Rendered at 11:41:53 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
AnotherGoodName 17 hours ago [-]
This is true in every large company right now.
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.
redox99 17 hours ago [-]
> 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.
Animats 15 hours ago [-]
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 )
Nice, it got a press mention, with a quote from Crew, one of the law firm's partners.
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.
Suppressed inventions aren't "very rare" per se. You simply don't hear about those that aren't being rescinded while the inventor is still alive.
According to official data from the U.S. Patent and Trademark Office (USPTO), there were 6,653 active secrecy orders in effect at the end of fiscal year 2026. Roughly 100 to 350 new secrecy orders are added every year. Many remain locked away for decades or more.
If the U.S. government puts a secrecy order on a patent application against the inventor's will, they are legally forbidden from talking about it. The inventor cannot talk about how it works, publish research on it, sell it, or market it. They cannot even discuss the exact classified details with a regular lawyer unless that attorney has the proper security clearance.
alexgoodhart 12 hours ago [-]
Why are you Claude
leoc 13 hours ago [-]
For as long as the big companies are committed to focusing on scaling LLMs it does seem reasonable that they don't want their overall efforts led by people like LeCun or Hassabis who don't particularly want to scale LLMs. But it does feel as if there's something a bit extra about many LLM maxis' active hostility to deviationists.
geertj 13 hours ago [-]
I think Hassabis’ position is far more nuanced than LeCun’s. From what I can see, LeCun believes LLMs are a dead end for AGI while Hassabis position seems to be that some new technique is needed combined with LLMs.
nnurmanov 8 hours ago [-]
I am not sure if LLMs will stay eventually, but I am totally sure that they will be used to develop new types of models
wtetzner 17 hours ago [-]
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.
dragonwriter 15 hours ago [-]
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.
bigfatkitten 14 hours ago [-]
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.
publlus_enigma 11 hours ago [-]
Blue LEDs too, which have transformed lighting and displays.
catlifeonmars 11 hours ago [-]
Laser printers were a side project that almost got the inventor fired.
bitwize 10 hours ago [-]
And then they made back 200x the operating expenses of the lab (Xerox PARC) they came from over its entire lifetime.
WalterBright 14 hours ago [-]
Jet engines, too.
RetroTechie 14 hours ago [-]
> 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.
lovich 10 hours ago [-]
Yes but you forget that spending on compute is spending on machines that the company owns while spending on researchers is spending on other apes that trigger the social hierarchies of executives brains and who have the dreaded key word of “rights”
Companies will try to ruthlessly rid themselves of labor as long as those members of labor have agency and rights.
If someone disagrees they can convince me by explaining why a company wouldn’t choose to spend X on a machine they wholly own vs X on an employee they don’t.
There’s some room for argument that employees can do more than machines can but in the current AI zeitgeist arguing that we have machines with equivalent to human intelligence I don’t see how you wouldn’t see this coming if you believed the zeitgeist.
pyrale 14 hours ago [-]
> 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.
robotresearcher 12 hours ago [-]
Bell and Xerox became powerful because they invented game-changing technologies. They were fundamentally research-based organizations from the start.
vlovich123 13 hours ago [-]
Remind me again where the transformer comes from?
grumbelbart 4 hours ago [-]
Or residual networks / connections.
eli_gottlieb 11 hours ago [-]
> it happens in academia and corporate labs pick it up once it reaches the point where the basics show commercial promise.
Ah, well, good thing we've almost entirely defunded academic science.
dfdydx 15 hours ago [-]
It’s a simple exploration/exploitation trade off. Always explore, never exploit is probably not the optimal strategy every time.
bmitc 15 hours ago [-]
It seems to me that everyone is in "always exploit" mode.
Seems weird when it's become apparent that LLMs aren't really special sauce outside of just having the compute.
gscott 14 hours ago [-]
The right idea usually comes from a wrong one.
frays 16 hours ago [-]
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.
thatsabadlook 16 hours ago [-]
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.
frozenseven 15 hours ago [-]
>when he new better
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.
ForHackernews 13 hours ago [-]
I'm not sure he was wrong. No one has solved the fundamental problem with LLMs, which is that they cannot be trusted.
jack_pp 7 hours ago [-]
Most humans can not be trusted either, that's why you have processes in place to test and review work
johnnyanmac 4 hours ago [-]
Sure. And we made efforts over millenia to ensure that.
Let me know when LLM's get those. Otherwise I trust it as much as a human under anarchic rule. Probably less because I know exactly who controls it.
frozenseven 12 hours ago [-]
Ok, so we're talking about applying connectionist systems/models to the messiness of the real world. And "trust" isn't an on-off switch. The goal is to build systems that are smarter and more general. It's a process. I don't think even Yann LeCun has made any strong claims beyond that. He just thinks that his way of doing deep learning is better.
esseph 16 hours ago [-]
It's not a distraction if the research pans out and then they become the market leader for the next several years or more.
siva7 15 hours ago [-]
The job of the CEO is to assess the research risk correctly. Zuck made the right bet here to replace him.
hvb2 15 hours ago [-]
The guy that named his company after his own diversion?
It's his call for sure, but rather funny as an argument.
hikarudo 14 hours ago [-]
The fact that someone made a mistake before doesn't mean that they will always make wrong calls.
ImPostingOnHN 13 hours ago [-]
not as a matter of certainty, but it certainly increases the bayesian prior probability of their calls being wrong
Ok yeah but it’s not a certain bet. Resources are limited and you gotta allocate them efficiently.
10% chance to be market leader doesn’t sound as good as 60% second place to them for example
esseph 15 hours ago [-]
This is a prime example of why large companies have a problem innovating.
Once you get close to the top, the risks don't look worth it.
geertj 13 hours ago [-]
> It's not a distraction if the research pans out
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.
15 hours ago [-]
gamblor956 11 hours ago [-]
LeCun wasn't wrong. He simply had higher standards for results and accountability that LLMs are, by design, incapable of reaching.
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.
teruakohatu 9 hours ago [-]
You will find brilliant scientists in every discipline who are not the least bit interested in a particular branch of research because they/their lab didn’t invent it. That’s fine for public research but not helpful private research.
solyin 10 hours ago [-]
Compared to the user buying a Mac mini and following a tutorial off Twitter to install some random shit they found online?
Muse is imperfect, as is all software, but calling it pre-alpha is a bit much.
10 hours ago [-]
tensor 12 hours ago [-]
Odd. As I see it he was one of the few right about everything. This is also taking his comments about transformers scaling out of context. He specifically said they won’t lead to human level ai and they haven’t, despite exhausting the training data.
