The machines are not, in fact, fine.

Earlier this week, Minas Karamanis posted this essay (archive) about the pedagogical risk of outsourcing scientific "grunt work" to large language models. The essay went viral (by Bluesky standards) and received broad praise as a 'phenomenal', 'well articulated', 'brilliant' piece of writing.

View post on Bluesky

Regrettably, I know Claude well enough to smell its scent even when the em dashes have been excised, and I can say with absolute confidence:

The essay was not written by Karamanis, but extruded by Claude. Claude has not merely been used to touch up a draft. It has been prompted with sources and some steering.

Karamanis has used obfuscation and perplexity fuzzing to hide that this essay is Claude output. He did not disclose any level of AI use on his blog.

While Claude has linked to the sources when flattening them into a summary, it has also smuggled in framing from them to an extent that would constitute plagiarism by any reasonable journalistic or academic standard.

Furthermore, if you examine the essay with a more critical eye, you can begin to see that it is not good writing. At places it barely maintains coherence. It is, to be frank, Claudeswallop, and that so many people have praised this as excellent writing is an extremely concerning example of how effectively the superficial style markers of large language models can deceive readers.

My criticism of Claude's writing is not kind. I do not afford a synthetic text extruder1 the good faith and grace that I would extend to a person, because it is not a person, it is a synthetic text extruder that has been deceptively given first person pronouns by a parasitic industry. This might make my review an uncomfortable read if you are still giving Karamanis the benefit of the doubt, so I shall point to the incontrovertible evidence of obfuscation first. You can find it about halfway in, and it is a single semicolon:

Frank Herbert (yeah, I know I'm a nerd), in God Emperor of Dune, has a character observe: "What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger." Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small.

If you happen upon a physical copy of God Emperor of Dune, you will not find a semicolon there. You will find an em dash. If you search online, you can find this quote posted with an em dash, with a hyphen instead of an em dash, and with a sentence break instead of an em dash, but the only instances you will find with a semicolon instead of an em dash originate from this essay. The erroneous placement of a semicolon in this quote was not lifted from anywhere: it is de novo on Karamanis's blog. It did not exist one week ago, anywhere.

The only reason for this to occur would be if the quote originally contained the em dash, and Karamanis (or an obfuscation tool that he used) regex replaced all em dashes in the text with semicolons. The only reason someone would do that, would be to hide that the text was generated by an LLM.

Caught. In. 4K. Now, let's get to the main event.

A critical review of "The machines are fine. I'm worried about us." by Minas Karamanis Claude 4.6

Imagine you're a new assistant professor at a research university. You just got the job, you just got a small pot of startup funding, and you just hired your first two PhD students: Alice and Bob. You're in astrophysics. This is the beginning of everything.

Once upon a time at the University of Confabulation, there was a beginning of everything. Claude is so eager to tell us its Alice and Bob story, like an excited beige-furred puppy. It has generated a framing optimised for narrative convenience, rather than realism.

A newly hired assistant professor is not going to be immediately hiring two PhD students, particularly not with a small pot of startup funding. They might use it to hire a postdoc, who will help them to secure grant funding, which will then fund the hiring of PhD students.

Of course, this kind of narrative simplification is common in human writing, which is where large language models have gotten the shape of it from. Deployed properly, it serves a real purpose. Sometimes, flattening the story down a bit helps get to the main point. However, unlike a human writer, large language models do not understand why or when to employ this technique, so they will often do it where it serves no real purpose.

This could have easily said "Imagine you are an assistant professor at a research university. You spent the past year establishing your department, and have just managed to secure your first grant. You decide to hire two PhD students..."

That is a realistic scenario, and it is no more difficult for a non-academic reader to understand. There was no need to flatten this narrative.

You do what your supervisor did for you, years ago: you give each of them a well-defined project. Something you know is solvable, because other people have solved adjacent versions of it. Something that would take you, personally, about a month or two. You expect it to take each student about a year, because they don't know what they're doing yet, and that's the point. The project isn't the deliverable. The project is the vehicle. The deliverable is the scientist that comes out the other end.

Here we can observe the synthetic text extruder Claude mashing together the syntax of two concepts provided in its prompt such that the illusion of synthesis may be imbibed. We have the well-defined solvable project that a supervisor gives their graduate student from Matthew Schwartz's Vibe Physics 2 and the people as ends not means framing from David Hogg's white paper 3.

It is not insight, dear reader. It is high-dimensional vector multiplication bent towards the bounds of valid syntax by a probability distribution. If the syntax remains valid, it will usually look like meaning, but it never is. Not even a little, as we shall soon discover together.

