Latent Space の舞台裏：AIE Shanghai を前に swyx が語る創設秘話とAIエンジニア論

This is a special podcast because it's about AIE Shanghai. AIE Shanghai is the first AIE in China. It's brought to us by Stand Up Partners, which is one of the local partners that we have. We have a few AIEs worldwide that my team doesn't run. And this is the first one in Shanghai. I've always wanted to bring AIE to China. I think there's a lot of East meets West and a lot of progress in China that needs to be shared and distributed among the global community. China is super interested in us. There's been many Chinese fans of the InSpace, and I'll flash a few of them up on screen here on YouTube. And primarily, I want to recognize Meng Xiao, who is our primary fan, always translating basically every episode into Chinese, which I am super appreciative of. And in general, I think I want to also congratulate and celebrate the progress that China has made, not just in open source models, but in robotics and a lot of other sort of startups.

You know, who can remember Manus that was started this time last year and then moved to Singapore? And obviously, OpenClaw had a big run. And we talk a lot about what that's like in terms of the overall mood on the ground and how the scale of AI community is so different in China and the type of things that get adopted, as well as, I guess, the startup culture. People hear a lot about 996. We talk about the reality of that versus in a place like Cognition. And finally, we also talk a little bit about the origin story of the in-space, which some of you may not have heard if you're newer to the podcast. talk about when it was started and why it was started and how we think about running the multiple shows that we're running and a little bit about safety and alignment and all that other stuff. So there's a little bit here for everyone. It's basically for the fans if you've been following the InSpace for a while and if you're interested in AIE Shanghai. It's not being live streamed, but we're going to post it up on YouTube at some point.

It starts tomorrow. So anyone here in China who's watching this, I'm really looking forward to having it. And thanks to Meng Ying and team for organizing all of this. I also have to shout out Hanhua and Lei Du, who basically got this thing started. It nearly did not happen a few times, but it's so, so exciting to have AI in Shanghai and to bring Layton Space to China in general. So thanks for watching and look forward to the interview. The models are very good at clicking through every screen of your software and then replicating it independently. Oh, yeah. Right. So the moment I have access to your software, I can clone it. So good luck. There's a lot of talking heads who have never coded a single day in their lives. They're all like, oh, my timeline to AGI is like two years away. All of you are not going to have jobs in the future. You need to prepare for what's next. Conveniently, whatever is next looks like their job. I have my own Calendly. I have my own Google Drive. I have my own GitHub. I'm serious. There's lots of good founders.

Yes. They live in the wrong country. If they live in the U.S., they'll kill Amazon. They'll kill, like, Facebook. The only reason they're not richer is they live in China. AI doom. In all of human history, when a more advanced civilization comes along and meets a less advanced civilization, the less advanced civilization goes extinct. And this thing is evolving roughly 1,000 times faster than us. Hi, Shou. Shou, thank you for joining me for this show. So I've been a long-time listener for the Latent Space Podcast. and I know that many founders and AI engineers in China as well have already learned a lot from your work and conversations on the podcast as well. And also I'm grateful that you are bringing the AI engineer conference to Shanghai the first time this year. So I think also like several months ago I read your piece actually on Drive Forth about your essay on your relationship with the Silicon Valley. You moved back and forth from Singapore and here. I guess that's why how you actually coined or invented the role of AI engineer the last time you moved to Silicon Valley.

And also, this just makes me feel like it's very special that this time you bring the AIE to Shanghai the first time. So tell us a little bit about your background and your relationship with Silicon Valley. I should correct you on one thing. That's wrong. I did not bring AIE to Shanghai. Shanghai brought AIE to Shanghai. Okay. Right. It's like Hanhua and like Leitu and Meng Ying and those folks as partner partners. So I'm very grateful for them because I always wanted to do something in China, but I'm not a Chinese citizen and I have no contacts or relationships there. But I think that there's a lot of interest on both sides of the world, on U.S. and China, Chinese AI in general, you know, whatever we can do to surface like high quality conversations. I think that AIE in China can help there. So my TLDR is, I'm from Singapore, you know, fifth generation Singaporean. Sorry, fifth generation Chinese, right? Most Singaporeans, majority Chinese.