AGI will require architecture changes not simply more data.
mr_roboto 11 hours ago [-]
I'm quite confused by what you mean human level AI here. Maybe we haven't hit Einstein but we blew past the average person a long time ago.
paganel 4 hours ago [-]
> but we blew past the average person a long time ago.
We did?
haltingproblem 10 hours ago [-]
It is very jagged and some people don't consider that human level AGI - it can solve Maths at the genius level just like Deep Blue could play chess at the grandmaster level. At this point it is simply an opinion and I can see the other side too.
procone 10 hours ago [-]
Sorry, but what? We haven't done anything beyond strapping a large language model to a "harness".
At no point do I ever confuse a human being with a Claude session.
Even if I'm somehow fooled or confused for a minute, the context window / hallucinations / Claudeish will immediately give it away.
Sure, we passed the turing test for chatbots but we are very far away from AGI.
Exoristos 10 hours ago [-]
With this perspective on LLMs, you are presumably often "quite confused" if you interact with them to a significant extent.
godelski 6 hours ago [-]
> This is also taking his comments about transformers scaling out of context
There have been significant improvements in architectural changes, but since "pre-training" isn't being openly published people just lazily attribute it to "scale". Scale of what? Scale
> AGI will require architecture changes not simply more data.
As Sutton claimed, all along[0]. People read what they wanted to read. Unfortunately, they also push against researchers who were trying to explore other architectures and methods. I honestly believe that has held us back. There was always room for people to explore different paths. The problem was, and has always been, preventing or discouraging others from exploring new paths. It's ironic given that one of the most common reasons a paper is rejected is for "lack of novelty"[1]. But if someone does something novel then the reviewer simply compares it to the existing state of the art, which is an unfair comparison[2].
It's silly since people want to move fast, but we're really not good at recognizing what has actually allowed us to move forward in the past.
[1] An essentially meaningless statement. Near meaningless because it is not objective.
[2] You compare a novel technique against a technique that has had hundreds or thousands of researchers optimizing over several years. That's not even remotely fair.
airstrike 15 hours ago [-]
All statements he made about world models did not already turn out to be wrong. He may be right in the long term.
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.
AnotherGoodName 17 hours ago [-]
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.
ion_trapper 15 hours ago [-]
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.
GIFtheory 12 hours ago [-]
Ironically, Yann is kind of known in academic circles for being the guy working on boring, unsexy stuff (deep learning) when all the other academics were doing sexy cutting edge SVMs and stuff. That’s probably why he got the Meta job in the first place.
johnnyanmac 4 hours ago [-]
Yeah, being a trendsetter pretty much means you had to be working on something boring and unsexy at first. I can't really remember the last time a major innovation came because someone knew exactly what to invent, everyone agreed it was important, and we all ran full speed ahead on it.
Perhaps the atomic bomb? Not being alarmist, but that seems like one of the few instances in modern history. I also thought of the space race, but even rockets as an innovative concept started centuries beforehand as a tool of war.
srean 14 hours ago [-]
They have different jobs.
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.
benatkin 16 hours ago [-]
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.
soon_to_be_sb 12 hours ago [-]
>LLMs won't be able to do this or that) turned out to be wrong
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.
jackb4040 11 hours ago [-]
You can't. It's AI booster propaganda. Yann LeCun obviously understands transformers better than random anons on this venture capital fan forum. This means he also understands their limitations, which is too complex for people who just want their preferred billionaire to win.
nonethewiser 9 hours ago [-]
> "I take an object, I put it on the table, and I push the table... there's no text in the world, I believe, that explains this... your GPT-5000, or whatever it is, it's never gonna learn about this."
Lex fridman podcast 2022
ChatGPT answers this just fine.
He also said they will be “ Writing aids, not much more" and they “Can't plan.”
No one said hes an idiot. He was just clearly wrong about LLM capabilities.
soon_to_be_sb 2 hours ago [-]
>> "I take an object, I put it on the table, and I push the table... there's no text in the world, I believe, that explains this... your GPT-5000, or whatever it is, it's never gonna learn about this."
As far as I know, those things are still true. Unless there is text in the training material that the text in the prompt can map to, or there is text in training material that describe how to map text in the prompt to text in the training material, LLMs cannot process the prompt.
So may be he is wrong about what could be in the training material. But I don't think he is wrong in the fundamental..
This is sort of what I mention in my original comment. These claims, when pushed, turns out not to hold water...
ViscountPenguin 8 hours ago [-]
Yann is smart but obviously not omniscient, I don't think it does the ML community a service to pretend that elders are never incorrect (or even badly incorrect). This type of thing is how we got the cult around Schmidhubering.
jeremyjh 13 hours ago [-]
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.
overfeed 7 hours ago [-]
> Almost all statements Yann LeCun made back then (LLMs won't be able to do this or that) turned out to be wrong
You're being uncharitable. He would have been right if Chain of Thought hadn't been pulled out of a hat, and no one saw it coming, or how u reasonably effective it would be.
Today, no one can know if there will be a future step-change improvement to LLMs at the same level as CoT, and conversely, no one can be absolutely certein there won't be - so it shouldn't be a firable offense because neither position obvious; even though it will look like it in hindsight.
redox99 3 hours ago [-]
CoT wasn't pulled out of a hat. It was obvious to any researcher that it was needed. It just took some time and stronger base models and longer context to actually be implemented. People were already doing "CoT" by telling models to think step by step before answering, primitive harnesses and similar tricks.
Even a nobody like me was playing with a sort of CoT with llama finetunes before o1 came out.
Only this year I'd say we picked all the low hanging fruit that has been obvious for a while that was needed.
empiricus 2 hours ago [-]
Hi, I am curious, what do you think are the parts that are obvious but still work in progress? Maybe continuous multimodal input?
Hmorgan12 4 hours ago [-]
The most confusing thing to me about AI and LLM progress in the past 4 years is that I can't clearly see how all novel patterns add up, or what was the right point.
I think adding harness + MCP + chain of thought + specialized data generation resulted in the world where we have now - the one where you don't have to type code manually, but sometimes do still have to think and sometimes don't.
But LLMs without all this tooling and calls under the hood are still kind of... LLMs. If there's a space to hallucinate - they will hallucinate.
killerstorm 9 hours ago [-]
It's not so much they _turned_ wrong, they were obviously wrong at the time he made statements, or shortly after. He is just extremely biased against transformers, and it's easy to see why - transformers are already invented, and as a scientist he wants to invent something new.
More specifically:
* he said that autoregressive transformer is fundamentally flawed: if it makes one error, all further generated tokens make no sense; that's, obviously, bullshit: there are many ways to implement backtracking, and there are many papers demonstrating backtracking at a time he did that lecture. And, of course, it just doesn't matter - reasoning model can just write "Oh, that's wrong. Let's try a different thing". - it's just not derailed by one mistake.
