is why, the moment you ask it for something specific and true about an actual system, it will confabulate with total fluency: invent a field name that sounds right and does not exist, describe a procedure that reads correctly and fails in contact with the real device. The generalist competence is real. The specific grounding is absent, and cannot be bluffed into existence.
So Karp is right that the premium moves toward the specific. But notice what "specific" actually is in this account. It is not a credential. It is contact with ground truth — the part of knowledge that comes from having done the thing, in the world, and knowing how it actually behaves. That is a different cut than the one his slogan makes.
III. The self-refuting witness¶
Here is the part that I think Karp, of all people, should see most clearly: he is the living counterexample to his own line.
Alex Karp holds an undergraduate degree in philosophy from Haverford, a law degree from Stanford, and a doctorate in social theory from Frankfurt, completed under the heirs of the Frankfurt School. There is no more perfectly "generalized, not specific" résumé in American business. By the literal terms of his own warning, the young Alex Karp — by his own account unsure who would give him a first job — was exactly the person who should have been "effed." Instead he built one of the most valuable companies in the world.
This is not a gotcha. It is the most useful data point in the whole conversation, because it tells you what actually created the value. Karp did not succeed by acquiring a vocation. He succeeded by taking deep, general, conceptual training — the ability to think rigorously about institutions, power, meaning, and how systems of people behave — and applying it to a specific, hard, real domain: building software that intelligence and defense institutions could actually trust. The value was not in the philosophy alone, and not in the domain alone. It was in the pairing, governed by judgment about which problems mattered and which solutions were real.
His own life argues against his slogan and for a better one. What survived — what compounded into a fortune — was not generality discarded in favor of a trade. It was generality grounded in a domain and directed by judgment. That is not the death of the generalist. It is the only thing that ever made the generalist valuable in the first place, now thrown into sharp relief because the machine has taken over the part that was never the point.
IV. The wrong axis¶
Philosophy has a precise vocabulary for the distinction Karp is groping toward, and it is older than the labor market.
Aristotle separated three kinds of knowing. Episteme is theoretical knowledge — the universal, the demonstrable, what can be written in a book. Techne is craft — the know-how of making, the vocational. And phronesis is practical wisdom: the capacity to judge the right action in a particular, concrete, ambiguous situation, where no rule fully determines the answer. Phronesis is not theory and it is not a trade. It is the seasoned judgment that lets an experienced person look at a situation that has never occurred before and know what to do.
A language model is a magnificent engine of episteme — it has compressed an astonishing amount of the universal and the demonstrable. It can be given techne in narrow, well-specified slices. What it cannot have is phronesis, because phronesis is built only one way: through accumulated experience of particulars, in a body, in a world, with consequences. Michael Polanyi named the residue of that experience tacit knowledge — "we can know more than we can tell." The senior analyst who looks at a clean tool output and says this is wrong, I don't know why yet is exercising tacit knowledge and phronesis at once. There is no prompt that contains it, because the person holding it cannot fully articulate it either. It is the part of expertise that resists being written down — which is precisely the part a system trained on what was written down can never acquire.
So the real axis is not generalist versus specialist. It is commoditizable synthesis versus augmentable judgment. On one side: anything that is, in the end, the production of the probable competent answer — and that is now cheap, whether it wears a Yale sweater or a tool belt. On the other side: phronesis, tacit grounding, the judgment that knows when the fluent answer is wrong. That survives. And it cuts across Karp's categories, not along them. There are vocational jobs that are pure pattern execution, and they are as exposed as any pundit. There are "humanities" capacities — ethical judgment under ambiguity, knowing which question is the real one, recognizing when a confident system is confidently wrong — that are the most augmentation-resistant skills there are.
V. The neurodivergence tell¶
Karp offers two hedges against obsolescence: vocational training, or neurodivergence — and he credits his own dyslexia with Palantir's success. The vocational hedge gets all the attention. The second one is the more revealing, because it accidentally unifies the whole argument.
Why would neurodivergence be a hedge against a machine? Because a language model is, by construction, a regression to the conventional — it produces the most probable continuation, the consensus shape of a thought. Neurodivergent cognition is, very often, structured deviation from the probable: it sees the connection the average mind skips, frames the problem in the way the training distribution would never weight highly. It is, almost definitionally, the thing the model is worst at generating, because the model is an averaging machine and this is non-average by nature.