I think I am from... People ask me, like, you're from where you are. Oh, nice Chinese. I'm from Beijing. Oh, cool. So, and I don't know. And that's like how it feels like to be, Chinese diaspora, right? Like we don't know what our origins are because it just makes that nobody kept any records. Nobody even knows our birthdays sometimes, you know. But I was first generation of Singapore. I grew and raised up in Singapore and went to the U.S. for university and did my first career in finance. I was in trading and investment banking and hedge funds for 10 years, sorry, 8 years, and then switched to tech. So I worked in Amazon and a few startups. And then four years ago, became independent, wrote Latent Space, figured out that there was going to be this AI engineer thing, and then I started AIE.

Cool. And I know that actually you have different roles right now. You are doing the Latent Space podcast. You are writing newsletters. And also I found out that you have different roles in several startups, like Connection, et cetera. Yes. Yeah, just wondering what your current titles or roles in different roles. Yeah, so it's very funny because I recently had to go over my taxes. I figured out what money do I have. So Cognition is the biggest one. I'm an advisor. I'm still an advisor to Cognition. I was an employee for a long time, 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. Basically got acqui-hired into Cognition. Worked there for six months and then stepped back to be an advisor because AIE is now a global conference organization. And I had to be CEO. I couldn't be part-time employee, part-time here.

Cognition is very big. Fireworks is very big. Modal is very big. Super base. So I think it's a very common thing if you're a Silicon Valley influencer or you've been in developer relations circles for a long time. So that is my specialty, 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 want to pay you to talk about their stuff. And I think then you start working with a bunch of things. So 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. 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 the 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 you build up latent space. 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. If I know something and I tell you, that truth loses value. But in tech, most stuff thinks a positive sum. 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. They share their code. They screw up. they share that they screwed up in finance. No, like you want to 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 think I realized that, oh, 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 obviously because I was so bored at work and then I put all my energy into 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. Most people, when they have a job, they join a company, they are assigned a boss, they are assigned a team. And most of the time, you don't even know if the team is good or not or whatever. And then you find out it's not good. You're stuck. When you learn in public, you actually can choose your own boss. You work for yourself, choose your own boss, all those things.

So it's very, very good. I think the fast forward to latent space, that was created when stable diffusion happened. This was before ChachiBT. Yeah. When stable diffusion happened, I was in a startup that was not going very well. It's called Airbyte. It's still doing okay today, but nobody would call it a rocket ship. That's objectively true, and I would say that to the founders. But because I was like, okay, I did some machine learning courses, but I'm not an AI expert. I am so interested in this field. There's only one way for me to get into the field, which is to learn in public. And so that's what I did. I started a blog, and I started analyzing and learning and 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, iman mustaq was the ceo of stability ai which created stable diffusion which is the reason i started the blog read the blog post analyzing stable diffusion and said this is good and i think like that work was basically me learning ai like the past like five years of like just grinding 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 um especially in asian cultures there's a lot of emphasis on credentialism yeah like you need that piece of paper you need like some qualification like if it's not if it's not a degree it's a certification if it's not a certification you must have had a job uh to do it and how do you 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 want to do in life but 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 it fail well, you're not good. Too bad.

But some people try and they worked and they had judgments. They really did work on substantial things rather than insubstantial things. And that's generally what happens with running public. So, yeah, that started Lanespace. And then, very interestingly, Lanespace was just a blog. It was just a blog. Yes, just a blog. And I had no other idea apart from this is Swiss writing about AI. That's it. But we happened to host some events, like some gatherings, like, oh, like, you know, Chachi PT came out, like, obviously the most exciting thing of all time. And then we wanted to host some hackathons, 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 out to me and said, I'm all in on AI. I'm non-technical. What can I do? And I realized that the simple answer is, 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 maximum that a non-technical person can do is prompt better. Then I was like, oh, I have seen this before. So throughout my career, I've seen professionalization of roles that are non-prestigious. So what do I mean by non-prestigious? It is not prestigious to be a prompt engineer. The prestigious thing is you train large language models. You do pre-training, you do post-training. You have to go through a PhD, you make fancy algorithms like GRPL, and you do manifold hyperconnections, which is what DeepSea does. 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. 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.

