Latent.Space 2024 Year in Review

**Alessio** (0:03)
Hey, everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host, Swyx, for the 100th time today.

**Swyx** (0:12)
Yay! And we're so glad that everyone has followed us in this journey. How do you feel about it? 100 episodes?

**Alessio** (0:19)
Yeah, almost two years that we've been doing this. We've had four different studios. We've had a lot of changes. We used to do this lightning round when we first started that we didn't like and we tried to change the questions.

**Swyx** (0:32)
Because every answer was cursor and flupexity.

**Alessio** (0:34)
Yeah, exactly. I love mid-journey. I was like, do you really not like anything else? Like, what's the unique thing? And I think, yeah, we've also had a lot more research-driven content. You know, we had Triedao, we had Jeremy Howard, we had more folks like that. I think we want to do more of that, too, in the new year, like having some of the Gemini folks both on the research and the applied side. Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, oh, we should do a podcast. And I think we kind of caught the right wave, obviously. And I think your Rise of the AI Engineer podcast just kind of give people some word to congregate. And then the AI Engineer Summit. And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineer industry as a whole, which is almost like, like even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect it would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today?

**Swyx** (1:31)
Yeah, my sign of that that we have won is that Gartner puts it at the top of the high curve right now. So Gartner has called the peak in AI engineering. I did not expect to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know how quickly it could happen. And obviously, there's a chance that I could be wrong. But I think like most people have come around to that concept.
Hacker News hates it, which is a good sign. But there's enough people that have defined it. GitHub, when you launch GitHub Models, which is the hugging face clone, they put AI engineers in the banner, like above the fold, like in big letters. So I think it's like kind of arrived as a meaningful and useful definition. I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June, because I think there's a lot of doubtful questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had. In some sense, I actually anticipated that as well. So I intentionally did not put a firm definition there, because most of the successful definitions are necessarily under specified. And it's actually useful to have different perspectives. And you don't have to specify everything from the outset.

**Alessio** (2:45)
Yeah, I was at AWS re.Invent and the line to get into the AI engineering talk, so to speak, which is applied AI and whatnot, was like there are hundreds of people just in line to go in. I think that's kind of what enabled people, right? Which is what you're going to talk about is, hey, you don't actually need a PhD, just use the model. And then maybe we'll talk about some of the blank spots that you get as an engineer with the earlier posts that we also had on the Substack. But yeah, it's been a heck of a two years.

**Swyx** (3:14)
Yeah, you know, I was going to view the conference as like, NeurIPS is, I think, like 16, 17,000 people. And the Latent Space Live event that we held there was 950 signups. I think the AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase. But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious. Like it will always be low status because at the end of the day, you're manipulating APIs or whatever. But you're wrapping GPTs. But there's going to be an increasing stack and an art to doing these things well. And I think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll talk about the trends here that apply.