Note the following comment by Jerry Ling: "The effect goes away if you search properly using the original submission date instead of the most recent submission date. By using most recent submission date, your analysis is biased because we’re so close to the beginning of 2026 so ofc we will see a peak that’s just people who have recently modified their submission."
The last-modified-date effect is even more important, because it can be used to support whatever the latest fad is, without needing to adapt data or arguments to the specifics of that fad.
Can people please not post links with vague titles like this? I had to click through and read half the article to even figure out what this was about, and I wasn’t interested.
Me too. So as a service to the community: the article is about a noticeable increase of submissions about high-energy theory to arXiv due to mediocre articles quickly produced with or by AI and how to deal with that.
These days, it feels like, every article about something big happening is about an AI doomsday scenario, AI bubble "finally" bursting or AGI being reached.
Maybe one exception is milestones in nuclear fusion, but even that is very much rare compared to these.
Guidelines say "please use the original title, unless it is misleading or linkbait" in that case I'd argue that it's linkbait so changing it is justified
In most of the world the past decades there has been no thought behind who should get university education. It has been given that after high school you should aim for university. I have studied software engineering in the most prestigious university in my country and from 100+ students in my group there were only a few (myself excluded) who actually had some interest in academic work and desire to pursue it. Most of us were just coasting - passing exams and writing mediocre papers without any goal to have those papers ever being read by someone after the graduation.
I think that university level and other kinds of formal education should be segregated. Universities should host fewer students and being able to provide them with higher rewards for actually meaningful work and I believe that a flood of mediocre quality papers (but let's admit it, in fact they are low quality in their content and perhaps good in their presentation) will lead us to rebuild the education system.
OTOH, weakening the ties between the industry and science can harm both of them. Right now in the university people get a rough idea of how science works, and most of them then go to work in the industry, which sounds like a right proportion. Nobody is reading papers below PhD level anyway, so I don't think that it's undergrad papers that are a problem
Indeed. Also the usual criticism about education not being adequate training for the workforce bla bla is simply because education is not to train a worker in the first place. There's no way to train someone other than to let them do the damn job. Yes teaching mathematics and reading and writing is probably a prerequisite but how is Shakespeare relevant? It's just a confusion of two things: learning for the sake of learning, and learning for the sake of employability.
I'm not arguing for one or the other. I'm just saying that I would also hate it if university CS for example just became a web bootcamp to churn out as many code monkeys as fast as possible. There is a place for just vocational training, and there is another place for a more platonic kind of learning, and just sending everyone off to university and tying employability to a degree is really stupid.
Alas it can't be fixed now, because 1. For profit universities 2. HR needing a quick filter 3. States needing to standardize some kind of path for kids
If you look at the beginning of XX century, university education was much less accessible with much fewer participants, and the results were much more impressive than today across all disciplines
There was also a lot of relatively low-hanging fruit in most fields, because we basically didn't have the technology before, or simply didn't bother to look.
There's a ton of low hanging fruit now - more than ever. Every question answered raises multiple new questions. If there is a lack of opportunity to answer interesting questions, it is certainly not because the questions aren't there.
But someone needed to realise it's a low-hanging fruit in the first place. By the end of the XIX century the general agreement was that physics was pretty much done, we are now just polishing the details.
Instead of 1 reviewer, have 10; also, don't we benefit as a society when everyone is more highly educated? Sure, we have a ways to go before we get there, namely with regards to resistance to disinformation training and including more resistance to populism / fascism in the curriculum so that we have a chance to build better and more equal societies.
I dunno, I think society is best served by educating as many people as possible. I would much rather live in a world where anyone who wants a quality education can get one.
We should teach people what we expect will be relevant in their lives, which includes basic math, science, government, history, and other subjects. Although some kids still won't learn, we should try. Anyone interested in a particular subject should be able to explore it further, since interest makes it relevant to them. And we should also give people mandatory but brief exposure to many difference fields, in case they become interested.
But at a certain point, you're wasting time and effort trying (and failing) to teach students what they're unlikely to, and ultimately won't, use afterward. "You can lead a horse to water, but you can't make them drink." Meanwhile, as GP noted, students who are interested in a "quality education" can't get one, because the quality is diminished by number of students, many who aren't interested. In order to provide the best education to the most people, we must optimize; cutting people who aren't learning means we can better educate those who are.
The key is to focus education on actual skill learning, rather than just focussing on exam preparation which is typically learning specific "magic" words and phrases (e.g. "condensation", "the powerhouse of the cell").
Learning specific physics formulas has its place, but learning the principles behind the formulas is far more valuable, though harder to measure.
If there is anything good I hope we can get from AI transformation it's making our approach to education less utilitarian. We should educate ourselves because it's good to be educated, and because it's good to be around educated people, not because education gets you a good job or makes you are more valuable unit in the economy
Or AI will double down in the Dunning Krueger effect, where true mastery not only diminishes, but people collectively take low erfort AI answers as the baseline truth.
Agree - but in the US so much more could and should be done at the primary and secondary level before we even talk about the tertiary one. It's actually pretty good compared to other developed countries but a lot could be gotten out of more investment there.
I'm not convinced that will really happen. "AI" just doesn't give reliable output, and even if humans don't either, they are still far less prone to error. And errors matter, a lot.
looks like history runs in cycles ... Knowledge was strictly guarded and the powers that be used to decide who gets an education. Looks like you are espousing the same, discounting all the good that has come about because of open education.
I feel that you didn't read my comment carefully enough (although I could have been more clear). To me university was really good and eye-opening but looking back, I had no place in academia. A good college would have been better. Consuming knowledge and creating knowledge are in somewhat different categories and most people are consumers.
I don't think it is true either, considering the broad claims made.
The thing to be changed is research incentives, not getting the bar even higher. Take the Francesca Gino case, for example. I don't think anyone can argue that Harvard's bar is "not high enough".
I didn't read it as simply making the bar higher for entering universities. For example here in Germany there are Universitäten (Universities) and Fachhochsculen (sort of like vocational academies, but they still award bachlor and master degrees).
Most people who studied computer science with me at university weren't interested in computer science at all but just wanted a good vocational training for entering the lucrative carrer of "software developer". I think it would benefit both them and employers if they would have instead attended a good vocational school for software development.
I didn't mean that at all. I am a regular developer who got into this field at a lucky time and am as worried about the future employment prospects as many are.
Either the institution develops and teaches methods and traditions that are beneficial for people in general, in which case it ought to be a good idea to offer them broadly, or it is used for gatekeeping and stratifying, in which case I think it should be abolished.
Well… it is happening. You can’t put spilled milk back to bottle. You can do future requirements that will try to stop this behaviour.
E.g. in the submission form could be a mandatory field “I hereby confirm that I wrote the paper personally.” In conditions there will be a note that violating this rule can lead to temporary or permanent ban of authors. In the world where research success is measured by points in WOS, this could lead to slow down the rise of LLM-generated papers.
Unironically, maybe they should be scored by LLMs? My first thought was that the reviewers could score the papers but that would lead to even more group-think.
Ideally whoever is paying the academics should just be paying attention to their work and its worth, but that would be crazy.
> This approach dismisses the cases where Ai submissions generally are better.
You’re perhaps missing the not so subtle subtext of Peter Woit’s post, and entire blog, which is:
While AI is getting better, it’s still not _good_ by the standards of most science. However it’s as good as hep-th where (according to Peter Woit) the bar is incredibly low. His thesis is part “the whole field is bad” and part “Arxiv for this subfield is full of human slop.”
I don’t have the background to engage with whether Peter Woit’s argument has merit, but it’s been consistent for 25+ years.
My comment was more an answer to the proposed gatekeeping of science as a human activity.
Yes, Ai is still not good in the grand scheme of things. But everybody actively using it has gotten concerned over the past 2 months by the leap frigging of LLMs - and surprised as they thought we had arrived at the plateau.
We will see in a year or two if humans still hold an advantage in research - currently very few do in software development, despite what they think about themselves.
The other side of the coin is: automating science as a machine activity.
Is that what we want? I agree with you that the use of language models in science is an inevitable paradigm shift, but now is the time to make collective decisions about how we're going to assimilate this increasingly super-human "intelligence" into academic practices, and the rest of daily life. Otherwise we will be the ones being assimilated by a force beyond our control.
The progress is so rapid that the only people who might have control over the process are the ones with self-interest, mainly financial, and not aligned with - in some aspects opposed to - the interests of humanity.
Its already automated. Do you think astronomers manually count stars or medical scientists manually run chemical reactions? Why is automation by ai wrong when all other automations were beneficial?
The single most valuable part of science is keeping the gates: not adding things to the corpus of scientific knowledge unless they can be properly substantiated.
I assume hep = high energy physics in this context. PI = professor who received a government grant.
Peer review has never really been blind and I suspect PIs will reject papers from "outsiders" even if they are higher quality. This already happens to some extent today when the stakes are lower.
But peer review (circa 1965-2010[1]) is just the prior iteration of the problem[2]; the wave of crap[3] produced by publish or perish (crica 1950-present[4]). Rejecting papers by outsiders is irrelevant; the problem is we want to determine which papers are good/interesting/worth considering out of the fire hose of bilge, and, though we were already arguably failing at this, the problem just got harder.
(I say arguably, because there is always the old "try it yourself and see if it actually works" trick, but nobody seems to be fond of this; it smacks of "do your own research" and we're lazy monkeys at heart, who would much rather copy off of someone else's homework.)
Kinda. PI is principal investigator and usually they’re a professor with a grant (the grant being the thing they are the principal of investigating). That part is right. But they’re not really directly in the review loop. For some fields where things are small enough that folks can recognize style such as it exists, you could see reviewers passing over unfamiliar work and promoting familiar work. That was not the issue.
The issue was that it still was kind of hard to produce crappy mid rate papers, so you kind of needed the infrastructure of a small lab to do that. Now you don’t. The success rate for those mediocre papers produced by grad students and postdocs will go way down. It is possible that will cease to be a useful signal for those early career researchers.
Peer review isn’t the issue here. His comments are about Arxiv, which is a preprint server. Essentially anyone can publish a preprint. There’s no peer or other review involved.
This is a common misconception. People without academic affiliation (based on their email address) require someone to vouch for them before they can submit to arxiv. And papers submitted to arxiv (with or without affiliation) are reviewed, and many are rejected.
arXiv does not review everything pushed to the site.
