Latent Space: The AI Engineer Podcast
00:06:33 · Shawn Wang
[00:06:33] S0: Yeah. So it's very funny because I recently had to go over my taxes, like, figure out, like, what money do I have. So Cognition is the biggest one is adviser. I'm still an adviser to Cognition. Nice. I was an employee for a long time... For for for a while. So, basically, the idea was I have three companies. I have AIE, I have Latent Space, and then I have Miscellaneous. It used to be Small AI, which is my personal sort of startup vehicle. Yeah. Basically, got acqui hired into Cognition, worked there for six months, and then stepped back to be an adviser because AIE is now, you know, a global conference conference organization, and I I had to be CEO. I couldn't be part time employee, time here. Cognition is very big. Fireworks is very big. Modal is very big. Supabase. So I think it's a very common thing if you're on a Silicon Valley influencer or you've been in developer relations circles for a long time. So that that is my specialty in... Which is that I am technical, but I also write and speak. And that is a good combination of skills to have for people because any startup will wanna pay you to, like, talk about their stuff. And I think, you know, then then you start working with a bunch of things. So, like, a lot of... This is not a normal career. I actually don't even recommend it for most people, but it is my role in life that I can advise a few different companies and work with them in exchange for stock, and that's the rough idea.
00:07:57 · S1
[00:07:57] S1: I just want to deep dive a bit more into the early days of Latent Space, because I know I read your essay that you start to learn in public and then tell everyone what you learned in early days, and then you felt the need, you need to transfer the conversation, you talk with different researchers online, and then become the early conversation of the Latent Space, probably just, you know, talking about the past days, how to build up Latent Space.
00:08:24 · Shawn Wang
[00:08:24] S0: There's kind of two different things that you're mixing together, right? The first thing is the principle of learning in public, which is something I realized when I left finance and I worked in tech. Why is tech so much better... Going so much better for me? It feels so much more comfortable. And the very core reason for people who are interested in both fields, both are high paying jobs, like prestigious jobs. The difference is finance is zero sum. Right? If I know something and I tell you that that truth lose... Loses value. Right. But in tech, it is... Most stuff is... Things are positive some. If I know something I tell you, it increases value. And that's a very different paradigm for people who are crossing over from finance to tech, and it's very liberating. It means that people will get on stage and they open source their work. Right. They share their code. They screw up. They share that they screwed up. In finance, no. Like, you wanna pretend that you never screwed up and everything is top secret. And I think that is something that I really took to heart, and I I think I realized that... Oh, the the other thing that happened to me was my first job was at Two Sigma, which is still in finance, but I was on the software engineering side. And I realized that I had a bad boss. So then I got really bored, and I started to do a lot of writing and speaking because at work, I wasn't able to do the things that I wanted to do because they didn't let me. But, like, obviously, because I was so bored at work, and then I put all my energy into the the stuff outside of work, then obviously that thing took off because no one else is doing that. And by comparison, that becomes you being able to choose your own boss. Right? Like, most people, when they have a job, they join a company, they are assigned a boss, they assign a team. Most of the most of the time, don't even know if, like, the team is good or not or, like, whatever. And then you find out you're not good. You're stuck. When you learn in public, you actually can choose your own boss. You choose... You you, like, work for yourself, choose your own boss, all those things. So it's very, good. I think the... Fast forward to Latent Space, that was created when stable diffusion happened. This is before ChatGPT. Yeah. When stable diffusion happened, I was in a startup that was not going very well.
