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.
BTW, I do think a highly educated society should give everyone capability to review or at minimum distinguish good papers
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.
Learning specific physics formulas has its place, but learning the principles behind the formulas is far more valuable, though harder to measure.
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".
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.
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.
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.
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.
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.
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.
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.
(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...
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.
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
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.
Stories: https://pastebin.com/Zc4jXvp4
Comments: https://pastebin.com/cFuczTWJ
The number of new submitters has indeed increased significantly since the beginning of 2026.
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.
Old accounts from multiple social media platforms has a $$$$$ value.
"When a metric becomes a target, it ceases to be a good measure" - Goodhart's law
Now that I think of this, whoever solves this well will have the next hyperscaler.
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."
I suppose we’re entering TURBO mode for of ‘making many books there is no end’.
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.
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.
Given that arXiv lacks peer review, I'm not clear what quality bar is being referenced here.
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...
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 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
Is the value in knowing how to do an operation by hand, or is the value in knowing WHICH operation to do?
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.
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.
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
But open-weight LLMs are a pretty decent compromise.
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.
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.
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, DuneYet 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.
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."
We have been stuck in the procedural treadmill for decades. If anything this AI boom is the first major sign of that finally cracking.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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 thing they currently lack is the social skills, ambition, and accountability to share a piece of software and get adoption for it.
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.
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.
no shit - could've asked literally anyone that's finished their phd to save yourself the conjecturing/hypothesizing about this fact.
I agree that the system of publishing papers to gain prestige to gain resources to publish papers was already broken pre AI.
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.
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.
what would be a better one?
Ask me how I know.
"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. "
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.
This kind of pattern is gonna get repeated in a lot of sectors when previous practices that were merely unsustainable become unsustained.
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.
Maybe one exception is milestones in nuclear fusion, but even that is very much rare compared to these.
To keep the original, with added clarity from the opening paragraph. Could use an emdash for irony.