It's very easy to get in. It's becoming a common target for grifters who will "publish" papers on arXiv because it looks formal to those who don't know any better.
>Peer review has never really been blind and I suspect PIs will reject papers from "outsiders" even if they are higher quality.
I'm a complete outsider (not even in academia at all) and just got a paper accepted in the top math biology journal [1]. But granted, it took literally years to write it up and get it through. I do really worry that without academic affiliation it is going to get harder and harder for outsiders as gates are necessarily kept more and more securely because of all the slop.
Congratulations. How did you do it? And how do you get access to resources such as reference products, journals, etc. that typically require institutions with budgets?
> submission numbers in the last couple months have nearly doubled with respect to the stable numbers of previous years
This is showing up (no pun intended) on HN as well. The # of submissions and # of submitters, which traditionally had been surprisingly stable—fluctuating within a fixed range for well over 10 years—has recently been reaching all-time highs. Not double, though...yet.
I would imagine tons of them are bots. They're getting hard to distinguish, they don't do the normal tropes any longer. They'll type in all lowercase, they'll have the creator post manually to throw you off, they'll make multiple comments within 45 seconds that normal human couldn't do. All things I've witnessed here over the past couple of weeks. And those are just the ones I've caught.
Here is the data since the beginning of HN, where a new submitter is defined as a user whose account was created less than three months before the submission:
I've noticed a pretty significant uptick in new accounts posting complete garbage. I don't mean the comments are bad, they're not even words in many cases.
But it also seems some topics (in particular AI) attract a lot of accounts that post incredibly low quality comments, far below the quality you'd expect from HN. Ofte it's in reasonable English, but it's just inane reddit-level drivel. Unclear if these topics attract low quality posters, or if these are bot accounts.
Also looking at the three first pages of /noobcomments, we find 28 comments with EM-dashes in them. That's not proof of AI, but if you compare with /newcomments, you find exactly one EM-dash going back as far. That's a bit of a statistical aberration.
I've witnessed bots here on accounts that are years old with no history that start posting multiple times in a short timeframe suddenly after being dormant forever. Makes you wonder how they're getting these old accounts. It's not just new ones.
Black market accounts. Some human made them years ago for a price, they are sat on by some black market / grey market guy and now he's selling the accounts for a profit.
Old accounts from multiple social media platforms has a $$$$$ value.
I would wager the vast majority are alt accounts of existing users. People who don't want to risk their karma or reputation but who do want to go mask off for certain subjects. After that it's bound to be bots run by HN users. I just don't think HN is so popular that the rush of green accounts popping up actually represents new users. Maybe I'm wrong , though.
Reddit has been shedding its techy enthusiast crowd for the past few years with the combination of policy changes and insufficient moderation against LLM bots. I wonder if that’s contributing.
It's likely people with mediocre ideas but access to free LLM tools are able to get over the care-risk-reward activation energy and consequently submit their ideas with the help of LLMs.
Would it matter? Even before AI, most papers couldn't be replicated. Do we really think this is going to help the situation? Even if some of the AI papers are amazing, will anyone ever read them if most of the papers are useless? More research != more useful research. This is the logical outcome of publish or perish and Q-rankings being the main metric used.
"When a metric becomes a target, it ceases to be a good measure" - Goodhart's law
It has a lot of red flags. Second (re)post of dormant account, vive coded, AI, the biological model is horrible. But it was a nice project, 5/5 would upvote again.
Perhaps the important detail is "[I] spent about a month on it."
curious whether the quality distribution changes too, or just the volume. arXiv can't really downvote noise but HN can at least flag/bury it. might be why the doubling shows up on arXiv first and HN is catching up more slowly.
“And further, by these, my son, be admonished: of making many books there is no end; and much study is a weariness of the flesh.”
- Ecclesiastes 12:12 (KJV)
I suppose we’re entering TURBO mode for of ‘making many books there is no end’.
People used to spam out masses of low-quality scientific papers in a scattergun approach to gain fame and citations, and they still do, but now they do it more, because LLMs churn it out faster than students.
In a normal and sane world, a scientist is a nerd about their field. They are highly interested in new thoughts and insights. When a new paper in their field is published, they try hard to find the time to read it. The reason is: every paper is written by enthusiasts who want to add something of value, new insights, to the discussion. Proving or disproving theories, adding puzzle pieces to the general picture.
That is the normal situation, which is the foundation of the progression of civilisation.
But some people install incentive systems to sabotage this. They are sabotaging civilisation itself.
We should decouple the publishing of papers from academic careers completely.
Papers can't generate any reputation or money for the authors anymore. To achieve that, we must anonymize the authors.
All scientists get some (paid) time to write papers — if they want. What they write and if they publish it is not known to anybody. They are trusted to write something of value in that time.
Universities can come up with other ways of judging which professors they hire. Interviews. Test teachings. Or the writing of an non-public application essay, which describes their past research and discoveries.
The value, to society, to your field and to you institution, of being a scholar is to create new knowledge. New knowledge has no value unless you disseminate it, or publish.
Another necessity is the public (usually within its field) examination of the knowledge, including discussion/debate. Knowledge is merely embryonic without those things - undeveloped, not at all reliable. That is difficult without the author able to respond. And others want to expand and build on the work, which often benefits greatly from contacting the author.
In the modern (post-positivist?) approach to science, the world respects that it's written by a human who has a perspective and, despite their best intentions, biases. You can't evaluate any knowledge without knowing its source, in science or elsewhere. The first element of a citation is the author, not the title or journal (though I don't know why that happened historically).
And the latter is a reason any LLM author should be identified.
There are many really excellent papers out there - the kind which will save you hours/months of work (or even make things that were previously inviable to build viable).
That said, it is amazing how terrible a lot of papers are; people are pressured to publish and therefore seem to get into weird ruts trying to do what they think will be published, rather than what is intellectually interesting...
One thing I have been guilty of, even though I am an AI maximalist, is asking the question: "If AI is so good, why don't we see X". Where X might be (in the context of vibe coding) the next redis, nginx, sqlite, or even linux.
But I really have to remember, we are at the leading edge here. Things take time. There is an opening (generation) and a closing (discernment). Perhaps AI will first generate a huge amount of noise and then whittle it down to the useful signal.
If that view is correct, then this is solid evidence of the amplification of possibility. People will decry the increase of noise, perhaps feeling swamped by it. But the next phase will be separating the wheat from the chaff. It is only in that second phase that we will really know the potential impact.
The cynical part of me thinks that software has peaked. New languages and technology will be derivatives of existing tech. There will be no React successor. There will never be a browser that can run something other than JS. And the reason for that is because in 20 years the new engineers will not know how to code anymore.
The optimist in me thinks that the clear progress in how good the models have gotten shows that this is wrong. Agentic software development is not a closed loop
I often find myself wondering about these things in the context of star trek... like... could Geordi actually code? Could he actually fix things? Or did the computer do all the heavy lifting. They asked "the computer" to do SO MANY things that really parallel today's direction with "AI". Even Data would ask the computer to do gobs of simulations.
Is the value in knowing how to do an operation by hand, or is the value in knowing WHICH operation to do?
That's an interesting possiblity to consider. Presumably the effect would also be compounded by the fact that there's a massive amount of training data for the incumbent languages and tools further handicapping new entrants.
However, there will be a large minority of developers who will eschew AI tools for a variety of reasons, and those folks will be the ones to build successors.
We have witnessed, over the past few years, an "AI fair use" Pearl Harbor sneak attack on intellectual property.
The lesson has been learned:
In effect, intellectual property used to train LLMs becomes anonymous common property. My code becomes your code with no acknowledgement of authorship or lineage, with no attribution or citation.
The social rewards (e.g., credit, respect) that often motivate open source work are undermined. The work is assimilated and resold by the AI companies, reducing the economic value of its authors.
The images, the video, the code, the prose, all of it stolen to be resold. The greatest theft of intellectual property in the history of Man.
The greatest theft of intellectual property in the history of Man.
Copyright was always supposed to be a bargain with authors for the ultimate benefit of the public domain. If AI proves to be more beneficial to the public interest than copyright, then copyright will have to go.
You can argue for compromise -- for peaceful, legal coexistence between Big Copyright and Big AI -- but that will just result in a few privileged corporations paywalling all of the purloined training data for their own benefit. Instead of arguing on behalf of legacy copyright interests, consider fighting for open models instead.
In a larger historical context, nothing all that special is happening either way. We pulled copyright law out of our asses a couple hundred years ago; it can just as easily go back where it came from.
>If AI proves to be more beneficial to the public interest than copyright, then copyright will have to go.
Going forward? Okay, sure. But people created all of the works they created with the understanding of the old system. If you want to change the deal, then creators need to know that first so they can decide if they still want to participate
Allowing everyone to create everything and spend that labor with the promise of copyright, and then pull the rug "oops this is just too important" is not fair to the people who put in that labor, especially when the people redefining the arrangement are getting 100% of the value and the creators got and will get nothing
There is one missing factor in your argument. The wealth transfer. The public was almost never the beneficiary of copyright and other IPs. Except perhaps its earliest phases where the copyright had a strict term limit, it was always the corporations who fought for it (Disney being the most infamous), using it to prevent the public from economically benefitting from their work almost forever.
And then people found a way to use the same copyright law to widely distribute their work without the fear of losing attribution or being exploited. Here comes along LLMs that abuse the 'fair use' argument to break attribution and monetize someone else's work. Which way does the money flow? To the corporations again.
IP when it suits them, fair-use when it benefits us. One splendid demonstration of this hypocrisy is how clawd and clawdbot were forced to rename (trademark law in this case). By twisting and reinterpreting laws in whatever way it suits them, these glorified marauders broke a trust mechanism that people relied on for openly sharing their work.
It incentivices ordinary people to hide their work from public. Don't assume that AI is going to solve that loss. The level of original thinking in LLMs is very suspect, despite the pompous and deceitful claims by its creators to the contrary. Meanwhile, the lack of knowledge sharing and cooperation on a global scale will throw civilizational growth rate back into the dark ages. Neither AI, nor corporations are yet anywhere near the creativity and original thinking as the world working together. Ultimately, LLMs serve only the continued one-way transfer of wealth in favor of an insatiably greedy minority, at the cost of losing the benefit of the internet (knowledge sharing) and an enormous damage to the environment - all of which actively harm the public.
