Chapter 1: What is the vision behind Reflection AI's open-weight models?
We want to build the most powerful agenda models in the world and make them open weight and accessible to everyone. You can see that there are like many frontier open models coming out of China. We are like really bullish that we'll just repeat the same in the West. Open science, publications, sharing your findings, uploading your code.
is something that the only way to really ensure that the progress is actually accessible by everyone and also that we can speed up the progress of intelligence and of science as a whole. Most people will just like choose the open one because like it gives you more flexibility, transparency, all else being equal, open wins every day. It is insane like how far we've come.
I've been doing AI for like almost 14 years now and kind of like thinking where the industry was when I started and like where it's now like the coding agent is like day and night. I don't see why open source cannot like be at the frontier. We built it in America, but like we don't just build it for America, right? Like we build it for the world.
So a team of former Google DeepMind researchers just raised $2 billion, you heard that right, to build America's answer to DeepSeek. So today we're going to talk about the future of open source AI and why it might determine who controls the next generation of technology. Welcome, humans, to the latest episode of The Neuron Podcast.
Chapter 2: How does open science contribute to the advancement of AI?
I'm Corey Knowles, editor of The Neuron, and as always, I'm joined by our trusty writer, Grant Harvey. How are you today, Grant?
Doing good, Corey. I'm really excited to have this chat. I'm really pumped on this. Very bullish on open source here. Excellent.
Well, today we have a special guest. We're joined by Yanis Antonoglou, co-founder at Reflection AI. Yanis helped create AlphaGo at DeepMind, the AI that famously beat the world champion in the Game of Go back in 2016. But now he's building what they're calling Frontier Open Intelligence, powerful AI models that anyone can download and use for free. Yanis, welcome to The Neuron.
It's great to have you.
It's great to be here. Thank you so much for the invitation.
Excellent. I guess my first question would be, so as someone who built AlphaGo, you know how powerful this technology can be. Why make it freely available? What's the upside for our listeners who maybe don't understand much about open source?
Yeah, that's a really good question. And maybe I can just take a couple of minutes to say a few things about me and about my background. So I'm Yanis, I'm the co-founder and CTO of Ram Technology at Reflection. We started Reflection with Misha, my co-founder, about a year and a half ago. Before that, I was a long-time DeepMinder. I actually joined DeepMind really early in 2012.
before an acquisition. It was like a startup at the time. I was like one of the founding engineers. And then I did a lot of the deep reinforcement learning research that came out of DeepMind while I was there. I worked on DQN, AlphaGo, AlphaZero, NuZero, and also did RLHF for Gemini before I left. So I actually have like a lot of
kind of like experience and a lot of, you know, I've spent most of my career doing research and just like really trying to push the boundaries of like the frontier and like, you know, just ensuring that we have like the most powerful algorithms for reinforcement learning and AI in general.
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Chapter 3: What are the benefits of open-source AI for end users?
We want to just put them out there, make them accessible to everyone, and ensure that there's a solid AI infrastructure alternative that has been built in this country.
That's really interesting because you're right. All of that is coming out of China right now. And they're good models. You know, when you take a minute and you look at Quinn, you look at what can be done with Kimmy, those are really good models. But none of that's coming out of here. Most American companies that are doing anything seem to be focusing on really small models right now.
It makes actually what you did unique because you raised $2 billion on the idea of open source. And that was something that was kind of unheard of a couple months ago, I think. I mean, this was coming seven months after you raised like at a $545 million valuation, right? So what convinced investors to double down on you and open source?
Yeah, I mean, I guess like... The thing that's really important in order to be able to build these models is to have the know-how and have the people.
So the reason why our investors really believed in us is because they saw that we had attracted really, really good talent from the top labs, and they saw that we could actually attract more of them and ensure that we don't just bring the people in, but we have them... they can work really nicely and productively together. And they really believe in the mission, like this is the
You know, the AI market is crazy. You know, people get like crazy offers from like other places. And what it boils down to is like whether people feel inspired by the mission and the vision of what they're building. And, you know, we at Reflection have a really strong and powerful message and vision. And that has really attracted some of the best people.
And, you know, I think that like we will continue to attract the best people. And this is kind of like the thing that you need. You need the resources and the people to really make use of those resources.
I mean, I guess the question behind the question I was wondering is, do you think that the mentality of open source and how competitive it could be has changed in the industry? Do you think that, I guess more broadly, do you think that there is a chance that open source could even surpass the big labs, especially if you're financed enough to compete with them?
Yeah, I mean, I guess like you're touching upon like the question of, you know, how do you make money to just like keep kind of like pushing to the frontier? Sure. And I don't see why open source cannot like be at the frontier. Like, you know, if something like there is an opportunity here to...
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Chapter 4: What innovative architectures is Reflection AI developing?
What do you look for in a coding agent that you use personally? And what's your favorite right now and why?
Yeah, I mean, I guess like... One thing that, I mean, first of all, I want to just like, you know, pause here and just like say that coding agents have had, like the progress that we've seen in terms of capabilities over the past two years has been incredible.
I think like every six months, you just like, you know, use your best kind of like coding agent tool and you just like get a completely, you're like... you're mind blown. I feel like maybe we've grown like to, uh, I think that maybe now we're like too desensitized, like, you know, a new world comes out, it does like incredible things and you're like, yeah, but like, does it go on its own?
Chapter 5: How does Reflection AI plan to compete with established labs?
Not, not yet. Totally.
I couldn't agree more covering the news on this every day. Couldn't agree more.
But, uh, it is, uh, it is insane. Like how far we've come. Like, you know, I've been, I've been doing AI for like like almost 14 years now and kind of like thinking where the industry was when I started and like where it's now with like the coding agents, like on a day and night. At the same time, I really, I mean, I like Cloud Code.
I think that like it's a really, really good piece of technology. I think that like Atropic has done really good work there, just like building that. So, you know, good to them. I've also used Kershner in the past, and that's also something that I found useful.
To me, I feel like we're getting to the point, we're not quite there yet, but I feel like we'll just get there in the next few months, where the... Like the programmer spends more time kind of like designing and just like architecting and less time about like really kind of like, you know, writing the code.
I mean, even today, you still need to just like go into the weeds and just like write the code. But like these agents become more and more powerful. So I think like we're not that far away from actually just like being the conductors of like an agent orchestra that like writes the code. And that's what I want to see in the next year.
When you just think about the difference in the last year, it's come a long way. And there are a lot of new players. It's become a competitive part of the industry even.
Yes, yes. There are many, many players out there. And I think this is because... there's almost like a discovery that we need to do. Once you have these powerful agents, what does coding look like in this new world? We need to kind of like almost like rethink how we just like code and what coding looks like. And then you can just like think of different ways and different paths of doing that.
And there are like, yeah, many kind of like different companies trying to just like almost do slightly different things. At the same time, you know, most converge to coding agents as like the solution, like in some way, in some form. So, I don't know, I think it's too early to just like say exactly what coding will look like in even like three years from now.
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