The end of Microsoft Research's AI Frontiers group
hunch.net
hunch.net
The transformer based approach is seen as a highway to race down in terms of training and building better models with minimal organizational distractions allowed while every company puts its foot down on this highway. Exploratory research into other AI methods is highly discouraged for now and organizations are encouraging transformer based scale out and iteration as a top priority.
As an example Meta literally fired it's previously head of AI who held views that transformers won't scale. He wanted to veer off the highway and heavily fund exploratory new types of models. They got someone willing to scale the current method out. Essentially the messaging it "Foot down, there's a straight highway ahead don't divert".
I have the view this makes sense too. Current top models are mere (mere in big datacenter terms) trillions of parameters right now. We definitely haven't hit the ceiling and everyone's noticing "Hey this Opus 5.5 with the increased parameters seems much better than the last one". Well that's a trillion parameter model. We'll soon start hitting 10's of trillions and 100's of trillions of parameter models. Unless there's a ceiling which nobody expects they should keep scaling just as they have been. I really do agree that it'd be insane to veer off this highway with exploratory research at this particular point in time. Foot down, get to 10 trillion parameters, see if the scaling holds, if so off to 100 trillion parameters. If the scaling continues to hold we'll be asking the AI to do the exploratory research for us soon enough.
Almost all statements Yann LeCun made back then (LLMs won't be able to do this or that) turned out to be wrong. They were right to fire him.
RCA developed the first good color TV cameras. They were monsters. Originally, electronic TV (as opposed to mechanical scanning) used Zworykin's iconoscope. This needed huge amounts of light. It didn't have a photo-multiplier stage to boost sensitivity, but it did accumulate charge from light over the whole frame time. There was an alternative approach, Farnsworth's image dissector, which also needed huge amounts of light. It had a photo-multiplier stage but only the light from the dot currently being scanned by the scanning beam mattered.
So RCA threw money at the problem. They developed the image orthicon camera tube. It combined the advantages of both approaches, integration over a full frame and a photo-multiplier stage. It was insanely expensive, costing thousands of dollars in the 1950s. It needed about twenty different voltages and multiple independent power supplies. It worked reasonably well.
Then came color. That took three image orthicons (RGB) inside a box the size of a small refrigerator, along with optics to split the incoming image into three colors. Plus a rack of analog electronics per camera, connected through a large cable. Plus an alignment problem from hell, with a huge number of adjustment knobs.[1]
Scaling worked! From the 1950s to the 1970s, color TV broadcasting was powered by those monsters.
Now, independent of this, other people were working on other approaches. Vidicons, silicon detectors inside camera tubes, and finally fully solid state image sensors. Eventually, in 1983, after thirty years of image orthicon monsters, RCA produced a usable solid state camera. Today, a cell phone camera costs about US$10 in bulk, and it's far better than the monsters.
It took thirty years of low-priority development to get past the giant image orthicon cameras.
Electronic fluorescent lamp ballasts had a different problem. That was a suppressed invention. The magnetic ballast industry was dominated by Magnetek. Someone invented a solid state replacement, which was smaller and cheaper, and licensed their patent to Magnetek for royalties. Magnetek just sat on it, not wanting to gut their core market. The inventor hired Townsend, Townsend and Crew in Palo Alto and sued. (Read this in the law firm's brag book in their office.) Eventually, the inventor got a hundred million or so, solid state ballasts went into production, and Magnetek seems to be gone. (The company called Magnetek today, which makes crane controls, is an offshoot of a Litton unit, and seems to be unrelated.)
There are also parallels from the steam locomotive industry, the Baldwin Locomotive Works being the best known. Baldwin actually built the first Diesel locomotive. They made a few half-hearted tries at Diesels over several decades, then gave up and went back to steam. Baldwin ceased all locomotive production in 1956. The corporation lingered on for a while, not doing much.
[1] https://www.tvcameramuseum.org/rca/tk40a/pix/rcatk40aart.pdf
Suppressed inventions are very rare, but that's one of them.
One of the few others is Airadar, from 1973.[1] Not "AI", "air radar". This was a phased-array radar for small aircraft. Way ahead of its time. So far ahead that the USAF was upset about it being available commercial before it was in use by the military.
[1] https://books.google.com/books?id=NWzlTqj0gQ4C&pg=PA64&lr=&r...
