I can’t speak for other startups, but I applied to the most recent YC batch with my idea for making AI proactive instead of reactive, and pre-being selected I’ve published a paper on recursive self-improvement mapped to the Epoch AI data.
I contacted a professor from a university in the UK and he responded since he was working on similar work, then asked me if I wanted to meet with him. We talked for about an hour since we had overlapping results and different methods, specifically different assumptions.
I say all that to say, as a physics student getting my undergrad, simply doing independent research and speaking to experts about it enabled me to network with someone I otherwise likely wouldn’t know. For young people getting into any business, research is a great way to meet new people.
ChatGPT 5.6 Sol ultra still can get basic things written cleanly. I still need to tweak it quite a few times to get it right. Super useful but if you vibe code you’re in a huge mess after 1 billion tokens.
That's also a reason the big labs stopped. Publishing is most valuable to people who have no other way to get the attention of smart strangers. Once you can hire nearly anyone and everyone already returns your calls, the main remaining effect of publishing is to tell your competitors which things worked.
This is what happens to every field as it turns from a science into an industry. Chemists published freely until dyes started being worth money, and then the interesting work moved into company labs and stopped coming out.
Which is a very ironic and selfish situation when your business model dependent mostly on model training based on available published data, academic and non-academic.
That has nothing to do with anything. If you publish a copyleft paper, that doesn't compel someone who makes a product based on your paper to publish more papers.
The GNU RPL (research public license), a viral knowledge license. By reading this paper you are legally obligated to openly publish all vaguely related future research that you perform.
Your RPL wouldn't be enforceable. Copyright doesn't deal with abstract ideas passing through people's minds. Even the GPL is kind of in a gray area because the virality feature and its definition of "derivative work" have never been tested in court, to my knowledge. Maybe under contract law, no idea. If nothing else, I'd love to hear a verdict.
Well it was a joke and is obviously quite silly but I believe it would be enforceable to the extent that the licensor could terminate the agreement and sue for damages. If I can agree to pay you not to talk about something (ie an NDA) or not to work in a field (ie a non-compete clause) then why can't I pay you to be required to publish all future work you do in a given area? ("All future work" might well be overly broad; you'd likely have to restrict it to only a few years but I'm no lawyer.)
I don't believe the GPL is in a grey area. When you license a work all sorts of strings are commonly attached. Rather IIUC no one has gone out of the way to test the GPL largely because it is clearly within bounds, plus any violation has the potential to be a PR disaster since violations are quite literally examples of looting the commons.
There are other reasons. One is for career development of your researchers. Another is to be good citizens, in good standing, in the community of scientific researchers. Another is to flex on people and buy class and respectability - to comport oneself as the “old money” does.
This academic publish and industry doesn't is an incorrect view. Academics also hold off from publishing groundbreaking science in order to protect their research edge. You shouldn't see this as industry bad as driven by profits and academia is free from career incentives and money.
> Chemists published freely until dyes started being worth money
Notably this is exactly what patents are intended to combat. And while US IP law is clearly very broken it does at least largely accomplish this stated goal. Much (but certainly not all) industrial chemistry has made it into the academic literature.
Not that the same logic necessarily applies to AI research (ie algorithms aka math and their implementations). And I'm actually happy about that because the cost of doing the research is so much lower. There's a long list of reasons that the average person living in a residential area can't do industrial chemistry as a hobby.
To your dye example, yttrium indium manganese blue was the first commercially viable inorganic blue pigment discovered in ~200 years, is the only known environmentally safe one, and was openly published in the literature. It's also under an exclusive license. (TBF though unless the chemical is unusually difficult to synthesize not publishing would be rather pointless in this day and age given the utterly absurd capabilities of modern analytical techniques.)
It depends on the field, and to some extent on the particular patent officer assigned, but one of the failings of the US patent system is many patents don't actually provide enough details to reproduce the result. And even if they do they don't (usually) include ancillary information like the discovery process, properties of the invention etc.
Happily, current AI is interacted with in a reactive loop. I open the Claude app, CLI, whatever, say my prompt, get an output.
I personally wanted an AI that was able to reach out to me about my life before I had to reach out to it. An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.
My personal setup tracks my mouse movement, keyboard, what’s on my screen, and keeps track of what I’m working on through files on my PC. It can update the backend and then restart it on it’s own, meaning I can develop the thing itself while being away from my PC.
The capabilities are more than what I’ve listed, but I want to avoid being too preachy about something I made. Here’s the repo if you wanted to take a look, it’s open-source and connects to the iPhone app:
I'm very interested in this kind of thing as a kind of ADHD brain augment, like it's monitoring my slack, github, email, calendar, active terminals, etc, and helps me prioritize what I should work on as well as weighing whether this or that ping is worth interrupting me for.
I assumed that's what openclaw basically was, but is Orb different from that? And is it fundamentally a different model from the request/response, or is it just request/response in an autonomous loop?
Exactly what my thoughts were when I first heard about Openclaw, that’s the exact idea of Orb that you pointed out. Letting you make less decisions, right now AI gives you answers but still requires decisions based on the outputs it gives you. This would deepen the actual ability of agents in those channels you listed.
On a fundamental level the backend was designed to do as little LLM calls as possible, for instance it’ll do scans of my screen every 15 seconds, log what’s on it and what’s going on, and store it in a local database, then Orb reviews the entire database every 6 hours for me. Then it’ll schedule wakeups for itself throughout the day, up to 4 so it doesn’t waste my tokens, and schedule notifications based on the last database dump it made.
I have my Claude Code, Codex, and Grok Build all useable by using the “claude -p; codex -p…etc” so you can also use multiple CLI’s in conjunction at the same time on different projects or the same project.
So your question about a loop is kind of right, but it really just collects your data all day and stores it locally on your PC then calls the LLM of your choice and it reviews all the data and makes those proactive moves we’ve discussed. You could theoretically get it to always be scanning by an LLM but that would be a drastic waste of money from what I’ve seen since most things don’t require a call.
Okay interesting, so maybe openclaw does a lot less than I thought it did; really I have no excuse not to be just trying it myself regardless, given that I'm sitting on a 9070 XT.
It doesn't look like you're building directly on openclaw, so is that coming from a place of different goals or philosophy, or what?
Openclaw is great from when I’d used it for a couple months, the key difference is that for Openclaw you’ll have to actually schedule the cron jobs or automated tasks yourself, so it’s a decision on your part to make the AI do a thing whereas Orb is a decision on the part of the AI (after your approval) to do the thing. So it’s a kind of shift of agency. Openclaw could definitely do many similar things, it just would do them only after you specifically instructed it to.
It’s built as it’s own backend and philosophy wise, I want my personal AI to connect to everything in my life and have complete context over all data that I own, be compiled into a neat stack of data accumulating, then when it thinks it’s appropriate to do/say something, it’ll do it without my involvement.
Nice card btw, I got my RTX 5070 a couple months ago and it runs like a dream.
> An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.
I don't mean to downplay your work, but I think you should come up with a better example use case. Automating away interactions with friends is pretty much the last thing I want AI to do.
Good point, another example would be for when I was training my own small LM a while back, the target was about 170M parameters and was trained on 2B tokens worth of movie subtitles.
The run stalled mid-step around 80M parameters, Orb notified me that it stalled, asked if I wanted to resume at the last checkpoint and kill the stalled version. I simply press “yes” and continue doing whatever I was doing.