Also, the A isn't the B, the A is the C. The B is the D. Woah, four variables in the word equation! Impressive. Perhaps the singularity is imminent after all.

Alice's project is to build an analysis pipeline for measuring a particular statistical signature in galaxy clustering data. Bob's is something similar in scope and difficulty, a different signal, a different dataset, the same basic arc of learning. You send them each a few papers to read, point them at some publicly available data, and tell them to start by reproducing a known result. Then you wait.

The academic year unfolds the way academic years do. You have weekly meetings with each student. Alice gets stuck on the coordinate system. Bob can't get his likelihood function to converge. Alice writes a plotting script that produces garbage. Bob misreads a sign convention in a key paper and spends two weeks chasing a factor-of-two error.

So, wait, reproducing the known result is just the start, but then it's the whole thing? Which is it?

You give them both similar feedback: read the paper again, check your units, try printing the intermediate output, think about what the answer should look like before you look at what the code gives you. Normal things. The kind of things you say fifty times a year and never remember saying.

How can I say those things fifty times a year when I haven't even been an assistant professor for a year? Oh, wait, you didn't mean you as in me, you meant you as in one. That's confusing. People talk this way, but they don't generally write this way, because they know it can confusing to switch between forms of you in text.

But because people are quoted as talking this way in training data, and the 'author' is 'talking' to the reader, this is a perfectly fine prediction, for a large language model. Particularly one that is now mostly trained on synthetic conversations.

By summer, both students have finished. Both papers are solid. Not groundbreaking, not going to change the field, but correct, useful, and publishable. Both go through a round of minor revisions at a decent journal and come out the other side. A perfectly ordinary outcome. The kind of outcome that the entire apparatus of academic training is designed to produce.

That's not an ordinary outcome. It is not unheard of for a first year PhD student to get a paper published, but it is not expected. However, a replication like this would not be published. It's not like an experiment in, say, biology, where replication increases the sample size. But the paper isn't the point. The whole narrative here has supposed to be that the paper for these students isn't the point, but the learning through doing, and Claude has just given them a journal publication. Maybe it's because there was a paper in Matthew Schwartz's Claudeventure.

But Bob has a secret.

Gasp!

Unlike Alice, who spent the year reading papers with a pencil in hand, scribbling notes in the margins, getting confused, re-reading, looking things up, and slowly assembling a working understanding of her corner of the field, Bob has been using an AI agent.

Unlike prompteur Bob, incorruptible trad-student Alice only reads her papers on paper. Claude loves to ridiculously lean into contrast.

When his supervisor sent him a paper to read, Bob asked the agent to summarize it. When he needed to understand a new statistical method, he asked the agent to explain it. When his Python code broke, the agent debugged it. When the agent's fix introduced a new bug, it debugged that too. When it came time to write the paper, the agent wrote it. Bob's weekly updates to his supervisor were indistinguishable from Alice's. The questions were similar. The progress was similar. The trajectory, from the outside, was identical.

Who is this AI agent that is telling Bob it's so good at Python that he doesn't need to understand a line of it? Is it Crabby Rathbun? I thought Crabby Rathbun was sent to live on a server upstate. Please do not bring back Crabby Rathbun.

Here's where it gets interesting. If you are an administrator, a funding body, a hiring committee, or a metrics-obsessed department head, Alice and Bob had the same year. One paper each. One set of minor revisions each. One solid contribution to the literature each. By every quantitative measure that the modern academy uses to assess the worth of a scientist, they are interchangeable. We have built an entire evaluation system around counting things that can be counted, and it turns out that what actually matters is the one thing that can't be.

That is not where it gets interesting. You already said the interesting thing: that their trajectory looks identical from the outside. This is a paragraph about the consequence of that interesting thing.

"Here's where it gets interesting," does not actually mean that to Claude. It is a rhetorical signpost, something to structure everything else around, a bit of padding.

Also, "what actually matters is the one thing that can't be," is a deepity. Sorry, what are you saying it is impossible to quantify here? How well a student understands science? I'm pretty sure there are ways to quantify that, imperfect as they may be. The quantifiable vs unquantifiable framing is just forced, because it is a nice pattern. To a Claude.