* he said that GPT-1000 can't possible have understanding of physical world, when GPT-4 already existing at that time could solve a problem he described.
So it's not like he was bad at predicting things, he's intellectually dishonest.
redox99 3 hours ago [-]
Yeah that slide of him "proving" autoregressive is flawed was really the nail in the coffin. I stopped listening to him after that.
Nevermark 12 hours ago [-]
Exactly.
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.
0xbadcafebee 15 hours ago [-]
In the short term. In the long term, his research on learning will be the next revolution.
mschuster91 15 hours ago [-]
It's Meta, lol. They wasted 80 billion $ on Zuck's fever dream called Metaverse.
Andrew_nenakhov 13 hours ago [-]
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.
outrun86 10 hours ago [-]
Well, don’t care about graphics yet. Unclear what will happen once those gamers hit 20, 30 years old.
WalterBright 13 hours ago [-]
Musk initially started his rocket company by going to Russia and trying to buy Russian rockets. The Russians spat on his shoe.
The rest is history.
mschuster91 13 hours ago [-]
That at least was a reasonable idea. The Russians had a lot of old Soviet era ICBMs they had no need for anymore (plus, IIRC an agreement with the US to disassemble them), so using their engines as a cheap powerplant until SpaceX nailed reusability made sense.
That failed and SpaceX was then forced to develop its own brand new engines.
16 hours ago [-]
ViktorRay 8 hours ago [-]
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.
The idea that someone should be fired just because they were wrong seems to be something that would cause issues if one wanted to think long term.
This approach will make people afraid of being wrong. It will lead them to avoid challenging dogmatic assumptions in large organizations and inevitably lead to group think.
People really shouldn’t be punished for saying wrong things. Long term it’s better for a company if they aren’t punished for that. Helps to let people freely criticize dogmas or propose new ones.
jack_pp 7 hours ago [-]
If what you say is true then they would have abandoned LLMs. You're saying that it would have benefited them?
HDThoreaun 5 hours ago [-]
Depends on your role. If youre in charge of research in a hundred billion dollar org you should absolutely be fired if you devote your org to pie in the sky research instead of creating tangible value. If your job is just doing the research obviously you shouldnt be punished if your experiments dont work. Yann was fired because his main job was balancing product development with research and he didnt do that.
incrudible 2 hours ago [-]
None of these second-rate LLMs are creating tangible value. Nobody has made any money developing LLMs yet, and when the capex runway ends, the second-rate models won't see much (if any) use at all, compute is too precious for that.
whatthejesus 16 hours ago [-]
Umm no. There’s the old factory way to build things like Ford model T; and then there’s the f*king Toyota method!
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.
dragonwriter 15 hours ago [-]
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.
0xbadcafebee 15 hours ago [-]
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.
freecodeio 16 hours ago [-]
[dead]
slipnslider 16 hours ago [-]
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?
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.
pyrophane 12 hours ago [-]
Another dumb question: can the model be smarter than the data?
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?
augment_me 10 hours ago [-]
"Smarter" is ill-defined.
Are you smart if you see that cognitive scientists had something figured out, connected it to something in your field of chemistry and made a breakthrough treatment for some disease?
Does any of our existing intelligence build on completely unknown information?
You could argue that any new solution/emergent intelligence is an association of existing information in new ways.
It also brings us to what is brute forcing? Humans are brute forcing stuff most of the time. You will have 10 stupid thoughts for every good thought. 10 stupid ideas for every good idea, you will discard most of them so fast that you won't think of that you did that. It's very rare, if not near impossible, that a non-mathematician with no prior knowledge, goes into a room on day 1 of deciding to think about a conjecture they don't know anything about, and solve it on the blackboard. They need to use their collect associations, formalize their axioms, think of first principles (known information), and associate it.
So to answer your question you need to answer:
1) What does "smarter" refer to? Is it accumulation of information about the universe?(if yes, a database is smart) Is it an efficient, abstracted representation of accumulated information? Is it prediction of the future state of the universe given a learned abstraction?
Or going further, is it solving tasks useful for the human in the world? Is it acting as an agent with it's own goals and interests in the universe? What if the goals are hard for humans to understand?
Does it count as smart if you make an evolutionary approach that spawn 1000 instances and the most fit one survives and spawns 1000 mutations of itself?
2) What does "genuinely new approaches" refer to? Is it fine to use math axioms to prove things? Does interpolation between two existing solutions count as new? At what granularity of concept does novelty happen (Does using two higher abstraction concepts, like combining some new, modern neural network architecture with a database count as a new thing?), even if the underlying lower abstraction of the same information can be expressed as storage, latency and compute, and is not novel?
---
As you can imagine, this is all not really defined or agreed upon. Here are some examples of projects that are set up to drive these ideas:
The Hutter Prize poses that intelligence is defined by compression, so if you manage to compress the English Wikipedia efficiently, you are closer to better intelligence (http://prize.hutter1.net/)
Compute: Intel CPU
The ARC-AGI foundation poses that being able to be goal-oriented and understand higher level abstractions is intelligence: https://arcprize.org/leaderboard
All of this also exists in the context of the compute medium. It could be, that you need a certain capacity of memory/computation to perform whatever we have defined as intelligent tasks. Hutter Prize takes the bet of that to make intelligence, you don't need a lot of computation, an Intel CPU laptop and 50GB RAM should be enough.
A lot of the big labs are instead taking the bet on that you just scale everything, make trillion-sized models and things will fall out of that automatically, for this you will need a very different compute medium (data centers) compared to (a laptop).
eli_gottlieb 11 hours ago [-]
> Another dumb question: can the model be smarter than the data?
Data isn't "smart", it doesn't contain intelligence. I would argue that LLMs are functionally intelligent, they have learned to what functionally amounts to thinking by training on text (and images, in some cases). They are capable of performing experiments autonomously and make decisions based on out-of-distribution data. Besides (current) lack of online learning, they are limited by their biases and working memory - but so are humans, and I'd like to see a convincing argument on why these mathematical laws should not also apply to humans, which are arguably intelligent.
retr0rocket 10 hours ago [-]
[dead]
joe_the_user 14 hours ago [-]
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.
samuelknight 14 hours ago [-]
'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.
dfdydx 15 hours ago [-]
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.
sbierwagen 16 hours ago [-]
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)
luke5441 14 hours ago [-]
From your link:
> 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...
stymaar 14 hours ago [-]
Interestingly enough labs do train with 100s or even 1000s times more tokens than what's proposed by the paper as an optimal value.