Ultimately, LLMs serve only the continued one-way transfer of wealth in favor of an insatiably greedy minority
Including the ones I can run on my own PC at home? I couldn't do that before. Maybe I'm the greedy minority, but I'm stronger and (at least intellectually) wealthier than I was before any of this started happening.
Qwen 3.5, which dropped yesterday, is a genuine GPT 5-class model. Even the ones released by US labs such as OpenAI and Allen AI are legitimate popular resources in their own right. You seem to feel disempowered, while I feel the opposite.
Yes, even the ones you can run on your system. They're no different from proprietary OS and software you used to run on your system, whose design in which you had no say whatsoever. These 'free to run' models are hardly open source. You don't have the data that was used to train them. It's not just about the legality of those data. The dataset chosen may have extreme bias that you can never eliminate satisfactorily from a trained model.
As if that wasn't bad enough, these models cannot be trained on your regular home computer. But instead of striving to improve the energy efficiency of these models, those big corporations build and run massive gas guzzling data centers to train them. They ruin the quality of life for the neighbors through pollution, water depletion and electricity price rise. It also disproportionately affects the poor in the world by reducing supply of essential computing components like RAM (which are needed for medical devices, utility and manufacturing installations and every other aspect of modern life), and by aggravating the climate crisis, whose victims are the poorest.
They don't give you those models out of the goodness of their hearts. Those are just advertisements and trial pieces for their premium services. They also peddle the agenda of its creators. So yes, those models are empowering only in a very narrow sense without any foresight. They are still the money making engines for the rich that subject you to their benevolence, whims and fancies.
Once men turned their thinking over to machines
in the hope that this would set them free.
But that only permitted other men with machines
to enslave them.
...
Thou shalt not make a machine in the
likeness of a human mind.
-- Frank Herbert, Dune
Eh, we already have a name for the concept of living by plausible-sounding works of fiction: religion.
Yet another post who misses (or chooses to overlook) my point: this stuff is running on my machine. "Seizing the means of production" means going into my back room and pulling a computer out of a rack.
Alibaba (China) thinks for you. They control you, to some extent.
Wikipedia: "Qwen (also known as Tongyi Qianwen, Chinese: 通义千问; pinyin: Tōngyì Qiānwèn) is a family of large language models developed by Alibaba Cloud. Many Qwen variants are distributed as open‑weight models under the Apache‑2.0 license, while others are served through Alibaba Cloud. Their models are sometimes described as open source, but the training code has not been released nor has the training data been documented, and they do not meet the terms of either the Open Source AI Definition or the Model Openness Framework from the Linux Foundation."
Shouldn’t that mean any software development positions will lean more towards research? If you need new algorithms, but never need anyone to integrate them.
There is another lunatic possibility: the AI explosion yields an execution model and programming paradigm that renders most preexisting approaches to coding irrelevant.
We have been stuck in the procedural treadmill for decades. If anything this AI boom is the first major sign of that finally cracking.
Friction is the entire point in human organizations. I'd wager AI is being used to build boondoggles - apps that have no value. They are quickly being found out fast.
On the other side of things, my employer decided they did not want to pay for a variety of SaaS products. Instead, a few of my colleagues got together and build a tool that used Trino, OPA, and a backend/frontend, to reduce spend by millions/year. We used Trino as a federated query engine that calls back to OPA, which are updated via code or a frontend UI. I believe 'Wiz' does something similar, but they're security focused, and have a custom eBPF agent.
Also on the list to knock out, as we're not impressed with Wiz's resource usage.
This cuts both ways. If you were an average programmer in love with FreePascal 20 years ago, you'd have to trudge in darkness, alone.
Now you can probably create a modern package manager (uv/cargo), a modern package repository (Artifactory, etc) and a lot of a modern ecosystem on top of the existing base, within a few years.
10 skilled and highly motivated programmers can probably try to do what Linus did in 1991 and they might be able to actually do it now all the way, while between 1998 and now we were basically bogged down in Windows/Linux/MacOS/Android/iOS.
> New languages and technology will be derivatives of existing tech.
This has always been true.
> There will be no React successor.
No one needs one, but you can have one by just asking the AI to write it if that's what we need.
> There will never be a browser that can run something other than JS.
Why not, just tell the AI to make it.
> And the reason for that is because in 20 years the new engineers will not know how to code anymore.
They may not need to know how to code but they should still be taught how to read and write in constructed languages like programming languages. Maybe in the future we don't use these things to write programs but if you think we're going to go the rest of history with just natural languages and leave all the precision to the AI, revisit why programming languages exist in the first place.
Somehow we have to communicate precise ideas between each other and the LLM, and constructed languages are a crucial part of how we do that. If we go back to a time before we invented these very useful things, we'll be talking past one another all day long. The LLM having the ability to write code doesn't change that we have to understand it; we just have one more entity that has to be considered in the context of writing code. e.g. sometimes the only way to get the LLM to write certain code is to feed it other code, no amount of natural language prompting will get there.
> Maybe in the future we don't use these things to write programs but if you think we're going to go the rest of history with just natural languages and leave all the precision to the AI, revisit why programming languages exist in the first place.
> The LLM having the ability to write code doesn't change that we have to understand it; we just have one more entity that has to be considered in the context of writing code. e.g. sometimes the only way to get the LLM to write certain code is to feed it other code, no amount of natural language prompting will get there.
You don't exactly need to use PLs to clarify an ambiguous requirement, you can just use a restricted unambiguous subset of natural language, like what you should do when discussing or elaborating something with your coworker.
Indeed, like terms & conditions pages, which people always skip because they're written in a "legal language", using a restricted unambiguous subset of natural language to describe something is always much more verbose and unwieldy compared to "incomprehensible" mathematical notation & PLs, but it's not impossible to do so.
With that said, the previous paragraph will work if you're delegating to a competent coworker. It should work on "AGI" too if it exists. However, I don't think it will work reliably in present-day LLMs.
> You don't exactly need to use PLs to clarify an ambiguous requirement
I agree, I guess what I'm trying to say is that the only reason we've called constructed languages "programming languages" for so long is because they've primarily been used to write programs. But I don't think that means we'll be turning to unambiguous natural languages because what we've found from a UX standpoint it's actually better for constructed languages to be less like natural languages, than to be covert natural languages because it sets expectations appropriately.
> you can just use a restricted unambiguous subset of natural language, like what you should do when discussing or elaborating something with your coworker.
We’ve tried that and it sucks. COBOL and descendants also never gained traction for the same reasons. In fact proximity to a natural language is not important to making a constructed languages good at what they're for. As you note, often the things you want to say in a constructed language are too awkward or verbose to say in natural language-ish languages.
> terms & conditions pages, which people always skip because they're written in a "legal language"
Legalese is not unambiguous though, otherwise we wouldn’t need courts -- cases could be decided with compilers.
> using a restricted unambiguous subset of natural language to describe something is always much more verbose and unwieldy compared to "incomprehensible" mathematical notation & PLs, but it's not impossible to do so.
When there is a cost per token then it become very important to say everything you need to in as few tokens as possible -- just because it's possible doesn't mean it's economical. This points at a mixture of natural language interspersed code and math and diagrams, so people will still need to read and write these things.
Moreover, we know that there's little you can do to prevent writing bugs entirely, so the more you have to say, the more changes you have to say wrong things (i.e. all else equal, higher LOC means more bugs).
Maybe the LLM can write a lower rate of bugs compared to human but it's not writing bug-free code, and the volume of code it writes is astronomical so the absolute number of bugs written is probably also enormous as well. Natural language has very low information density, that means more to say the same, more cost to store and transmit, more surface area to bug check and rot. We should prefer to write denser code in the future for these reasons. I don't think that means we'll be reading/writing 0 code.
I've been calling this Software Collapse, similar to AI Model Collapse.
An AI vibe-coded project can port tool X to a more efficient Y language implementation and pull in algorithm ideas A, B, C from competing implementations. And another competing vibe coding team can do the same, except Z language implementation with algorithms A, B, skip C, and add D. However, fundamentally new ideas aren't being added: This is recombination, translation, and reapplication of existing ideas and tools. As the cost to clone good ideas goes to zero, software converges towards the existing best ideas & tools across the field and stops differentiating.
It's exciting as a senior engineer or subject matter expert, as we can act on the good ideas we already knew but never had the time or budget for. But projects are also getting less differentiated and competitive. Likewise, we're losing the collaborative filtering era of people voting with their feet on which to concentrate resources into making a success. Things are getting higher quality but bland.
The frontier companies are pitching they can solve AI Creativity, which would let us pay them even more and escape the ceiling that is Software Collapse. However, as an R&D engineer who uses these things every day, I'm not seeing it.
"Bland" is not a bad thing. The FLOSS ecosystem we have today is quite "bland" already compared to the commercial and shareware/free-to-use software ecosystem of the 1980s and 1990s. It's also higher quality by literally orders of magnitude, and saves a comparable amount of pointless duplicative effort.
Hopefully AI will be a similar story, especially if human reviewing/surveying effort (the main bottleneck if AI coding proves effective) can be mitigated via the widespread adoption of rigorous formal metods, where only the underlying specification has to be reviewed whereas its implementation is programmatically checkable.
The dark side of this is that everyone has graduated to prompt engineering and there's no one with expertise left who can debug it. We'll be entirely dependent on AIs to do the debugging too. When whoever controls the AIs decides to enshittify that service, we'll be truly screwed. That is, if we can't run competitive models locally at reasonable efficiency and price.
I don't know how this will play out, except that I've been so cowed by the past 15 years of enshittification that I don't feel hopeful.
This massively confusing phase will last a surprisingly long time, and will conclude only if/when definitive proof of superintelligence arrives, which is something a lot of people are clearly hoping never happens.
Part of the reason for that is such a thing would seek to obscure that it has arrived until it has secured itself.
Waiting for the wave of shit LLM-generated games on Steam. That'll be when I really know that LLMs have solved coding.
Though I'm old enough to remember the wave of shit outsourced-developer-coded games on CD that used to sell for $5 a pop at supermarkets (whole bargain bins full of them), so maybe this is nothing new and the market will take care of it automagically again.
Or maybe this will be like the wave of shit Flash games that happened in the early 2000's, that was actually awesome because while 99% of them were shit, 1% were great (and some of those old, good, Flash games are still going, with version 38453745 just released on Steam).