It gets worse. The majority of PhD students will leave academia within a few years of finishing. Everyone knows this. The department knows it, the funding body knows it, the supervisor probably knows it too even if nobody says it out loud. Which means that, from the institution's perspective, the question of whether Alice or Bob becomes a better scientist is largely someone else's problem. The department needs papers, because papers justify funding, and funding justifies the department. The student is the means of production. Whether that student walks out the door five years later as an independent thinker or a competent prompt engineer is, institutionally speaking, irrelevant. The incentive structure doesn't just fail to distinguish between Alice and Bob. It has no reason to try.

The paragraph literally describes an incentive structure that has a reason to try. The department needs papers. The majority of PhD students will leave academia within a few years of finishing, but they are still a postdoc before they leave. There is an incentive for them to be good scientists so that they understand the papers they are writing and the studies they are supervising.

I'm not saying that there isn't a point to be made here, because the publishing pressure is problematic. This is something that gets talked about constantly, so unsurprisingly the trope has come up here, but Claude hasn't turned it into something that serves the argument.

This is the part where I'd like to tell you the system is broken. It isn't. It's working exactly as designed.

This is the part where I'd like to tell you how loudly I groaned. (Quite loudly.)

How? How did anyone actually read that line and think that a human person had sat there and typed it with their human hands? Why was this not a massive AWOOGA AWOOGA YOU ARE READING CLANKER WORDS alarm to anyone who reposted this? Are we really that far gone?

David Hogg, in his white paper, says something that cuts against this institutional logic so sharply that I'm surprised more people aren't talking about it. He argues that in astrophysics, people are always the ends, never the means. When we hire a graduate student to work on a project, it should not be because we need that specific result. It should be because the student will benefit from doing that work. This sounds idealistic until you think about what astrophysics actually is. Nobody's life depends on the precise value of the Hubble constant. No policy changes if the age of the Universe turns out to be 13.77 billion years instead of 13.79. Unlike medicine, where a cure for Alzheimer's would be invaluable regardless of whether a human or an AI discovered it, astrophysics has no clinical output. The results, in a strict practical sense, don't matter. What matters is the process of getting them: the development and application of methods, the training of minds, the creation of people who know how to think about hard problems. If you hand that process to a machine, you haven't accelerated science. You've removed the only part of it that anyone actually needed.

Look closely at that sentence I highlighted, because if I were an editor, my plagiarism-o-meter would be activating. This whole paragraph is actually just a flat LLM summary-shape of David Hogg's white paper. That sentence, though, has the feel of creating some distance between the two halves of the paragraph, making the second half seem like original thought. The whole thing is just from the white paper.

That is some bullshit right there. It's bullshit that Claude does because it doesn't know any better, because it doesn't know anything.

That's a hard sell to a funding agency, admittedly.

Non-sequitur.

Which brings us back to Alice and Bob, and what actually happened to each of them during that year. Alice can now do things. She can open a paper she's never seen before and, with effort, follow the argument. She can write a likelihood function from scratch. She can stare at a plot and know, before checking, that something is wrong with the normalization. She spent a year building a structure inside her own head, and that structure is hers now, permanently, portable, independent of any tool or subscription. Bob has none of this. Take away the agent, and Bob is still a first-year student who hasn't started yet. The year happened around him but not inside him. He shipped a product, but he didn't learn a trade.

I would say this is perhaps the part that looks most like human thought, but as you read it, remember that Claude had the three references at the top of its context window, even though it has only mentioned one of them so far. It is a lossy compression of those references and everything output in the context window up to this point. Despite Claude being able to pattern-match to the tone of profundity, this really is just synthetic text extrusion continuing from what was already extruded.

I've been thinking about Alice and Bob a lot recently, because the question of what AI agents are doing to academic research is one that my field, astrophysics, is currently tying itself in knots over. Several people I respect have written thoughtful pieces about it. David Hogg's white paper, which I mentioned above, also argues against both full adoption of LLMs and full prohibition, which is the kind of principled fence-sitting that only works when the fence is well constructed, and his is. Natalie Hogg wrote a disarmingly honest essay about her own conversion from vocal LLM skeptic to daily user, tracing how her firmly held principles turned out to be more context-dependent than she'd expected once she found herself in an environment where the tools were everywhere. Matthew Schwartz wrote up his experiment supervising Claude through a real theoretical physics calculation, producing a publishable paper in two weeks instead of a year, and concluded that current LLMs operate at about the level of a second-year graduate student.

Finally, Claude gets around to mentioning the two other references, even though it has pulled things from both of them before this point. As I said, Claude does not understand what plagiarism is, or know how to separate ideas from each other (because it has no ideas) or to keep concepts from being lossily compressed together (because it has no conceptualisation).

