Latent Space: The AI Engineer Podcast
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.
00:15:45 · Shawn Wang, S1
[00:15:45] S0: Do they use ComfyUI? Yes. Oh, wow. [00:15:47] S1: And also and also then they, like, gradually invite more people to learning public and contribute more contributions on online document, which was on the Lark, which was facial under Bai Dansba, which was on the Lark. So everybody contribute their knowledges, share their use cases about stable, efficient, ComfyUI, and then become larger and larger. Actually, think right now, the online community has around a million users learning together and several million users, like, learning together. So become the largest AI community in China as well. So that's also, I think, that happened exactly at the same time, I think you start to feel like you need a... We need a new word for this type of engineer, and then as... Which is you just published the essay of the AI engineer.
00:16:36 · Shawn Wang
[00:16:36] S0: Yeah. I think people... So here are some of the alternatives. I think you can best understand it by the alternatives that people were trying at the time. So there was AI Tinkerers, which is another significant community. I I know Joe and Alex well. And then there are people who are saying LLM engineers or applied AI engineer or all these all these other terminology. And I think, basically, you have to focus on the job title that people want to have. And I think that for us, that was very key. And I think I picked this up from the James Clear. Is it James Clear that wrote Atomic Habits? I think so. I I forget I forget I forget the authorship relationship, but I think it's James Clear. Basically, if you want to deeply understand or connect with somebody, you have to connect with them not on the level of their job, not on the level of their activities or hobbies or whatever, it is their identity. Like, who are you? Right. What what do you aspire to be? If you can give them the path towards being the better version of themselves, then you can build a community around that. And so a lot of non AI people wanted to be in AI. And what is the path for them? While you give them a job title that pays money, Like, then they will be loyal to you. Yeah. Yeah. Yeah. Right. And so so, Yeah. Now there's a lot more AI engineers in the world.
00:18:00 · S1
[00:18:00] S1: Yeah. And to follow-up just to my interest as well. So I know that there are just a lot of works or to do, all right, as an AI engineer. I know you mentioned harness, a of developments happen in the AI. So do you think the role of the world AI engineer has been evolved as well, or is different from what you just mentioned, like three years ago, once you started this
00:18:24 · Shawn Wang
[00:18:24] S0: work? Honestly, it hasn't really changed that much. I think that what's hot within AI Engineering has changed, but the job title is pretty much correct. I think there are people... In the early days, it used to be RAG and managing context and all that. And then more recently, it's been coding agents, it's been harness engineering. It's been maybe loop engineering or whatever. Yeah. There's also other disciplines on memory and all these things. So for those who are interested in all the fields of AI engineering, in AIE World's Fair, which is the the the flagship biggest conference, we have 30 different tracks of what it means to be an AI engineer, and evals is part of that. Multimodal is part of it, you know, all that stuff. So I think it has shifted over time. I think agent engineer maybe is is one of them or AI architect, you know, so AI engineers, like, the individual contributor version of this, but who is, like, the company level, company wide boss, and AI architect is... Seems to be the emerging term for for that that person that decides this is how everybody in the company does AI. Yeah. That's AI architect. So... And then, like, there's there's all the all the other, like, AI designer, AI PM, all these things. So it's just, like, who does that work and who writes code and who is responsible for checking that code? If if something fails, are you responsible? No. You're not an AI engineer. Right. You know, if the the person who wakes up in, 4AM in the morning to go and fix it, that's the engineer. Yeah. Right? And so we wanna serve those people, and that's the real core of it. The other problem with AI is that there's a lot of bullshit. There's a lot of talking heads who have never coded a single day in their lives, but they are very big picture. They're very, very senior. They're like, oh, I'm CTO, blah blah blah. And... But they haven't... They don't touch code. Mhmm. And so a lot of AI conferences and communities focus on those guys because they have big fancy titles, but then they all sound the same. They're all like, oh, my timeline to AGI is, like, two years away. You know, all of you are gonna have... Not gonna have jobs in the future. You need to prepare for what's next. Conveniently, whatever is next looks like their job. So it's very self serving.