this post was submitted on 28 Mar 2026
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Same places as usual: Academia and open source foundations.
That's where 99% of all advancements in AI come from. You don't actually think Big AI is paying as many people to do computer science and mathematics research as all the universities in the world (with computer science programs)?
It's the same shit as always: Big companies commercialize advancements and discoveries made by scientist and researchers from academia (mostly) and give almost nothing back.
Big AI has partnerships with tons of schools and if it weren't for that, they wouldn't be advancing the technology as fast as they are. In fact, the only reason why many of these discoveries are made public at all is because of the agreements with the schools that require the discoveries/papers be published (so their school, professors, researchers, and students can get credit).
Like I was saying before: You don't need a trillion dollars in data centers to do this stuff. Almost all the GPUs and special chips being used (and preordered, sigh) by Big AI are being used to serve their customers (at great expense). Not for training.
Training used to be expensive but so many advancements have been made this is no longer the case. Instead, most of the resources being used in "AI data centers" (and research) is all about making inference more efficient. That's the step that comes after you give an AI a prompt.
Training a super modern AI model can be done with a university's data center or a few hundred thousand to a few million dollars of rented GPUs/compute. It doesn't even take that long!
Generative AI improves at a ridiculously fast rate. In nearly all the ways you could think of: Training, inference (e.g. figuring out user intent), knowledge, understanding, and weirder, fluffier stuff like "creativity" (the benchmarks of which are dubious, BTW).
Before we spin into a tangent about theory and "what ifs" etc, care to link me to all these great models from academics and open-source institutions?
Because right now, the only companies I see making advancements in "AI" are burning through obscene amounts of cash, with no end in sight.
And there is no evidence the cost of inference is going down, and even Anthropic admits training will continue burning resources.
You seem to be unaware that it only takes about four NVIDIA HGX H100 nodes (32 GPUs) to train something like qwen3.5:122b. That model is about as good as ChatGPT was six months to a year ago (for the usual use cases). That would take a long ass time though (over a year) so you'd want probably 50-100 HGX H100s (or lots of the newer, cheaper ARM-based hardware devices).
The weights for qwen3.5:122b are open. That means that if you've got the hardware (loads of universities and non-profits have waaaay TF more than 4 HGX H100 nodes) you can continue modern AI development. Everything you need is right there on Huggingface! Deepseek's stuff is also open I think but I forget. Aside: In my head, I hold the qwen models as "the gold standard" based on many articles I've read about them but AI moves so fast, there might be better stuff out on any given day! I haven't read AI news in like a week so I could be all wrong and qwen3.5 is now sooo obsolete, hehe (that's how it feels to follow AI news, anyway 🤣).
Even more interesting: qwen3.5:122b isn't just an LLM. It does visual reasoning (e.g. give it a picture of a plant and ask it to identify it, count the number of screws in an image, estimate distances, etc) as well as the usual LLM stuff. You can read all about it here:
https://ollama.com/library/qwen3.5:122b
...and if you install
ollamaand spend $20 on ollama.com's cloud service you can actually try it out without having to own enough GPUs to cover the 245+GB requirement. I highly recommend that service! You can try out all the latest & greatest models on your local PC (or phone!) for any purpose you want for a $20. Whenever a new model is out they usually have it up on their servers within a day or two and it's fast, too.FYI: I've used ollama cloud to evaluate models for coding (web dev with Python back end) and qwen3.5:122b is fantastic. It's not as good as Claude Opus 4.6 but it's close (and cheap) enough that you can just make up for the mistakes with extra instances that check the output with a critical eye (the latest trick in AI-based coding to get good output).
For reference, the University of Texas at Austin has data centers with 4,000 NVIDIA Blackwell (B200/GB200) GPUs, Harvard has 1,144 GPUs, and the University of Cambridge & Bristol (in the UK) has some monstrous mix of Intel and AMD GPUs. All three are perfectly capable of training new models from scratch or using continuing development on existing open-weight models like Deepseek and Qwen.
Generative AI isn't going anywhere. Furthermore, advancements in that space happen so fast that it's likely that in a few years we won't need so many GPUs/VRAM to train models. Especially if ternary models (and similar, like Google's TurboQuant tech) take off.
I know this is a long comment but I want to point something else out: If OpenAI and Anthropic go bust, that would flood the market with cheap GPUs. It would be a total price collapse and you can bet your ass that clever universities and service providers (like Amazon compute, but 3rd party) would snap those up and bring down prices across the board.