For non-technical users and non-antisocial people, remembering things you forgot so you don’t let people down. You told your sister you’d send her some pictures two hours ago but it can see you’re scrolling on reddit and the photos are on your desktop, so it assumes you forgot and reminds you.
The idea was that the biggest issue with the usefulness of an agent is that it has too little context about who I am, it needs more data. So I run all of my data through a smart router, then the local database, then the LLM reviews it and uses reasoning on what’s been collected.
Personally I don't think I'm ready to hand over total access to my digital life until I can self-host the model capable enough to act on it, but either way there is definitely some cool work to be done in the model harnesses for this.
It's very common to experiment with ideas using your own data or personal niche, then branch out by finding analogies in real world business applications or (less commonly) a consumer tool with a cleaner pitch. The important thing is talking to other people to find those usecases or refine the public angle.
Yes! And, whether it stems from research or not, putting yourself out there and talking to other people is one of the most fundamentally important things you can do for your career and your personal development as a human being.
I've been at two startups that have done genuine world first fundamental research.
The first tried to publish novel results for 3 years in tier 1 journals before finally doing a preprint and telling the tier one publishers to jump in a fire.
The second, and ongoing, isn't publishing anything because of my experience with the first.
That and avoiding openAI and Anthropic copying our results and leaving us with nothing to show for six months of work. The papers only come with the pitch deck.
Why wouldn't they just skip the journals and publish the papers themselves? Sharing the research is the important part unless these are academics who are trying to get tenure, grants or such.
The article is complaining that research isn't peer reviewed and research is turning into blogs. I'm explaining why that's the best outcome possible and why in a field as hot as large dl models you won't even get that.
I thought parent post was saying that they didn't publish them at all after they had so much trouble with the journals. If they did publish the papers themselves then I don't see the problem. The article itself read to me as your standard journal rent seeking and I wasn't really responding to it.
>Why wouldn't they just skip the journals and publish the papers themselves? Sharing the research is the important part
To answer your question, sharing the research isn't the important part. These are researchers in the private sector, in a highly competitive field. Arguably, doing the research isn't the important part either. The important part is making money, and the research is a means to that end. Publishing the research doesn't further that goal, and, according to GP, it's so difficult that it may not be worthwhile trying.
For the individual employees, having your name on a peer reviewed paper in a prestigious journal makes you far more employable elsewhere.
Moreso if your current employer is a stealth AI startup which hasn't yet produced much notable.
It also can encourage employees to work for fewer wages... 'If you do great work you can publish and then get a $$$$$$ job offer at OpenAI, or you can stay here and get equity in a fast growing startup. If however your work doesn't do well, you walk away from a bankrupt startup with worthless shares and minimum wage'
Anthropic and OpenAI have proven themselves to be aspiring monopolists through their positions and actions. Why would you want to share your research with them?
If your perception of a moat is worth more than the GDP of most countries, publishing anything must seem like a bad idea. Much less helping others build upon your work.
There seems to be this mentality among a large number of people who publish open data/research/software that everything is fine as long as no one else makes money using it. The second that happens, they're furious and act as though someone unfairly profited off of their "unpaid labor" (read: that thing they did purely for the love of the game). I think it has to be some sort of feeling of resentment and/or stupidity that _they_ were the person with a million dollar idea that let it slip through their fingers.
It's not that difficult. You invest effort into research in the hope it'll give you a competitive edge. If someone else can come along and reproduce your results practically at a much lesser cost, then you lose that competitive edge. You're back at square one, except you've lost the cost of the research.
That's not really a common attitude in academia, unfortunately.
The most common attitude is something closer to "I want to be well known and respected by smart and influential people". In short, prestige.
People using your work is a side effect of success, but simultaneously threatens your ownership of your "brand". If they significantly improve upon the thing you did, your work could be made irrelevant, your prestige ruined.
Arguably, the point of a startup isn’t to make money, it’s to attract attention and then investors.
Once the business is making money it’s not really a startup any more, it’s a going concern.
OP is an undergrad student. Attracting attention is what will make them stand out. And sharing research at this stage will probably expose them to research others don’t want to publish but do want to share with other researchers.
If you want people to copy it, but want a 20 year temporary monopoly, you can publish an open source step-by-step guide for someone to recreate what you’re doing with the patent office.
The downside is frontier computer science in several domains is decades beyond the state-of-the-art from academic literature. This is the situation that patents were designed to disincentivize.
There is not an obvious path to making this research public again. That's the reality. For open source to be competitive it needs to be approximately as efficient as the best systems we design.
Publishing CS papers at the top venues requires using in-group language and formalism that is pretty much inaccessible to someone who has not done a PhD in that specific narrow field.
LLMs are pretty good at this, hilariously. This means a genuinely good paper by an outsider has significantly less chance of getting good reviews than AI slop.
There is almost no benefit in publishing frontier research as a startup. People can try to argue it but it is not defensible. Publishing that kind of thing is a flex that companies risking nothing can do.
Cool for Google. Problematic if you are a startup.
The article is vague about the companies in the paper, for some reason.
In the paper, OpenAI is at the top of the chart for cumulative citations. MEGVII, Hugging Face, Waymo, Momenta, Preferred Netowkrs, Anthropic, Owkin, and Databricks, and Aibee follow (in that order). Yes, that is citations, not publications, but they explain that they're trying to use that as a proxy for significance, albeit an imperfect one.
Companies like Google aren't included because they aren't unicorn startups.
Google published a lot. I still wonder this original transformer paper, and the attention, etc. Why would they allow it to let go in the open? Perhaps because was intended for translation first before someone decided to loop it over itself? Or was so obscure to fellow researchers what do they actually publish?
It took 6 years to get from Attention Is All You Need to a consumer product. Up until ChatGPT’s release, LLMs were obscure and really were just fancier autocomplete. Deepmind was largely a speculative research division until Google decided to play catch up, and even that took a bit of time as they figured out how to proceed in a way that wouldn’t cannabalize search. So in short they didn’t realize the potential. Thats my somewhat naive take.
What the blogificafion of AI research has done is allowed all kinds of claims and terminology related to AI to be introduced and taken up in a manner replicating social media dynamics. And that is simply not healthy. We’re fast reaching a place where any claim can be backed up with a set of numbers from a number of experiments run in some gamified environment or the other, with little concern for if it all adds up to anything.
It’s a vicious loop, because this same junk then goes in to train the next models which help spit out the next set of models AND blogs/papers.
The net effect is not dissimilar to setting termites loose in a library.
No traditional research is mostly done by post-docs and have a phd level of education rather than a tech bro that passed leetcode. That seems like a good bar to have; not too mention the whole peer review thing, hard to really understand anything if you purposely withhold it and tell people to kick rocks.
Eh. Peer review isn't central to science. The point of peer review is: you have a claim someone makes; if it's been peer-reviewed, you can build further research on it without having verify the claim yourself. If you're building technology directly, any result you use you're going to verify inherently, because it either works as advertised or your tech doesn't work. The external verification doesn't add that much in that case.
Top line difference is speed. The impact of speed is deeper and gets felt over time.
Traditional research publications are no angels. They gatekeep research, and also allow financial incentives to drive them to publish junk with their stamp on it.
But a flood of papers doesn’t actually mean more knowledge. In bypassing this route entirely, AI has swiftly lost the ability to engage with itself as a field. And the cost of that is only beginning to be felt.
