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.
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 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...
You are right that arxiv is an invite-only website, but once you are in, there is no peer review of any form.
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
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.
in every domain, simultaneously
essentially, the end of the progress of humanity
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.
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 thing they currently lack is the social skills, ambition, and accountability to share a piece of software and get adoption for it.
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
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.
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.
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.
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.
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.
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. As the cost to clone good ideas goes to zero, software converges towards the best ideas 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.
I suppose we’re entering TURBO mode for of ‘making many books there is no end’.
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.
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 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.
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.
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.
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.
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.
Given that arXiv lacks peer review, I'm not clear what quality bar is being referenced here.
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.
sealeck•1h ago
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...
CoastalCoder•44m ago
/jk