It makes me sad because there are so many unexplored areas of AI research, even on text generation. Billions are invested in almost one idea, which very realistically isn't the most optimal way to do text generation, but a lot of companies are afraid of taking risks. I still wouldn't put $1b on JEPA but I prefer a world where we can try things like that.
LLMs have very favourable exploration<>consolidation dynamics. An LLM alternative would need to display fundamental improvements at relatively large scales which is extremely challenging for new research.
> there’s a breakdown of the basic deal in MSR: do good work and good things happen
This really hit home for me. This is the contract that held for a *long* time at Microsoft and has been broken and rebroken with current leadership. It’s culture-destroying.
He didn't say they were not doing transformers research. In fact, he specifically mentioned a type of transformer they were working on. And labs like Qwen and DeepSeek (at least) have shown how incredible results can be when you innovate in the area of LLMs.
I think the real point of the post is that office politics and poor management got in the way of innovation due to infighting and poor execution in transfer of technology from research to product.
Companies with billions of $, stopping R&D on the most revolutionizing technology/ breakthrough is something I can’t understand. I do understand there’s an AI Race to win, and that is with transformer and current LLMs architectures, but if research could find a better, more efficient approach they would re-shape this race, lead it, or even dominate it.
The problem is the "if". There is no guarantee that there's a "better, more efficient approach" to be found.
So far, the attempts to replace the transformer LLM with something else have yielded either "the same transformer LLM but with this one tweak that makes it 10% better" or "a radically different architecture that's a bitch to work with, and performs, best case, about the same as the transformer LLM".
By now, I'm firmly in the camp of "the architecture is good enough". Rifle is fine. Transformer is fine. Get better at the transformer before you look at anything else.
I think it’s more likely that some future LLM model finds a more efficient architecture than a human does, at this point. So yeah I’d go all in on LLMs too.
But is it a race to zero? Maybe AI will make very expensive things (eg software engineering) very cheap, but there will be enough competition that the margins are tiny and don't actually justify putting crazy dollar amounts into research
That's exactly what's happening. Software -> 0 marginal cost (obviously still need engineers to actually make the architecture). LLMs -> Price of Energy / Amortized Cost of GPUs. "AI Labs" -> Owned by Creditors
From an engineer, who’s devastated by this, i have to admit/raise my concern: why do we still need engineers to make architecture? Why is everyone saying that’s a must? For me the toughest problem was coding a huge repo/pkgs, architecture should be much of an easier task to do. I’ve seen it do sys design, scaled architecture better than 10X engineers. With good planning, context and prospective info -> it can produce extremely good architecture even for complex systems.
I think the dichotomy of open-research vs. commercially-driven innovation might be easier to coordinate, with three stages, not two:
1. Open research: Great creative freedom, with great resource constraints. Lots of ideas, lots of things tried, at insignificant cost relative to commercial work.
2. Results filtering, vetting, regularization: Opportunistic selection of apparently successful work from stage 1, for tentative testing at increasing scale. The best ideas end up with moderate budgets that verify benefits, which are reworked for compatibility before any handoff to stage 3.
3. Commercial efforts: aimed strictly at improving quality and economics of current revenue sources.
Direct negotiation between #1 and #3 involves too much dissonance over globally complementary but locally incompatible values.
#2 can be a relatively small, high payoff group, keeping #1 and #3 from butting heads, while facilitating them both.
Almost as fascinating is that the article references Digg as the source - genuinely hadn't heard of Digg for maybe a decade? I thought the DVD encryption fiasco + reddit had ended it?
On the flip side, frontier models are so good and relatively cheap now that it's possible to pursue your pet research project on your own as long as resources are not a constraint.
Hmm... So strange. It seems obvious(?) that both research and scale are needed, and that one should never have one starve the other. In fact research can influence the scale needed, and vice versa.
I guess when money is concerned people will always seek out the the most reliable and predictable path - at the risk of being potentially left behind if it turned out to the wrong (or at least suboptimal) path.
