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.
> 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.
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.
Historically, that happens now and then. Examples include color television cameras and fluorescent lamp ballasts.
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.
(So it must be those Orthicon cameras which cause the distinctive weirdnesses of early TV images like the reflections from highly-polished surfaces (such as the bells of brass instruments and saxophones) which gleam black? https://www.youtube.com/watch?v=2uTQQKU6ge0&t=111s )
Even if he was wrong about those statements, exploring other types of models isn't necessarily a bad thing. Especially if we can find ones that perform well with less demanding hardware requirements.
Its not a bad thing, but exploratory research as opposed to reaearch that finetunes along a path already paved by exploratory research is something that doesn’t usually happen much in for-profit product-oriented firms when there is such a path available; it happens in academia and corporate labs pick it up once it reaches the point where the basics show commercial promise. CS is a little bit of an exception because the capital cost of basic research isn’t, proportionally, as high, and because the promise-to-product path is often short enough that having done the basic work in house can be a meaningful advantage in getting to market first (though, see Google on much of modern AI for a counterexample).
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.
Those exploratory "side quests" in industry are how we got the transistor, the liquid crystal display, Shannon's information theory, the laser and practical photovoltaic cells, among many other foundational technologies upon which we rely today.
> and corporate labs pick it up once it reaches the point where the basics show commercial promise.
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.
> CS is a little bit of an exception because the capital cost of basic research isn’t, proportionally, as high, and because the promise-to-product path is often short enough that having done the basic work in house can be a meaningful advantage in getting to market first (though, see Google on much of modern AI for a counterexample)
...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.
The issue isn't the exploration itself, it's the opportunity cost. When a figure of Yann LeCun's influence pushes a specific direction, it pulls key talent and resources away from mainstream LLM work. That kind of organizational distraction has a massive cost, and would've stopped Meta from delivering a polished product like Muse sooner.
The mainstream is wrong and in a lot of ways hazardous. He had every right to say he wasn't willing to sign is name on the wrong technology when he new better. When you hire smart people you listen to them. Elsewise you get what you deserve.
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.
Absolutely agree fwiw. Scale out over LeCunns desired exploratory research was absolutely the right call. Called out because it seems like MS is making a similar call.
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.
I empathize with LeCunn but he is a prime example of why scientists often make poor leaders. It’s too tempting to always do the cool and interesting thing instead of drilling down on the boring but reliable thing that actually creates value. There is of course a place for this kind of eternal curiosity and I hope he’s found it, but hyper-competitive industry may not be it.
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.
I'm becoming more sympathetic to LeCun's views, but being able to train something that acts the tiniest bit like those who made the training data (authors, professors, researchers, etc) is an absolute juggernaut and will be for some time.
They surprised almost everyone, but he remained skeptical well after it was clear we could not put boundaries on the capabilities gained from scaling, and also from new RL techniques. Still, I do not think he is wrong about world models and continuous learning being fundamental to AGI, though the latter issue he was himself dismissive of until relatively recently.
It's amusing that their grand dream was basically a clone of Roblox, just with better VR tech and far more advanced graphics. But thing is, kids playing Roblox absolutely do not care about graphics.
TPS (and Lean, which it played a role in inspiring) is largely about finding what works empirically and ruthlesslessly optimizing around it.
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.
Ruthlessly optimizing isn't the best goal, because people "optimize" all the quality out, and lack of quality leads to lack of sales and increased cost. Ford/GM may have "optimized" their line for efficiency, but the increase in defects caused more losses. Companies that practice Lean tend to forget this and focus only on speed and cost, leading to enshittification. TPS focuses more on quality and its effects on lowering cost and increasing sales.
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.
This is a dumb question since I know very little about LLMs and transformers but I keep hearing similar things such as "more GPUs/parameters/compute" leads to a predictable scaling in intelligence.
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?
'Scaling law' is the prediction that increasing the training data and model size will cause the result to be better. It's not a law, but it was the prediction that OpenAI staked their fledgling startup on to get to the ChatGPT moment.
It should be noted that these laws aren't, well, laws; "a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.". Notice the word empirical here.