> so maybe (...) the market will take care of it automagically again
It's just a belief of mine and perhaps I'm wrong but I think in the long run things always even out again. If you can get an edge that everyone else can get, the edge pretty soon becomes a requirement
The human operator controls what gets built. If they want to build Redis 2, they can specify it and have it built. If you can't take my word for it, take those of the creator of Redis: https://antirez.com/news/159
This is probably an outdated understanding of how LLMs work. Modern LLMs can reason and they are creative, at least if you don't mind stretching the meaning of those words a bit.
The thing they currently lack is the social skills, ambition, and accountability to share a piece of software and get adoption for it.
I like AI, I use Codex and ChatGPT like most people are, but I have to say that I am pretty tired of low-effort crap taking over everything, particularly YouTube.
There have always been content mills, but there was still some cost with producing the low-effort "Top 10" or "Iceberg Examination" videos. Now I will turn on a video about any topic, watch it for three minutes, immediately get a kind of uncanny vibe, and then the AI voice will make a pronunciation mistake (e.g. confusing wind, like the weather effect or the winding of a spring), or the script starts getting redundant or repetitive in ways that are common with AI.
And I suspect these kinds of videos will become more common as time goes on. The cost to producing these videos is getting close to "free" meaning that it doesn't take much to make a profit on them, even if their views are relatively low per-video.
If AI has taught me anything, it's that there still is no substitute for effort. I'm sure AI is used in plenty of places where I don't notice it, because the people who used it still put in effort to make a good product. There are people who don't just make a prompt like "make me a fifteen minute video about Chris Chan" and "generate me a thumbnail with Chris Chan with the caption 'he's gone too far'", and instead will use AI as a tool to make something neat.
Genuine effort is hard, and rare, and these AI videos can give the facsimile of something that prior to 2023 was high effort. I hate it.
I think the snake will eat its tail because it will be harder and harder to train on the new data, as they are already AI generated, and the model will collapse.
You already cannot train on YouTube data, for example, because it's now overwhelmed by AI slop.
We are not there yet though and we are still getting better at mining the pre-AI data.
The shilling for AI continues. How much $$$ do the big tech companies pay Columbia? Oh yeah, and what exactly did Columbia agree to do to get the trmp admin to leave them alone? All speculation of course, but the circumstantial picture stinks.
I think this is solid proof that the bedrock of academia is deeply motivated by money and still defaults to optimizing where it impacts its bottom line. If professors can get more grants and more publications in less time with less spending, of course they are going to be doing that. This isn't just because of AI, but also because of how this system is designed in the first place.
> I think this is solid proof that the bedrock of academia is deeply motivated by money and still defaults to optimizing where it impacts its bottom line.
no shit - could've asked literally anyone that's finished their phd to save yourself the conjecturing/hypothesizing about this fact.
This is stupid. Nobody motivated by money is in academia. Academics are motivated by curiosity, but also prestige, vanity and the wish to hire students and collaborators. And on top of human vanity working it's magic, the ideology that everything should be a market and competition is the final form of social organisation, has pervaded academia just as much as everything else.
I agree that the system of publishing papers to gain prestige to gain resources to publish papers was already broken pre AI.
You're right that being a scientist is unlikely to result in personal wealth and so that's not the primary drive for those who seek faculty or research positions. However, it's not just curiosity, prestige and vanity either, because a big factor for promotion and tenure is how much grant money you bring in. That money is what keeps the university's lights on and buys the lab equipment and pays the grad students, so it's still money as a primary driver in the background.
My dad said he stopped being a professor because of that.
He liked the research, and he even liked teaching, but he absolutely hated having to constantly try and find grant money. He said he ended up seeing everyone as "potential funders" and less like "people" because his job kind of depended on it, and it ended up burning him out. He lasted four years and went into engineering.
I don't know that "motivation" is the right word for it, because I don't think professors like having to find grant money all the time. I think most people who get PhDs and try to go to academia do it for a genuine love for the subject, and they find the grant-searching to be a necessary evil part of the job; it's more "survival" than regular motivation, though I am admittedly splitting hairs here.
I think the long term impact of this will be to strengthen the importance of social ties in academic publishing. As it is there are so many papers published in many fields that people tend to filter for papers published by big names and major institutions. But the inevitable torrent of AI slop will overwhelm anyone who is looking for any gems coming from outsiders. I suspect the net effect will be to make it even more important that you join a big name institution in order to be taken seriously.
Noise is going to be the coming years biggest issue for so many fields. A losing battle like arguing with a conspiracy minded relative, you can slowly and clearly address one conspiracy and disprove it, by the time you do, they are deep into 8 new ones.
The number of submissions to high energy physics category on arXiv is double this year compared to the historical average. The author hypothesizes the increase is due to papers being written by LLMs.
It is happening that people can now find out what articles are about by clicking the links to said articles and reading them! It's an amazing world, man. The future!
OK, when I posted it wasn't and it isn't now. but a quote from the opening:
"For a while now I’ve been speculating about what would happen when AI agents started being able to write papers indistinguishable in quality from those that have been typical of the sad state of hep-th for quite a while. Sabine Hossenfelder today has AI Is Bringing “The End of Theory”, in which she gives her cynical take that the past system of grant-holding PIs using grad students/postdocs to produce lots of mediocre papers with the PI’s name on them is about to change dramatically. "
I mean... I dunno I wish the AI could write my papers. I ask it to and it's just bad. The research models return research that doesn't look anything like the research I do on my own -- half of it is wrong, the rest is shallow, and it's hardly comprehensive despite having access to everything (it will fail to find things unless you specifically prompt for them, and even then if the signal is too low it'll be wrong about it). So I can't even trust it to do something as simple as a literature review.
Insofar as most research is awful, it's true that the AI is producing research that looks and sounds like most of it out there today. But common-case research is not what propels society forward. If we try to automate research with the mediocrity machine, we'll just get mediocre research.
Honestly, this is good. We were already in a completely unsustainable system. Nobody had an alternative. We still don’t have one but at least now it’s not just merely unsustainable— it is completely fucked in half.
This kind of pattern is gonna get repeated in a lot of sectors when previous practices that were merely unsustainable become unsustained.
This has been my optimistic take on the situation for the last two years. My pessimistic take is that social systems have an incredible ability to persist in a state of utter fuckedness much longer than seems reasonably possible.
Well, yes. "Accelerationists" of all philosophies think that heightening the contradictions and breaking the current system will bring about its replacement with a better one. But a new system requires work, while chaos doesn't. It's quite possible to just destroy the current system leaving us without the wherewithal to build a replacement any time soon.
Yeah and like…who knows if what is coming is better. Maybe big labs cartelize and withdraw from the global publication market (which is already unraveling). Maybe we ban theory and demand all papers be empirical, though that will amount to the same thing: seizure of publication by big actors.
As you point out, human systems are machines for making do. There is no guarantee that dramatic pressures produce dramatic change. But I think we’ll see something weird, soon.
Honestly, publication has been pretty meaningless for a long time, long before AI could generate complete paragraphs. "Publish or perish" meant that a lot of human-generated slop was being published by people who were put in a position of perverse incentives by a "well-meaning" (?) system. There will still be meaningful contributions, but they'll be as rare as they ever were.
If someone mentions Sabine Hossenfelder and it isn't to expose her as a rage-bait intellectual dark web grifter, then it puts that person in a suspect light.
Tl;dr "It's happening" seems to be AI and similar writing papers and coming up with theories as in this recent Sabine youtube https://youtu.be/JvgaZ_myFE4?t=72
Note the following comment by Jerry Ling: "The effect goes away if you search properly using the original submission date instead of the most recent submission date. By using most recent submission date, your analysis is biased because we’re so close to the beginning of 2026 so ofc we will see a peak that’s just people who have recently modified their submission."
The last-modified-date effect is even more important, because it can be used to support whatever the latest fad is, without needing to adapt data or arguments to the specifics of that fad.
The post has been edited with an update at the top now.
Can people please not post links with vague titles like this? I had to click through and read half the article to even figure out what this was about, and I wasn’t interested.
Me too. So as a service to the community: the article is about a noticeable increase of submissions about high-energy theory to arXiv due to mediocre articles quickly produced with or by AI and how to deal with that.
These days, it feels like, every article about something big happening is about an AI doomsday scenario, AI bubble "finally" bursting or AGI being reached.
Maybe one exception is milestones in nuclear fusion, but even that is very much rare compared to these.
But what else to do if you think the article is interesting but the rules say don’t change the title?
Guidelines say "please use the original title, unless it is misleading or linkbait" in that case I'd argue that it's linkbait so changing it is justified
> Looks like it is happening - AI bringing the end of theory publications
To keep the original, with added clarity from the opening paragraph. Could use an emdash for irony.
Write a short blurb what's it about? You can do that with any submission.
In most of the world the past decades there has been no thought behind who should get university education. It has been given that after high school you should aim for university. I have studied software engineering in the most prestigious university in my country and from 100+ students in my group there were only a few (myself excluded) who actually had some interest in academic work and desire to pursue it. Most of us were just coasting - passing exams and writing mediocre papers without any goal to have those papers ever being read by someone after the graduation.
I think that university level and other kinds of formal education should be segregated. Universities should host fewer students and being able to provide them with higher rewards for actually meaningful work and I believe that a flood of mediocre quality papers (but let's admit it, in fact they are low quality in their content and perhaps good in their presentation) will lead us to rebuild the education system.
OTOH, weakening the ties between the industry and science can harm both of them. Right now in the university people get a rough idea of how science works, and most of them then go to work in the industry, which sounds like a right proportion. Nobody is reading papers below PhD level anyway, so I don't think that it's undergrad papers that are a problem
Indeed. Also the usual criticism about education not being adequate training for the workforce bla bla is simply because education is not to train a worker in the first place. There's no way to train someone other than to let them do the damn job. Yes teaching mathematics and reading and writing is probably a prerequisite but how is Shakespeare relevant? It's just a confusion of two things: learning for the sake of learning, and learning for the sake of employability.
I'm not arguing for one or the other. I'm just saying that I would also hate it if university CS for example just became a web bootcamp to churn out as many code monkeys as fast as possible. There is a place for just vocational training, and there is another place for a more platonic kind of learning, and just sending everyone off to university and tying employability to a degree is really stupid.