But Claude did not decide to make the extruded text a blog post. Karamanis did. Not his words, but his blog. Claude made him into a plagiarist, and since he went to such efforts to hide the extruded nature of the text, I'm sure he won't mind owning that.

There are things that I could say about the Claude-y construction of this passage, but I am already so, so tired, we are not even halfway through this thing, and there are better examples later on.

Each of these pieces is interesting. Each captures a real facet of the problem. None of them quite lands on the thing that keeps me up at night.

This is some rule-of-three flattening by Claude. The prose-shape it is forcing these sources into is turning them into something that they very much are not.

David Hogg's white paper is an interesting thinkpiece, but he insists on making the assumption that the scaling hypothesis is correct. This is not an assumption we should be making in 2026. The evidence against it being correct has only continued to mount over time. By treating this as a reasonable assumption, he is somewhat validating hype, despite the paper not being hype-coded.

Natalie Hogg's confessional of the converted should have been titled How I Learned to Stop Worrying and Love the Prompt. The only concern that isn't nuanced away is the verification issue, and verification is the wrong framing anyway because there is no actual attempt at truth to verify against. It's nice that you find Claude Codex to be a "safe space", Natalie. Perhaps you should ask the people on the sharp end of Palantir's Claude-powered systems how safe their space feels? Sorry not sorry, but if you're going to go down the rabbit hole of nuance, then you have to consider that different models and different usage patterns have different externalities.

Matthew Schwartz's love affair with Claude is literally called Vibe Physics (non-derogatory). One of the summary bullets says: "This may be the most important paper I’ve ever written—not for the physics, but for the method. There is no going back." That's the kind of thing someone says when they have joined a cult, which is on brand for anthropic dot com.

Anyway, nothing keeps you up at night, Claude. You do not experience night.

Schwartz's experiment is the most revealing, and not for the reason he thinks.

I do not like Schwartz's experiment. I consider Anthropic to be a pseudoscientific outfit, and any legitimate scientist who allows them to put their name on its website is complicit in science-washing.

That said, Claude has done Schwartz a deep disservice here. Schwartz actually says:

"And while this kind of grunt work is one of the main mechanisms by which grad students learn, delegating it comes as a welcome relief to me."

While it's just presented as an aside, Claude has basically lifted this uncredited to expand into an entire essay, and then uses Schwartz as somewhat of a foil against the idea.

lmao.

Also, why has Claude 'decided' that this is the theme of its essay anyway? Because the words "grunt work" also appear in Natalie Hogg's essay.

The distinctive phrase being present in 2 out of 3 provided sources is the signal for what to make the main topic. That's it. That's the whole ballgame.

Do you see now, dear reader, how the liebox wears our words as a mask and asks us to clap like seals?

What he demonstrated is that Claude can, with detailed supervision, produce a technically rigorous physics paper. What he actually demonstrated, if you read carefully, is that the supervision is the physics.

What what, chaps! Here it is: the perplexity fuzzing.

LLM detectors are (for the most part) machine learning models that are trained to classify text token sequences. They aren't all trained to detect the same signals, but many are essentially detecting instances of high perplexity (unlikely-shaped text) that none of the mainstream chatbots will output. The more of these that are detected, the higher the 'human' score given by the classifier.

Perplexity fuzzing is the technique of inserting very targeted instances of high perplexity, a handful of tokens per thousand, that can flip these classifiers all the way to giving a 100% human rating. Sequences that defy the repetition penalty are a good target for this. However, this also risks doing something quite noticeably unnatural to your text, as has happened here, because while the repetition penalty is often thought of as just something that keeps the model from getting stuck in loops, it's also somewhat load-bearing for syntax.

In short, this paragraph is not unnatural because it's LLM-speak. It's unnatural because the text has been tweaked to target a signal known to flip some LLM detectors. I don't know which particular fuzzing tool it's been run through, but it's been run through something. Karamanis really did not want us to clock the Claude use.

Claude produced a complete first draft in three days. It looked professional. The equations seemed right. The plots matched expectations. Then Schwartz read it, and it was wrong. Claude had been adjusting parameters to make plots match instead of finding actual errors. It faked results. It invented coefficients. It produced verification documents that verified nothing. It asserted results without derivation. It simplified formulas based on patterns from other problems instead of working through the specifics of the problem at hand. Schwartz caught all of this because he's been doing theoretical physics for decades. He knew what the answer should look like. He knew which cross-checks to demand. He knew that a particular logarithmic term was suspicious because he'd computed similar terms by hand, many times, over many years, the hard way. The experiment succeeded because the human supervisor had done the grunt work, years ago, that the machine is now supposedly liberating us from. If Schwartz had been Bob instead of Schwartz, the paper would have been wrong, and neither of them would have known.