And the answer to your question is basically just “synthetic data”.
stymaar 14 hours ago [-]
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.
sroussey 12 hours ago [-]
If you don’t know which data has the highest value, though more at it. Chinchilla is probably still true, but with that caveat. And it could be like advertising… you know a lot of it is worthless, but you don’t know which part.
SideQuark 9 hours ago [-]
More parameters without significantly more data fails; it loses the ability to generalize and simply memorizes.
And we’ve consumed pretty much all the data, and that part is getting harder as data is now expensive, Balkanized by owners, litigated and charged for. Couple that with model collapse as models eat their own output (not solved for any models, despite more than a decade of the problem), and add that lots of previously human curated datasets (stack overflow style), and it very well may be the models are not getting much better.
robrenaud 9 hours ago [-]
Very large models trained with sgd variants don't tend to over fit, especially when using few epochs. See double descent.
The coding/math breakthroughs were caused by training models on the subset of their own outputs which pass a verifier, see reinforcement learning from verified rewards.
SideQuark 8 minutes ago [-]
More data or parameters can break double descent; it's not a magic free ride, e.g.,
That's the historical wisdom, but there are reasons to believe the opposite: that past a certain point more parameters are always better. Basically, that overfitting don't matter, because a large enough model will memorize and build the general solution simultaneously. Then weight decay and SGD noise encourage forgetting of the memorized solution so the general solution takes over.
This is what I thought a year ago, too. But how does it explain what has happened since?
MantisShrimp90 11 hours ago [-]
That depends on your definition of scale. Yes! We were to literally convert the whole world and its resources into data centers.
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.
doctorpangloss 6 hours ago [-]
"citizens all over the world" will not lift a finger for CAFOs or trawl fishing. i really don't think anyone "fighting data centers tooth and nail" is doing so because of a belief in the limits of scaling or any understanding of scarcity.
siva7 16 hours ago [-]
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.
Frost1x 15 hours ago [-]
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.
andriy_koval 15 hours ago [-]
> still don't want to accept the reality that their research has become obsolete.
I see it that most of legacy research is not obsolete but can be significantly amplified by AI.
lordnacho 2 hours ago [-]
Why wouldn't the explore/exploit balance leave at least a little to the explore side? Huge amounts of money are going into AI, why not save a little bit to keep an eye on the things that don't currently look like they're going to scale up?
Like maybe a couple dozen people on an explore team?
jmull 15 hours ago [-]
What makes sense is to do both.
Race down the transformer highway as fast as possible and explore other AI methods.
One need not preclude the other.
knollimar 15 hours ago [-]
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
Fordec 13 hours ago [-]
Also in an exponential competition environment, the difference between 100% commitment and 98% commitment is very possibly not even the difference between a win and second place, but outright lapping by the winner and humiliation at the scale of the gap for second place.
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.
fallingbananna 15 hours ago [-]
My understanding is that the resources are limited and even AI labs with all the funding in the world don't have enough hardware.
caminante 15 hours ago [-]
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.
joe_the_user 12 hours ago [-]
Theoretically one need doesn't preclude the other. The OP gives a detailed argument and his own experience of one need precluding the other in practice.
Both of these approaches require quite a bit of institutional support notably.
lanthissa 12 hours ago [-]
it literally does when gpu's are scarce.
markasoftware 15 hours ago [-]
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.
qlte 15 hours ago [-]
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.
dekhn 16 hours ago [-]
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...
Brystephor 13 hours ago [-]
Im at a company with its own set of machine learning engineers, a few separate organizations internally have them so probably a few hundred MLEs in total.
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.
TomJansen 12 hours ago [-]
Isn't the thing with transformers is that they work really well, but only if you have a lot of training data? If data is sparse, you need more specialized models
liuliu 16 hours ago [-]
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.
sbierwagen 16 hours ago [-]
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.
jurgenburgen 13 hours ago [-]
Synapses and LLM parameters are not equivalent.
cite_please 12 hours ago [-]
In Silicon Valley they are.
mschuster91 15 hours ago [-]
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.
Frost1x 15 hours ago [-]
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.
qlte 15 hours ago [-]
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.
15 hours ago [-]
slashdave 13 hours ago [-]
The extra parameters are useful for optimization. Deep-learning relies on highly singular solutions.
mikert89 15 hours ago [-]
Yup, we havent seen any limits yet. We have discovered a general learning algorithm. We are in for big things
mrandish 12 hours ago [-]
> there's a straight highway ahead
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.
falcor84 12 hours ago [-]
I've been hearing a lot of "credible AI researchers" talk about these diminishing returns of transformer-based architectures since 2023 (at the least), and I'm still waiting to see this promised plateau materialize, because I definitely can't see it in https://metr.org/time-horizons/ or in my personal experience.
mrandish 10 hours ago [-]
Assuming super-linear growth will continue based on the first 3 years of a new technology? Almost every physical, computational and economic system in history, from Dennard scaling in silicon to transonic aviation speed looked super-linear until it hit the inflection point in its eventual S-curve.
A Copernican/Lindy baseline suggests the growth rate in LLM intelligence will peak in around another 2-4 years and I'm leaning on the low side because while the growth of LLM intelligence per dollar and per month are still growing in absolute terms, the growth rate of both seems to already be falling as power-law constraints in compute and data bottlenecks take hold.
famouswaffles 2 hours ago [-]
There's just not much value to a 'Every technology hit a brick wall eventually' take. Anyone can spout that. Why even bring 'credible AI researchers' if that's all it amounts to. What is relevant is the foresight to predict when and at what capabilities. It doesn't change much of anything if LLMs 'plateau' right after they can replace any human at any job.
sigbottle 15 hours ago [-]
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.
Incipient 10 hours ago [-]
I would say I used to believe how the models would scale, now I'm a bit more skeptical. I would be incredibly surprised if oai, anthropic, etc hadn't at least TRIED this yet. They have the compute to do it. They're either sitting on it and saving it for a rainy day, or there is something wrong with it.
flatline 10 hours ago [-]
We know nothing about their internal models that, for instance, produced revolutionary results in mathematics. The public no longer gets to see what’s at the frontier, and we may never see much of it. Also, the cost to train these is immense. China literally does not have enough chips in circulation to compete. I’m not sure where US lab capabilities tap out but I suspect they are at least partly bound by GPU production as well at this point.
octoberfranklin 10 hours ago [-]
We know that if this immense cost had in fact been spent, and the internal models really were that revolutionary, these companies would be selling access to it for cash instead of taking on mountains of debt.
We definitely know that.
flatline 9 hours ago [-]
I agree they would not shut up about it if they had something to reveal, so we probably have a semi-accurate vision of the capabilities if not the implementation details.
gradus_ad 15 hours ago [-]
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?