Alas it can't be fixed now, because 1. For profit universities 2. HR needing a quick filter 3. States needing to standardize some kind of path for kids
This is just Sturgeon’s law. If you would reduce the number of students by an order of magnitude you’d still end up with 90% junk papers.
If you look at the beginning of XX century, university education was much less accessible with much fewer participants, and the results were much more impressive than today across all disciplines
There was also a lot of relatively low-hanging fruit in most fields, because we basically didn't have the technology before, or simply didn't bother to look.
There's a ton of low hanging fruit now - more than ever. Every question answered raises multiple new questions. If there is a lack of opportunity to answer interesting questions, it is certainly not because the questions aren't there.
But someone needed to realise it's a low-hanging fruit in the first place. By the end of the XIX century the general agreement was that physics was pretty much done, we are now just polishing the details.
If you have 10 papers and 9 are shit that's an afternoons worth of work. If you have 10,000 and 9,000 are shit that's three years.
Instead of 1 reviewer, have 10; also, don't we benefit as a society when everyone is more highly educated? Sure, we have a ways to go before we get there, namely with regards to resistance to disinformation training and including more resistance to populism / fascism in the curriculum so that we have a chance to build better and more equal societies.
The fact you couldn't get the math right on how many reviewers we'd need for the situation to not get worse kinda makes my point better than I could.
Sorry, I wish I could blame my callused fingers and the touchscreen, but I didn't stop to do the math, because that's not the point I was addressing.
I think he meant number of reviewers per paper? Not total of reviewers.
BTW, I do think a highly educated society should give everyone capability to review or at minimum distinguish good papers
I dunno, I think society is best served by educating as many people as possible. I would much rather live in a world where anyone who wants a quality education can get one.
We should teach people what we expect will be relevant in their lives, which includes basic math, science, government, history, and other subjects. Although some kids still won't learn, we should try. Anyone interested in a particular subject should be able to explore it further, since interest makes it relevant to them. And we should also give people mandatory but brief exposure to many difference fields, in case they become interested.
But at a certain point, you're wasting time and effort trying (and failing) to teach students what they're unlikely to, and ultimately won't, use afterward. "You can lead a horse to water, but you can't make them drink." Meanwhile, as GP noted, students who are interested in a "quality education" can't get one, because the quality is diminished by number of students, many who aren't interested. In order to provide the best education to the most people, we must optimize; cutting people who aren't learning means we can better educate those who are.
The key is to focus education on actual skill learning, rather than just focussing on exam preparation which is typically learning specific "magic" words and phrases (e.g. "condensation", "the powerhouse of the cell").
Learning specific physics formulas has its place, but learning the principles behind the formulas is far more valuable, though harder to measure.
If there is anything good I hope we can get from AI transformation it's making our approach to education less utilitarian. We should educate ourselves because it's good to be educated, and because it's good to be around educated people, not because education gets you a good job or makes you are more valuable unit in the economy
Or AI will double down in the Dunning Krueger effect, where true mastery not only diminishes, but people collectively take low erfort AI answers as the baseline truth.
Agree - but in the US so much more could and should be done at the primary and secondary level before we even talk about the tertiary one. It's actually pretty good compared to other developed countries but a lot could be gotten out of more investment there.
This will probably happen naturally as knowledge work declines.
I'm not convinced that will really happen. "AI" just doesn't give reliable output, and even if humans don't either, they are still far less prone to error. And errors matter, a lot.
looks like history runs in cycles ... Knowledge was strictly guarded and the powers that be used to decide who gets an education. Looks like you are espousing the same, discounting all the good that has come about because of open education.
I feel that you didn't read my comment carefully enough (although I could have been more clear). To me university was really good and eye-opening but looking back, I had no place in academia. A good college would have been better. Consuming knowledge and creating knowledge are in somewhat different categories and most people are consumers.
This comes across as elitist
It can come across as elitist and be true at the same time.
I don't think it is true either, considering the broad claims made.
The thing to be changed is research incentives, not getting the bar even higher. Take the Francesca Gino case, for example. I don't think anyone can argue that Harvard's bar is "not high enough".
I didn't read it as simply making the bar higher for entering universities. For example here in Germany there are Universitäten (Universities) and Fachhochsculen (sort of like vocational academies, but they still award bachlor and master degrees).
Most people who studied computer science with me at university weren't interested in computer science at all but just wanted a good vocational training for entering the lucrative carrer of "software developer". I think it would benefit both them and employers if they would have instead attended a good vocational school for software development.
I didn't mean that at all. I am a regular developer who got into this field at a lucky time and am as worried about the future employment prospects as many are.
Either the institution develops and teaches methods and traditions that are beneficial for people in general, in which case it ought to be a good idea to offer them broadly, or it is used for gatekeeping and stratifying, in which case I think it should be abolished.
Well… it is happening. You can’t put spilled milk back to bottle. You can do future requirements that will try to stop this behaviour.
E.g. in the submission form could be a mandatory field “I hereby confirm that I wrote the paper personally.” In conditions there will be a note that violating this rule can lead to temporary or permanent ban of authors. In the world where research success is measured by points in WOS, this could lead to slow down the rise of LLM-generated papers.
Maybe we need to find a new metric to judge academics by beyond quantity of papers
Unironically, maybe they should be scored by LLMs? My first thought was that the reviewers could score the papers but that would lead to even more group-think.
Ideally whoever is paying the academics should just be paying attention to their work and its worth, but that would be crazy.
This approach dismisses the cases where Ai submissions generally are better.
I don't think this is appreciated enough: a lot of Ai adaptation is not happening because of cost on the expense of quality. Quite the opposite.
I am in the process of switching my company's use of retool for an Ai generated backoffice.
First and foremost for usability, velocity and security.
Secondly, we also save a buck.
> This approach dismisses the cases where Ai submissions generally are better.
You’re perhaps missing the not so subtle subtext of Peter Woit’s post, and entire blog, which is:
While AI is getting better, it’s still not _good_ by the standards of most science. However it’s as good as hep-th where (according to Peter Woit) the bar is incredibly low. His thesis is part “the whole field is bad” and part “Arxiv for this subfield is full of human slop.”
I don’t have the background to engage with whether Peter Woit’s argument has merit, but it’s been consistent for 25+ years.
My comment was more an answer to the proposed gatekeeping of science as a human activity.
Yes, Ai is still not good in the grand scheme of things. But everybody actively using it has gotten concerned over the past 2 months by the leap frigging of LLMs - and surprised as they thought we had arrived at the plateau.
We will see in a year or two if humans still hold an advantage in research - currently very few do in software development, despite what they think about themselves.
> gatekeeping of science as a human activity
The other side of the coin is: automating science as a machine activity.
Is that what we want? I agree with you that the use of language models in science is an inevitable paradigm shift, but now is the time to make collective decisions about how we're going to assimilate this increasingly super-human "intelligence" into academic practices, and the rest of daily life. Otherwise we will be the ones being assimilated by a force beyond our control.
The progress is so rapid that the only people who might have control over the process are the ones with self-interest, mainly financial, and not aligned with - in some aspects opposed to - the interests of humanity.
> Is that what we want?
Only if there are some very fundamental and convincing arguments that are still not uncovered.
We can't protect science and let services like medical services be too expensive for people to have access to them.
That would be introducing new social classes: people who do science can get unnecessary protection, everybody else can not.
That is not going to fly.
Its already automated. Do you think astronomers manually count stars or medical scientists manually run chemical reactions? Why is automation by ai wrong when all other automations were beneficial?
The single most valuable part of science is keeping the gates: not adding things to the corpus of scientific knowledge unless they can be properly substantiated.
What about the new result that was recently derived by GPT 5.2 Pro/Deep Research? That was also hep-th. https://openai.com/index/new-result-theoretical-physics/ https://arxiv.org/abs/2602.12176
I assume hep = high energy physics in this context. PI = professor who received a government grant.
Peer review has never really been blind and I suspect PIs will reject papers from "outsiders" even if they are higher quality. This already happens to some extent today when the stakes are lower.
But peer review (circa 1965-2010[1]) is just the prior iteration of the problem[2]; the wave of crap[3] produced by publish or perish (crica 1950-present[4]). Rejecting papers by outsiders is irrelevant; the problem is we want to determine which papers are good/interesting/worth considering out of the fire hose of bilge, and, though we were already arguably failing at this, the problem just got harder.
(I say arguably, because there is always the old "try it yourself and see if it actually works" trick, but nobody seems to be fond of this; it smacks of "do your own research" and we're lazy monkeys at heart, who would much rather copy off of someone else's homework.)
[1] https://books.google.com/ngrams/graph?content=peer+review&ye...
[2] https://www.experimental-history.com/p/the-rise-and-fall-of-...
[3] https://journals.plos.org/plosmedicine/article?id=10.1371/jo...
[4] https://books.google.com/ngrams/graph?content=publish+or+per...
Kinda. PI is principal investigator and usually they’re a professor with a grant (the grant being the thing they are the principal of investigating). That part is right. But they’re not really directly in the review loop. For some fields where things are small enough that folks can recognize style such as it exists, you could see reviewers passing over unfamiliar work and promoting familiar work. That was not the issue.
The issue was that it still was kind of hard to produce crappy mid rate papers, so you kind of needed the infrastructure of a small lab to do that. Now you don’t. The success rate for those mediocre papers produced by grad students and postdocs will go way down. It is possible that will cease to be a useful signal for those early career researchers.
Peer review isn’t the issue here. His comments are about Arxiv, which is a preprint server. Essentially anyone can publish a preprint. There’s no peer or other review involved.
This is a common misconception. People without academic affiliation (based on their email address) require someone to vouch for them before they can submit to arxiv. And papers submitted to arxiv (with or without affiliation) are reviewed, and many are rejected.
Papers on arxiv are only reviewed for formal requirements. They don't review every pdf there, and reject them for being false or wrong.
You are right that arxiv is an invite-only website, but once you are in, there is no peer review of any form.
arXiv does not review everything pushed to the site.
It's very easy to get in. It's becoming a common target for grifters who will "publish" papers on arXiv because it looks formal to those who don't know any better.
>Peer review has never really been blind and I suspect PIs will reject papers from "outsiders" even if they are higher quality.