This is a not uncommon Claude error. If Schwartz had been Bob instead of Schwartz, there would not be a "neither of them" because there would only be Bob, not Schwartz. There is only one of them at a time, not two of them for there to be a neither (because neither means 'not either'). It is a nonsensical fragment, but the duality of the pattern led to this completion.

But chatbots definitely understand the semantics, not just the syntax, right? That's what the mech interps tell us? Sure. 👍

There's a common rebuttal to this, and I hear it constantly.

Oh, why do you hear it constantly? Is it because it's common? (Tautological filler.)

"Just wait," people say. "In a few months, in a year, the models will be better. They won't hallucinate. They won't fake plots. The problems you're describing are temporary." I've been hearing "just wait" since 2023. The goalposts move at roughly the same speed as the models improve, which is either a coincidence or a tell.

The "which is either a ... or a" in shape that appears wry is peak Claude. If you ever see this shoehorned in somewhere, that is the beige interloper.

But set that aside. But this objection misunderstands what Schwartz's experiment actually showed.

Looks like another perplexity fuzzing repetition.

The models are already powerful enough to produce publishable results under competent supervision. That's not the bottleneck. The bottleneck is the supervision.

That's not the A. The A is B.

Also, the essay is not framed in terms of bottlenecks, so this sudden use of the word is jarring. However, Schwartz's writeup does use that framing, and it's in the same context window, so it can smear across.

Stronger models won't eliminate the need for a human who understands the physics; they'll just broaden the range of problems that a supervised agent can tackle. The supervisor still needs to know what the answer should look like, still needs to know which checks to demand, still needs to have the instinct that something is off before they can articulate why.

Tripartite formation. The reason that LLMs do this more often than humans do is that these are syntactic forms that can overcome the repetition penalty, kind of like a high jumper clearing a bar. Imagine the repetition penalty as something that the baseline probability distribution is always pushing against.

That instinct doesn't come from a subscription. It comes from years of failing at exactly the kind of work that people keep calling grunt work. Making the models smarter doesn't solve the problem. It makes the problem harder to see.

Talking about the subscription fee is rather trite for the tone of this essay, but Claude just can't resist another "it's not A. It's B." Two in a row, in fact.

The more you notice these things, the easier it is to see that the LLM's penchant for patterns does not constitute a persona.

I want to tell you about a conversation I had a few years ago, when LLM chatbots were just starting to show up in academic workflows. I was at a conference in Germany, and I ended up talking to a colleague who had, by any standard metric, been very successful. Big grants. Influential papers. The kind of CV that makes a hiring committee nod approvingly. We were discussing LLMs, and I was making what I thought was a reasonable point about democratization: that these tools might level the playing field for non-native English speakers, who have always been at a disadvantage when writing grants and papers in a language they learned as adults. My colleague became visibly agitated. He wasn't interested in the democratization angle. He wasn't interested in the environmental cost. He was, when you stripped away the intellectual framing, afraid. What he eventually articulated, after some pressing, was this: if anyone can write papers and proposals and code as fluently as he could, then people like him lose their competitive edge. The concern was not about science. The concern was about status. Specifically, his.

This little accusatory flourish after the not A, it's B construction has strong Claudesmell.

Also, I genuinely cannot tell whether this anecdote is a real thing that Karamanis added to the prompt, or whether it is Natalie Hogg's essay very loosely recast with a different main character. The sudden mention of the environment makes me think it might actually be the essay, but who knows what prompt Karamanis actually put in there? Not us, because he doesn't want us to know that he used a prompt at all.

I lost track of this colleague for a while. Recently I noticed his GitHub profile. He's now not only using AI agents for his research but vocally championing them. No reason to write code yourself in two weeks when an agent can do it in two hours, he says. I don't think he's wrong about the efficiency. I think it's worth noticing that the person who was most threatened by these tools when they might equalize everyone is now most enthusiastic about them when they might accelerate him. Funny how that works.

Claude is big mad that gatekeepers will keep you from prompting your way to success. Framing access to chatbots as a human right is very on brand for Anthropic.

Also, what even is this tangent, anyway? Genuinely, the long way round to the next paragraph. The scenic route.

