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.
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.
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.
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?
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.
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.
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.
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'
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.
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.
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.
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.
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.
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.
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.
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?
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.
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.*
> 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.
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?
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.
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.
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.
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?
"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.”"
------------
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.
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.
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.
> 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.
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.
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.)
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.
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.
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.
Isn't the POINT of publishing research because you want others to copy it?
For startups, no, because the point of that is to make money.
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.
That would clearly demonstrate it had real substance beyond hot air or puffery.
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'
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.
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.
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.
The main difference is reproducibility and peer review process.
On the first, I mostly don’t care.
On the second, that’s mostly unavoidable if they want to keep their IP.
They are not perfect, but institutions like peer review or universities do provide a framework and incentives for quality.
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.
what makes them a top ai startup
The AI usage has mostly been getting it to explain error messages
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.
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?
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.
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.
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.*
edit: edited.
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.
A distinction should be made between the old nonprofit OpenAI and the current organization. They don't publish technical research anymore.
Like I said, I think you're bringing some assumptions to this conversation that aren't based on the how the industry came about.
Example: https://arxiv.org/abs/2607.24653
Exceptions to some like Deepmind, Nvidia, and Thinking Machines
Fortunately
[1] https://arstechnica.com/ai/2025/06/anthropic-destroyed-milli...
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.
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
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.
The scanning process destroys the book.
Internet Archive scans their books one page at a time keeping the book intact and stored in a warehouse.
> "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.
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.
50% of startups contributing to public research is actually a crazy good outcome. That’s far more than I had expected.
------------
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.
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).
Think Renaissance Technologies or similar.
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.
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.
...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.
(Not saying that's how it should be, but that's how it has been.)
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.
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.
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!
To be fair — from about half of the papers.
We're not racing towards anything. We've been going in circles for years.
We reached the NASCAR-racing equivalent of scientific research.