But even in computing, when there is a promising way forward that the basic reaearch has been done on, its hard to justify side quests.
How does that work when the research team moved elsewhere? Re-hire talent? That takes time - the 1 thing you don't have in a fast-moving field like AI. Post sums it up nicely:
"Do the math—how much is it worth to halve training and inference cost given that the industry has investments approaching a trillion dollars?"
How much is these researchers' salaries, compared to what MSFT spends on compute? Chances are it's peanuts in comparison.
...And because they have monopoly money. Basically, research labs are usually vanity spending for corporations with too much money. Xerox parc was funded by a monopoly. Bell labs was funded by a monopoly. Google funds its moonshots with its monopoly.
Ah, well, good thing we've almost entirely defunded academic science.
Seems weird when it's become apparent that LLMs aren't really special sauce outside of just having the compute.
10% chance to be market leader doesn’t sound as good as 60% second place to them for example
Once you get close to the top, the risks don't look worth it.
It's his call for sure, but rather funny as an argument.
It is because even if it would pan out, it would mean temporarily losing AI leadership, reducing stock price, losing the ability to hire talent, and by virtue of that losing the opportunity permanently.
He was against LLMs, reinforcement learning, etc. Claiming that those are a dead-end. He was wrong. His own "JEPA" ideas have basically gone nowhere. Meta was right in moving on without him.
Zuckerberg wanted to placate his shareholders with a shiny new thing now.
would've stopped Meta from delivering a polished product like Muse sooner.
Muse is not polished, unless usability and programming standards have fallen so far in the LLM vibe coding era that a pre-alpha state is now considered polished.
Muse is imperfect, as is all software, but calling it pre-alpha is a bit much.
AGI will require architecture changes not simply more data.
LLMs work well for search problems with the help of a strong verifier.
As it turns out, every problem is ultimately a search problem. But not every domain has an easily available verifier.
The author states "I consider the scaling-only focus to be both misdirected and not fun.". I get the point but also totally get the focus on scale out for now.
For a corporation it is to make money.
For a researcher it is to explore and illuminate. That this exploration and illumination often leads to a landfall of new wealth generation is a happy accident. This ensures that the monied class would bet some of their capital on them, at least the wise ones.
Where can I read more about this? I have seen people make this claim for a while, and when I looked deep, it used to turn out to be wrong.
Yann LeCun has the syndrome where someone who sees the value of B (non-text modalities, i.e. physical reality) cannot stop themselves from constantly critiquing A (LLMs), even though clearly A has its own advantages.
I think he is a smart guy. But he constantly deploys the crackpot's PR playbook.
Which is superior, the big f*king monstrosity or the elegant on-demand, small batches, reduce waste?
Meta will go in the shitter and Deepseek will make the small batches.
It does (Lean at least) incorporate controlled experimentation, but tightly controlled with a both a laser focus on what the goal is and tight resource constraints. And Lean (at least several writers on Lean) favors taking multiple paths with a a quick and low risk and a longer-payoff, higher-risk, higher reward path for some efforts—one source used the US Navy’s two-path approach to SLBMs with Polaris and Poseidon as a model for this—but that's different than “maybe there is something down this path” basic research, which is outside of the concern a production model like TPS/Lean and often completely outside the model of a product-oriented firm; basic research is something firms consume and incorporate more than generate, because until the basics have been proven out, the rationally perceived chance of any particular avenue paying off is near zero for any firm.
Toyota is also a good example of what not to do. Their lack of earlier research investment in EVs has led to being severely behind the market, in addition to their lack of focus on quality causing poorer sales (their quality used to be one of the only reasons to buy them). KIA basically stole their playbook by creating loads of good-quality cheap hybrids and EVs. Toyota could've had that market if the invested more ahead of time in research.
And that's what's happening with AI. While there are plenty of players now, it's the ones that aren't looking ahead that will be left behind. Nothing scales forever; when you hit the ceiling, you're trapped in a box of your own making.
The rest is history.
That failed and SpaceX was then forced to develop its own brand new engines.
Is that observed scaling part of some equation or law? Is there a limit to how far that scaling goes before a ceiling or diminishing returns hit? Or is it simply based on more GPUs/parameters/compute plus how much human knowledge is in existence and will scale predictably forever?
And you can in practice often see a large network follow an expected training trajectory for loss/entropy.
In practice data is getting more sparse than compute, so you are usually overtraining using more tokens and being suboptimal.
