As if anyone riding this wave and making billions is not sitting on top of thousands of papers and millions of lines of open source code. And as if releasing llama is one of the main reasons we got here in AI…
Wait.. are you saying it wasn't? Just releasing it in that form was a big deal ( and heavily discussed on HN, when it happened ). Not to mention, a lot of the work that followed on llama partly because it let researches and curious people dig deeper into internals.
R1 is a 650b monster no one can run locally.
This is like complaining an electric bike inky goes up to 80km/h
The full version... If you have to ask you can't afford it.
We can laugh at that (like I like to do with everything from Facebook's React to Zuck's MMA training), or you can see how others (like Deepseek and to a lesser extent, Mistral, and to an even lesser extent, Claude) are doing the same thing to help themselves (and each other) catch up. What they're doing now, by opening these models, will be felt for years to come. It's draining OpenAI's moat.
Innovation ALWAYS follows this path. Something is invented in a research capacity. Someone implements it for the ultra rich. The price comes down and it becomes commoditized. It was inevitable that “good enough” models became ultra cheap to run as they were refined and made efficient. Anybody looking at LLMs could see they were a brute forced result wasting untold power because they “worked” despite how much overkill they were to get to the end result. Them becoming lean was the obvious next step, now that they had gotten pretty good to the point of some diminishing returns.
Really this should be an indictment of corporate bloat, having hundreds of thousand headcount companies distracted by performance reviews, shareholders, marketing, rebuilding the same product they launched two years ago under a new name.
Yeah.
There are some shorter words or acronyms for it though, roughly equivalent to your about 30-word paragraph above:
IBM DEC Novell Oracle MS Sun HP ... MBA , all in their worse days or incarnations or ...
Because we saw, what a week ago the leading indicator that the money people were now feeling happy they were in charge which was that weird not-government US$500 billion investment in AI announcement. And we saw the same being breathlessly reported when Elon Musk founded xAI and had "built the largest AI computer cluster!"...as though that statement actually meant anything?
There was a whole heavily implied analogy going on of "more money (via GPUs) === more powerful AIs!" - ignoring any reality of how those systems worked, their scaling rules or the fact that inferrence tended to run on exactly 1 GPU.
Even the internet activist types bought into this, because people complaining about image generators just could not be convinced that the Stable Diffusion models ran locally on extremely limited hardware (the number of arguments where people would discuss this and imply a gate while I'm sitting their with the web GUI in another window on my 4 year old PC).
Riding hype, and dumping at the first sign of issues, follows that perfectly well.
Regulatory capture only benefits you nationally. You might even get used to it.
They can “profit” (benefit in product development) from it.
They just can't profit (return gains to investors) much from it, because that requires a moat rather than a market free for all that devolves into price competition and drives market clearing price down to cost to produce.
All that cloak and dagger stuff comes at a cost, so it's only worth paying if you think you can maintain your lead while continuing to pay it. If the open source community is able to move faster because they are more focused on results than you are, you might as well drop the charade and run with them.
It's not clear that that's what will happen here, but it's at least plausible.
They just didn't.
Worse for the proprietary labs is how much they've trumpeted safety regulations. They can't just release a model without extensive safety testing, or else their entire regulatory push falls apart. DeepSeek can just post a new model to Hugging Face whenever they feel like it — most of their Tiananmen-style filtering isn't at the model level, it's done manually at their API layer. Ditto for anyone running finetunes. In fact, circumventing filtering is one of the most common reasons to run a finetune... A week after R1's release, there are already uncensored versions of the Llama and Qwen distills published on HF. The open source ecosystem publishes faster.
With massively expensive training runs, you could imagine a world where model development remained very centralized and thus the few big labs would easily fend off open-source competition: after all, who would give away the results of their $100MM investment? Pray that Zuck continues? But if the training runs are cheap... Well, there are lots of players who might be interested in cutting out the legs from the centralized big labs. High Flyer — the quant firm that owns DeepSeek — no longer is dependent on OpenAI for any future trading projects that use LLMs, for the cost of $6MM... Not to mention being immune from any future U.S. export controls around access to LLMs. That seems very worthwhile!
As LeCun says: DeepSeek benefitted from Llama, and the next version of Llama will likely benefit from DeepSeek (i.e. massively reduced training costs). As a result, there's incentive for both companies to continue to publish their results and techniques, and that's bad news for the proprietary labs who need the LLMs themselves to be profitable and not just the application of LLMs to be profitable... Because the open models will continue eating their margins away, at least for large-scale deployments by competent tech companies (i.e. like Linux on servers).
They kinda did: https://en.wikipedia.org/wiki/Azure_Linux
DeepSeek did something legitimately innovative with their addition of Group Relative Policy Optimization. Other firms are certainly free to innovate as well.
Open source means we need to be able to reproduce what they’ve built - which means transparency on the training data, training source code, evaluation suites, etc. For example, what AI2 does with their OLMo model:
isn't LeCun basically admitting that he and his team didn't have the creative insight to utilize current research and desperately trying to write off the blindside with exceptionalism?
not a good look tbh
The thing is that the steam engine guys researched thermodynamics and developed the mechanics and tooling which allowed the diesel engine to be invented and built.
Also, for every breakthrough like DeepSeek which is highly publicized, there are dozens of fizzled attempts to explore new ideas which mostly go unnoticed. Are these wasted resources, too?
Resource allocation in this context isn’t at all binary.
Given your take, this is a meaningless question, no?
As you point out, all resource usage that lead up to the creation of the diesel engine were necessary preconditions. While one might be able to imagine a parallel universe where the diesel engine was created in another way without all the things in between that might feel like a waste, that is not this universe. In this one, it took what it took.
Same goes for AI. That AI researcher had to eat that sandwich double wrapped in plastic, subsequently placed in another plastic bag in order to get to where he got. Which might feel like a "waste of resources". I am sure you can easily imagine a parallel universe where he didn't eat something that used up so much plastic. But that was the precondition necessary in this universe.
So, ultimately, either everything is a waste of resources or nothing is. And there is no meaning in trying to find a distinction between those two.
LeCun is in a different part of the organization - FAIR (FaceBook AI Research), and isn't even the head of that. He doesn't believe that LLMs will lead to AGI, and is pursuing a different line of research.
Looking back at the PDP handbook, it's not even clear that LeCun deserves the credit for CNNs, and he himself gives credit for the core "weight sharing" idea to Rumelhart.
Chollet's claim to fame seems to be more as creator of Keras than researcher, which has certainly been of great use to a lot of people. He has recently left Google and is striking out to pursue his own neuro-symbolic vision for AGI. Good luck to him - seems like a nice and very smart guy, and it's good to see people pursuing their own approaches outside of the LLM echo chamber.
They literally used GPT and Llama to help build DeekSeek, it responds thinking that it's GPT in countless queries (which people have been posting screenshots of). They 'cheated' exactly as Musk did to build xAI's model/s. So much of this is laughable scaremongering and it's absolutely not an accomplishment of large consequence.
It's a synth LLM.
“To people who see the performance of DeepSeek and think: ‘China is surpassing the US in AI.’ You are reading this wrong. The correct reading is: ‘Open source models are surpassing proprietary ones.’ DeepSeek has profited from open research and open source (e.g., PyTorch and Llama from Meta). They came up with new ideas and built them on top of other people’s work. Because their work is published and open source, everyone can profit from it. That is the power of open research and open source.”
[1] https://www.forbes.com/sites/luisromero/2025/01/27/chatgpt-d...