SJMG 14 hours ago [-]
If/when this happens the value generation will be focused on application and the training compute will be shifted towards inference.
sidibe 8 hours ago [-]
At this point I think it'll be a lot easier to find out what happens then and what to do next if you're using the frontier AI
mullingitover 11 hours ago [-]
I'm still convinced the model advancements are largely US firms jingling a set of keys in front of investors/markets/the public to distract while they scramble to build serious patent moats around the harness/orchestration/infrastructure technologies.
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.
srean 17 hours ago [-]
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.
pvab3 14 hours ago [-]
Aren't there scaling laws with corpus size and parameter count?
MisterTea 15 hours ago [-]
> 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?
hn_throwaway_99 16 hours ago [-]
> If the scaling continues to hold we'll be asking the AI to do the exploratory research for us soon enough.
Obviously we'll see how it pans out, but other approaches feel a bit like trying to ignore the bitter lesson.
paganel 4 hours ago [-]
> Hey this Opus 5.5 with the increased parameters seems much better than the last one
The LLMs still hallucinate way too much when there's no computer code involved, but the computer programmers in here won't be aware of that.
zer00eyz 16 hours ago [-]
> 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).
tehjoker 15 hours ago [-]
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".
ACCount39 14 hours ago [-]
> 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.
tehjoker 11 hours ago [-]
At least for now, those keys haven't been completely unlocked, so parameter scaling is the primary way corporations are achieving increases in ability. If they hit the hardware ceiling, maybe the research direction changes then.
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.
zer00eyz 8 hours ago [-]
> so parameter scaling is the primary way corporations are achieving increases in ability
Thinking tokens have nothing to do with parameter count and where some of the biggest gains have come from.
We're really past the point of scaling parameters being viable. We simply lack the content to make "bigger" better at this point.
jgalt212 16 hours ago [-]
> 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?
mbgerring 15 hours ago [-]
From mass layoffs of tech company employees.
jasomill 12 hours ago [-]
There’s a good example of a model that won’t scale.
motoboi 14 hours ago [-]
Get the transformer to help accelerate research. That's how you get a new architecture.
Anthropic, which is more open about this has shown the numbers, it works.
gxs 14 hours ago [-]
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
HDThoreaun 5 hours ago [-]
The companies would rather have all the skunkworks people working on making the LLMs better.
surgical_fire 16 hours ago [-]
> 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.
AnotherGoodName 15 hours ago [-]
Well 4.6 was released Feb this year.
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.
surgical_fire 14 hours ago [-]
> 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).
octoberfranklin 10 hours ago [-]
> "Hey this Opus 5.5 with the increased parameters seems much better than the last one". Well that's a trillion parameter model.
Opus is 5T, not 1T.
> We'll soon start hitting 10's of trillions
Fable is already 10T.
> "Foot down, there's a straight highway ahead don't divert". I have the view this makes sense too.
The roadblock they ignored is politics.
Three days ago the states where most of the new datacenters are being built (Texas, Ohio, Georgia and others) elected governors who campaigned on halting datacenter construction. The highway metaphor implies money in => intelligence out. Turns out there's at least one other ingredient, and we just ran out of it.
eastbound 15 hours ago [-]
> 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.
joe_the_user 15 hours ago [-]
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.
jurgenburgen 13 hours ago [-]
> it/he
It
rib3ye 14 hours ago [-]
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.
konschubert 14 hours ago [-]
If not in the US then in other countries.
thesmtsolver2 13 hours ago [-]
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.
We are repeating the mistake again.
mullingitover 11 hours ago [-]
> 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.
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.
thesmtsolver2 7 hours ago [-]
> The 'US' isn't a monolithic thing
Yeah, we all know that. What next? You are going to tell me "US politicians" aren't a monolithic thing? Or that we are made up of atoms?
Who voted for the politicians? Who clamored for cheaper goods and fell for the lies?
smokel 17 hours ago [-]
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.
frolix 10 hours ago [-]
The real news is "The End of Microsoft Research". Langford's group is just one of many that have been parted out over the last year or so. The division that was T&R under Kevin Scott was quietly dissolved about a year ago and MSR was reorged under Azure Security with the AzSec leader as hatchetman for the department. MSR itself doesn't have a future beyond a few years at most I reckon. I don't suspect Kevin Scott will be around much longer either since his department keeps getting reduced and is fully eligible for VEO.
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.
doctorpangloss 6 hours ago [-]
simply owning most of OpenAI via a convertible note on very cheap inference sold to it could turn out to be a bigger deal than the rest of what microsoft does.
sheepscreek 17 hours ago [-]
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.
dang 16 hours ago [-]
Ok we'll use that above. Thanks!
Zigurd 16 hours ago [-]
> There is no end in sight to real AI frontiers.
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?
xlayn 17 hours ago [-]
I see this playing the same way in the other big companies that have a pressure to deliver (cough *value for shareholders*)
iagiagiag 13 hours ago [-]
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.
alansaber 13 hours ago [-]
LLMs have very favourable exploration<>consolidation dynamics. An LLM alternative would need to display fundamental improvements at relatively large scales which is extremely challenging for new research.
sidibe 8 hours ago [-]
It's a lot easier to explore these other areas with the help of LLMs.
rhyperior 13 hours ago [-]
> 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.
9 hours ago [-]
ilaksh 15 hours ago [-]
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.
fraywing 17 hours ago [-]
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.
uptrails 17 hours ago [-]
The article is about a guy leaving Microsoft and the disbanding of an internal team called "AI Frontiers".
fraywing 17 hours ago [-]
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.
joe_the_user 17 hours ago [-]
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.
joegibbs 4 hours ago [-]
If they make more efficient models won’t people just use the big data centres to make and run even bigger more powerful models than they can currently support? Or massive swarms of agents to look at all different avenues of a problem?
senordevnyc 15 hours ago [-]
Do you think humanity is just going to run out of problems that need the smartest intelligence?
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.
kylestanfield 15 hours ago [-]
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.
s_u_d_o 16 hours ago [-]
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.
ACCount39 14 hours ago [-]
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.
nonethewiser 8 hours ago [-]
And getting the transformer better may make finding the next thing easier
hobo123 2 hours ago [-]
Me neither. When revenues are growing every year, a fixed chunk of researchers' salaries (maybe growing a few % each year) doesn't really hurt, but R&D offers a nice upside.
Xenoamorphous 13 hours ago [-]
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.
0xpgm 3 hours ago [-]
Big corporations, by nature, are not rational in the decisions they make.
That's why they'll always be room for startups.
move6729 16 hours ago [-]
They are losing tons of money on it. Simple as.