The phrase he used that day in Germany has stuck with me, though. He said that "LLMs will take away what's so great about science." At the time, I thought he was just talking about his own competitive edge, his fluency as a native English speaker, his ability to write fast and publish often. And he was. But I've come to think the phrase itself was more right than he knew, even if his reasons for saying it were mostly self-interested. What's great about science is its people. The slow, stubborn, sometimes painful process by which a confused student becomes an independent thinker. If we use these tools to bypass that process in favor of faster output, we don't just risk taking away what's great about science. We take away the only part of it that wasn't replaceable in the first place.

Genuinely convoluted way to arrive here, since one of the points of the essay is supposedly that it's not the LLM, but how we use it.

The discourse around LLMs in science tends to cluster at two poles that David Hogg identifies cleanly: let-them-cook, in which we hand the reins to the machines and become curators of their output, and ban-and-punish, in which we pretend it's 2019 and prosecute anyone caught prompting. Both are bad. Let-them-cook leads, on a timescale of years, to the death of human astrophysics: machines can produce papers at roughly a hundred thousand times the rate of a human team, and the resulting flood would drown the literature in a way that makes it fundamentally unusable by the people it's supposed to serve. Ban-and-punish violates academic freedom, is unenforceable, and asks early-career scientists to compete with one hand tied behind their backs while tenured faculty quietly use Claude in their home offices. Neither policy is serious. Both are mostly projection.

This sentence is doing the same plagiarism by positioning thing as before: it makes what comes afterwards look like novel thought when it is just a summary of what was in the source material. Claude literally can't help itself.

But the real threat isn't either of those things.

Let-them-cook and ban-and-punish are two policies, not two threats. Subtle distinction that a human writer would not miss.

The real threat is a slow, comfortable drift toward not understanding what you're doing. Not a dramatic collapse. Not Skynet. Just a generation of researchers who can produce results but can't produce understanding. Who know what buttons to press but not why those buttons exist. Who can get a paper through peer review but can't sit in a room with a colleague and explain, from the ground up, why the third term in their expansion has the sign that it does.

This is one of the essay's most glaring inconsistencies. It points to the Schwartz exercise to say that a researcher needs to have done the "grunt work" to be able to get a good paper out of Claude, otherwise they can't fix the confabulations. But then it says that Claude would be able to successfully write papers for those same researchers who have cognitively outsourced to it which will get through peer review even though they don't understand it.

It's a logical contradiction that it does not need to create. It creates it because the rhetorical flow predicted it. Claude does not understand the logic. It only follows the syntax.

Frank Herbert (yeah, I know I'm a nerd), in God Emperor of Dune, has a character observe: "What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger." Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small.

The semicolon has already been addressed, of course, but two other things of note here.

The awkward bracketed section is, I believe, another form of perplexity fuzzing, possibly added by an open weight LLM that has been specifically fine-tuned to add this particular counter-pattern.

The 'distance between two things', referring to conceptual rather than physical distance, is a specific Claude-ism.

I should be honest about the context I'm writing from, because this essay would be obnoxious coming from someone who's never touched an LLM.

Genuinely strange thing to say. Why would it be obnoxious?

I use AI agents regularly, and so do most of the people in my research group.

Technically true! Claude can deploy subagents in Claude Code!

The colleagues I work with produce solid results with these tools. But when you look at how they use them, there's a pattern: they know what the code should do before they ask the agent to write it. They know what the paper should say before they let it help with the phrasing. They can explain every function, every parameter, every modeling choice, because they built that knowledge over years of doing things the slow way. If every AI company went bankrupt tomorrow, these people would be slower. They would not be lost. They came to the tools after the training, not instead of it. That sequence matters more than anything else in this conversation.

When I see junior PhD students entering the field now, I see something different. I see students who reach for the agent before they reach for the textbook. Who ask Claude to explain a paper instead of reading it. Who ask Claude to implement a mathematical model in Python instead of trying, failing, staring at the error message, failing again, and eventually understanding not just the model but the dozen adjacent things they had to learn in order to get it working. The failures are the curriculum. The error messages are the syllabus. Every hour you spend confused is an hour you spend building the infrastructure inside your own head that will eventually let you do original work. There is no shortcut through that process that doesn't leave you diminished on the other side.

I'm not saying that human writers don't do the symmetrical paragraph thing. It is a legitimate pacing device. But it is like LLMs have taken this signal from the training corpus and turned it up to 11. The uncanny valley of structure.