The headline makes good bait for AI skeptics. But this is a team of 50 people with a grandiose name.
The more interesting idea is that MS has abandoned AI research entirely, presumably because their zillions invested in other companies are a better bet in that area.
If MS abandoned a promising idea, pursuing it outside makes perfect sense. I am sure there are investors willing to fund this. This could even be an opening to attack the moat of the current AI frontier model companies.
It is notable that with the advancements of 24/7 coding agents with basic research capabilities the premise:
> a clever idea pursued by one person it was likely to arrive late and unconvincing because one person could not move fast enough and the baselines which one person could assemble just were not strong enough to really show that an idea was worth taking flight
may no longer be true. The limiting factors are: frontier model token and compute budgets, researcher skill, drive, foresight and imagination, not the ability to work in teams. Of course, working in teams still helps, and benefits from internal use, peer review and promotion of the research line are certainly there, but compared to some time prior to 2026, it's pretty much not a factor.
Ironically now by collaborating with AI one person can absolutely move fast enough. I used to work in orgs like that and am now vastly more productive than I ever was back then. It could take me months to convince a peer of an idea that now only takes minutes to explain to AI.
This seems to track with a recent blog post from MAI regarding their approach to pre-training their first Frontier level model which seems promising but does seem to have a focus on scaling.
but Mircosoft AI model is growing greatly and recently beat the nanobanana in generate video domain. I can't image the situation of them are bad as you explained.
I'll play devil's advocate. Of course John Langford thinks what he and his group were doing held a lot of promise, and the work was fun and challenging and super important. But in the end you also need to live in the present and deliver. In the words of Jerry Maguire, "Show me the money". He had years to show Microsoft the money, and in the same time other people at other firms showed their bosses the money, big time.
Yes, leave it to the Chinese to advance the state of the art. America throws money at it, the rest brains. Because they don't have the HW.
Same happened to computer chess in the 80ies. Clever small SW was leading the tournaments, until Ken Thompson could persuade IBM to throw hardware at it, Big Blue. This win was shortsighted. HW didn't scale and nobody profited from it. SW did, esp. reinforcement learning.
(In the context of Microsoft Research) Man, the history of how large corps fumble major wins should be studied.
I remember during my thesis how Microsoft Academic (a Google scholar/Scopus alternative) was ridiculously good at its job.
I'm talking, "input the key words and filter out the wrong fields and you've got 20 highly relevant papers that no other search term has found" level.
...Of course, they proceeded to close it down.
I can't even express in words how mad and disappointed I was and still am at Microsoft for this alone. It felt the closest to a genie or an oracle in being amazing. (The old Swype vs modern Gboard is probably a close example for those who'd used both.) It's just... crazy, frustrating, and crazy frustrating.
Executives chasing short-term returns. Google did 20 years of AI research, even creating custom chips, just so we could get to this point. Without long-term strategy Microsoft will perpetually be the also-ran. (which I guess is fine with shareholders?)
Real research at most big companies is dead. Unfortunately, what has replaced it is mostly venture-backed startups, where the timeline to a return is still expected to be quite short.
As a result, you end up with companies doing basic research under the guise of developing a product, announcing timelines which are never going to be met. Fusion energy and quantum computing startups are some of the most egregious examples of this.
Agreed. I used to be an industrial researcher at one of the last companies that didn’t require their researchers to think of short-term productization goals. However, things changed at my former company, and I ended up leaving for a tenure-track position at a community college.
I don’t know what industrial research in CS is like in the era of agentic coding. On one hand I suppose building prototypes is faster than ever, but on the other hand I genuinely don’t know how well LLMs do with harder programming tasks. It’s one thing to have an agent work on a CRUD app, but I wonder how effective agents are at handling things like parallelism, distributed computing, load-balancing, and other topics that are harder to write than the average CRUD app. I am also aware of efforts such as Sakana AI’s automated scientific discovery research, and I wonder if many of the incremental, short-term research topics many companies pursue can be automated.
