Can Open Source Keep AI Power From Concentrating?

AI is becoming more powerful, but the resources needed to build it are increasingly concentrated. Does it have to stay that way? MTS host Sofia Du heads to the Open Source AI Summit in San Francisco to ask researchers and founders across the AI stack whether open source can create a more distributed future for AI. Lukas Kiser, co author of the landmark Attention is All You Need paper, argues that today's concentration may be a property of our current technology, not an inevitable feature of AI. Transformers thrive on enormous amounts of data and compute, but they're less than a decade old, and the next breakthrough could change those economics. From open models and access to compute to companies owning their own intelligence, Sofia explores where AI power is actually concentrating, what openness can change, and what it still leaves unsolved. Joining me is Lukas Kiser, an AI researcher and the co author of Attention Is All You Need, the paper that introduced the transformer that completely changed modern day AI. Where is modern day AI and where is power currently concentrating? There was a very important moment sometime around end of last year, start of this year, where the coding agents started to work. The nature of this AI is that you need to train it on very expensive data centers and on a lot of data. So there's big companies that do this, and then they charge for it, right? That concentrates it in the sense to get the, like, to me, even the current models are not smart enough. So there still could be much smarter. There may be some research breakthroughs that are needed to make them smarter with less data. But since research breakthroughs, know, sometimes they come, sometimes they don't, they're not like a business proposition. But the big companies are like, okay, but you can do things without research breakthrough, which is to go bigger, bigger, bigger. Now, that is very concentrating. You go bigger, bigger, you need billions of dollars. You need to scrape data from every corner of the internet. So the current state of the technology is a bit concentrating, but we should remember that it's just the current state. Sure, transformers are great, but they're not even 10 years old. I'm sure will be other things. Maybe we'll make it possible to train with less data, more diverse models. Like transformers are really good if you train them on the whole Internet, and then they're reasonably smart. But if you try to say, you know, you just learned about this, then they're just stupid, right? It doesn't work that well. But it doesn't work because maybe we need a research breakthrough that will make it work. But the transformer needs to be everything. And that's a problem, but it's a research problem that hopefully gets solved. With the transformer, architecturally, you need a lot of resources. And the more resources you have as a company, as a model, …