I wonder about this a lot.
There are all these publications from OpenAI and Anthropic that are solving complex math & science problems.
Are they just "brute forcing" it? Able to try a lot more "known" or reasonable approaches given existing knowledge?
Or are they showing "emergent intelligence" and able to try genuinely new approaches that can't be expected from the knowledge in their dataset?
There's a mathematical sense in which it cannot be: the data-processing inequality (https://en.wikipedia.org/wiki/Data_processing_inequality). The extent to which that sense is totally counterintuitive to how things normally work in ML nowadays has led to investigating different foundations for information theory: https://arxiv.org/abs/2601.03220.
These empirical observations on quantities which aren't "intelligence" since no one has yet defined intelligence.
(This moves the argument to "is being really good at predicting the next token the same as being intelligent?" which has happily diverted many minds for years now)
> for every doubling of model size the number of training tokens should also be doubled
Well, I don't think many claim that increasing the model size while also increasing the training tokens doesn't make sense.
The problem is how to get the training tokens...
And the answer to your question is basically just “synthetic data”.
But the whole problem is that these things aren't acting like other technological revolutions like the internet or Google where lots of profit comes from relatively little spend. They scale linearly maybe if you are super smart and good about your design.
Meanwhile, citizens all over the world are fighting data centers tooth and nail because we are hitting a scaling limit in terms of the resources people will allow to be put into this highway project and its equally unwise and untethered from reality to pretend this doesn't matter in a strategic sense.
The one thing that is clear is if it can become more cost effective it’s world changing. Even at its subsidized standards now it’s world changing. And it’s reasonably classified as AI IMO, but it’s not self aware.
I think a lot of your general traditional researchers are fundamentally looking to understand ourselves through machines as proxies in a sense. Something self aware that senses, learns, adapts, so on. The current path isn’t there or necessarily clearly the key to achieving that. That may be the disconnect. I think LLMs may give us some hints at that but not the full picture.
Still incredibly powerful for things we once considered nearly impossible.
I see it that most of legacy research is not obsolete but can be significantly amplified by AI.
More and more, the talk of using generative models (e.g. transformers) is coming up to replace existing models in all concepts. Anything from understanding data, image recognition, labeling, ranking, etc.
As someone who is not an MLE and really doesnt understand these models well, it seems like the transformer approach is a "one size fits all" solution that may not be optimal for all problem categories but is good enough that its not worth investing in specialized models to get incremental improvements.
Race down the transformer highway as fast as possible and explore other AI methods.
One need not preclude the other.
Going close to as fast as possible while saving a stream for the other approaches makes sense as an approach, but let's not ignore that there's a compromise/tradeoff here. Might not matter in orgs with near infinite resources
For an example of what this looks like at a more pronounced scale just look at the discourse around European models and how far behind Minstral are perceived to be.
Look at what happened to Meta where they squandered a lead, had some mixed results, and reorged to more product certainty and away from fundamental research.
Both of these approaches require quite a bit of institutional support notably.
So yeah, LLMs keep getting more powerful/useful but because of an industry wide pivot not a "straight highway" as imagined in the GPT-4 era, when simply going bigger and bigger seemed like a reasonable roadmap for the foreseeable future.
Which many credible, AI researchers believe is already starting up an increasingly steep scaling wall of diminishing returns that eventually ends at a brick wall.
LLMs need an awful lot of handholding all the time, meanwhile humans get pretty good at it on their own.
I mean we literally do this every few seconds already with natural birth processes.
Trying to estimate equivalent "parameter size" of the brain when we only superficially understand behavior of individual neurons let alone how intelligence emerges from the connectome seems too speculative to be a particularly useful metric.
I think that we're well into diminishing returns already. The models have improved, but to me the far bigger advances have been in feeding them contexts, building them into loops, etc.
I don't even think much more construction is really necessary in data centers. We can do a lot more with the power/HVAC we have, especially once the ASICs start coming online.
It's like we invented the wheel in 2022, the crowds are fighting over capital to build wheel factories, and savvier tech firms are quietly patenting automobile designs while everyone's focused on making slightly rounder wheels.
Feels like this is leaving major breakthroughs on the table for the competition. Sure, transformers might get us much farther. But if someone figures out an approach that unlocks a performance boost or better resolution then they are going to kill the competition.