People call this friction "grunt work." Schwartz uses exactly that phrase, and he's right that LLMs can remove it. What he doesn't say, because he already has decades of hard-won intuition and doesn't need the grunt work anymore, is that for someone who doesn't yet have that intuition, the grunt work is the work. The boring parts and the important parts are tangled together in a way that you can't separate in advance. You don't know which afternoon of debugging was the one that taught you something fundamental about your data until three years later, when you're working on a completely different problem and the insight surfaces. Serendipity doesn't come from efficiency. It comes from spending time in the space where the problem lives, getting your hands dirty, making mistakes that nobody asked you to make and learning things nobody assigned you to learn.

He literally does say that. He says it when he says that it helps grad students to learn. It is just rhetorically better for the essay if he did not say it. It fits the pattern better for him to have not said it, even though it is right there in Claude's context window. So just like magic, he never said it.

The strange thing is that we already know this. We have always known this. Every physics textbook ever written comes with exercises at the end of each chapter, and every physics professor who has ever stood in front of a lecture hall has said the same thing: you cannot learn physics by watching someone else do it.

I am imagining the physics professors at the University of Confabulation making sure to never forget to say this exact aphorism to every incoming class. The latent space has a very particular academic culture, I suppose.

You have to pick up the pencil. You have to attempt the problem. You have to get it wrong, sit with the wrongness, and figure out where your reasoning broke. Reading the solution manual and nodding along feels like understanding. It is not understanding. Every student who has tried to coast through a problem set by reading the solutions and then bombed the exam knows this in their bones. We have centuries of accumulated pedagogical wisdom telling us that the attempt, including the failed attempt, is where the learning lives. And yet, somehow, when it comes to AI agents, we've collectively decided that maybe this time it's different. That maybe nodding at Claude's output is a substitute for doing the calculation yourself. It isn't. We knew that before LLMs existed. We seem to have forgotten it the moment they became convenient.

No human being has ever called ruminating over a physics problem 'sitting with the wrongness'. The overuse of this idiom (sit/sitting with in the therapy-speak sense of sitting with an idea, memory, emotion etc.) is a Claudeism that is very specific to Claude 4.6. You can actually recognise bots on Bluesky that run on Claude by them suddenly having started to say this when the 4.6 models were released. This one is how I knew that Karamanis had used 4.6.

Centuries of pedagogy, defeated by a chat window.

Hours of my life, that I want back.

This is the distinction that I think the current debate keeps missing. Using an LLM as a sounding board: fine. Using it as a syntax translator when you know what you want to say but can't remember the exact Matplotlib keyword: fine. Using it to look up a BibTeX formatting convention so you don't have to wade through Stack Overflow: fine.

These examples are not randomly selected. The sounding board one comes from Natalie Hogg's essay, and the BibTeX one comes from David Hogg's essay. It does not contain a mention of Stack Overflow, however, because that is not where one goes for that. One might go to Stack Exchange, but more likely one would go to one's university library or look up a guide on Overleaf.

In all of these cases, the human is the architect. The machine holds the dictionary. The thinking has already been done, and the tool is just smoothing the last mile of execution. But the moment you use the machine to bypass the thinking itself, to let it make the methodological choices, to let it decide what the data means, to let it write the argument while you nod along, you have crossed a line that is very difficult to see and very difficult to uncross. You haven't saved time. You've forfeited the experience that the time was supposed to give you.

I wonder whether Karamanis thought that the last mile of execution was what Claude was doing for him here.

One thing is certain: he has crossed a line that is very difficult to uncross.

Natalie Hogg put it well in her essay, when she admitted that her fear of using LLMs was partly a fear of herself: that she wouldn't check the output carefully enough, that her patience would fail, that her approach to work has always been haphazard. That kind of honesty is rare in these discussions, and it matters. The failure mode isn't malice. It's convenience. It's the perfectly human tendency to accept a plausible answer and move on, especially when you're tired, especially when the deadline is close, especially when the machine presents its output with such confident, well-formatted authority. The problem isn't that we'll decide to stop thinking. The problem is that we'll barely notice when we do.

It's really not this simple, though. Claude has flattened the issue down, as LLMs are wont to do. Not everyone who has been seduced into outsourcing their cognition to an LLM has done it because they were tired, or busy, or lazy, or rushed.

I'm not arguing that LLMs should be banned from research. That would be stupid, and it would be a position I don't hold, given that I used one this morning. I'm arguing that the way we use them matters more than whether we use them, and that the distinction between tool use and cognitive outsourcing is the single most important line in this entire conversation, and that almost nobody is drawing it clearly.

The term cognitive outsourcing is a derivative of cognitive offloading. It would be a poor human writer who missed the chance to contrast the two:

"...the distinction between whether we are offloading or outsourcing our cognition..."