I’m a bit pessimistic about the state of research these days, from funding to publish-or-perish to the effects of generative AI (automation, AI slop publications, AI slop reviews). Then again, I’m a bit of a curmudgeon, and maybe things are better than I think.
Remember Bell labs, XEROX parc? Ask yourself why that existed and why research came out of them.
Monopoly.
So - many people don’t want to admit this but monopolies can be good from an R&D perspective, but without strong oversight by regulators - money will instead spent on R&D projects that specifically raise entry barriers.
I can show you many more examples of monopolies which did not produce PARC or Bell Labs than those which did. It's possible such research happened in spite of dominating the market, not because of it. Anyway, you cannot have both monopolies and strong regulators. These are incompatible situations. Monopolies occur because regulation doesn't exist or is captured.
I meant competent, but regulator strength in the way you're implying at its limit becomes monopolistic itself. So when I speak of regulation strength with respect to monopoly, I am speaking to the ability of a regulator to effectively both understand their duty and carry it out.
I have been shorting most of the Quantum Computing startups for about a year. They have been printing. Missed out on Fusion! (?) These were one of few examples of the market leaving money on the table. Others examples have been Nikola. Usually the scammyiest companies don't IPO.
It seems as if Microsoft is losing developers now.
The whole "let's go into AI" is not really compatible
with oldschool software jobs. AI drains humans dry.
I mean we are already seeing the hype cycle coming to
a close, with the stock market showing this, but key
people leaving kind of means that Microsoft is losing
its profile here. Add to this that many don't like Win11
and you see a huge paradigm shift here - and tons of
associated problems.
"In practice, there are substantial issues because
the philosophy of the ex-Inflection leadership of
MAI is incompatible."
AI is killing people here - not necessarily literally
(though we have to see how much economic damage AI
has caused) but figuratively speaking. The counter-wave
is coming here.
> If the research products were truly innovative and useful, he wouldn't need to force Microsoft to adopt them.
That's just patently not true. History is littered with products that were very innovative and useful (and often far superior to competing products), but were canned, bought by competetors, or shelved in other way - often because they threatened the profits being made by some entity (though that's not the only reason).
Autogen is open source. I don't know the availability of other projects.
Generally engineers in megacorps aren't shy of picking up the latest AI tools. I suppose Microsoft may have barriers around this though, curious to discover more details.
Yeah, I agree. What he describes reflects the exact challenges I've experienced literally everywhere. He is basically saying work/life is hard so he looking for a better playground. I do not know his contributions to AI so maybe he deserves to be better listened to but I find that never reflects corpo life.
It makes me sad because there are so many unexplored areas of AI research, even on text generation. Billions are invested in almost one idea, which very realistically isn't the most optimal way to do text generation, but a lot of companies are afraid of taking risks. I still wouldn't put $1b on JEPA but I prefer a world where we can try things like that.
LLMs have very favourable exploration<>consolidation dynamics. An LLM alternative would need to display fundamental improvements at relatively large scales which is extremely challenging for new research.
> there’s a breakdown of the basic deal in MSR: do good work and good things happen
This really hit home for me. This is the contract that held for a *long* time at Microsoft and has been broken and rebroken with current leadership. It’s culture-destroying.
He didn't say they were not doing transformers research. In fact, he specifically mentioned a type of transformer they were working on. And labs like Qwen and DeepSeek (at least) have shown how incredible results can be when you innovate in the area of LLMs.
I think the real point of the post is that office politics and poor management got in the way of innovation due to infighting and poor execution in transfer of technology from research to product.
Companies with billions of $, stopping R&D on the most revolutionizing technology/ breakthrough is something I can’t understand. I do understand there’s an AI Race to win, and that is with transformer and current LLMs architectures, but if research could find a better, more efficient approach they would re-shape this race, lead it, or even dominate it.
The problem is the "if". There is no guarantee that there's a "better, more efficient approach" to be found.
