Latent Space: The AI Engineer Podcast
00:44:06 · S1, Shawn Wang
[00:44:06] S1: Uh-huh. [00:44:06] S0: So you know DocuSign? Yes. $10,000,000,000 company. Yeah. Or maybe 7 now. Yeah. What is DocuSign? You you send a PDF, they say like, oh, yep. Sign. They give you a certificate. Oh, that's it. Yeah. Right? And they they send... They charge you something like $10 per per PDF or a first signature or something, or for envelope, they call it. Yeah. And, like, yeah, I I... There's an open source DocuSign, and I host it for myself. And you know what's the difference? Because I have my own Drive and I have my own DocuSign, the moment that the DocuSign is signed, it goes straight into the correct folder on the drive. [00:44:43] S1: Oh, yeah. Because you integrate this together. [00:44:45] S0: Yes. And that does not exist in your DocuSign. Right. Right. Yes. Right. So, like, my data room has directly the DocuSign, so every... Everything that I didn't have to touch because it already has Right. Everything. [00:44:57] S1: My one thing special about DocuSign is created... Like, because it was built long time ago, and it creates a network effect. Everyone joins the network, everyone using it. Are you still thinking it's a mode for this kind of software? Fuck no. No?
00:44:57 · S1, Shawn Wang
[00:44:57] S1: My one thing special about DocuSign is created... Like, because it was built long time ago, and it creates a network effect. Everyone joins the network, everyone using it. Are you still thinking it's a mode for this kind of software? Fuck no. No? [00:45:10] S0: No. Electronic signatures are legally enforceable everywhere. And DocuSign has no patent on it. I can do my electronic signature. What... The the network is the email address. Is it? Yeah. It it is so stupid. And, like, I can keep going about why I made each of each of those things. It doesn't matter. It's not like I'm encouraging everyone to do this. I think that whatever problem you face in your business or your life, you really can find open source version of it Yes. Host it for yourself, customize it, integrate. Okay. I want this plus this. Boom. They just work because I fucking use them all the time. I copy here, paste there, copy here, paste there. Inside of every 1,000,000 copy paste, I think there is a fantastic startup and product to be built. [00:45:52] S1: Great. And I guess next question, next year, I'm actually quite interested in about your conversation with Diogo from Jeff. Because also it was actually a really also a very hotly discussed topic and product. [00:46:05] S0: Yeah. What is the Chinese developers thinking about it?
00:46:05 · Shawn Wang, S1
[00:46:05] S0: Yeah. What is the Chinese developers thinking about it? [00:46:08] S1: I think it just creates a new way of thinking about using, like, LMs from a totally different angle. Yes. And then I even heard that, you know, one podcast podcaster from China, she didn't know anything about coding, but she she used Jeff to judge, you know, the clips from their podcast, whether it would go viral. And then Jeff gives, you know, gives the answer and and then he will post it on on the network. So I found out so many great use cases based on Jeff. So I just wonder what's your take with there and what probably [00:46:45] S0: Yeah. So for those who didn't listen to the podcast, we were the first podcast You are the first one. Because I forced him, because I have friends with him for four years and say, you give me the first floor, I'm gonna kill you. No, he's obviously like very, very accomplished. He was the co author on Instruct GPT, which became Chat GPT. And I think the interesting thing about Jeff is that it is the kind of the original vision of AI engineering for me, which is that instead of... Right now, AI engineering is is what we talked about, is a big harness Yeah. Around a model. And it's like, I'll do a bunch of work, and then I'll hand off to a model and say, please, god model, give me the right answer, and and you can take ten days to think about it. Jeff is like, well, no. Models are for us. We don't work for the model. [00:47:36] S1: Right.
00:47:36 · S1, Shawn Wang
[00:47:36] S1: Right. [00:47:37] S0: And so I will reduce the scope of the model to only one thing, which is to make choices or to estimate probabilities, and I will I will put it in the line on path of my critical work. So if you think about what is the failure mode of of what the LLM... Current LLM paradigm is, is that you have the big traditional SaaS, and then your LLM is this window that's a chatbot. Mhmm. Right? Mhmm. That is basically what everything is. Or you are... You look like Codex or ChatGPT or Claude Agent. And it's like chat window here Yeah. Menu here, that's it. For what Jeff represents and what system one models represent, this is why he doesn't call it decision model, he calls it system one models. Mhmm. It is that they are in the path of code. You write it as casually as you write an if statement inside of a normal programming language. And I think that is really what he means by smarter software, which I really, really endorse, which is like, your software should just be at every point smarter instead of at one point smart, and then everything else is dumb. That's really dumb. [00:48:37] S1: Mhmm. [00:48:38] S0: Yeah. I think the the speed stuff is almost secondary to the reliability, which is what he really is trying to say. That's why the company is called TypeSave. But, yeah, the speed is nice.
00:48:38 · Shawn Wang, S1
[00:48:38] S0: Yeah. I think the the speed stuff is almost secondary to the reliability, which is what he really is trying to say. That's why the company is called TypeSave. But, yeah, the speed is nice. [00:48:49] S1: I just found out that Diogo himself also a very, you know, such a character, and and he's really passionate about what he's been doing. So I just wonder, you know, you've talked to a lot of like researchers and developers or AI engineers. What do you think? Why was Diogo that made the JAF? Was it something special or different from other kinds of researchers or people that you've talked with? Because I find it quite special about Bob, about him talking to what he's building and what he's believing.
00:49:22 · Shawn Wang, S1
[00:49:22] S0: He has a nontraditional background himself. He didn't go to, Stanford PhD and, you know, the kind of... Those kind of things. So he has that sympathy with a regular developer, but he also worked on ChatGPT. Mhmm. I think that a lot of people who... Maybe they went through the traditional ML researcher path, you go to a top university and then you work on some interesting paper, and then you get hired into a lab and you work on a lab, they're in the big models cult. Everything will be solved by making our model bigger, by having more more flops in our training run, and more parameters and more, more, more, more. Right. Right? And so it takes someone who doesn't come from that mold to really say, well, actually, there's another way to do this, which is smaller stuff that is sprinkled everywhere instead of one big one. And I think that is... By the way, Diogo was not the first to do that. There's been thousands of people who've tried to say smaller model is good. Right. Faster, cheaper, better, whatever. But somehow Diogo worked. Mhmm. One, because he has the credibility of OpenAI. Two, he made it into a system one versus system two thing, which I think makes the communication very clear to the point where you can play Doom or whatever it was with... So there's some game demo that he that he showed in the launch post that was very clear, like, there's no way you will ever use LLM to do this. [00:50:46] S1: Mhmm.