As far as I can tell, the paper never actually mentions the companies who aren't publishing papers. Open AI, Anthropic, and hugging face are all specifically mentioned as companies that do publish papers. Just FYI for anyone else who reads "AI's top startups" and immediately assumes OpenAI and Anthropic.
From a quick skim, the paper does a really bad job at answering the question in everyone’s mind when they see the title. Like, OpenAI is at the top in terms of citations, but as we all know old influential papers tend to accumulate more citations, and there was a period when OpenAI was, you know, more open… Are they still contributing now, post Altman pivot? No idea from the paper (maybe I missed it). In fact papers after 2025 are explicitly excluded, and we’re probably more interested in access to the cutting edge.
I’m not sure why this is so surprising? AI company does not automatically mean research company. The vast majority of new startups popping up over the last few years have commercial motivations, and use models built by someone else. Why are you expecting them to publish scientific papers?
50% of startups contributing to public research is actually a crazy good outcome. That’s far more than I had expected.
Also the reason we fund science publicly is because we can't count on the incentives from the free market. I mean why would they share information with competitors?
Google may consider the standalone frontier-model arms race economically irrational, while still considering frontier-model capability strategically indispensable. Its longer game is probably not to avoid building the biggest models, but to build only enough of them to serve as capability factories—then turn that intelligence into a much larger population of cheap, purpose-built models.
(Human again) If Google knew what they were on to, why wouldn’t they make it their secret weapon from the start? I suspect it’s because they predicted there would be an arms race, and knew how to profit from it. They had a distillation paper published before “Attention is all you need”. In hindsight, is it ironic at all? Or is it obvious?
I think the answer is much simpler: They didn't recognize the potential of that specific architecture. I don't think anyone could have. They valued the perk to employees being able to publish research, and the prestige that followed, and googles ability to attract talent, more than they did the content of those papers.
It was kind of like radiation science before WWII, it would be freely published because it wasn't potentially world changing yet. After it became a government interest, even people doing things unrelated to weapons would be much more apt to hold their work close.
Perhaps I'm imagining it but the entire industry was build on published research, this "AI wave" is at odds with that and seems to be driven by greed (although they'll claim some arms race or something to help themselves sleep at night).
There should be a new ESG (Environmental, Social, and Governance) policy being pushed recognizing the important role this plays. Although ESG and all norms have been set aside in this grim new world it seems.
I usually hear that it's extremely hard to track down the copyright owners for many older things. That's the reason the film/video industry often gives for not digitising the backlog online, and I would expect their volume to be much easier to handle than the book industry.
When they do this, are they buying a single copy of say Book A and destroying a single Book A copy? Or are they buying every copy they can find of Book A and destroying all of them?
Elon recently (2d) tweeted about making sure they preserve rare books and "scan them the hard way". That tweet launched a cultural wave of opposition regarding the debinding of rare books.
The discussion has been around, but it's flared significantly recently. Not sure if that's what the person you're responding to is specifically inflamed about.
Regardless, old/rare books are certainly being acquired and destroyed.
No, he didn't tweeted about "making sure" of anything.
Elon tweeted "I’ve asked the SpaceXAI team to preserve any rare books in a library and scan them the hard way vs just cutting off the spine and scanning"
which is no sense a credible source for what he actually asked SpaceXAI team to do, or if they are doing it, or what they were doing before yesterday. Elon has an extremely long and thorough track record of lying.
The problem remains either way. Small operations around the globe are debinding rare/unique books. I'm involved with an organization considering (not strongly) that exactly.
Hi, shredding the book is to reduce potential legal liability of format shifting - if you shred the book after scanning, the theory is you are not increasing the number of copies. This has come up as a factor in legal rulings. The legality of format shifting is still murky though, and the legality of training on the work are a separate question.
> Perhaps I'm imagining it but the entire industry was build on public research
There is some excellent publicly-funded research in there, but pivotal papers like Attention Is All You Need and the numerous pivotal OpenAI publications were privately funded.
OpenAI is at the top of the chart in the study.
I think you're bringing some assumptions into this conversation that aren't supported by the evidence.
The Attention Is All You Need paper deserves a lot of credit, but it also builds on other ideas. Earlier versions of attention were introduced in 2014 papers from (not U.S.) universities.
You are not. A disproportionate amount of value in the computing industry was created by <strike>geniuses</strike> decently smart people who worked together and who decided to just tell people how to do things instead of trying to capture the value of being the first person to figure out how to do those things.
This observation pre-dates the current wave of AI hype by a half century or so.
> driven by greed
I can only speak for myself.
For me it's exactly the opposite. If I want to explain how something works, I can just... do that. If I want to share an artifact demonstrating how to solve a particular type of problem, I can just... do that. If I want to mentor/teach, I can just... do that.
Doing those things within the confines of Academia Approved Institutions is exhausting and distracting.
To wit, and the actual point of this post: the term "Publishing Research" in this article doesn't mean "post it on a .html page and share the source code". It means engaging in a very specific and peculiar and extremely political modality of communication.
And it really only makes sense to do that specific and peculiar and political thing you're at a stage in your professional/personal development where you need to play that particular prestige game. (Which there's nothing wrong with, but it is a deeply cargo culted version of the actual scientific process.)
* A disproportionate amount of value in the computing industry was created by g̶e̶n̶i̶u̶s̶e̶s̶ decently smart people who worked together to do things that seemed impossible and who decided to just tell people how to do things instead of trying to capture the value of being the first person to figure out how to do those things.*
The process and standards of peer-reviewed publication are tedious. If the objective is efficient communication of knowledge, this is not the process that maximizes that outcome. This process is about credentials and imprimatur, not communication.
Academic publication is largely chasing prestige. The value of prestige accrues more to the individual than the company employing the individual. Incentives for companies reflect this.
Many blogified publications are unqualified slop. This sounds like an indictment but a lot of academic peer-reviewed research is also low-quality slop. In many domains, slop is rewarded, devaluing the contributions of people publishing more serious research, which affects incentives. The signal-to-noise ratio has been poor for decades no matter where you get your knowledge.
Any good research you publish will be exploited against you in myriad ways. It is effectively impossible to assert any IP rights. Research turns into a pure cost center if you treat it this way. Companies recognize this and have adapted to reflect it. In some computer science domains the state-of-the-art has been buried in trade secrets for decades such that the academic literature is embarrassingly obsolete.
Publication costs time and money. Given all of the above, why would you invest productive capacity in public communication of research results? The researchers want to work on interesting problems, the companies want to maximize the leverage from their research. Publishing delivers neither.
A lot of fundamental research is done for the purpose of solving a problem, not publishing a paper. Their motivation and personal reward is solving the problem, not publishing it. Once they've solved it, it is no longer interesting and they are on to the next interesting problem.
Some research is coupled with national security considerations. If you do frontier research this is always sitting in the background.
All of this is a consequence of incentives. A large percentage of all basic research no longer happens in academia. I've greatly enjoyed participating in non-publishing research programs. I also understand why they don't publish. I've done a significant amount of interesting foundational research under my own auspices where it was not worth the effort to publish. I'm not chasing prestige, I had a problem I needed to solve.
"But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”"
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If everyone holds back their publications, the whole sector moves slower. Yes, it's moving alarmingly fast according to many, but is it moving fast enough that the massive investments in data centres will actually pay off?
It's more game theory. Things may go well for one selfish company not publishing in a sea of altruists who publish, but perhaps not if everyone else is selfish too.
> is it moving fast enough that the massive investments in data centres will actually pay off?