So far, the attempts to replace the transformer LLM with something else have yielded either "the same transformer LLM but with this one tweak that makes it 10% better" or "a radically different architecture that's a bitch to work with, and performs, best case, about the same as the transformer LLM".
By now, I'm firmly in the camp of "the architecture is good enough". Rifle is fine. Transformer is fine. Get better at the transformer before you look at anything else.
They are losing tons of money on it. Simple as.
I think it’s more likely that some future LLM model finds a more efficient architecture than a human does, at this point. So yeah I’d go all in on LLMs too.
But is it a race to zero? Maybe AI will make very expensive things (eg software engineering) very cheap, but there will be enough competition that the margins are tiny and don't actually justify putting crazy dollar amounts into research
That's exactly what's happening. Software -> 0 marginal cost (obviously still need engineers to actually make the architecture). LLMs -> Price of Energy / Amortized Cost of GPUs. "AI Labs" -> Owned by Creditors
From an engineer, who’s devastated by this, i have to admit/raise my concern: why do we still need engineers to make architecture? Why is everyone saying that’s a must? For me the toughest problem was coding a huge repo/pkgs, architecture should be much of an easier task to do. I’ve seen it do sys design, scaled architecture better than 10X engineers. With good planning, context and prospective info -> it can produce extremely good architecture even for complex systems.
I think the dichotomy of open-research vs. commercially-driven innovation might be easier to coordinate, with three stages, not two:
1. Open research: Great creative freedom, with great resource constraints. Lots of ideas, lots of things tried, at insignificant cost relative to commercial work.
2. Results filtering, vetting, regularization: Opportunistic selection of apparently successful work from stage 1, for tentative testing at increasing scale. The best ideas end up with moderate budgets that verify benefits, which are reworked for compatibility before any handoff to stage 3.
3. Commercial efforts: aimed strictly at improving quality and economics of current revenue sources.
Direct negotiation between #1 and #3 involves too much dissonance over globally complementary but locally incompatible values.
#2 can be a relatively small, high payoff group, keeping #1 and #3 from butting heads, while facilitating them both.
Almost as fascinating is that the article references Digg as the source - genuinely hadn't heard of Digg for maybe a decade? I thought the DVD encryption fiasco + reddit had ended it?
It got rebooted a couple years back. One of the founders of reddit was involved too lol.
https://en.wikipedia.org/wiki/Digg#2025%E2%80%93present:_The...
Seems like it quickly pivoted quickly to cash in on "AI" hype.
Relevant: https://www.quantamagazine.org/when-chatgpt-broke-an-entire-...
On the flip side, frontier models are so good and relatively cheap now that it's possible to pursue your pet research project on your own as long as resources are not a constraint.
Great link, thanks for sharing. I missed that when it came out. I'd love to read something similar about other fields, like chess AI up to 1996.
Hmm... So strange. It seems obvious(?) that both research and scale are needed, and that one should never have one starve the other. In fact research can influence the scale needed, and vice versa.
I guess when money is concerned people will always seek out the the most reliable and predictable path - at the risk of being potentially left behind if it turned out to the wrong (or at least suboptimal) path.
The headline makes good bait for AI skeptics. But this is a team of 50 people with a grandiose name.
The more interesting idea is that MS has abandoned AI research entirely, presumably because their zillions invested in other companies are a better bet in that area.
If MS abandoned a promising idea, pursuing it outside makes perfect sense. I am sure there are investors willing to fund this. This could even be an opening to attack the moat of the current AI frontier model companies.
Curse of money strikes again. $4tn market cap company thinks team of 50 researchers too expensive.
Yeah but to have research be actually useful, you kinda have to have a full-on frontier lab.
Or be content to publish academic papers, which is what this team was doing.
John was the creator of Vowpal Wabbit, which used to be a popular ML library: https://en.wikipedia.org/wiki/Vowpal_Wabbit
It was my introduction to feature hashing.