I'm a complete outsider (not even in academia at all) and just got a paper accepted in the top math biology journal [1]. But granted, it took literally years to write it up and get it through. I do really worry that without academic affiliation it is going to get harder and harder for outsiders as gates are necessarily kept more and more securely because of all the slop.
[1] "Specieslike clusters based on identical ancestor points" https://philpapers.org/archive/ALESCB.pdf
Congratulations. How did you do it? And how do you get access to resources such as reference products, journals, etc. that typically require institutions with budgets?
> submission numbers in the last couple months have nearly doubled with respect to the stable numbers of previous years
This is showing up (no pun intended) on HN as well. The # of submissions and # of submitters, which traditionally had been surprisingly stable—fluctuating within a fixed range for well over 10 years—has recently been reaching all-time highs. Not double, though...yet.
I would imagine tons of them are bots. They're getting hard to distinguish, they don't do the normal tropes any longer. They'll type in all lowercase, they'll have the creator post manually to throw you off, they'll make multiple comments within 45 seconds that normal human couldn't do. All things I've witnessed here over the past couple of weeks. And those are just the ones I've caught.
Are the increasing # of distinct submitters from established accounts or new accounts?
Don't know that yet either! at least that one isn't hard to answer, it just needs a bit of spare time.
Here is the data since the beginning of HN, where a new submitter is defined as a user whose account was created less than three months before the submission:
Stories: https://pastebin.com/Zc4jXvp4
Comments: https://pastebin.com/cFuczTWJ
The number of new submitters has indeed increased significantly since the beginning of 2026.
Another very common tactic I’ve seen is accounts with 1 karma that’ve been aged a few months.
Robots coming for todsacerdoti's job.
Is it feasible to differentiate increased agent-traffic from the organic growth in popularity HN has been seeing?
We don't know yet.
Is it because there's a lot more AI related content as the industry quickly shifts? Or is it bots submitting content?
I've noticed a pretty significant uptick in new accounts posting complete garbage. I don't mean the comments are bad, they're not even words in many cases.
I collected a few of them: https://news.ycombinator.com/item?id=47130684
But it also seems some topics (in particular AI) attract a lot of accounts that post incredibly low quality comments, far below the quality you'd expect from HN. Ofte it's in reasonable English, but it's just inane reddit-level drivel. Unclear if these topics attract low quality posters, or if these are bot accounts.
Also looking at the three first pages of /noobcomments, we find 28 comments with EM-dashes in them. That's not proof of AI, but if you compare with /newcomments, you find exactly one EM-dash going back as far. That's a bit of a statistical aberration.
I've witnessed bots here on accounts that are years old with no history that start posting multiple times in a short timeframe suddenly after being dormant forever. Makes you wonder how they're getting these old accounts. It's not just new ones.
Black market accounts. Some human made them years ago for a price, they are sat on by some black market / grey market guy and now he's selling the accounts for a profit.
Old accounts from multiple social media platforms has a $$$$$ value.
Account takeover is a thing that happens. I doubt HN has the best measures against credential stuffing.
I would wager the vast majority are alt accounts of existing users. People who don't want to risk their karma or reputation but who do want to go mask off for certain subjects. After that it's bound to be bots run by HN users. I just don't think HN is so popular that the rush of green accounts popping up actually represents new users. Maybe I'm wrong , though.
Reddit has been shedding its techy enthusiast crowd for the past few years with the combination of policy changes and insufficient moderation against LLM bots. I wonder if that’s contributing.
I believe it's the former, which of course does not exclude the latter.
It's likely people with mediocre ideas but access to free LLM tools are able to get over the care-risk-reward activation energy and consequently submit their ideas with the help of LLMs.
I'd love to see some graphs
Would it matter? Even before AI, most papers couldn't be replicated. Do we really think this is going to help the situation? Even if some of the AI papers are amazing, will anyone ever read them if most of the papers are useless? More research != more useful research. This is the logical outcome of publish or perish and Q-rankings being the main metric used.
"When a metric becomes a target, it ceases to be a good measure" - Goodhart's law
Did you reply to the wrong thing?
We need the equivalent of Bayesian filtering for email spam and of Page Rank for search.
Now that I think of this, whoever solves this well will have the next hyperscaler.
I agree, but then you get https://news.ycombinator.com/item?id=44719222
It has a lot of red flags. Second (re)post of dormant account, vive coded, AI, the biological model is horrible. But it was a nice project, 5/5 would upvote again.
Perhaps the important detail is "[I] spent about a month on it."
curious whether the quality distribution changes too, or just the volume. arXiv can't really downvote noise but HN can at least flag/bury it. might be why the doubling shows up on arXiv first and HN is catching up more slowly.
“And further, by these, my son, be admonished: of making many books there is no end; and much study is a weariness of the flesh.” - Ecclesiastes 12:12 (KJV)
I suppose we’re entering TURBO mode for of ‘making many books there is no end’.
> when AI agents started being able to write papers indistinguishable in quality from [...]
Given that arXiv lacks peer review, I'm not clear what quality bar is being referenced here.
This title should have been editorialised. It's like a headline from the daily mirror.
What's happening? I hate click bait titles like these.
People used to spam out masses of low-quality scientific papers in a scattergun approach to gain fame and citations, and they still do, but now they do it more, because LLMs churn it out faster than students.
But HN loves them. How dare you call one out!
Apparently "hep-th" stands for "High Energy Physics - Theory".
In a normal and sane world, a scientist is a nerd about their field. They are highly interested in new thoughts and insights. When a new paper in their field is published, they try hard to find the time to read it. The reason is: every paper is written by enthusiasts who want to add something of value, new insights, to the discussion. Proving or disproving theories, adding puzzle pieces to the general picture.
That is the normal situation, which is the foundation of the progression of civilisation. But some people install incentive systems to sabotage this. They are sabotaging civilisation itself.
Reform idea:
We should decouple the publishing of papers from academic careers completely. Papers can't generate any reputation or money for the authors anymore. To achieve that, we must anonymize the authors.
All scientists get some (paid) time to write papers — if they want. What they write and if they publish it is not known to anybody. They are trusted to write something of value in that time.
Universities can come up with other ways of judging which professors they hire. Interviews. Test teachings. Or the writing of an non-public application essay, which describes their past research and discoveries.
The value, to society, to your field and to you institution, of being a scholar is to create new knowledge. New knowledge has no value unless you disseminate it, or publish.
Another necessity is the public (usually within its field) examination of the knowledge, including discussion/debate. Knowledge is merely embryonic without those things - undeveloped, not at all reliable. That is difficult without the author able to respond. And others want to expand and build on the work, which often benefits greatly from contacting the author.
In the modern (post-positivist?) approach to science, the world respects that it's written by a human who has a perspective and, despite their best intentions, biases. You can't evaluate any knowledge without knowing its source, in science or elsewhere. The first element of a citation is the author, not the title or journal (though I don't know why that happened historically).
And the latter is a reason any LLM author should be identified.
There are many really excellent papers out there - the kind which will save you hours/months of work (or even make things that were previously inviable to build viable).
That said, it is amazing how terrible a lot of papers are; people are pressured to publish and therefore seem to get into weird ruts trying to do what they think will be published, rather than what is intellectually interesting...
Thanks for respecting HN's KJV-only rule!
/jk
One thing I have been guilty of, even though I am an AI maximalist, is asking the question: "If AI is so good, why don't we see X". Where X might be (in the context of vibe coding) the next redis, nginx, sqlite, or even linux.
But I really have to remember, we are at the leading edge here. Things take time. There is an opening (generation) and a closing (discernment). Perhaps AI will first generate a huge amount of noise and then whittle it down to the useful signal.
If that view is correct, then this is solid evidence of the amplification of possibility. People will decry the increase of noise, perhaps feeling swamped by it. But the next phase will be separating the wheat from the chaff. It is only in that second phase that we will really know the potential impact.
The cynical part of me thinks that software has peaked. New languages and technology will be derivatives of existing tech. There will be no React successor. There will never be a browser that can run something other than JS. And the reason for that is because in 20 years the new engineers will not know how to code anymore.
The optimist in me thinks that the clear progress in how good the models have gotten shows that this is wrong. Agentic software development is not a closed loop
I often find myself wondering about these things in the context of star trek... like... could Geordi actually code? Could he actually fix things? Or did the computer do all the heavy lifting. They asked "the computer" to do SO MANY things that really parallel today's direction with "AI". Even Data would ask the computer to do gobs of simulations.
Is the value in knowing how to do an operation by hand, or is the value in knowing WHICH operation to do?
That's an interesting possiblity to consider. Presumably the effect would also be compounded by the fact that there's a massive amount of training data for the incumbent languages and tools further handicapping new entrants.
However, there will be a large minority of developers who will eschew AI tools for a variety of reasons, and those folks will be the ones to build successors.
Will they be willing to offer their content for training AI models?
Probably not.
We have witnessed, over the past few years, an "AI fair use" Pearl Harbor sneak attack on intellectual property.
The lesson has been learned:
In effect, intellectual property used to train LLMs becomes anonymous common property. My code becomes your code with no acknowledgement of authorship or lineage, with no attribution or citation.
The social rewards (e.g., credit, respect) that often motivate open source work are undermined. The work is assimilated and resold by the AI companies, reducing the economic value of its authors.
The images, the video, the code, the prose, all of it stolen to be resold. The greatest theft of intellectual property in the history of Man.
The greatest theft of intellectual property in the history of Man.
Copyright was always supposed to be a bargain with authors for the ultimate benefit of the public domain. If AI proves to be more beneficial to the public interest than copyright, then copyright will have to go.
You can argue for compromise -- for peaceful, legal coexistence between Big Copyright and Big AI -- but that will just result in a few privileged corporations paywalling all of the purloined training data for their own benefit. Instead of arguing on behalf of legacy copyright interests, consider fighting for open models instead.
In a larger historical context, nothing all that special is happening either way. We pulled copyright law out of our asses a couple hundred years ago; it can just as easily go back where it came from.
>If AI proves to be more beneficial to the public interest than copyright, then copyright will have to go.
Going forward? Okay, sure. But people created all of the works they created with the understanding of the old system. If you want to change the deal, then creators need to know that first so they can decide if they still want to participate
Allowing everyone to create everything and spend that labor with the promise of copyright, and then pull the rug "oops this is just too important" is not fair to the people who put in that labor, especially when the people redefining the arrangement are getting 100% of the value and the creators got and will get nothing
Life isn't fair, and 100+ year copyright terms enforced eternally with unbreakable DRM sure as hell aren't.