It may be moving so fast that they do not pay off, i.e. they get commoditized too fast. This all depends on Ai demand in the end. If you can keep the GPUs saturated (at a profitable rate), then it really shouldn't matter how much a token costs. If you bought too many GPUs, or paid to much for them in a hype cycle, you may need higher token prices to ROI than the market wants to pay given alternatives.
Many AI startups open source part of their work and write blog posts without publishing papers, including the startup I work for. Sometimes, startups just have for more pressing things to do and don't have time to write up scientific papers for publishing.
Also, there are simply too many AI papers that make peer-review publishing quite meaningless these days (e.g., AAAI this year got more than 50K submissions).
I don't expect non-foundational or non-frontier startups to do, or publish much heavy hitting research. "AI" has become such a ubiquitous description, it is probably easier to find non-AI startups.
That was my first thought. In my experience, particularly with trading firms (the ones doing real science based prediction trading, not TA bullshit) do not publish ANYTHING at all, and haven't for 40+ years.
I'm under the impression that the majority of startups go nowhere, so the fact that they don't have this property is not itself a major statement about the field as a whole.
Once something goes commercial, academics have to consider what progress would be research-worthy, rather than a half-baked prototype for a product or feature.
Companies can’t be expected to publish their confidential and proprietary information about their feature development, and academics should consider projects that would have higher impact.
If you’re taking up a seat in a PhD program tinkering with would-be feature ideas for an existing major tech company, you should really just get hired by that tech company, where the resources are abundant and the degree is not required.
> Companies can’t be expected to publish their confidential and proprietary information about their feature development,
Of course they can. Simply eliminate trade secret protections and NDAs, which have literally zero purpose for society and in fact undermine patents' incentive to publish. Give a one year grace period to apply for a patent. Patent protection should be designed to be only as long as needed to try to maximize the development/spread of technology, e.g. maybe 2-3 years for rapidly developing areas like ML.
We should all be expected to contribute whatever knowledge we find to the commons. It makes us all richer.
Frontier AI research is not only under incredible commercial pressure but it’s also a geopolitical advantage.
I don’t think Dario wants to be a monopolist. But history is full of brilliant people who focused on the social good as priority #1 and lost the game early.
Once you lose the game and go bankrupt you can’t do anything. Same as politics, if you’re not elected you can’t change anything. Of course there are many lines not worth crossing.
All startups barely publish their research, because doing that would be incredibly stupid for the most part, because they want to sell the stuff they invent not give it away for free.
Bernie Sanders and Donald Trump have plans that would put lots of public money into the biggest Ai companies (sovereign wealth fund). They are getting a lot of money through government contracts, which is ultimately funded by the public through taxes.
How we wish to consider public funding and required sharing of gains is an open debate.
> Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.
...today? (emphasis is mine) And the author was so excited that decided to write up a paper and submit it to Science all in one day? Yeah, right. That's all you need to know who is moonshooting behind this paper.
There is no reason to publish anything in the AI era until you have reaped as much benefit from it as you are satisfied with. "Building in public" and "Researching in public" now means someone can swoop in with an AI and copy you instantly and become your competition overnight. Keep secrets.
Even at work, I have come to realize if I simply horde my accumulated custom AI built tools and productivity boosting tricks for myself, I can make myself more competitive as an employee.
I think this is how you get hired now, not by having a good resume, but by making claims of having special processes and personal tooling design that gets massive productivity ROI.
> “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”
We're not racing towards anything. We've been going in circles for years.
Commercial competition has always had a dark forest character - disclosing trade secrets can lead to loss of competitive edge and in the worst case, like the dark forest, death of the company.
(Not saying that's how it should be, but that's how it has been.)
I can’t speak for other startups, but I applied to the most recent YC batch with my idea for making AI proactive instead of reactive, and pre-being selected I’ve published a paper on recursive self-improvement mapped to the Epoch AI data.
I contacted a professor from a university in the UK and he responded since he was working on similar work, then asked me if I wanted to meet with him. We talked for about an hour since we had overlapping results and different methods, specifically different assumptions.
I say all that to say, as a physics student getting my undergrad, simply doing independent research and speaking to experts about it enabled me to network with someone I otherwise likely wouldn’t know. For young people getting into any business, research is a great way to meet new people.
Recursive self improvement is the beginning of the end.
it's actually the end of the beginning, there's walls that self-improving models hit that are never overcome even when given vast amounts of time.
ChatGPT 5.6 Sol ultra still can get basic things written cleanly. I still need to tweak it quite a few times to get it right. Super useful but if you vibe code you’re in a huge mess after 1 billion tokens.
That's also a reason the big labs stopped. Publishing is most valuable to people who have no other way to get the attention of smart strangers. Once you can hire nearly anyone and everyone already returns your calls, the main remaining effect of publishing is to tell your competitors which things worked.
This is what happens to every field as it turns from a science into an industry. Chemists published freely until dyes started being worth money, and then the interesting work moved into company labs and stopped coming out.
Which is a very ironic and selfish situation when your business model dependent mostly on model training based on available published data, academic and non-academic.
That's why we should enforce copyleft
That has nothing to do with anything. If you publish a copyleft paper, that doesn't compel someone who makes a product based on your paper to publish more papers.
The GNU RPL (research public license), a viral knowledge license. By reading this paper you are legally obligated to openly publish all vaguely related future research that you perform.
There's already an RPL, incidentally: https://en.wikipedia.org/wiki/Reciprocal_Public_License
Your RPL wouldn't be enforceable. Copyright doesn't deal with abstract ideas passing through people's minds. Even the GPL is kind of in a gray area because the virality feature and its definition of "derivative work" have never been tested in court, to my knowledge. Maybe under contract law, no idea. If nothing else, I'd love to hear a verdict.
Well it was a joke and is obviously quite silly but I believe it would be enforceable to the extent that the licensor could terminate the agreement and sue for damages. If I can agree to pay you not to talk about something (ie an NDA) or not to work in a field (ie a non-compete clause) then why can't I pay you to be required to publish all future work you do in a given area? ("All future work" might well be overly broad; you'd likely have to restrict it to only a few years but I'm no lawyer.)
I don't believe the GPL is in a grey area. When you license a work all sorts of strings are commonly attached. Rather IIUC no one has gone out of the way to test the GPL largely because it is clearly within bounds, plus any violation has the potential to be a PR disaster since violations are quite literally examples of looting the commons.
Sounds dystopian.
i wish more people would publish the things that didn't work. i'd like everything, but the exploration of searched negative space is so wasteful.
There are other reasons. One is for career development of your researchers. Another is to be good citizens, in good standing, in the community of scientific researchers. Another is to flex on people and buy class and respectability - to comport oneself as the “old money” does.
This academic publish and industry doesn't is an incorrect view. Academics also hold off from publishing groundbreaking science in order to protect their research edge. You shouldn't see this as industry bad as driven by profits and academia is free from career incentives and money.
> Chemists published freely until dyes started being worth money
Notably this is exactly what patents are intended to combat. And while US IP law is clearly very broken it does at least largely accomplish this stated goal. Much (but certainly not all) industrial chemistry has made it into the academic literature.
Not that the same logic necessarily applies to AI research (ie algorithms aka math and their implementations). And I'm actually happy about that because the cost of doing the research is so much lower. There's a long list of reasons that the average person living in a residential area can't do industrial chemistry as a hobby.