00:10:36 · Shawn Wang
It's called Airbyte. It's still doing okay today, but nobody will call it a rocket ship. That's just... That's objectively true, and I will say that to the founders. But because I was like, okay. I have... I I did, like, some machine learning courses, but I'm not an AI expert. I am so interested in this field. There's there's only one way for me to get into the field, is to learn in public. And so that's what I did. I started a blog, and I started analyzing and learning and, like, writing up my learnings into a blog. And at first, nobody will notice, but then eventually, if you start talking about people's things, like, literally, Emad Mostaque, who is the CEO of Stability AI, which created Stable Diffusion, which is the the reason I started the blog, read the blog post analyzing stable diffusion and said this is good. And I think that work was basically me learning AI, like the past five years of just grinding and and and writing about that. And I think that is really the... You know, like, you are not qualified, and I'm still probably not that qualified today, but I'm a bit more qualified than I used to be four years ago. So that's good. And I think that's the only way you can learn. Like, I think, especially in Asian cultures, there's a lot of emphasis on credentialism. Yeah. Like,. Right? Like, you need that piece of paper. You need, like, some qualification. Like, if it's not if it's if it's not a degree, it's a certification. If it's not a certification, you must have had a job to do it. And how do get the job? You must have a degree. Like, it's just like a infinite loop of things that basically mean that when you're 16, you need to decide what you're do in But, like, I think life is a lot more flexible than that, and you can choose to pivot if you want to. You just have to be really persistent and also realize that just because you work very hard, you're not entitled to win. You have to work very hard and be good. I think a lot of people try, a lot of people fail, and they're like, why didn't they fail? Well, you're not good. Too bad. But some people try and they they worked and and they had judgments. They really did work on substantial things rather than insubstantial things, and that's what... That's generally what happens with learning public. So so, yeah, that start...
00:12:45 · Shawn Wang
This started Latent Space, and then very, very interestingly, Latent Space was just a blog. It was just Stars was just a blog. Yes. Just a blog, and I had no other no other idea apart from this is Swyx's writing about AI. That's it. But we happened to host some events, like, gatherings, like, oh, like, you know, ChatGPT came out, like, obviously, the most exciting thing of all time. And then we wanted to host some hackathon, so we hosted a hackathon. And at the hackathon, that's where the idea for AI Engineer came about, which is the realization... Actually, it was this woman that came up to me and said, I'm I'm all in on AI. I'm nontechnical. What can I do? And I realized that the simple answer is based... Compared to an engineer, she can do nothing. Engineers are about to be so much more powerful than the non engineer because engineers can control 100 AIs at once and program it. Whereas the the maximum that a non technical person can do is prompt better. Then I was like, oh, like, I think I have seen this before. So my my career... Throughout my career, I've seen, like, professionalization of roles that are nonprestigious. So what do I mean by nonprestigious? It is not prestigious to be a prompt engineer. The prestigious thing is you train large language models. You do pretraining. You do posttraining. You have to go through a PhD, you make fancy algorithms like GRPO, you know, and and you you do manifold hyperconnections, which is what DeepSeek does. Yeah. That's prestigious, and that continues to be prestigious. But I think the overcorrection that people have is that looking at everything else that is not that ML research, that is worthless. That actually was untrue. You actually do want to build harnesses, and harnesses do not require you to know advanced math or have a lot of experience in supercomputing. Like, actually, you just need to be a very good software engineer that is open minded enough to understand how to put AI into traditional software code, which is a harness. Mhmm. We didn't have the word for harness at the time, but it was very clear that engineers would be able to do things like this that normal people cannot. And they would be in demands, and there's not only that, companies are trying to hire this kind of people, and developers want to become this kind of people. They want they want a term to call themselves something.
00:14:57 · Shawn Wang, S1
And so all you have to do is figure out the right term, which is... The simplest one is AI engineer, and make it a industry, make it a discipline. And so that's what I did. [00:15:06] S1: Actually, I can share with you a side story in China is that at the time when you just mentioned advent of stable diffusion, the open source of stable diffusion, it was actually at the time that, right now, the largest AI community in China, which is called WaytoAGI, actually, that was also started from a bunch of people who are ambitious or lovers of those stable diffusion technology. I want to transform into different, like, images generation. And also it become the foundation of the ComfyUI. You're definitely familiar with that ComfyUI. [00:15:40] S0: Yes. Comfy is famous. We've had him on the podcast. [00:15:43] S1: Yeah. Yeah. Yeah. And in China as well. And then so [00:15:45] S0: Do they use ComfyUI? Yes. Oh, wow.