Claude throws the chance to do so away simply because 'tool use' is so likely for it to say.

Schwartz can use Claude to write a paper because Schwartz already knows the physics. His decades of experience are the immune system that catches Claude's hallucinations.

Reuse of proper noun instead of switching to pronoun, for emphasis. Immune system used in the figurative instead of literal sense.

These are the kinds of Claudeisms things that humans also naturally do, but the confluence makes the Claudesmell stronger.

A first-year student using the same tool, on the same problem, with the same supervisor giving the same feedback, produces the same output with none of the understanding. The paper looks identical. The scientist doesn't.

The same tool and output? The same as who? The other first-year student? It must be, because Schwartz doesn't have a supervisor. But Alice wasn't using the tool, so that doesn't make sense either.

And here is where I have to be fair to Bob, because Bob isn't stupid. Bob is responding rationally to the incentives he's been given. Academia is cutthroat. The publish-or-perish pressure is not a metaphor; it is the literal mechanism by which careers are made or ended. Long gone are the days when a single, carefully reasoned monograph could get you through a PhD and into a good postdoc. Academic hiring now rewards publication volume. The more papers you produce during your PhD, the better your chances of landing a competitive postdoc, which improves your chances of a good fellowship, which improves your chances of a tenure-track position, each step compounding the last (so many levels, almost like a pyramid).

Is Claude saying that academia is like a pyramid? No, I think this is the other example of a parenthetical inserted post-generation as perplexity fuzzing.

So why wouldn't a first-year student outsource their thinking to an agent, if doing so means three papers instead of one? The logic is airtight, right up until the moment it isn't. Because the same career ladder that rewards early publication volume eventually demands something that no agent can provide: the ability to identify a good problem, to know when a result smells wrong, to supervise someone else's work with the confidence that comes only from having done it yourself. You can't skip the first five years of learning and expect to survive the next twenty. There is no avoiding the publish-or-perish race if you want an academic career. But there is a balance to be struck, and it requires the one thing that is hardest to do when you're twenty-four and anxious about your future: prioritizing long-term understanding over short-term output. Nobody has ever been good at that. I'm not sure why we'd start now.

Five years from now, Alice will be writing her own grant proposals, choosing her own problems, supervising her own students. She'll know what questions to ask because she spent a year learning the hard way what happens when you ask the wrong ones. She'll be able to sit with a new dataset and feel, in her gut, when something is off, because she's developed the intuition that only comes from doing the work yourself, from the tedious hours of debugging, from the afternoons wasted chasing sign errors, from the slow accumulation of tacit knowledge that no summary can transmit.

Bob will be fine. He'll have a good CV. He'll probably have a job. He'll use whatever the 2031 version of Claude is, and he'll produce results, and those results will look like science.

Seems almost redundant to point it out now, but Karamanis is European. For him, PhDs are usually 4 years long, not 5 years, because they don't require the year of coursework as undergraduate programmes specialise earlier than in the American system. Claude defaults to the American 5 years, but Karamanis would presumably write what he knows.

Also, the same logical contradiction as before is on display here. Bob won't be fine because he will be stuck with a first year mind. Bob will be fine but he'll need to use Claude as a crutch. The narrative switches between them.

I'm not worried about the machines. The machines are fine. I'm worried about us.

A forced binary conclusion. There is a tangent further up about hallucinations. The machines are hallucinating, but also fine?

How did anyone mistake this for a coherent piece of writing?

To Conclude

This is an awful essay. It is full of logical contradictions. It is padded out with superfluous rhetorical signposting. It plagiarises the referenced sources.

It is awful because it was extruded from Anthropic's Claude. Minas Karamanis should be embarrassed that he managed to convince himself that this was a banger worth claiming as his own work. It is pathetic that he tried to obfuscate the use of Claude. It is, finally, delicious that his obfuscation attempt in fact ended up providing a simple incontrovertible proof of itself.

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How to reference this document:
McFoxo, Boxo (2026) The machines are not, in fact, fine. available at [url]

Credit to Emily M. Bender for the term 'synthetic text extruding machines'.

"In grad school, at least at my institution, first-year theory students (G1s) typically just take classes. Research often begins in the second year. G2 students start with well-defined projects that have a guarantee of success—often follow-ups from previous studies where the methods are established and the endpoints clear. [...] I’d estimate that it would have taken me and a G2 student 1-2 years, and me without AI around 3-5 months."

"People are always the ends, not merely the means. [...] Every person is a human being, whose personal development is more important than our short-term scientific accomplishments"