To your dye example, yttrium indium manganese blue was the first commercially viable inorganic blue pigment discovered in ~200 years, is the only known environmentally safe one, and was openly published in the literature. It's also under an exclusive license. (TBF though unless the chemical is unusually difficult to synthesize not publishing would be rather pointless in this day and age given the utterly absurd capabilities of modern analytical techniques.)
It depends on the field, and to some extent on the particular patent officer assigned, but one of the failings of the US patent system is many patents don't actually provide enough details to reproduce the result. And even if they do they don't (usually) include ancillary information like the discovery process, properties of the invention etc.
Curious what you mean by proactive? Could you share a bit more?
Happily, current AI is interacted with in a reactive loop. I open the Claude app, CLI, whatever, say my prompt, get an output.
I personally wanted an AI that was able to reach out to me about my life before I had to reach out to it. An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.
My personal setup tracks my mouse movement, keyboard, what’s on my screen, and keeps track of what I’m working on through files on my PC. It can update the backend and then restart it on it’s own, meaning I can develop the thing itself while being away from my PC.
The capabilities are more than what I’ve listed, but I want to avoid being too preachy about something I made. Here’s the repo if you wanted to take a look, it’s open-source and connects to the iPhone app:
https://github.com/getorb/Orb-Backend
I'm very interested in this kind of thing as a kind of ADHD brain augment, like it's monitoring my slack, github, email, calendar, active terminals, etc, and helps me prioritize what I should work on as well as weighing whether this or that ping is worth interrupting me for.
I assumed that's what openclaw basically was, but is Orb different from that? And is it fundamentally a different model from the request/response, or is it just request/response in an autonomous loop?
Exactly what my thoughts were when I first heard about Openclaw, that’s the exact idea of Orb that you pointed out. Letting you make less decisions, right now AI gives you answers but still requires decisions based on the outputs it gives you. This would deepen the actual ability of agents in those channels you listed.
On a fundamental level the backend was designed to do as little LLM calls as possible, for instance it’ll do scans of my screen every 15 seconds, log what’s on it and what’s going on, and store it in a local database, then Orb reviews the entire database every 6 hours for me. Then it’ll schedule wakeups for itself throughout the day, up to 4 so it doesn’t waste my tokens, and schedule notifications based on the last database dump it made.
I have my Claude Code, Codex, and Grok Build all useable by using the “claude -p; codex -p…etc” so you can also use multiple CLI’s in conjunction at the same time on different projects or the same project.
So your question about a loop is kind of right, but it really just collects your data all day and stores it locally on your PC then calls the LLM of your choice and it reviews all the data and makes those proactive moves we’ve discussed. You could theoretically get it to always be scanning by an LLM but that would be a drastic waste of money from what I’ve seen since most things don’t require a call.
Okay interesting, so maybe openclaw does a lot less than I thought it did; really I have no excuse not to be just trying it myself regardless, given that I'm sitting on a 9070 XT.
It doesn't look like you're building directly on openclaw, so is that coming from a place of different goals or philosophy, or what?
Openclaw is great from when I’d used it for a couple months, the key difference is that for Openclaw you’ll have to actually schedule the cron jobs or automated tasks yourself, so it’s a decision on your part to make the AI do a thing whereas Orb is a decision on the part of the AI (after your approval) to do the thing. So it’s a kind of shift of agency. Openclaw could definitely do many similar things, it just would do them only after you specifically instructed it to.
It’s built as it’s own backend and philosophy wise, I want my personal AI to connect to everything in my life and have complete context over all data that I own, be compiled into a neat stack of data accumulating, then when it thinks it’s appropriate to do/say something, it’ll do it without my involvement.
Nice card btw, I got my RTX 5070 a couple months ago and it runs like a dream.
What’s so innovative to send an auto message every x time or based on events: “wake up and check if you got anything to do”.
OR
“Event x happened at y time”
> An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.
I don't mean to downplay your work, but I think you should come up with a better example use case. Automating away interactions with friends is pretty much the last thing I want AI to do.
Good point, another example would be for when I was training my own small LM a while back, the target was about 170M parameters and was trained on 2B tokens worth of movie subtitles.
The run stalled mid-step around 80M parameters, Orb notified me that it stalled, asked if I wanted to resume at the last checkpoint and kill the stalled version. I simply press “yes” and continue doing whatever I was doing.
For non-technical users and non-antisocial people, remembering things you forgot so you don’t let people down. You told your sister you’d send her some pictures two hours ago but it can see you’re scrolling on reddit and the photos are on your desktop, so it assumes you forgot and reminds you.
The idea was that the biggest issue with the usefulness of an agent is that it has too little context about who I am, it needs more data. So I run all of my data through a smart router, then the local database, then the LLM reviews it and uses reasoning on what’s been collected.
That second one resonates a bit better for me.
Personally I don't think I'm ready to hand over total access to my digital life until I can self-host the model capable enough to act on it, but either way there is definitely some cool work to be done in the model harnesses for this.
It's very common to experiment with ideas using your own data or personal niche, then branch out by finding analogies in real world business applications or (less commonly) a consumer tool with a cleaner pitch. The important thing is talking to other people to find those usecases or refine the public angle.
Yes! And, whether it stems from research or not, putting yourself out there and talking to other people is one of the most fundamentally important things you can do for your career and your personal development as a human being.
I've been at two startups that have done genuine world first fundamental research.
The first tried to publish novel results for 3 years in tier 1 journals before finally doing a preprint and telling the tier one publishers to jump in a fire.
The second, and ongoing, isn't publishing anything because of my experience with the first.
That and avoiding openAI and Anthropic copying our results and leaving us with nothing to show for six months of work. The papers only come with the pitch deck.
Can you share a link to the preprint?
https://www.biorxiv.org/content/10.1101/2021.12.02.471005v2....
Why wouldn't they just skip the journals and publish the papers themselves? Sharing the research is the important part unless these are academics who are trying to get tenure, grants or such.
The article is complaining that research isn't peer reviewed and research is turning into blogs. I'm explaining why that's the best outcome possible and why in a field as hot as large dl models you won't even get that.
I thought parent post was saying that they didn't publish them at all after they had so much trouble with the journals. If they did publish the papers themselves then I don't see the problem. The article itself read to me as your standard journal rent seeking and I wasn't really responding to it.
>Why wouldn't they just skip the journals and publish the papers themselves? Sharing the research is the important part
To answer your question, sharing the research isn't the important part. These are researchers in the private sector, in a highly competitive field. Arguably, doing the research isn't the important part either. The important part is making money, and the research is a means to that end. Publishing the research doesn't further that goal, and, according to GP, it's so difficult that it may not be worthwhile trying.
For the individual employees, having your name on a peer reviewed paper in a prestigious journal makes you far more employable elsewhere.
Moreso if your current employer is a stealth AI startup which hasn't yet produced much notable.
It also can encourage employees to work for fewer wages... 'If you do great work you can publish and then get a $$$$$$ job offer at OpenAI, or you can stay here and get equity in a fast growing startup. If however your work doesn't do well, you walk away from a bankrupt startup with worthless shares and minimum wage'
Same reason movies released on YouTube aren’t treated with the same respect as those released in theaters.
> That and avoiding openAI and Anthropic copying our results and leaving us with nothing to show for six months of work.
Isn't the POINT of publishing research because you want others to copy it?
Anthropic and OpenAI have proven themselves to be aspiring monopolists through their positions and actions. Why would you want to share your research with them?
And crucially, you should want to others to build upon it, like you build upon so many that came before you.