IncreasePosts 16 hours ago [-]
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
move6729 15 hours ago [-]
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
s_u_d_o 11 hours ago [-]
From an engineer, who’s devastated by this, i have to admit/raise my concern: why do we still need engineers to make architecture? Why is everyone saying that’s a must? For me the toughest problem was coding a huge repo/pkgs, architecture should be much of an easier task to do. I’ve seen it do sys design, scaled architecture better than 10X engineers. With good planning, context and prospective info -> it can produce extremely good architecture even for complex systems.
nonethewiser 8 hours ago [-]
Im not sure I would say architecture specifically. Just anything on the input and output.
move6729 6 hours ago [-]
It cant lmao (skill issue)
jphoward 17 hours ago [-]
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?
natebc 17 hours ago [-]
It got rebooted a couple years back. One of the founders of reddit was involved too lol.
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.
zulban 11 hours ago [-]
Great link, thanks for sharing. I missed that when it came out. I'd love to read something similar about other fields, like chess AI up to 1996.
shermantanktop 17 hours ago [-]
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.
thepace 16 hours ago [-]
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.
2OEH8eoCRo0 17 hours ago [-]
Curse of money strikes again. $4tn market cap company thinks team of 50 researchers too expensive.
shermantanktop 10 hours ago [-]
Yeah but to have research be actually useful, you kinda have to have a full-on frontier lab.
Or be content to publish academic papers, which is what this team was doing.
17 hours ago [-]
Nevermark 12 hours ago [-]
I think the dichotomy of open-research vs. commercially-driven innovation might be easier to coordinate, with three stages, not two:
1. Open research: Great creative freedom, with great resource constraints. Lots of ideas, lots of things tried, at insignificant cost relative to commercial work.
2. Results filtering, vetting, regularization: Opportunistic selection of apparently successful work from stage 1, for tentative testing at increasing scale. The best ideas end up with moderate budgets that verify benefits, which are reworked for compatibility before any handoff to stage 3.
3. Commercial efforts: aimed strictly at improving quality and economics of current revenue sources.
Direct negotiation between #1 and #3 involves too much dissonance over globally complementary but locally incompatible values.
#2 can be a relatively small, high payoff group, keeping #1 and #3 from butting heads, while facilitating them both.
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.
linuxhansl 10 hours ago [-]
Hmm... So strange. It seems obvious(?) that both research and scale are needed, and that one should never have one starve the other. In fact research can influence the scale needed, and vice versa.
I guess when money is concerned people will always seek out the the most reliable and predictable path - at the risk of being potentially left behind if it turned out to the wrong (or at least suboptimal) path.
outworlder 13 hours ago [-]
This is the canary in the coal mine. The canary is dead. The AI Winter is coming.
This scaling approach is unsustainable, it is generating diminishing returns and ballooning costs. It is already blowing up.
E-Reverance 11 hours ago [-]
Did you read the article? it literally showcases research published 2 weeks ago on how to make *better models*
mlmonkey 10 hours ago [-]
John Langford was the creator of "VW" ("Vee Dub", or "Vowpal Wabbit"), an ML framework that was quite efficient back in the day ( ~ 20 years ago ).
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.
goodmythical 16 hours ago [-]
>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...
jltsiren 16 hours ago [-]
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.
xondono 16 hours ago [-]
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.
12 hours ago [-]
aleph_minus_one 16 hours ago [-]
>
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.
srean 16 hours ago [-]
I largely agree. For US though, academia has not been a good place lately.
mlazos 15 hours ago [-]
Wait until you hear where academia gets its funding (hint: it is not no strings attached)
doomroot13 16 hours ago [-]
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.
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.
13 hours ago [-]
redmaple892 17 hours ago [-]
The Microsoft AI (MAI) part stood out to me. I bet the politics of being in Mustafa's group were not fun.
fooker 11 hours ago [-]
Who's this Mustafa and what does a MSR team getting disbanded have do do with it?
georgeburdell 15 hours ago [-]
Is this the same group that did ResNet? Bad news
brokensegue 16 hours ago [-]
That's a name I haven't heard in a while
credit_guy 11 hours ago [-]
I'll play devil's advocate. Of course John Langford thinks what he and his group were doing held a lot of promise, and the work was fun and challenging and super important. But in the end you also need to live in the present and deliver. In the words of Jerry Maguire, "Show me the money". He had years to show Microsoft the money, and in the same time other people at other firms showed their bosses the money, big time.
JaydenTeoh 6 hours ago [-]
this is not true. even with ideas with provable/strong scalability, there was still a lack of resources (especially GPUs) to validate such ideas at scale. ultimately, "showing the money" still requires some initial faith from those higher up who control the resources.
rurban 12 hours ago [-]
Yes, leave it to the Chinese to advance the state of the art. America throws money at it, the rest brains. Because they don't have the HW.
Same happened to computer chess in the 80ies. Clever small SW was leading the tournaments, until Ken Thompson could persuade IBM to throw hardware at it, Big Blue. This win was shortsighted. HW didn't scale and nobody profited from it. SW did, esp. reinforcement learning.
skeledrew 17 hours ago [-]
New AI lab incoming?
sehw 17 hours ago [-]
Yeah, in India.
toppa33 10 hours ago [-]
Microsoft's now shown researchers their good work isn't safe.
Harukadia 12 hours ago [-]
but Mircosoft AI model is growing greatly and recently beat the nanobanana in generate video domain. I can't image the situation of them are bad as you explained.
AndrewKemendo 17 hours ago [-]
Well I never thought I’d see Digg used as a primary source
This is certainly an interesting timeline in this section of the light cone
(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.
zodwick 16 hours ago [-]
Not what i expected, but good read nonetheless
dreamcompiler 6 hours ago [-]
I'm about 90% certain that there are numerous huge inefficiencies in these big transformer models. Inefficiencies so big that eliminating them would make it possible to run the equivalent of the latest frontier models on your phone.
But nobody wants to look for them too hard because centralized AI as a service is where all the money is. So everybody keeps building bigger rather than smarter.
textwriter 16 hours ago [-]
Mr. S Altman just said almost all Startups will die within a couple years. This is sad.
0xbadcafebee 15 hours ago [-]
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?)
antonvs 17 hours ago [-]
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.
linguae 16 hours ago [-]
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.
eurieidd 16 hours ago [-]
There’s actually a bizarre irony.
Remember Bell labs, XEROX parc? Ask yourself why that existed and why research came out of them.
Monopoly.
So - many people don’t want to admit this but monopolies can be good from an R&D perspective, but without strong oversight by regulators - money will instead spent on R&D projects that specifically raise entry barriers.
15 hours ago [-]
soulofmischief 16 hours ago [-]
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.
jyrkesh 15 hours ago [-]
> Anyway, you cannot have both monopolies and strong regulators...