Feels like the American industry is high on all the capitol being shoveled into the furnace without caring how efficient the furnace is. At some point someone is going to make a breakthrough and burst that bubble. This could crater the value as we are currently betting on the low power efficiency of the models as the barrier of entry and point of profit. Of course that would lead to cheaper AI but is that profitable vs local models once (or if) it becomes affordable to do so?
Already happening: https://news.ycombinator.com/item?id=50397994
Obviously we'll see how it pans out, but other approaches feel a bit like trying to ignore the bitter lesson.
US rightfully placed a bunch of labor/environmental regulations around manufacturing which moved to China but stupidly didn't place restrictions on goods manufactured with severe labor and environmental violations.
We are repeating the mistake again.
The 'US' isn't a monolithic thing. US market leaders convinced US politicians to open US markets to China, and the lack of labor and environmental regulations was a feature, not a bug in the plan.
Anthropic, which is more open about this has shown the numbers, it works.
Where's the money coming for those 10X and 100X ventures?
This is a false dichotomy if I ever saw one
Why not hedge and just keep an ai skunkworks team working? These companies are big enough that they can have these people far away from the main work without distraction or without starving those teams of bandwidth
Chinchilla Law says no, we wont. We're likely well past the point of diminishing returns but MOE hides a fair bit of it.
We already know that power laws apply to scaling so 10x the size is going to result in minor gains - where costs become a real factor (as if they weren't already).
FTA "The core philosophy there is scaling, and aggressively not research."
This is where the real gains for AI come from - good enough and CHEAP is going to win the day here (brute force, mixture of models). It's the engineers displacing the academics - and that will mean a bloodbath for revenue (see DeepInfer for what the end game looks like).
Bioinspired algorithms may eventually lead to vastly improved intelligence per a weight too.
I don't think we should do this, but I feel doomed in that I suspect that we "can".
I'm incredibly skeptical of anything that goes "oh, we need to split AI into specialist systems".
I suspect that a lot of the delta between "our best models" and "base models they're trained on" lies in relatively low amounts of parameter changes driven by SFT and RL.
Model capacity is not the limit - the limit is our ability to drive those changes efficiently. Even things like "good research taste" could be represented as tiny in relation to total model capacity, if they could be trained into a model efficiently.
I forget who said it, but a trenchant insight is that current neural networks are generalized function approximators and essentially they are doing through brute force what biology accomplishes elegantly. Our hardware (both in the sense of our brains in terms of elegance and our computer chips in terms of raw throughput) is amazing.
Moore's law: Computing power is multiplied by 10 every 6 months? Can't wait for the optimization and rationalization phase, where we'll recover a trillionth of that power to run powerful databases.
I should also say that I use ChatGpt daily and enjoy it for researching algorithms. I tried to set up Claude last week and it/he was so overwhelming as to be unusable. So I think frontier models can be "unwieldy" or something for average use.
It
Is this true?
I barely notice any improvements ever since... 4.6? At least for software development.
Opus makes the same sort of mistakes and has the same sort of strengths ever since that point in my perception. My impressions is that increasing the number of parameters clearly has diminishing returns.
In fact, the best improvement I got from Claude was Haiku 5.5. Good cheap workhorse to implement plans.
As in your statement to me indicates that you clearly see the scaling working over longer timespans. You chose a relatively recent 2026 model as your baseline after all, not a 2025 model or earlier. You clearly see that the 2026 model is better than ones in the past.
Might i suggest that you perhaps just don't see the improvement as clearly when comparing iterations that are fairly close together? If you're not anecdotally seeing improvement between an early 2026 model and one from ~6 months later perhaps just give it another iteration? Personally i do see 5.5 as much better but i can understand not seeing a lot of difference to 4.6 since they literally aren't that far apart in releases.
No, you can't. Because you invalidate your argument by adding later:
> Personally i do see 5.5 as much better
It has to be one or the other.
Either each minor release Anthropic farts is a major improvement from what came before, or I don't see much difference because it has been very little time in between releases. Holding both arguments as true would require me to be excessively apologetic for a company that desires a 2T IPO.
And to be clear, I do think that there is an argument that not much time has passed since Opus 4.6 and Chat GPT 5.3 Codex days.
Part of this conundrum may be because the models reached a good enough point for software development. Maybe walking 80% of the way was easier than the final 20%. I am nowadays more excited about how cheap models can be useful for it (hence my positive remark about Haiku 5.5 and Chinese models in general).