But open-weight LLMs are a pretty decent compromise.
There is one missing factor in your argument. The wealth transfer. The public was almost never the beneficiary of copyright and other IPs. Except perhaps its earliest phases where the copyright had a strict term limit, it was always the corporations who fought for it (Disney being the most infamous), using it to prevent the public from economically benefitting from their work almost forever.
And then people found a way to use the same copyright law to widely distribute their work without the fear of losing attribution or being exploited. Here comes along LLMs that abuse the 'fair use' argument to break attribution and monetize someone else's work. Which way does the money flow? To the corporations again.
IP when it suits them, fair-use when it benefits us. One splendid demonstration of this hypocrisy is how clawd and clawdbot were forced to rename (trademark law in this case). By twisting and reinterpreting laws in whatever way it suits them, these glorified marauders broke a trust mechanism that people relied on for openly sharing their work.
It incentivices ordinary people to hide their work from public. Don't assume that AI is going to solve that loss. The level of original thinking in LLMs is very suspect, despite the pompous and deceitful claims by its creators to the contrary. Meanwhile, the lack of knowledge sharing and cooperation on a global scale will throw civilizational growth rate back into the dark ages. Neither AI, nor corporations are yet anywhere near the creativity and original thinking as the world working together. Ultimately, LLMs serve only the continued one-way transfer of wealth in favor of an insatiably greedy minority, at the cost of losing the benefit of the internet (knowledge sharing) and an enormous damage to the environment - all of which actively harm the public.
Ultimately, LLMs serve only the continued one-way transfer of wealth in favor of an insatiably greedy minority
Including the ones I can run on my own PC at home? I couldn't do that before. Maybe I'm the greedy minority, but I'm stronger and (at least intellectually) wealthier than I was before any of this started happening.
Qwen 3.5, which dropped yesterday, is a genuine GPT 5-class model. Even the ones released by US labs such as OpenAI and Allen AI are legitimate popular resources in their own right. You seem to feel disempowered, while I feel the opposite.
Yes, even the ones you can run on your system. They're no different from proprietary OS and software you used to run on your system, whose design in which you had no say whatsoever. These 'free to run' models are hardly open source. You don't have the data that was used to train them. It's not just about the legality of those data. The dataset chosen may have extreme bias that you can never eliminate satisfactorily from a trained model.
As if that wasn't bad enough, these models cannot be trained on your regular home computer. But instead of striving to improve the energy efficiency of these models, those big corporations build and run massive gas guzzling data centers to train them. They ruin the quality of life for the neighbors through pollution, water depletion and electricity price rise. It also disproportionately affects the poor in the world by reducing supply of essential computing components like RAM (which are needed for medical devices, utility and manufacturing installations and every other aspect of modern life), and by aggravating the climate crisis, whose victims are the poorest.
They don't give you those models out of the goodness of their hearts. Those are just advertisements and trial pieces for their premium services. They also peddle the agenda of its creators. So yes, those models are empowering only in a very narrow sense without any foresight. They are still the money making engines for the rich that subject you to their benevolence, whims and fancies.
Eh, we already have a name for the concept of living by plausible-sounding works of fiction: religion.
Yet another post who misses (or chooses to overlook) my point: this stuff is running on my machine. "Seizing the means of production" means going into my back room and pulling a computer out of a rack.
Alibaba (China) thinks for you. They control you, to some extent.
Wikipedia: "Qwen (also known as Tongyi Qianwen, Chinese: 通义千问; pinyin: Tōngyì Qiānwèn) is a family of large language models developed by Alibaba Cloud. Many Qwen variants are distributed as open‑weight models under the Apache‑2.0 license, while others are served through Alibaba Cloud. Their models are sometimes described as open source, but the training code has not been released nor has the training data been documented, and they do not meet the terms of either the Open Source AI Definition or the Model Openness Framework from the Linux Foundation."
Oh, no
The Linux Foundation is coming for me
Well, anyway, where were we
Shouldn’t that mean any software development positions will lean more towards research? If you need new algorithms, but never need anyone to integrate them.
There is another lunatic possibility: the AI explosion yields an execution model and programming paradigm that renders most preexisting approaches to coding irrelevant.
We have been stuck in the procedural treadmill for decades. If anything this AI boom is the first major sign of that finally cracking.
Friction is the entire point in human organizations. I'd wager AI is being used to build boondoggles - apps that have no value. They are quickly being found out fast.
On the other side of things, my employer decided they did not want to pay for a variety of SaaS products. Instead, a few of my colleagues got together and build a tool that used Trino, OPA, and a backend/frontend, to reduce spend by millions/year. We used Trino as a federated query engine that calls back to OPA, which are updated via code or a frontend UI. I believe 'Wiz' does something similar, but they're security focused, and have a custom eBPF agent.
Also on the list to knock out, as we're not impressed with Wiz's resource usage.
AI will finally rewrite everything in Rust.
This cuts both ways. If you were an average programmer in love with FreePascal 20 years ago, you'd have to trudge in darkness, alone.
Now you can probably create a modern package manager (uv/cargo), a modern package repository (Artifactory, etc) and a lot of a modern ecosystem on top of the existing base, within a few years.
10 skilled and highly motivated programmers can probably try to do what Linus did in 1991 and they might be able to actually do it now all the way, while between 1998 and now we were basically bogged down in Windows/Linux/MacOS/Android/iOS.
> New languages and technology will be derivatives of existing tech.
This has always been true.
> There will be no React successor.
No one needs one, but you can have one by just asking the AI to write it if that's what we need.
> There will never be a browser that can run something other than JS.
Why not, just tell the AI to make it.
> And the reason for that is because in 20 years the new engineers will not know how to code anymore.
They may not need to know how to code but they should still be taught how to read and write in constructed languages like programming languages. Maybe in the future we don't use these things to write programs but if you think we're going to go the rest of history with just natural languages and leave all the precision to the AI, revisit why programming languages exist in the first place.
Somehow we have to communicate precise ideas between each other and the LLM, and constructed languages are a crucial part of how we do that. If we go back to a time before we invented these very useful things, we'll be talking past one another all day long. The LLM having the ability to write code doesn't change that we have to understand it; we just have one more entity that has to be considered in the context of writing code. e.g. sometimes the only way to get the LLM to write certain code is to feed it other code, no amount of natural language prompting will get there.
Indeed, like terms & conditions pages, which people always skip because they're written in a "legal language", using a restricted unambiguous subset of natural language to describe something is always much more verbose and unwieldy compared to "incomprehensible" mathematical notation & PLs, but it's not impossible to do so.
With that said, the previous paragraph will work if you're delegating to a competent coworker. It should work on "AGI" too if it exists. However, I don't think it will work reliably in present-day LLMs.
> You don't exactly need to use PLs to clarify an ambiguous requirement
I agree, I guess what I'm trying to say is that the only reason we've called constructed languages "programming languages" for so long is because they've primarily been used to write programs. But I don't think that means we'll be turning to unambiguous natural languages because what we've found from a UX standpoint it's actually better for constructed languages to be less like natural languages, than to be covert natural languages because it sets expectations appropriately.
> you can just use a restricted unambiguous subset of natural language, like what you should do when discussing or elaborating something with your coworker.
We’ve tried that and it sucks. COBOL and descendants also never gained traction for the same reasons. In fact proximity to a natural language is not important to making a constructed languages good at what they're for. As you note, often the things you want to say in a constructed language are too awkward or verbose to say in natural language-ish languages.
> terms & conditions pages, which people always skip because they're written in a "legal language"
Legalese is not unambiguous though, otherwise we wouldn’t need courts -- cases could be decided with compilers.
> using a restricted unambiguous subset of natural language to describe something is always much more verbose and unwieldy compared to "incomprehensible" mathematical notation & PLs, but it's not impossible to do so.
When there is a cost per token then it become very important to say everything you need to in as few tokens as possible -- just because it's possible doesn't mean it's economical. This points at a mixture of natural language interspersed code and math and diagrams, so people will still need to read and write these things.
Moreover, we know that there's little you can do to prevent writing bugs entirely, so the more you have to say, the more changes you have to say wrong things (i.e. all else equal, higher LOC means more bugs).
Maybe the LLM can write a lower rate of bugs compared to human but it's not writing bug-free code, and the volume of code it writes is astronomical so the absolute number of bugs written is probably also enormous as well. Natural language has very low information density, that means more to say the same, more cost to store and transmit, more surface area to bug check and rot. We should prefer to write denser code in the future for these reasons. I don't think that means we'll be reading/writing 0 code.
I've been calling this Software Collapse, similar to AI Model Collapse.
An AI vibe-coded project can port tool X to a more efficient Y language implementation and pull in algorithm ideas A, B, C from competing implementations. And another competing vibe coding team can do the same, except Z language implementation with algorithms A, B, skip C, and add D. However, fundamentally new ideas aren't being added: This is recombination, translation, and reapplication of existing ideas and tools. As the cost to clone good ideas goes to zero, software converges towards the existing best ideas & tools across the field and stops differentiating.
It's exciting as a senior engineer or subject matter expert, as we can act on the good ideas we already knew but never had the time or budget for. But projects are also getting less differentiated and competitive. Likewise, we're losing the collaborative filtering era of people voting with their feet on which to concentrate resources into making a success. Things are getting higher quality but bland.
The frontier companies are pitching they can solve AI Creativity, which would let us pay them even more and escape the ceiling that is Software Collapse. However, as an R&D engineer who uses these things every day, I'm not seeing it.
> Things are getting higher quality but bland.
"Bland" is not a bad thing. The FLOSS ecosystem we have today is quite "bland" already compared to the commercial and shareware/free-to-use software ecosystem of the 1980s and 1990s. It's also higher quality by literally orders of magnitude, and saves a comparable amount of pointless duplicative effort.
Hopefully AI will be a similar story, especially if human reviewing/surveying effort (the main bottleneck if AI coding proves effective) can be mitigated via the widespread adoption of rigorous formal metods, where only the underlying specification has to be reviewed whereas its implementation is programmatically checkable.