Look at what Karp's two hedges have in common. Vocational, hands-on expertise survives because it is grounded in particulars the model has never touched. Neurodivergent insight survives because it is idiosyncratic in a way the model cannot average toward. Both are simply names for what the model cannot produce — the specific and the non-generic. He is right twice and mislabels it twice. The unifying principle isn't "trades and ADHD." It is: value is migrating to wherever human judgment departs from the most probable competent output, because the most probable competent output is exactly the thing now available for free.
VI. The trap inside the prescription¶
There is a deeper problem with Karp's prescription, and it is one I've written about separately: it eats its own seed corn.
Hubert Dreyfus spent a career arguing that expertise is not stored rules but embodied, intuitive mastery — and, crucially, that it is acquired in stages. The novice follows rules; the expert has internalized so much experience that they perceive situations directly and act without deliberation. You do not arrive at expert phronesis by being handed it. You build it by grinding through the lower rungs — the "generic" work, the synthesis, the drafting, the cases that felt routine until the one that wasn't taught you something no rule could.
Karp's advice — abandon the generalist track, the AI does that now — quietly assumes the experts already exist. But domain experts are manufactured out of smart generalists who spent years doing the generic work that the AI is now eating. If you automate away the apprenticeship, you do not produce a generation of instant specialists. You starve the pipeline that produces specialists at all, and pair it with the cognitive-offloading problem: a person who never struggles through the generic work never builds the tacit grasp that becomes judgment. The stable end-state Karp describes may be reachable only through a transition that destroys the mechanism for reaching it. That is not a reason to dismiss his observation. It is a reason to treat "let the AI do the entry-level thinking" as the most dangerous half of his advice.
VII. The un-augmented anything¶
So what is the durable position? Not the pure specialist, and not the pure generalist. It is the centaur — and that word is the whole argument compressed.
The centaur model, which the rest of this lab's work is built on, holds that a human paired with a machine outperforms either alone, because each supplies what the other structurally cannot: the machine brings scale, speed, and the commoditized synthesis; the human brings ground truth, phronesis, accountability, and the calibrated skepticism to know when the fluent output is wrong. In the systems I build, this is not a metaphor — it is an enforced architecture. The model generates and synthesizes; a deterministic layer verifies its claims against the actual world; a human holds the judgment and the authority for anything that matters. The human half is not there because the human is smarter. It is there because the human holds the one thing the model cannot: contact with the real, and responsibility for the outcome.
What this reveals is a competence Karp's framing misses entirely. Being a good centaur — knowing what to delegate, how to direct an AI, how to audit it, when to overrule it — is itself a skill. It is learnable and teachable; encoding it is literally what my work does. And it is a skill most people, including a great many of Karp's prized specialists, do not yet have. A tradesman who cannot direct and check an AI is exposed the same way the generalist is. A domain expert who has also learned to drive the machine is not just safe — they are the most leveraged worker in the economy, because they can aim a tireless generalist at the specific problems only they can ground.
That is the sharper, more defensible version of Karp's line, and it is the one I would stake the future on: the smart generalist isn't obsolete — the un-augmented anything is. What AI commoditizes is the production of the probable competent answer. What it cannot touch is the judgment that knows whether the answer is true, whether the question was right, and whether the action is worth taking. Pair that judgment with the machine and you have not been replaced. You have been amplified. Strip the augmentation away — from the generalist, the specialist, the welder, or the Yale graduate alike — and that is the profile with no future.
Coda¶
I want to be careful not to claim more certainty than anyone has. The economics of this transition are genuinely unsettled, and Karp may be more right than I am about how brutal the near term will be for people whose work was generic and who never get the chance to ground it. His warning is worth taking seriously precisely because the commoditization is real and already here.
But a slogan that tells a generation of curious, capable young people that their general intelligence is now worthless is both false and corrosive — false because it misnames the axis, corrosive because it discourages exactly the people who could become the grounded, augmented experts the future actually rewards. The better message is harder to fit on a chyron: don't abandon the general mind. Ground it, and augment it. The man who said the generalist is finished is himself a philosophy PhD who grounded his general mind in a hard domain and built an empire. He should believe his own life over his own line. If you're working on the same questions — about what AI commoditizes, what it can't, and how expertise is actually built — I'd like to compare notes.