These empirical observations on quantities which aren't "intelligence" since no one has yet defined intelligence.
Your phrasing (_observed_ scaling _part of_ some equation) might indicate you assume / think there’s some theoretical equation that relate intelligence to those parameters and the we observe the data to confirm it. As far as I know, there isn’t - it’s purely empirical curve fitting. Train a model with different amount of compute / data / parameters, measure performance, fit curve.
Yes, the Chinchilla scaling laws from 2022: https://arxiv.org/abs/2203.15556 More parameters directly and mechanically means less prediction error for each token.
(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)
The Chinchilla paper is probably the poster child of “paper that aged like milk” in LLM field though. Everyone over the past 4 years have trained with several orders of magnitude more data than the Chinchilla optimal number.
I'm seeing the same pattern at other multinationals with huge R&D departments. Mostly colleagues with academic pedigree in traditional ai research who have been for years in denial that transformers are actually - in combination with recent advanced patterns in software development - the key to ai. it's really simple as that! except they weren't part of the revolution and still don't want to accept the reality that their research has become obsolete.
Is it the key to AI? It probably depends on the squishy definition of AI and what different people want from it. If your definition includes self awareness and sentience I’m not sure it is the key (I’m also not entirely convinced it isn’t, either), but it’s not clear either way in my opinion.
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.
as fast as possible implies spending the resources that way.
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
Except doing both..is..so..hard when the future payoff of one is more unclear and you are faced with quarterly earnings reports.
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.
100 trillion parameters doesn't make any sense if your dataset is ~30 trillion tokens. My uneducated guess is 1/10 compression ratio is good and that point to ~300 to ~1000 trillion tokens, which again, doesn't make any practical sense.
Depending on how you count, the human brain is between 100 trillion and 1 quadrillion parameters, which is one guess as to why we're so much more sample efficient at learning than LLMs are.
Unless quantum effects do infact play a role in consciousness. It’s not clear they do by any means, but if it did that could shift the understanding of our own minds and consciousness drastically. Otherwise we are just a dense complex network we can reproduce with enough money.
I mean we literally do this every few seconds already with natural birth processes.
I don't think you even need to go to hypothesized quantum stuff. A single biological neuron in isolation is a surprisingly complex system that resembles a small artificial neural net more than an artificial neuron in many ways. On top of an entire feedback system from diffusion of neurotransmitters which doesn't cleanly map to ANNs.
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.
One key difference between humans and (current) LLMs is that we can actually judge the material we learn from at the time we're learning it, contextualize it and discard garbage when we spot it.
LLMs need an awful lot of handholding all the time, meanwhile humans get pretty good at it on their own.
is scaling working? It is also my understanding that Opus 5.5 is probably a well under 10T parameter model. Probably a similar size class to Fable 5 earlier. And supposedly even GPT-4 was a 1T model. Clearly most of the gains are coming from something other than scaling up the model size. Yes, scaling up RL counts as scaling too, but the fact that labs have pivoted to a different axis of scaling is evidence that it's not just a "straight highway" to improve LLMs.
From the outside it seemed like GPT 4.5 was the apogee of parameter size scaling and since then a huge investment in RLVR became the new scaling axis.
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.
It's worth it to keep exploiting transformers while doing just a bit of exploration of alternatives because they work so well. Heck, just exploiting transformers more will accelerate exploration of alternatives...
This doesn't engage with the article in the precise way that transformer-based improvements and fundamental research that way are not being incorporated into models, at Microsoft AI, which is concerning.
What if scaling fails to hold at a certain point? Whether that is a steep or gradual dropoff in returns. What happens to the empires of accumulated compute? How much value do they immediately lose once the returns to scaling ceiling is reached?
When a company pays billions to OpenAI, any activity that does not do a glorious dance in praise and support of that investment is taken to be an act of disloyalty.
> Essentially the messaging it "Foot down, there's a straight highway ahead don't divert".
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?
> Hey this Opus 5.5 with the increased parameters seems much better than the last one"
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.
> Might i suggest that you perhaps just don't see the improvement as clearly when comparing iterations that are fairly close together?
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).
These corporations have an obscene amount of money
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
> 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.