> Monopolies occur because regulation doesn't exist or is captured.
IMO, the stronger the regulator, the stronger the incentive to capture it.
Unless by "strong" you mean "competent"?
soulofmischief 11 hours ago [-]
I meant competent, but regulator strength in the way you're implying at its limit becomes monopolistic itself. So when I speak of regulation strength with respect to monopoly, I am speaking to the ability of a regulator to effectively both understand their duty and carry it out.
17 hours ago [-]
the__alchemist 17 hours ago [-]
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.
dominotw 9 hours ago [-]
> scaling is straightforward
I remember IllyaS saying scaling doesnt work anymore so we are just RLcrazy. Is that not true?
outside1234 17 hours ago [-]
Microsoft is a really shitty place to work right now. There are a hundred stories like this all over the company like this.
rwmj 17 hours ago [-]
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.
vladkens 16 hours ago [-]
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.
keeda 13 hours ago [-]
Yep, from what I hear and see, it's like this everywhere. It actually started with the Twitter layoffs and has spread across the industry. At first, it seemed like a reaction to the ZIRP-era overhiring, but it's kept going long after correcting for it, and with AI has been cemented as the modus operandi for many companies.
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.
xondono 16 hours ago [-]
Something tells me the only “good thing” in those two companies is the pay.
They look more like complete organizational shit shows.
williamgators 16 hours ago [-]
Yay China!
shevy-java 15 hours ago [-]
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.
Almost inclined to agree, but the headline of Langford's post needs some additional context.
arikrahman 16 hours ago [-]
Appreciate this, much needed context.
dang 16 hours ago [-]
Ok let's put that link above!
16 hours ago [-]
pavlov 17 hours ago [-]
Hugged to death like it’s 2008 and you’ve just lit your LAMP stack.
srean 17 hours ago [-]
Thanks for the heads-up. I added an archive.is link to give it a breather.
lockprobeH4212 11 hours ago [-]
[flagged]
tramtamlinhcom 8 hours ago [-]
[flagged]
belkinpower 17 hours ago [-]
[flagged]
13 hours ago [-]
kvark 17 hours ago [-]
His complaints sound childish to me. If the research products were truly innovative and useful, he wouldn't need to force Microsoft to adopt them.
nocman 16 hours ago [-]
> 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).
kaashif 16 hours ago [-]
You think incumbent megacorps are all quick at adopting new, useful technologies?
Some are, some aren't. Some try, but botch it.
kvark 16 hours ago [-]
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...
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...
According to official data from the U.S. Patent and Trademark Office (USPTO), there were 6,653 active secrecy orders in effect at the end of fiscal year 2026. Roughly 100 to 350 new secrecy orders are added every year. Many remain locked away for decades or more.
If the U.S. government puts a secrecy order on a patent application against the inventor's will, they are legally forbidden from talking about it. The inventor cannot talk about how it works, publish research on it, sell it, or market it. They cannot even discuss the exact classified details with a regular lawyer unless that attorney has the proper security clearance.
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.
Companies will try to ruthlessly rid themselves of labor as long as those members of labor have agency and rights.
If someone disagrees they can convince me by explaining why a company wouldn’t choose to spend X on a machine they wholly own vs X on an employee they don’t.
There’s some room for argument that employees can do more than machines can but in the current AI zeitgeist arguing that we have machines with equivalent to human intelligence I don’t see how you wouldn’t see this coming if you believed the zeitgeist.
...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.
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.
Let me know when LLM's get those. Otherwise I trust it as much as a human under anarchic rule. Probably less because I know exactly who controls it.
It's his call for sure, but rather funny as an argument.
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 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.
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.
We did?
At no point do I ever confuse a human being with a Claude session.
Even if I'm somehow fooled or confused for a minute, the context window / hallucinations / Claudeish will immediately give it away.
Sure, we passed the turing test for chatbots but we are very far away from AGI.
It's silly since people want to move fast, but we're really not good at recognizing what has actually allowed us to move forward in the past.
[0] He speaks about Bitter Lesson directly at 9:57 but the earlier part gives better context: https://www.youtube.com/watch?v=21EYKqUsPfg
[1] An essentially meaningless statement. Near meaningless because it is not objective.
[2] You compare a novel technique against a technique that has had hundreds or thousands of researchers optimizing over several years. That's not even remotely fair.
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.
Perhaps the atomic bomb? Not being alarmist, but that seems like one of the few instances in modern history. I also thought of the space race, but even rockets as an innovative concept started centuries beforehand as a tool of war.
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.
Lex fridman podcast 2022
ChatGPT answers this just fine.
He also said they will be “ Writing aids, not much more" and they “Can't plan.”
No one said hes an idiot. He was just clearly wrong about LLM capabilities.
As far as I know, those things are still true. Unless there is text in the training material that the text in the prompt can map to, or there is text in training material that describe how to map text in the prompt to text in the training material, LLMs cannot process the prompt.
So may be he is wrong about what could be in the training material. But I don't think he is wrong in the fundamental..
This is sort of what I mention in my original comment. These claims, when pushed, turns out not to hold water...
You're being uncharitable. He would have been right if Chain of Thought hadn't been pulled out of a hat, and no one saw it coming, or how u reasonably effective it would be.
Today, no one can know if there will be a future step-change improvement to LLMs at the same level as CoT, and conversely, no one can be absolutely certein there won't be - so it shouldn't be a firable offense because neither position obvious; even though it will look like it in hindsight.
Even a nobody like me was playing with a sort of CoT with llama finetunes before o1 came out.
Only this year I'd say we picked all the low hanging fruit that has been obvious for a while that was needed.
I think adding harness + MCP + chain of thought + specialized data generation resulted in the world where we have now - the one where you don't have to type code manually, but sometimes do still have to think and sometimes don't.
But LLMs without all this tooling and calls under the hood are still kind of... LLMs. If there's a space to hallucinate - they will hallucinate.
More specifically:
* he said that autoregressive transformer is fundamentally flawed: if it makes one error, all further generated tokens make no sense; that's, obviously, bullshit: there are many ways to implement backtracking, and there are many papers demonstrating backtracking at a time he did that lecture. And, of course, it just doesn't matter - reasoning model can just write "Oh, that's wrong. Let's try a different thing". - it's just not derailed by one mistake.
* he said that GPT-1000 can't possible have understanding of physical world, when GPT-4 already existing at that time could solve a problem he described.
So it's not like he was bad at predicting things, he's intellectually dishonest.
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.
The rest is history.
That failed and SpaceX was then forced to develop its own brand new engines.
The idea that someone should be fired just because they were wrong seems to be something that would cause issues if one wanted to think long term.