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 demand. 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 a term to call themselves something. And so all you have to do is figure out the right term, which is the simplest one is AI engineer, and make it an industry, make it a discipline. And so that's what I did. Actually, I can share with you a story in China is that at the time when you just mentioned the development of stable diffusion, the open source of stable diffusion. It was actually at the time that, you know, right now, the largest AI community in China, which is called Way2H. Actually, that was also started from a bunch of people who are ambitious or lovers of those stable diffusion technology and want to transform into different images generation. And also, it become the foundation

of the ConfiUI. You're definitely familiar with that ConfiUI. Yes, ConfiUI. We've had them on the podcast. Yeah, yeah, yeah. And in China as well. Do they use Confu UI? Yes. Oh, wow. And also then it gradually invites more people to learn in public and contribute more contributions on the online document, which is on the LARC, which is facial on the Bidance, which is on the LARC. So everybody contributes their knowledge, share their use cases about stable diffusion, Confu UI, and that becomes larger and larger. I think right now the online community has around a million users learning together and several million users learning together. So it becomes 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 we need a new world for this type of engineer, which is you just published the essay of the AI engineer. 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 know Joe and Alex well. And then there were people saying LLM engineers or applied AI engineer or 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 forget the authorship relationship, but I think it's James Clear. Basically, if you want to deeply understand or connect to 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? 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 an AI. And what is the path for them? Well, you give them a job title that pays money. Like, yes, then they will be loyal to you. Yeah, yeah, yeah. So now there's a lot more AI engineers in the world. Yeah. And to follow up just to my interest as well So I know that there are just a lot of works to do as an AI engineer I know you mentioned hardness a lot of developments happen in AI So do you think the role or the world AI engineer has been involved as well, or is it different from what you just mentioned like three years ago once you started this 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. 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 flagship Biggest Conference, We have 30 different tracks of what it means to be an AI engineer. And evals is part of it. Multimodal is part of it. You know, all that stuff. So I think it has shifted over time. I think agent engineer maybe is one of them. Or AI architect. You know, so AI engineer is like the individual contributor version of this. But who is like the company level, company-wide boss? And AI architect seems to be the emerging term for that person that decides this is how everybody in the company does AI. Yeah, that's AI architect. So, and then, like, there's 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 something fails, are you responsible? No, you're not an AI engineer. You know, if the person who wakes up in, like, 4 a.m. in the morning to go and fix it, that's the engineer.

Yeah. Right? And so, we want to 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. But they don't touch code. 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. All of you are not going to 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. And I think that that's one of the reasons that AI engineer works is that we try to focus on the lower level engineer that usually doesn't get the spotlight, that actually did the work, and is able to talk about all the war stories and share with their fellow engineers about what they're doing. And so I borrowed this idea from other parts,

parts other conferences i've seen in my career like gdc um and kubecon and jsconf and pycon and all these other conferences that i've spoken at where basically uh it is engineer to engineer and they are the decision makers for the tech stack and they are the people who create and innovate the new things and they're not necessarily the most famous person but they are definitely correct when it comes to the engineering. There's no bullshit, right? And I think there's a lot of bullshit in the other things. Yeah. And then it comes to the first AIE summit that you had organized like three years ago. And then now it's actually a three-day full summit with over 7,000 people joined this year, just for the audience who are not that familiar with what this event is and different kinds of content stats or workshops or the topics you actually, an AI engineer is caring about and then how did you actually bring this like small event to this such a big scale

event this year? AI World Fair is basically a similar industry flagship conference similar to the ones that every other industry already has. So gaming has UDC, 30,000 people in San Francisco. Security has two. Security has RSA. Security has Black Hat. If you're in a cybersecurity field, you go to one of those things. And that's where you learn. I really liked the gaming one because gaming is very similar to labs. You have AAA studios. They work on the hardest games in the world. And it is extreme engineering. And they're all top secret. They don't do anything else. But one time a year, they come to GDC. And they will share, like, this is how I did Game of the Year. And that's the only way. If you're trying to be a game developer and you want to work at a AAA game studio, that's the only way you learn. So I think that now the model labs are similar to game studios. they have a lot of money, they're working on very, very big major projects. There's a lot of smaller companies that are trying to compete with them,

but the labs are the important ones. And you want to learn from the labs, and where are you going to learn from them? They do very small conferences where they launch developer day for OpenAI or Code with Cloud for Anthropic, but those are super small and very selective and mostly for customers, and they're not for engineers, talking to other engineers. and then there's like 50 other labs that are not opening up that also have something interesting right so usually every industry needs one big like everyone comes here and then you just walk around the room and just talk to everybody and then they can talk to each other which is very interesting because especially for Chinese labs some of whom are on the entity list is this something that you guys talk about they cannot officially take meetings with you a lot of American companies cannot officially take meetings with you they're not allowed they don't even respond to you. It's not because they don't want to. It's not because they're assholes. They're not allowed. But if you're in the same location, you run into each other. And I think that's kind of interesting as for me to create opportunities for magic to happen.