If your perception of a moat is worth more than the GDP of most countries, publishing anything must seem like a bad idea. Much less helping others build upon your work.
There seems to be this mentality among a large number of people who publish open data/research/software that everything is fine as long as no one else makes money using it. The second that happens, they're furious and act as though someone unfairly profited off of their "unpaid labor" (read: that thing they did purely for the love of the game). I think it has to be some sort of feeling of resentment and/or stupidity that _they_ were the person with a million dollar idea that let it slip through their fingers.
It's not that difficult. You invest effort into research in the hope it'll give you a competitive edge. If someone else can come along and reproduce your results practically at a much lesser cost, then you lose that competitive edge. You're back at square one, except you've lost the cost of the research.
Hence why most companies don't publish research and most software is proprietary. Publishing and open source only make sense in certain situations.
Open source doesn't mean, it's available to the general public.
What's your point?
That's not really a common attitude in academia, unfortunately.
The most common attitude is something closer to "I want to be well known and respected by smart and influential people". In short, prestige.
People using your work is a side effect of success, but simultaneously threatens your ownership of your "brand". If they significantly improve upon the thing you did, your work could be made irrelevant, your prestige ruined.
Huh? Wouldn’t their prestige be improved if some other group spent a lot of effort improving on it?
That would clearly demonstrate it had real substance beyond hot air or puffery.
You could be proven wrong and wither in to obscurity.
Better to silo yourself and move in to the penthouse.
I would presume that if their results end up being more impactful or interesting, you end up being a footnote and lose the spotlight.
For researchers, yes.
For startups, no, because the point of that is to make money.
So why is op trying to publish?
Arguably, the point of a startup isn’t to make money, it’s to attract attention and then investors.
Once the business is making money it’s not really a startup any more, it’s a going concern.
OP is an undergrad student. Attracting attention is what will make them stand out. And sharing research at this stage will probably expose them to research others don’t want to publish but do want to share with other researchers.
> Arguably, the point of a startup isn’t to make money, it’s to attract attention and then investors.
Investors with money.
If you want people to copy it, but want a 20 year temporary monopoly, you can publish an open source step-by-step guide for someone to recreate what you’re doing with the patent office.
The patents are effectively unenforcible. People stopped patenting frontier computer science research 20 years ago. That is the current reality.
good.
The downside is frontier computer science in several domains is decades beyond the state-of-the-art from academic literature. This is the situation that patents were designed to disincentivize.
There is not an obvious path to making this research public again. That's the reality. For open source to be competitive it needs to be approximately as efficient as the best systems we design.
"Drop a preprint like it's a mic" is ML's new "publish".
Publishing CS papers at the top venues requires using in-group language and formalism that is pretty much inaccessible to someone who has not done a PhD in that specific narrow field.
LLMs are pretty good at this, hilariously. This means a genuinely good paper by an outsider has significantly less chance of getting good reviews than AI slop.
There is almost no benefit in publishing frontier research as a startup. People can try to argue it but it is not defensible. Publishing that kind of thing is a flex that companies risking nothing can do.
Cool for Google. Problematic if you are a startup.
The article is vague about the companies in the paper, for some reason.
In the paper, OpenAI is at the top of the chart for cumulative citations. MEGVII, Hugging Face, Waymo, Momenta, Preferred Netowkrs, Anthropic, Owkin, and Databricks, and Aibee follow (in that order). Yes, that is citations, not publications, but they explain that they're trying to use that as a proxy for significance, albeit an imperfect one.
Companies like Google aren't included because they aren't unicorn startups.
databricks is a top ai startup?. i thought they did spark hosting or something.
what makes them a top ai startup
$160 billion valuation and market capture among some top (non-AI, traditional enterprise) companies, I think.
Yeah, but that capture is usually just hosted spark usage.
The AI usage has mostly been getting it to explain error messages
they also have a pretty handy button right next to the error to ftfy
Google published a lot. I still wonder this original transformer paper, and the attention, etc. Why would they allow it to let go in the open? Perhaps because was intended for translation first before someone decided to loop it over itself? Or was so obscure to fellow researchers what do they actually publish?
It took 6 years to get from Attention Is All You Need to a consumer product. Up until ChatGPT’s release, LLMs were obscure and really were just fancier autocomplete. Deepmind was largely a speculative research division until Google decided to play catch up, and even that took a bit of time as they figured out how to proceed in a way that wouldn’t cannabalize search. So in short they didn’t realize the potential. Thats my somewhat naive take.
Google itself was shipping transformer-based features to consumers within 2 years, notably BERT for websearch.
What the blogificafion of AI research has done is allowed all kinds of claims and terminology related to AI to be introduced and taken up in a manner replicating social media dynamics. And that is simply not healthy. We’re fast reaching a place where any claim can be backed up with a set of numbers from a number of experiments run in some gamified environment or the other, with little concern for if it all adds up to anything. It’s a vicious loop, because this same junk then goes in to train the next models which help spit out the next set of models AND blogs/papers. The net effect is not dissimilar to setting termites loose in a library.
How is that different from traditional research publication? It's faster?
No traditional research is mostly done by post-docs and have a phd level of education rather than a tech bro that passed leetcode. That seems like a good bar to have; not too mention the whole peer review thing, hard to really understand anything if you purposely withhold it and tell people to kick rocks.
Plenty of research is performed by non PhDs.
The main difference is reproducibility and peer review process.
Science is a method of inquiry, not an institution or group of people with titles.
Sure, but it is good that people actually adhere to that method if inquiry.
They are not perfect, but institutions like peer review or universities do provide a framework and incentives for quality.
Eh. Peer review isn't central to science. The point of peer review is: you have a claim someone makes; if it's been peer-reviewed, you can build further research on it without having verify the claim yourself. If you're building technology directly, any result you use you're going to verify inherently, because it either works as advertised or your tech doesn't work. The external verification doesn't add that much in that case.
Two separate axes here: whether the research is in a blog, and whether it’s peer reviewed.
On the first, I mostly don’t care.
On the second, that’s mostly unavoidable if they want to keep their IP.
Top line difference is speed. The impact of speed is deeper and gets felt over time.
Traditional research publications are no angels. They gatekeep research, and also allow financial incentives to drive them to publish junk with their stamp on it.
But a flood of papers doesn’t actually mean more knowledge. In bypassing this route entirely, AI has swiftly lost the ability to engage with itself as a field. And the cost of that is only beginning to be felt.
That plus leaning on legacy reputation of journals or schools, instead of open websites and virality.
> We’re fast reaching...
We already arrived at that destination a decade ago.
Probably even further back tbh.
There's just a lot more people playing the game now, without the social indoctrination that made it more tolerable in some circles.
It's not that bad, though. September is annoying but you kind of miss the eternal renewal once you're out.
As far as I can tell, the paper never actually mentions the companies who aren't publishing papers. Open AI, Anthropic, and hugging face are all specifically mentioned as companies that do publish papers. Just FYI for anyone else who reads "AI's top startups" and immediately assumes OpenAI and Anthropic.
From a quick skim, the paper does a really bad job at answering the question in everyone’s mind when they see the title. Like, OpenAI is at the top in terms of citations, but as we all know old influential papers tend to accumulate more citations, and there was a period when OpenAI was, you know, more open… Are they still contributing now, post Altman pivot? No idea from the paper (maybe I missed it). In fact papers after 2025 are explicitly excluded, and we’re probably more interested in access to the cutting edge.