This approach will make people afraid of being wrong. It will lead them to avoid challenging dogmatic assumptions in large organizations and inevitably lead to group think.
People really shouldn’t be punished for saying wrong things. Long term it’s better for a company if they aren’t punished for that. Helps to let people freely criticize dogmas or propose new ones.
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.
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?
Are you smart if you see that cognitive scientists had something figured out, connected it to something in your field of chemistry and made a breakthrough treatment for some disease?
Does any of our existing intelligence build on completely unknown information? You could argue that any new solution/emergent intelligence is an association of existing information in new ways.
It also brings us to what is brute forcing? Humans are brute forcing stuff most of the time. You will have 10 stupid thoughts for every good thought. 10 stupid ideas for every good idea, you will discard most of them so fast that you won't think of that you did that. It's very rare, if not near impossible, that a non-mathematician with no prior knowledge, goes into a room on day 1 of deciding to think about a conjecture they don't know anything about, and solve it on the blackboard. They need to use their collect associations, formalize their axioms, think of first principles (known information), and associate it.
So to answer your question you need to answer:
1) What does "smarter" refer to? Is it accumulation of information about the universe?(if yes, a database is smart) Is it an efficient, abstracted representation of accumulated information? Is it prediction of the future state of the universe given a learned abstraction?
Or going further, is it solving tasks useful for the human in the world? Is it acting as an agent with it's own goals and interests in the universe? What if the goals are hard for humans to understand?
Does it count as smart if you make an evolutionary approach that spawn 1000 instances and the most fit one survives and spawns 1000 mutations of itself?
2) What does "genuinely new approaches" refer to? Is it fine to use math axioms to prove things? Does interpolation between two existing solutions count as new? At what granularity of concept does novelty happen (Does using two higher abstraction concepts, like combining some new, modern neural network architecture with a database count as a new thing?), even if the underlying lower abstraction of the same information can be expressed as storage, latency and compute, and is not novel?
---
As you can imagine, this is all not really defined or agreed upon. Here are some examples of projects that are set up to drive these ideas:
The Hutter Prize poses that intelligence is defined by compression, so if you manage to compress the English Wikipedia efficiently, you are closer to better intelligence (http://prize.hutter1.net/)
Compute: Intel CPU
The ARC-AGI foundation poses that being able to be goal-oriented and understand higher level abstractions is intelligence: https://arcprize.org/leaderboard
Compute: NVIDIA T40s
Modded NanoGPT competition poses that intelligence is minimizing entropy/loss over a dataset in the least amount of steps: https://github.com/kellerjordan/modded-nanogpt
Compute: 8x NVIDIA H100
---
All of this also exists in the context of the compute medium. It could be, that you need a certain capacity of memory/computation to perform whatever we have defined as intelligent tasks. Hutter Prize takes the bet of that to make intelligence, you don't need a lot of computation, an Intel CPU laptop and 50GB RAM should be enough.
A lot of the big labs are instead taking the bet on that you just scale everything, make trillion-sized models and things will fall out of that automatically, for this you will need a very different compute medium (data centers) compared to (a laptop).
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”.
And we’ve consumed pretty much all the data, and that part is getting harder as data is now expensive, Balkanized by owners, litigated and charged for. Couple that with model collapse as models eat their own output (not solved for any models, despite more than a decade of the problem), and add that lots of previously human curated datasets (stack overflow style), and it very well may be the models are not getting much better.
The coding/math breakthroughs were caused by training models on the subset of their own outputs which pass a verifier, see reinforcement learning from verified rewards.
https://arxiv.org/pdf/1912.02292
There's plenty more. Read Google scholar
https://youtu.be/lKoJJxjUfdw is a really excellent lecture that changed how I think about it.
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.
Like maybe a couple dozen people on an explore team?
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.
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.
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.
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.
A Copernican/Lindy baseline suggests the growth rate in LLM intelligence will peak in around another 2-4 years and I'm leaning on the low side because while the growth of LLM intelligence per dollar and per month are still growing in absolute terms, the growth rate of both seems to already be falling as power-law constraints in compute and data bottlenecks take hold.
We definitely know that.
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.
The LLMs still hallucinate way too much when there's no computer code involved, but the computer programmers in here won't be aware of that.
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.
Thinking tokens have nothing to do with parameter count and where some of the biggest gains have come from.
We're really past the point of scaling parameters being viable. We simply lack the content to make "bigger" better at this point.
Where's the money coming for those 10X and 100X ventures?
Anthropic, which is more open about this has shown the numbers, it works.
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
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).
Opus is 5T, not 1T.
> We'll soon start hitting 10's of trillions
Fable is already 10T.
> "Foot down, there's a straight highway ahead don't divert". I have the view this makes sense too.
The roadblock they ignored is politics.
Three days ago the states where most of the new datacenters are being built (Texas, Ohio, Georgia and others) elected governors who campaigned on halting datacenter construction. The highway metaphor implies money in => intelligence out. Turns out there's at least one other ingredient, and we just ran out of it.
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.
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.
Yeah, we all know that. What next? You are going to tell me "US politicians" aren't a monolithic thing? Or that we are made up of atoms?
Who voted for the politicians? Who clamored for cheaper goods and fell for the lies?
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.
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.
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.
So far, the attempts to replace the transformer LLM with something else have yielded either "the same transformer LLM but with this one tweak that makes it 10% better" or "a radically different architecture that's a bitch to work with, and performs, best case, about the same as the transformer LLM".
By now, I'm firmly in the camp of "the architecture is good enough". Rifle is fine. Transformer is fine. Get better at the transformer before you look at anything else.
That's why they'll always be room for startups.
https://en.wikipedia.org/wiki/Digg#2025%E2%80%93present:_The...
Seems like it quickly pivoted quickly to cash in on "AI" hype.
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.
The more interesting idea is that MS has abandoned AI research entirely, presumably because their zillions invested in other companies are a better bet in that area.
Or be content to publish academic papers, which is what this team was doing.
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.
It was my introduction to feature hashing.
> 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.
I guess when money is concerned people will always seek out the the most reliable and predictable path - at the risk of being potentially left behind if it turned out to the wrong (or at least suboptimal) path.
This scaling approach is unsustainable, it is generating diminishing returns and ballooning costs. It is already blowing up.
https://vowpalwabbit.org/
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.
https://microsoft.ai/news/frontier-pre-training-at-microsoft...
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.
But nobody wants to look for them too hard because centralized AI as a service is where all the money is. So everybody keeps building bigger rather than smarter.
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"?
I remember IllyaS saying scaling doesnt work anymore so we are just RLcrazy. Is that not true?
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.
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 ?