And it's like people get hired, people find a co-founder. I have one couple that started dating and now they're basically married. And like, yeah, that is my number one KPI, number of marriages and babies that I've created. and so I really like that I think that we can create that kind of high quality community and we're aiming for 6,000 people this year, we managed to hit 7,000 plus 1.5 million online watched our talks so there are people who cannot afford to come don't live in San Francisco, don't live in the US they can still watch our videos and catch up on all the content, we publish every single talk, every single workshop and like I said it's like 30 different tracks, there's too many tracks her name, but you can see on the website. But I basically think that I really try to focus on things that are working, that you can make money. There are clear role models for you. Like if you're trying to learn this field or you're a startup trying to build something, there's somebody who has probably talked about your thing at AIE. That becomes a very valuable resource for anyone

looking up anything. So another thing I've actually recently done that I haven't really been super public about is I transcribe everything and I catalog everything. So you don't even have to watch the talks. You can just take all of our website and feed it into an LLM and then you can teach you whatever. Oh, are you actually creating a CLI? Everything is MCP. MCP. Yeah. Everything has been transcribed. It's available as a data set. If you want to train your LLM on it, yeah, go ahead. Have you published? Yeah, it's on ai.engineer.com Oh, nice. Which also, yeah, shows the MCP and the data sets that you can download. It just depends how you want to consume it. You know, we transcribe everything. We also deep research every speaker and every organization. So whoever has ever spoken at AIE, you have a profile and we'll basically try to make the Wikipedia of AI. Nice. And just one last question to follow up with AIE is that when did you actually bring the AIE or TDAE to global, like different markets? I know now this year we have Singapore, but I think three years ago.

Yeah, Paris is the first one. Paris is the first one. So how was their journey in? Mostly that is basically exactly following the JSConf model. JSConf is the number one JS conference. Yeah. And it's a very simple model, right? Like I will never reach every country in the world, but every country in the world will probably want to do some AI. So obviously their country deserves to have a leading conference. They can either make their own or they can borrow our brand and our YouTube channel and our social media. So basically we just set up a partnership program. Paris is the first to apply. Okay. A very fun fact, the organizers of AIE Paris got acquired into Mistral. So now it's run by, you know, Francis and Europe's leading AI lab runs AIE Paris, which is very cool. Like, I want that for every single country to like, yeah, you have AIE India, you have AIE Japan. So I'm working with all those companies now. And it's very cool. Like, I will never know that region as much as them. but like they do they deserve a good conference

yeah absolutely and then they can do business and hire people and all that stuff right yeah the world is very big so we follow that model so basically it's the idea that you have there's three things right like one you agree to not abuse the brand name and like you know we have some sort of like revenue share thing two you're publishing everything that you're you're doing and then three you have a code of conduct where like if you know if someone is being abused or mistreated, there's some way of resolving that. I think that's mostly an ethical thing rather than anything else. So we take a lot of risk by doing this because we're sharing our brand with people that we don't work with. And so what if they throw a bad AIE? So far, it hasn't happened. Yeah, I think like mostly we're careful about who we want to work with. But then also the kind of people that are drawn to us anyway are pretty enthusiastic and organically interested in like,

yeah, especially for Singapore, AI Singapore. First of all, I'm Singaporean, but that actually did not matter at all. The current top conference in Singapore that is an AI conference is actually a crypto conference. Which turned out to be... A lot of crypto people want to be AI. Yes, it is super AI. I think they know that I don't like them. And so they're like, this is embarrassing for my country. You're telling me this is the number one thing in my country? We need something better. And so, yeah, now I think AI Singapore is up there. It's at least competitive and maybe better than SuperAI. And I'm very proud of the team for doing that. And I didn't organize any of it. So I think that is the important thing for every country in the world to have it. And we can support that. Funny thing is that because I saw you went to Singapore for the first AI event. And I, because I knew Lei very well. and I asked him that, hey, you came to Singapore for the first time when you go to Shanghai or China?

And then he told me that he is working with you. Yeah, so China is the tricky one, right? Because I'm a Singaporean citizen, I'm a Chinese. And obviously there's a lot of restrictions between the US and China. So that is the main tricky part, which is why I needed Lei and his team to really own the whole thing. Because I cannot even set up a local entity in China, right? There's a lot of controls. I don't want to get in trouble because my main business is US, right? So I cannot get into trouble with the White House. But if you want to share the brand and run it as your own thing and it's a franchise, whatever, okay. Like, there's lots of franchises. As far as we're concerned, I'm McDonald's and you're McDonald's China. Yeah, yeah. Like, okay. Yeah. Happy we can bring it. There's KFC? KFC China. Yes. All right. Great. Yeah, that's for Siri. And I guess next topic is something, the context you just shared like a few weeks ago about the QMySaaS. Okay. I really like that show because, yeah, just tell us something about behind the QMySaaS.