Where's the money coming for those 10X and 100X ventures?
> Current top models are mere (mere in big datacenter terms) trillions of parameters right now. ... We'll soon start hitting 10's of trillions and 100's of trillions of parameter models.
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).
I'm not an AI booster (in a "should" not "can" sense), but in terms of "can" we are going to see next generation hardware (i.e. Vera Rubin GPU architecture) and improvements in model density (i.e. loosely improvements in intelligence per millions of weights), so the models will get bigger, the models will get more dense, and we may see more "expert models" i.e. in the sense that for a while there were human generalists that knew all of science and mathematics until the 1800s, after a certain point we had to specialize since there was too much to know. The models will need to partially specialize after hitting the hardware ceiling.
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".
> The models will need to partially specialize after hitting the hardware ceiling.
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.
> Foot down, get to 10 trillion parameters, see if the scaling holds, if so off to 100 trillion parameters
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.
It seems extremely likely to me that scaling produces some benefits, it will get better at particular things things continue but scaling by itself isn't heading to general intelligence.
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.
Ignoramus question: does the 100s of trillions of parameters world still exist with a constrained domestic data center build out? Democrats are signaling they are about to go psycho mode on data center contracts.
Like the sister comment, if not in US then in other countries.
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.
This refers specifically to "AI Frontiers", a research group within Microsoft Research that focuses on fundamental advances in AI. There is no end in sight to real AI frontiers.
Perhaps "The End of Microsoft Research's AI Frontiers Group"
would be a less ambiguous title.
Yeah, I went down the rabbit hole scratching my head the whole time. This is about the group at Microsoft Research that was disbanded or reorged into what is now Microsoft Research.
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?
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.
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
To me, all signs point to US over investment in hardware capacity to support the "frontier" when in reality the next major breakthroughs are likely radical optimizations that end in increased consumer training and inference efficiency.
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.
Yeah, I'm speaking to "scaling vs research" and where investment is going. Which is relevant to the article. Accelerating scale requires demand, closed frontier demand is what I'm questioning.
And gp's comment is relevant to the particular problems the guy (John Langford) cites in his comment about leaving. IE, that research first winds being only about scaling and then winds-up ignored because "just scale" can be an easier answer.
Everyone wants radically more efficient AI. That doesn’t mean it’s coming any time soon. And when models are twice as efficient, guess what happens next. We try making one twice as big for the same cost.
> 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.
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.
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?
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.
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.
If you value the freedom to pursue your own research direction and share your work freely, then choose academia. All the perks of working in an industry research role are provided in exchange for this freedom.
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.
>If you value the freedom to pursue your own research direction and share your work freely, then choose academia
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...
Academic research is free in the same sense as running your own business. You can try whatever you want if you can find the funding, but you still have to deal with people and regulations.
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.
That’s not the issue here, it’s not like AIF was doing purely academic work.
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.
>
If you value the freedom to pursue your own research direction and share your work freely, then choose academia.
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.
(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 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.
They've offered voluntary redundancy to a quite large proportion of the staff (a quarter?) so they'll also soon be losing anyone with employment prospects elsewhere.
I see a lot of news about waves of layoffs in large tech companies. Is MS the only shitty place now? I think all of them are, maybe except OpenAI & Anthropic, since they're building something new and are kind of on the "good" side right now.
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.
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
http://www.electricalfun.com/electroncafe/Image_Orthicon_tub...
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.
Seems weird when it's become apparent that LLMs aren't really special sauce outside of just having the compute.
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.
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.
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.
The rest is history.
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.
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.
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”.
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.
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
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.
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.
LLMs need an awful lot of handholding all the time, meanwhile humans get pretty good at it on their own.
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.
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?
Anthropic, which is more open about this has shown the numbers, it works.
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.
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).
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
Where's the money coming for those 10X and 100X ventures?
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.
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
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.
Perhaps "The End of Microsoft Research's AI Frontiers Group" would be a less ambiguous title.
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?
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.
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.
> 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.
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.
It was my introduction to feature hashing.
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.
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"?
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.
https://archive.is/AaY5O
(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 ?