It is notable that with the advancements of 24/7 coding agents with basic research capabilities the premise:
> a clever idea pursued by one person it was likely to arrive late and unconvincing because one person could not move fast enough and the baselines which one person could assemble just were not strong enough to really show that an idea was worth taking flight
may no longer be true. The limiting factors are: frontier model token and compute budgets, researcher skill, drive, foresight and imagination, not the ability to work in teams. Of course, working in teams still helps, and benefits from internal use, peer review and promotion of the research line are certainly there, but compared to some time prior to 2026, it's pretty much not a factor.
John Langford was the creator of "VW" ("Vee Dub", or "Vowpal Wabbit"), an ML framework that was quite efficient back in the day ( ~ 20 years ago ).
https://vowpalwabbit.org/
This is the canary in the coal mine. The canary is dead. The AI Winter is coming.
This scaling approach is unsustainable, it is generating diminishing returns and ballooning costs. It is already blowing up.
Did you read the article? it literally showcases research published 2 weeks ago on how to make *better models*
Ironically now by collaborating with AI one person can absolutely move fast enough. I used to work in orgs like that and am now vastly more productive than I ever was back then. It could take me months to convince a peer of an idea that now only takes minutes to explain to AI.
This seems to track with a recent blog post from MAI regarding their approach to pre-training their first Frontier level model which seems promising but does seem to have a focus on scaling.
https://microsoft.ai/news/frontier-pre-training-at-microsoft...
but Mircosoft AI model is growing greatly and recently beat the nanobanana in generate video domain. I can't image the situation of them are bad as you explained.
I'll play devil's advocate. Of course John Langford thinks what he and his group were doing held a lot of promise, and the work was fun and challenging and super important. But in the end you also need to live in the present and deliver. In the words of Jerry Maguire, "Show me the money". He had years to show Microsoft the money, and in the same time other people at other firms showed their bosses the money, big time.
Yes, leave it to the Chinese to advance the state of the art. America throws money at it, the rest brains. Because they don't have the HW.
Same happened to computer chess in the 80ies. Clever small SW was leading the tournaments, until Ken Thompson could persuade IBM to throw hardware at it, Big Blue. This win was shortsighted. HW didn't scale and nobody profited from it. SW did, esp. reinforcement learning.
Microsoft's now shown researchers their good work isn't safe.
New AI lab incoming?
Yeah, in India.
Is this the same group that did ResNet? Bad news
That's a name I haven't heard in a while
Well I never thought I’d see Digg used as a primary source
This is certainly an interesting timeline in this section of the light cone
Some more discussion on source: https://news.ycombinator.com/item?id=50406589
(In the context of Microsoft Research) Man, the history of how large corps fumble major wins should be studied.
I remember during my thesis how Microsoft Academic (a Google scholar/Scopus alternative) was ridiculously good at its job.
I'm talking, "input the key words and filter out the wrong fields and you've got 20 highly relevant papers that no other search term has found" level.
...Of course, they proceeded to close it down.
I can't even express in words how mad and disappointed I was and still am at Microsoft for this alone. It felt the closest to a genie or an oracle in being amazing. (The old Swype vs modern Gboard is probably a close example for those who'd used both.) It's just... crazy, frustrating, and crazy frustrating.
Executives chasing short-term returns. Google did 20 years of AI research, even creating custom chips, just so we could get to this point. Without long-term strategy Microsoft will perpetually be the also-ran. (which I guess is fine with shareholders?)
Real research at most big companies is dead. Unfortunately, what has replaced it is mostly venture-backed startups, where the timeline to a return is still expected to be quite short.
As a result, you end up with companies doing basic research under the guise of developing a product, announcing timelines which are never going to be met. Fusion energy and quantum computing startups are some of the most egregious examples of this.
Agreed. I used to be an industrial researcher at one of the last companies that didn’t require their researchers to think of short-term productization goals. However, things changed at my former company, and I ended up leaving for a tenure-track position at a community college.
