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.
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.
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.
Real research has always been long term as well as heavy manufacturing. It requires long-term planning with long-term budget, which is hard to get when money is expensive.
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.
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.
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?
> 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).
(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.
I used to work on the search index and query processing for Microsoft Academic. It was a great project, perhaps the most interesting thing I've ever worked on.
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.
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.
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
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.
More like, a large company has to be rational, as the article points out, throwing money at research for uncertain returns is not as straightforward as throwing it at scaling for known returns.
Startups are allowed to be irrational because investors know upfront that what their dollars are getting is a high variance outcome. People buy a stake in Microsoft when they want what they perceive to be a safe investment.
Me neither. When revenues are growing every year, a fixed chunk of researchers' salaries (maybe growing a few % each year) doesn't really hurt, but R&D offers a nice upside.
Or if you are Microsoft, you throw in the towel on leading in any given industry or technology, and you're happy to be third best at everything and leverage b2b dealmaking to stay relevant.
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.
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?)
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.
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.
> 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.
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[ 4.6 ms ] story [ 50.6 ms ] threadhttps://hunch.net/?p=13764006
Satya Nadella is a total loser. It’s easy to lead when things are good…
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.
Or be content to publish academic papers, which is what this team was doing.
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.
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.
> 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 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.
This is certainly an interesting timeline in this section of the light cone
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).
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.
It's still available here (separate from the papers that Microsoft Academic collected and indexed): https://www.microsoft.com/en-us/research/project/knowledge-e...
I haven't worked on this since 2016 so I don't know if it still works.
https://microsoft.ai/news/frontier-pre-training-at-microsoft...
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.
That's why they'll always be room for startups.
More like, a large company has to be rational, as the article points out, throwing money at research for uncertain returns is not as straightforward as throwing it at scaling for known returns.
Startups are allowed to be irrational because investors know upfront that what their dollars are getting is a high variance outcome. People buy a stake in Microsoft when they want what they perceive to be a safe investment.
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.
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.
> 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.
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.