Building toward a bright post-AGI future with Eric Steinberger from Magic.dev

**Sarah Guo** (0:05)
So welcome to No Priors. Today we're talking with Eric Steinberger, the co-founder and CEO of Magic. They're developing a software engineer co-pilot that will act more like a colleague than a tool. And Eric has a really fascinating background between work at Meta on different types of games, running Climate Science, which was a non-profit focused on the climate world, and now of course developing an incredibly interesting AI model and system. So welcome to No Priors today, Eric.

**Eric Steinberger** (0:30)
Thank you so much for having me.

**Sarah Guo** (0:31)
It's great. So you have a super eclectic background.
Could you tell us a little bit more about what you worked on in the early days, how that evolved into working on AI and sort of the path you've taken?

**Eric Steinberger** (0:43)
Yeah. Yeah. Thank you. So I guess when I was 14, I just had my midlife crisis and thought I had to do something important with my life, and spent a year trying to look at everything. I was pretty stupid and basically I'd look at things like string theory and all the things a 14-year-old would look at. I mean, okay, what can I spend my life on? Eventually, my mom got me a book on AI and I didn't read it. I'm sorry, but it was like the idea was sufficient. So I said, okay, this could do anything. So you should just do that and then it does everything. Then it seemed plausible that you'd need to do reinforcement learning. So I didn't know how to code at the time.
Then I learned to code over a couple of years. This was in high school times. Then it seemed plausible that you'd need to do reinforcement learning because otherwise you'd not be unbounded. So I just started working on RL, played around with things for a bit.
Eventually, I reached out to someone at DeepMind to basically do it. So I was pitching this multi-page email. I was like, could you do a mini PhD thing where I have a complete newbie, but if you can bash me, just please bash me like every two weeks and tell me how to be a good researcher. So eventually, I got like reasonable and then did some actual research work and worked with a few other people including Noam Brown, who led META on developing new RL algorithms to be more sample efficient and just generally better investor or whatever, was the goal at the time to solve whatever environments we were interested in at the time. So yeah, that's how I got into it. I have no background in language models when we started Magic at all. It just seemed, I just was totally not on my radar. I was like, oh, wait a second, like if you take this and this and put it together, like maybe this works. And so then I sort of, it felt like this huge relief of uncertainty, relief of like where AGI would come from, because you just put those two things together and then it will work, was the sort of hope. But yeah, I'm a very general background as an RL and trying to come up with algorithms that sort of, yeah, just like have better structures to be more sample efficient and faster or better convergence.

**Sarah Guo** (2:49)
And a little bit of an emphasis on magic is for a two-fold, on the one hand, you're doing a large scale custom model, specifically in part focused on code and that you're also building sort of at the product suite that can really help address coding and working on the software development side. How did you decide to start magic and why focus on that versus other aspects of AI?

**Eric Steinberger** (3:12)
It sort of came from a place of working backwards from AGL. If your end goal is to have a system that can do everything, you can reduce that to building a system that can build that system. And so that minimal system is a system that writes code and comes up with ideas and can validate those by writing code and running experiments, which is still in the same order of complexity as the full thing. But at least we don't have to train Zora. And we don't have to think about 10 billion other use cases that everyone building general domain products has to think about. We only have to think about code. So it's a lot simpler on all aspects except compute and slightly simpler on the aspect and slightly cheaper on the aspect of compute. I think it's not a lot cheaper. I probably overestimated how much cheaper it would get on the compute side at the beginning. But the other things are simpler, I think.