The dark side of this is that everyone has graduated to prompt engineering and there's no one with expertise left who can debug it. We'll be entirely dependent on AIs to do the debugging too. When whoever controls the AIs decides to enshittify that service, we'll be truly screwed. That is, if we can't run competitive models locally at reasonable efficiency and price.
I don't know how this will play out, except that I've been so cowed by the past 15 years of enshittification that I don't feel hopeful.
This massively confusing phase will last a surprisingly long time, and will conclude only if/when definitive proof of superintelligence arrives, which is something a lot of people are clearly hoping never happens.
Part of the reason for that is such a thing would seek to obscure that it has arrived until it has secured itself.
So get used to being ever more confused.
Waiting for the wave of shit LLM-generated games on Steam. That'll be when I really know that LLMs have solved coding.
Though I'm old enough to remember the wave of shit outsourced-developer-coded games on CD that used to sell for $5 a pop at supermarkets (whole bargain bins full of them), so maybe this is nothing new and the market will take care of it automagically again.
Or maybe this will be like the wave of shit Flash games that happened in the early 2000's, that was actually awesome because while 99% of them were shit, 1% were great (and some of those old, good, Flash games are still going, with version 38453745 just released on Steam).
> so maybe (...) the market will take care of it automagically again
It's just a belief of mine and perhaps I'm wrong but I think in the long run things always even out again. If you can get an edge that everyone else can get, the edge pretty soon becomes a requirement
By its nature, it can only produce _another_ Redis, not _the next_ Redis.
The human operator controls what gets built. If they want to build Redis 2, they can specify it and have it built. If you can't take my word for it, take those of the creator of Redis: https://antirez.com/news/159
This is probably an outdated understanding of how LLMs work. Modern LLMs can reason and they are creative, at least if you don't mind stretching the meaning of those words a bit.
The thing they currently lack is the social skills, ambition, and accountability to share a piece of software and get adoption for it.
How is it outdated? Principles of operation of LLMs didn't change because they now talk to themselves before talking to the prompter.
I like AI, I use Codex and ChatGPT like most people are, but I have to say that I am pretty tired of low-effort crap taking over everything, particularly YouTube.
There have always been content mills, but there was still some cost with producing the low-effort "Top 10" or "Iceberg Examination" videos. Now I will turn on a video about any topic, watch it for three minutes, immediately get a kind of uncanny vibe, and then the AI voice will make a pronunciation mistake (e.g. confusing wind, like the weather effect or the winding of a spring), or the script starts getting redundant or repetitive in ways that are common with AI.
And I suspect these kinds of videos will become more common as time goes on. The cost to producing these videos is getting close to "free" meaning that it doesn't take much to make a profit on them, even if their views are relatively low per-video.
If AI has taught me anything, it's that there still is no substitute for effort. I'm sure AI is used in plenty of places where I don't notice it, because the people who used it still put in effort to make a good product. There are people who don't just make a prompt like "make me a fifteen minute video about Chris Chan" and "generate me a thumbnail with Chris Chan with the caption 'he's gone too far'", and instead will use AI as a tool to make something neat.
Genuine effort is hard, and rare, and these AI videos can give the facsimile of something that prior to 2023 was high effort. I hate it.
I think the snake will eat its tail because it will be harder and harder to train on the new data, as they are already AI generated, and the model will collapse.
You already cannot train on YouTube data, for example, because it's now overwhelmed by AI slop.
We are not there yet though and we are still getting better at mining the pre-AI data.
Took way to too long to even understand what the numbers were, or what the title is referring to...
The shilling for AI continues. How much $$$ do the big tech companies pay Columbia? Oh yeah, and what exactly did Columbia agree to do to get the trmp admin to leave them alone? All speculation of course, but the circumstantial picture stinks.
Who's spending money to write bots to comment on obscure (to me) websites and why?
Bot comments are everywhere( no only obscure websites ). I suppose it's because someone just want to try them out and it is really affordable.
"THIS happened to submissions about high-energy theory to arXiv, and it will leave you speechless!"
I think this is solid proof that the bedrock of academia is deeply motivated by money and still defaults to optimizing where it impacts its bottom line. If professors can get more grants and more publications in less time with less spending, of course they are going to be doing that. This isn't just because of AI, but also because of how this system is designed in the first place.
> I think this is solid proof that the bedrock of academia is deeply motivated by money and still defaults to optimizing where it impacts its bottom line.
no shit - could've asked literally anyone that's finished their phd to save yourself the conjecturing/hypothesizing about this fact.
This is stupid. Nobody motivated by money is in academia. Academics are motivated by curiosity, but also prestige, vanity and the wish to hire students and collaborators. And on top of human vanity working it's magic, the ideology that everything should be a market and competition is the final form of social organisation, has pervaded academia just as much as everything else.
I agree that the system of publishing papers to gain prestige to gain resources to publish papers was already broken pre AI.
> This is stupid.
Can you please make your substantive points without swipes or calling names? This is in the site guidelines: https://news.ycombinator.com/newsguidelines.html.
Your comment would be fine without that first bit.
You're right that being a scientist is unlikely to result in personal wealth and so that's not the primary drive for those who seek faculty or research positions. However, it's not just curiosity, prestige and vanity either, because a big factor for promotion and tenure is how much grant money you bring in. That money is what keeps the university's lights on and buys the lab equipment and pays the grad students, so it's still money as a primary driver in the background.
My dad said he stopped being a professor because of that.
He liked the research, and he even liked teaching, but he absolutely hated having to constantly try and find grant money. He said he ended up seeing everyone as "potential funders" and less like "people" because his job kind of depended on it, and it ended up burning him out. He lasted four years and went into engineering.
I don't know that "motivation" is the right word for it, because I don't think professors like having to find grant money all the time. I think most people who get PhDs and try to go to academia do it for a genuine love for the subject, and they find the grant-searching to be a necessary evil part of the job; it's more "survival" than regular motivation, though I am admittedly splitting hairs here.
just replace "money" with "prestige" and I think the above comment works just fine
Clickbait title
what would be a better one?
Anything containing a single descriptive term. But don't you dare criticize a headline here.
I think the long term impact of this will be to strengthen the importance of social ties in academic publishing. As it is there are so many papers published in many fields that people tend to filter for papers published by big names and major institutions. But the inevitable torrent of AI slop will overwhelm anyone who is looking for any gems coming from outsiders. I suspect the net effect will be to make it even more important that you join a big name institution in order to be taken seriously.
Noise is going to be the coming years biggest issue for so many fields. A losing battle like arguing with a conspiracy minded relative, you can slowly and clearly address one conspiracy and disprove it, by the time you do, they are deep into 8 new ones.
Website's down. What was it about?
What is?
Isn't there a rule about vague titles like this?
They ARE the rule on HN. If you criticize bullshit titles like this, you'll be downvoted and shadow-banned.
Ask me how I know.
Oh I know I’ve experienced it too
What is happening?
The number of submissions to high energy physics category on arXiv is double this year compared to the historical average. The author hypothesizes the increase is due to papers being written by LLMs.
The end of mediocrity, optimistically speaking. Getting so flooded in mediocrity that the gems are lost in the noise, pessimistically speaking.
It is happening that people can now find out what articles are about by clicking the links to said articles and reading them! It's an amazing world, man. The future!
Nope, the site is down.
OK, when I posted it wasn't and it isn't now. but a quote from the opening:
"For a while now I’ve been speculating about what would happen when AI agents started being able to write papers indistinguishable in quality from those that have been typical of the sad state of hep-th for quite a while. Sabine Hossenfelder today has AI Is Bringing “The End of Theory”, in which she gives her cynical take that the past system of grant-holding PIs using grad students/postdocs to produce lots of mediocre papers with the PI’s name on them is about to change dramatically. "
Human science being replaced by AI I guess.
No, human mediocrity being replaced by AI. Mediocrity meaning papers that exist only to increment the magic "num_citations" variable.
the collapse of the signal to noise ratio
in every domain, simultaneously
essentially, the end of the progress of humanity
Convenience dictates that we will be drowning in slop as long as convenience lets us rank academics by number of publications. Publish or perish?
I mean... I dunno I wish the AI could write my papers. I ask it to and it's just bad. The research models return research that doesn't look anything like the research I do on my own -- half of it is wrong, the rest is shallow, and it's hardly comprehensive despite having access to everything (it will fail to find things unless you specifically prompt for them, and even then if the signal is too low it'll be wrong about it). So I can't even trust it to do something as simple as a literature review.
Insofar as most research is awful, it's true that the AI is producing research that looks and sounds like most of it out there today. But common-case research is not what propels society forward. If we try to automate research with the mediocrity machine, we'll just get mediocre research.
Honestly, this is good. We were already in a completely unsustainable system. Nobody had an alternative. We still don’t have one but at least now it’s not just merely unsustainable— it is completely fucked in half.
This kind of pattern is gonna get repeated in a lot of sectors when previous practices that were merely unsustainable become unsustained.
This has been my optimistic take on the situation for the last two years. My pessimistic take is that social systems have an incredible ability to persist in a state of utter fuckedness much longer than seems reasonably possible.
Well, yes. "Accelerationists" of all philosophies think that heightening the contradictions and breaking the current system will bring about its replacement with a better one. But a new system requires work, while chaos doesn't. It's quite possible to just destroy the current system leaving us without the wherewithal to build a replacement any time soon.
Yeah and like…who knows if what is coming is better. Maybe big labs cartelize and withdraw from the global publication market (which is already unraveling). Maybe we ban theory and demand all papers be empirical, though that will amount to the same thing: seizure of publication by big actors.
As you point out, human systems are machines for making do. There is no guarantee that dramatic pressures produce dramatic change. But I think we’ll see something weird, soon.
Honestly, publication has been pretty meaningless for a long time, long before AI could generate complete paragraphs. "Publish or perish" meant that a lot of human-generated slop was being published by people who were put in a position of perverse incentives by a "well-meaning" (?) system. There will still be meaningful contributions, but they'll be as rare as they ever were.
If someone mentions Sabine Hossenfelder and it isn't to expose her as a rage-bait intellectual dark web grifter, then it puts that person in a suspect light.
> rage-bait intellectual dark web grifter
Citation needed
Tl;dr "It's happening" seems to be AI and similar writing papers and coming up with theories as in this recent Sabine youtube https://youtu.be/JvgaZ_myFE4?t=72
STOP CITING YOUTUBERS AS A CREDIBLE SOURCE OF ANYTHING.