Anyway, there’s a repo link https://github.com/q5loisel/unicorn-AI-startup-publications so someone more determined at getting a better answer can poke at it.
I’m not sure why this is so surprising? AI company does not automatically mean research company. The vast majority of new startups popping up over the last few years have commercial motivations, and use models built by someone else. Why are you expecting them to publish scientific papers?
50% of startups contributing to public research is actually a crazy good outcome. That’s far more than I had expected.
When the second biggest player is named 'OpenAI', I would expect some papers to be published.
OpenAI and Anthropic are identified in the article and study as top publishers of AI research papers.
Also the reason we fund science publicly is because we can't count on the incentives from the free market. I mean why would they share information with competitors?
Yet none of them would have been here if Google hadn't published "Attention is all you need", the irony.
The authors of that paper are all at AI companies not named Google.
They wouldn't be if Google hadn't published "Attention is all you need".
Lab employees do seem to be "knowledge sharing" as they move employers as far as I can tell, so I don't think that's true,
The irony may only just be unfolding - check out this paper from 2015:
https://research.google/pubs/distilling-the-knowledge-in-a-n...
# LLM generated summary of the implied irony
Google may consider the standalone frontier-model arms race economically irrational, while still considering frontier-model capability strategically indispensable. Its longer game is probably not to avoid building the biggest models, but to build only enough of them to serve as capability factories—then turn that intelligence into a much larger population of cheap, purpose-built models.
(Human again) If Google knew what they were on to, why wouldn’t they make it their secret weapon from the start? I suspect it’s because they predicted there would be an arms race, and knew how to profit from it. They had a distillation paper published before “Attention is all you need”. In hindsight, is it ironic at all? Or is it obvious?
I think the answer is much simpler: They didn't recognize the potential of that specific architecture. I don't think anyone could have. They valued the perk to employees being able to publish research, and the prestige that followed, and googles ability to attract talent, more than they did the content of those papers.
Even Google wouldn't be here.
It was kind of like radiation science before WWII, it would be freely published because it wasn't potentially world changing yet. After it became a government interest, even people doing things unrelated to weapons would be much more apt to hold their work close.
Oh that built on a bunch of ideas that weren’t far off. It was a great discovery but like many it’s standing in tall shoulders
Perhaps I'm imagining it but the entire industry was build on published research, this "AI wave" is at odds with that and seems to be driven by greed (although they'll claim some arms race or something to help themselves sleep at night).
There should be a new ESG (Environmental, Social, and Governance) policy being pushed recognizing the important role this plays. Although ESG and all norms have been set aside in this grim new world it seems.
Forget publishing, companies like misanthropic are buying rare books and destroying it.
[1] https://arstechnica.com/ai/2025/06/anthropic-destroyed-milli...
What do you mean?
To scan them, they usually have to resort breaking them into individual pages.
they're refering to this: https://futurism.com/artificial-intelligence/ai-companies-de...
https://arstechnica.com/ai/2025/06/anthropic-destroyed-milli...
The scanning process destroys the book.
Note it doesn't have to, it's just faster to.
Internet Archive scans their books one page at a time keeping the book intact and stored in a warehouse.
They do: the judge in the Anthropic case said that destroying them means that the text is being transferred, and so therefore can be fair use.
> "The print original was destroyed. One replaced the other."
(I believe the reasoning is that the author is not being financially disadvantaged by more copies of their book existing).
So the current law prefers them to destroy.
Rare books seldom are under copyright and that’s not a concern then.
Is that easy to tell for each book, though?
I usually hear that it's extremely hard to track down the copyright owners for many older things. That's the reason the film/video industry often gives for not digitising the backlog online, and I would expect their volume to be much easier to handle than the book industry.
When they do this, are they buying a single copy of say Book A and destroying a single Book A copy? Or are they buying every copy they can find of Book A and destroying all of them?
Elon recently (2d) tweeted about making sure they preserve rare books and "scan them the hard way". That tweet launched a cultural wave of opposition regarding the debinding of rare books.
The discussion has been around, but it's flared significantly recently. Not sure if that's what the person you're responding to is specifically inflamed about.
Regardless, old/rare books are certainly being acquired and destroyed.
No, he didn't tweeted about "making sure" of anything.
Elon tweeted "I’ve asked the SpaceXAI team to preserve any rare books in a library and scan them the hard way vs just cutting off the spine and scanning"
which is no sense a credible source for what he actually asked SpaceXAI team to do, or if they are doing it, or what they were doing before yesterday. Elon has an extremely long and thorough track record of lying.
https://xcancel.com/elonmusk/status/2081844165881594362
Sure, I'm ambivalent to his veracity.
The problem remains either way. Small operations around the globe are debinding rare/unique books. I'm involved with an organization considering (not strongly) that exactly.
Hi, shredding the book is to reduce potential legal liability of format shifting - if you shred the book after scanning, the theory is you are not increasing the number of copies. This has come up as a factor in legal rulings. The legality of format shifting is still murky though, and the legality of training on the work are a separate question.
> Perhaps I'm imagining it but the entire industry was build on public research
There is some excellent publicly-funded research in there, but pivotal papers like Attention Is All You Need and the numerous pivotal OpenAI publications were privately funded.
OpenAI is at the top of the chart in the study.
I think you're bringing some assumptions into this conversation that aren't supported by the evidence.
Sorry I mean published i.e. available to the public.
I was also referring to published papers available to the public.
Like I said, I think you're bringing some assumptions to this conversation that aren't based on the how the industry came about.
> OpenAI is at the top of the chart in the study.
A distinction should be made between the old nonprofit OpenAI and the current organization. They don't publish technical research anymore.
The Attention Is All You Need paper deserves a lot of credit, but it also builds on other ideas. Earlier versions of attention were introduced in 2014 papers from (not U.S.) universities.
> Perhaps I'm imagining it
You are not. A disproportionate amount of value in the computing industry was created by <strike>geniuses</strike> decently smart people who worked together and who decided to just tell people how to do things instead of trying to capture the value of being the first person to figure out how to do those things.
This observation pre-dates the current wave of AI hype by a half century or so.
> driven by greed
I can only speak for myself.
For me it's exactly the opposite. If I want to explain how something works, I can just... do that. If I want to share an artifact demonstrating how to solve a particular type of problem, I can just... do that. If I want to mentor/teach, I can just... do that.
Doing those things within the confines of Academia Approved Institutions is exhausting and distracting.
To wit, and the actual point of this post: the term "Publishing Research" in this article doesn't mean "post it on a .html page and share the source code". It means engaging in a very specific and peculiar and extremely political modality of communication.
And it really only makes sense to do that specific and peculiar and political thing you're at a stage in your professional/personal development where you need to play that particular prestige game. (Which there's nothing wrong with, but it is a deeply cargo culted version of the actual scientific process.)
Let me help rewrite your first paragraph:
* A disproportionate amount of value in the computing industry was created by g̶e̶n̶i̶u̶s̶e̶s̶ decently smart people who worked together to do things that seemed impossible and who decided to just tell people how to do things instead of trying to capture the value of being the first person to figure out how to do those things.*
thx for notes; no complaints on my end.
edit: edited.
Some companies are sharing their research. Mostly not the American ones
Example: https://arxiv.org/abs/2607.24653
Exceptions to some like Deepmind, Nvidia, and Thinking Machines
> Although ESG and all norms have been set aside
Fortunately
Several things can be true simultaneously:
The process and standards of peer-reviewed publication are tedious. If the objective is efficient communication of knowledge, this is not the process that maximizes that outcome. This process is about credentials and imprimatur, not communication.