Perhaps "The End of Microsoft Research's AI Frontiers Group" would be a less ambiguous title.
Langford has been at MSR a very long time and has done amazing research doing that time. He's bounced around a bit at MSR during his tenure and it's a shame to lose him. AI Frontiers wasn't around that long.
Much of this is Microsoft performing a very large and uncommunicated strategy update. All updates of MSR reorgs are just that "MSR is becoming more lean and focused" when it's obvious this isn't the case. This gets into the conversation that many Microsoft employees tend to have these days which is "Why does culture no longer matter at Microsoft and why did Satya turn into another heartless CEO". I won't speculate there, but the place isn't nearly as fun as it used to be.
To the author: 100% need this kind of thinking. It’s a balancing act. Historically though, dedicated labs haven’t had the best track record at commercializing their tech. The skillset differs vastly from running a product-focused business.
That said, even within this narrow scope - there are areas ripe for research.
Some of my particular interests include:
(1) Latent thinking/reasoning, which could arguably be more efficient but also opaque is one
(2) Separation of “intelligence” from knowledge - ie. keep the ability to reason like a frontier model and make tool calls (calling the ‘oracle’ or searching the web) but remove all parameters associated with niche knowledge/facts.
These are a couple off the top of my head that are very much aligned to scaling.
That statement is broad enough to be unfalsifiable, but if you narrow it down just a bit to scaling LLMs, maybe there is an end in sight. At least financial markets are skeptical of there being no end in sight.
If scaling LLMs really was the road to God in a box, then compute demand would be infinite, and nobody building to meet that demand would have a junk debt rating. Did those money people just not get it?
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.
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.
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.
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.
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'm not sure how that, combined with open models inching up on frontier intelligence, is compatible with these massive AI IPO valuations?
It seems like closed model AI demand has a lot of contingencies that are ultimately fragile and possibly short sighted.
If so, then yeah, increasing efficiency 100x means we hit that point much faster.
If not, then it just means we make improvements 100x faster.
https://en.wikipedia.org/wiki/Digg#2025%E2%80%93present:_The...
Seems like it quickly pivoted quickly to cash in on "AI" hype.
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.
> 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.
It was my introduction to feature hashing.
This scaling approach is unsustainable, it is generating diminishing returns and ballooning costs. It is already blowing up.
https://microsoft.ai/news/frontier-pre-training-at-microsoft...
If your research aligns with the incentives of industry, you can get the best of both worlds in industry. This was the case for machine learning researchers in the post-AlexNet, pre-ChatGPT era. The good times can't last forever, though.
Perhaps it is more free, but my understanding has always been that professional academia still restricts freedom of research. You have to apply for funding, convince the board that your direction will be fruitul.
An archaeologist can't just dig wherever. They compete with their peers for a limitted funding pool. If the school's board is more interested in the bronze age than the paleolithic, you're probably not going to get your dino dig approved.
The same is true for CS, I assume. You can't just "i would like spend university resources developing brainfuck 2.0" because the other teams are more likely to get their funding over you getting yours.
Both corporations and university's have finite budgets, both with bottom lines and margins. The uni wants their research done in a popular field that will attract students because students generate income that funds research that attracts students that generate income that funds research that...
Universities are mostly just administrative organizations. They have little control over your research, they rarely fund it, and they are usually not even interested in it.
They just think it makes sense to explore things, rather than just rinse and repeat with more cash.
The good news for John is that when 99.99% of the competitors are doing all the same, it’s when the rewards for looking around can be the biggest. Those are the real moments for disruption, and companies disregard them at their peril.
The working environment in academia is often precarious :-( , so such people often have no alternatives to going into industry, even though they still deeply appreciate and are influenced by the values of academia.
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.
This is certainly an interesting timeline in this section of the light cone
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.
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 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.
> 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"?
From the outside, Microsoft seems to actually be one of the better places, and is making at least token efforts to reduce headcount in more amicable ways, as exemplified the buyout offer.
But in general, the strategy seems to be overload people until they burn out and quit, because hey, AI will replace them anyway.
They look more like complete organizational shit shows.
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.
(Original https://hunch.net/?p=13764006 )
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).
Some are, some aren't. Some try, but botch it.
Edit: Fara is open weight as well. Seems like a great match for https://www.microsoft.com/en-us/windows-365/agents ?