No Priors Ep. 79 | With Magic.dev CEO and Co-Founder Eric Steinberger

(music plays) So welcome to No Priors. Today we're talking with Erik 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 Erik has a really fascinating background between work on- at Meta on different types of games, running, uh, Climate Science, which was a nonprofit focused on the climate world, and now, of course, developing a- a incredibly interesting AI model and system. So welcome to No Priors today, Erik.

Uh, thank you so much for having me. It's great.

So- so you have a super eclectic background. Um, could you tell us a little bit more about your, you know, what you worked on in the early days, how that evolved into working on AI, and sort of the path you've taken?

Yeah. Um, yeah, thank you. Um, so I- I guess when I was 14, I just had my midlife crisis and, uh, thought I had to do something important with my life. Uh, and spent a year trying to look at everything. Uh, it was pretty stupid. And, uh, basically I looked at the things like string theory and like all the things a 14-year-old would look at and be like, "Okay, what can I spend my life on?" Uh, and, uh, eventually, uh, my mom got me a book on AI and I didn't read it, I'm sorry. Um, but it was like the idea, um, was sufficient. Uh, so I was like, "Okay, this could do anything." Uh, and so- so you should just do that, and then it does everything. And then, um, it seemed plausible that, uh, you'd need to do reinforcement learning. Um, so I didn't know how to code at the time. Um, then, uh, learned to code, uh, uh, over a couple years. This was sort of in high school times. Uh, and then, um, it seemed plausible that you'd need to do reinforcement learning because otherwise you'd not be unfounded. Um, so- so I sort of just started working on RL, uh, played around with things for a bit. Um, and, uh, e- eventually, uh, reached out to someone at DeepMind, uh, to basically I was like pitching this like multi-page email that was like, "Could you like do like a mini PhD thing," where I'm like, "I'm a complete newbie, but if you can bash me, just please bash me like every two weeks and like tell me how to be a good researcher." And so- so eventually, um, I got like reasonable and then did some actual research work and- and worked with, you know, a few other people, including Norman Brown, um, who on developing new RL algorithms, uh, to be more sample efficient and just generally better and faster or whatever, uh, was the goal at the time, um, to- to- to solve, uh, whatever environments we were interested in at the time. Um, so yeah, that's how I got into it. I- I have no background in language models when we started, um, Magic at all. It- it just seemed... I just was like totally not on my radar. Uh, I was like, "Oh, wait a second, like if you take this and this and put it together, like- like maybe this works." Um, and so- so then- then I- I sort of, it felt like this huge relief of, uh, uncert- like, uh, uh, certainty relief of like where AGI would come from, uh, 'cause you just put those two things together and then- and then they will work, uh, was the sort of hope. Um, but yeah, yeah, my original background is in RL, and, uh, trying to come up with algorithms that sort of, yeah, just like have better structures, uh, to be more sample efficient and faster or better conversions.