Academic publication is largely chasing prestige. The value of prestige accrues more to the individual than the company employing the individual. Incentives for companies reflect this.
Many blogified publications are unqualified slop. This sounds like an indictment but a lot of academic peer-reviewed research is also low-quality slop. In many domains, slop is rewarded, devaluing the contributions of people publishing more serious research, which affects incentives. The signal-to-noise ratio has been poor for decades no matter where you get your knowledge.
Any good research you publish will be exploited against you in myriad ways. It is effectively impossible to assert any IP rights. Research turns into a pure cost center if you treat it this way. Companies recognize this and have adapted to reflect it. In some computer science domains the state-of-the-art has been buried in trade secrets for decades such that the academic literature is embarrassingly obsolete.
Publication costs time and money. Given all of the above, why would you invest productive capacity in public communication of research results? The researchers want to work on interesting problems, the companies want to maximize the leverage from their research. Publishing delivers neither.
A lot of fundamental research is done for the purpose of solving a problem, not publishing a paper. Their motivation and personal reward is solving the problem, not publishing it. Once they've solved it, it is no longer interesting and they are on to the next interesting problem.
Some research is coupled with national security considerations. If you do frontier research this is always sitting in the background.
All of this is a consequence of incentives. A large percentage of all basic research no longer happens in academia. I've greatly enjoyed participating in non-publishing research programs. I also understand why they don't publish. I've done a significant amount of interesting foundational research under my own auspices where it was not worth the effort to publish. I'm not chasing prestige, I had a problem I needed to solve.
No one should be surprised by any of this.
"But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”"
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If everyone holds back their publications, the whole sector moves slower. Yes, it's moving alarmingly fast according to many, but is it moving fast enough that the massive investments in data centres will actually pay off?
It's more game theory. Things may go well for one selfish company not publishing in a sea of altruists who publish, but perhaps not if everyone else is selfish too.
> is it moving fast enough that the massive investments in data centres will actually pay off?
It may be moving so fast that they do not pay off, i.e. they get commoditized too fast. This all depends on Ai demand in the end. If you can keep the GPUs saturated (at a profitable rate), then it really shouldn't matter how much a token costs. If you bought too many GPUs, or paid to much for them in a hype cycle, you may need higher token prices to ROI than the market wants to pay given alternatives.
Many AI startups open source part of their work and write blog posts without publishing papers, including the startup I work for. Sometimes, startups just have for more pressing things to do and don't have time to write up scientific papers for publishing.
Also, there are simply too many AI papers that make peer-review publishing quite meaningless these days (e.g., AAAI this year got more than 50K submissions).
I don't expect non-foundational or non-frontier startups to do, or publish much heavy hitting research. "AI" has become such a ubiquitous description, it is probably easier to find non-AI startups.
Do startups in other fields constantly publish their research?
That was my first thought. In my experience, particularly with trading firms (the ones doing real science based prediction trading, not TA bullshit) do not publish ANYTHING at all, and haven't for 40+ years.
Think Renaissance Technologies or similar.
publications would likely lead to regulation, if the SEC was properly resourced.
In biotech sometimes. It's a form of advertising.
Author requests open weights from Sam Altman
Hmmmm I wonder why, so interesting
I'm under the impression that the majority of startups go nowhere, so the fact that they don't have this property is not itself a major statement about the field as a whole.
Once something goes commercial, academics have to consider what progress would be research-worthy, rather than a half-baked prototype for a product or feature.
Companies can’t be expected to publish their confidential and proprietary information about their feature development, and academics should consider projects that would have higher impact.
If you’re taking up a seat in a PhD program tinkering with would-be feature ideas for an existing major tech company, you should really just get hired by that tech company, where the resources are abundant and the degree is not required.
> Companies can’t be expected to publish their confidential and proprietary information about their feature development,
Of course they can. Simply eliminate trade secret protections and NDAs, which have literally zero purpose for society and in fact undermine patents' incentive to publish. Give a one year grace period to apply for a patent. Patent protection should be designed to be only as long as needed to try to maximize the development/spread of technology, e.g. maybe 2-3 years for rapidly developing areas like ML.
We should all be expected to contribute whatever knowledge we find to the commons. It makes us all richer.
Frontier AI research is not only under incredible commercial pressure but it’s also a geopolitical advantage.
I don’t think Dario wants to be a monopolist. But history is full of brilliant people who focused on the social good as priority #1 and lost the game early.
Once you lose the game and go bankrupt you can’t do anything. Same as politics, if you’re not elected you can’t change anything. Of course there are many lines not worth crossing.
Great.
How do we fix this.
It's like we know things are destined to go down in a flaming pile, and we're just watching so we can change it AFTER that happens.
Soz. Kinda a "dear diary" response, but am frustrated.
Who's this'Al' I keep seeing in the headlines?
Startups exist to profit, not necessarily publish.
All startups barely publish their research, because doing that would be incredibly stupid for the most part, because they want to sell the stuff they invent not give it away for free.
The stuff they invent isn't what's in the papers. The progress in AI is in the engineering hurdles.
What you just said is not in any relationship to reality.
Do they receive public funds?
Bernie Sanders and Donald Trump have plans that would put lots of public money into the biggest Ai companies (sovereign wealth fund). They are getting a lot of money through government contracts, which is ultimately funded by the public through taxes.
How we wish to consider public funding and required sharing of gains is an open debate.
> Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.
...today? (emphasis is mine) And the author was so excited that decided to write up a paper and submit it to Science all in one day? Yeah, right. That's all you need to know who is moonshooting behind this paper.
There is no reason to publish anything in the AI era until you have reaped as much benefit from it as you are satisfied with. "Building in public" and "Researching in public" now means someone can swoop in with an AI and copy you instantly and become your competition overnight. Keep secrets.
Even at work, I have come to realize if I simply horde my accumulated custom AI built tools and productivity boosting tricks for myself, I can make myself more competitive as an employee.
I think this is how you get hired now, not by having a good resume, but by making claims of having special processes and personal tooling design that gets massive productivity ROI.
Funny, as ONE PERSON with Claude, I've been on a tear when it comes to publishing my own research:
https://arxiv.org/search/?searchtype=author&query=Magarshak%...
So I know that smart people in those companies can definitely publish. In fact, a whole team should probably be publishing like no tomorrow!
Have you built the things described in those papers?
Yes, actually.
To be fair — from about half of the papers.
Trade secrets are back, baby!
More to the point, without determining how much work is “worthy” of a paper it is unclear how much this matters.
Most AI companies are either a product and marketing layer over a model or not meaningfully moving any dimension to be worthy of a paper.
Also, formal papers and blogs and “cards” are all being intertwined.
That’s because most of its slop, and not reproducible
Live by the non-determinism, die by the non-determinism.
> “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”
We're not racing towards anything. We've been going in circles for years.
> We're not racing towards anything. We've been going in circles for years.
We reached the NASCAR-racing equivalent of scientific research.
Feels more like 3 steps forward and 2 steps back.
gate keepers of antiquated practice decry people realizing they they dont need to get past the gate
No man is an island.
The age of the dark forest is upon us
Commercial competition has always had a dark forest character - disclosing trade secrets can lead to loss of competitive edge and in the worst case, like the dark forest, death of the company.
(Not saying that's how it should be, but that's how it has been.)