I don’t know what industrial research in CS is like in the era of agentic coding. On one hand I suppose building prototypes is faster than ever, but on the other hand I genuinely don’t know how well LLMs do with harder programming tasks. It’s one thing to have an agent work on a CRUD app, but I wonder how effective agents are at handling things like parallelism, distributed computing, load-balancing, and other topics that are harder to write than the average CRUD app. I am also aware of efforts such as Sakana AI’s automated scientific discovery research, and I wonder if many of the incremental, short-term research topics many companies pursue can be automated.
I’m a bit pessimistic about the state of research these days, from funding to publish-or-perish to the effects of generative AI (automation, AI slop publications, AI slop reviews). Then again, I’m a bit of a curmudgeon, and maybe things are better than I think.
There’s actually a bizarre irony.
Remember Bell labs, XEROX parc? Ask yourself why that existed and why research came out of them.
Monopoly.
So - many people don’t want to admit this but monopolies can be good from an R&D perspective, but without strong oversight by regulators - money will instead spent on R&D projects that specifically raise entry barriers.
I can show you many more examples of monopolies which did not produce PARC or Bell Labs than those which did. It's possible such research happened in spite of dominating the market, not because of it. Anyway, you cannot have both monopolies and strong regulators. These are incompatible situations. Monopolies occur because regulation doesn't exist or is captured.
> Anyway, you cannot have both monopolies and strong regulators...
> Monopolies occur because regulation doesn't exist or is captured.
IMO, the stronger the regulator, the stronger the incentive to capture it.
Unless by "strong" you mean "competent"?
I meant competent, but regulator strength in the way you're implying at its limit becomes monopolistic itself. So when I speak of regulation strength with respect to monopoly, I am speaking to the ability of a regulator to effectively both understand their duty and carry it out.
I have been shorting most of the Quantum Computing startups for about a year. They have been printing. Missed out on Fusion! (?) These were one of few examples of the market leaving money on the table. Others examples have been Nikola. Usually the scammyiest companies don't IPO.
Yay China!
It seems as if Microsoft is losing developers now.
The whole "let's go into AI" is not really compatible with oldschool software jobs. AI drains humans dry.
I mean we are already seeing the hype cycle coming to a close, with the stock market showing this, but key people leaving kind of means that Microsoft is losing its profile here. Add to this that many don't like Win11 and you see a huge paradigm shift here - and tons of associated problems.
"In practice, there are substantial issues because the philosophy of the ex-Inflection leadership of MAI is incompatible."
AI is killing people here - not necessarily literally (though we have to see how much economic damage AI has caused) but figuratively speaking. The counter-wave is coming here.
More here
https://archive.is/AaY5O
(Original https://hunch.net/?p=13764006 )
This should be the main link
Almost inclined to agree, but the headline of Langford's post needs some additional context.
Appreciate this, much needed context.
Ok let's put that link above!
Hugged to death like it’s 2008 and you’ve just lit your LAMP stack.
Thanks for the heads-up. I added an archive.is link to give it a breather.
His complaints sound childish to me. If the research products were truly innovative and useful, he wouldn't need to force Microsoft to adopt them.
> If the research products were truly innovative and useful, he wouldn't need to force Microsoft to adopt them.
That's just patently not true. History is littered with products that were very innovative and useful (and often far superior to competing products), but were canned, bought by competetors, or shelved in other way - often because they threatened the profits being made by some entity (though that's not the only reason).
You think incumbent megacorps are all quick at adopting new, useful technologies?
Some are, some aren't. Some try, but botch it.
Autogen is open source. I don't know the availability of other projects. Generally engineers in megacorps aren't shy of picking up the latest AI tools. I suppose Microsoft may have barriers around this though, curious to discover more details.
Edit: Fara is open weight as well. Seems like a great match for https://www.microsoft.com/en-us/windows-365/agents ?
Yeah, I agree. What he describes reflects the exact challenges I've experienced literally everywhere. He is basically saying work/life is hard so he looking for a better playground. I do not know his contributions to AI so maybe he deserves to be better listened to but I find that never reflects corpo life.