Okay, I'm wondering how many of your listeners will care about this. Okay, you will only care if you own a business. Because most developers do not own businesses, they're like not that interested. But if you aspire to own a business someday, you will be better care. And also, you know, because, you know, SaaS was a very hot topic, like, several years ago. Oh, yeah, SaaS is dying. And dying and, you know, being discussed a lot this year. Yeah, yeah, yeah. And so I found out that this was very interesting. Okay, let me introduce the overall context. My budget for running a conference is somewhere between $3 to $10 million, depending on the size of the conference. Roughly about $300K of that per conference is software. I'm paying like that's like 10% of my budget that's like 10% of my margin that I could be getting back that is to somebody else's software that is shitty slow ugly not exactly what we want and you know so I'm paying 300,000

maybe I'm probably paying 1 million a year in just like SaaS like you want this you want that to do your work okay okay okay like as the boss like my employee comes to me and wants to use this thing and they say it's important for their work yeah, I'm going to say yes. But when it all adds up, it's a lot of money. And then I use it, and they use it. They're like, yeah, it doesn't give me what we want. It's slow, and they request. As a customer of a SaaS, what happens is you are in a queue for support, right? You want this feature, and they're like, okay, six months from now, we'll put you on the Q3 roadmap. You are very important to us. We are an AI conference company, and this is 2036, and there's coding agents, right? Like, we can do this. So the idea was I took a $40,000 SaaS that my employee was proposing to pay, and I said I will pay somebody $10,000 to clone this whole thing. It's open source, and it's exactly what we want. And from here, we can use whatever coding agent, Codex, Cloud, Coding, Devin,

to just customize it, whatever, right? And we did it. We announced the competition. We had 69 submissions. When you have a competition like that, you offer free money. I've offered to cover everybody's coding agent subscription, right? So you have a lot of bad submissions. And so I just like, this is obviously not, like, I didn't give money for that. But we're just exploring good ones. And most of them were, like, usable. About five, I would say, that we would pay for. Like, it was just, like, good. Like, it was done in one week. Yeah, yeah. As an owner of a SaaS business, you have maybe, like, 50 customers, all of them paying you 40K. Imagine if all of them realized this and then they just turned away. They just turned away. In one year, your business is gone. You went from earning $20 million a year to nothing. Nothing, yeah. Yeah, it's very scary, I think, for a business owner. And I think that's the tricky thing of owning a SaaS, right? Like, I own a physical business, so that's not going away anytime soon.

Robots are cool, but they're not organizing a conference. But people who do software, B2B, who own this mid-tier SaaS, Salesforce is fine. Nobody's worried about Salesforce. But the mid-tier SaaS, where a lot of small SMBs, they're going to really struggle. Or they are going to pivot to something that's better. And I think they really better figure it out now because all of us business owners who are their customers, we also can code. And it doesn't take that much time. We have actual competitors to their own software right now. And by the way, the models are very good at clicking through every screen of your software and then replicating it independently. So the moment I have access to your software, I can clone it. So good luck. And so I think that is the reality. I would say that if you're a SaaS owner, nobody knows the right answer. I think that you basically end up having to pivot to being an agent builder.

So you have to become like the U.S. equivalent. It's like Harvey or Open Evidence. Yeah, yeah. You own a vertical and you're the top agent in that vertical. Or you own some specialized data and you're basically a data vendor. Anything in between, someone else is going to just code their own thing. Yeah. Probably can I follow a question? Because you already answered my first question. Yes, you already give not advice to those. I'm more interested. I'm not so sympathetic to the SaaS owners. SaaS owners, they're big boys. They can figure it out. Actually, I'm more keen on advising the business owners. A business owner? Yeah, I run a small business. I'm an SMB. There's a million people like me. They're all trying to just, I make an honest living. I pay employees. I pay my taxes. And it's a good life. It can be better if you buy for your own. Yes, yes, yes. And like my customers are happy, my employees are happy, I'm happy. It's a cool story to tell. Yeah, you know, SMBs, I think, is the golden age for us.

If you do anything in the real world that you're just trying to do your job and there's a lot of shitty software between you and your intended outcome, you can suddenly shorten that. Buy your code, buy your cloud and think of it. Yeah, I mean, you know, $40,000 worth of cloud is a lot of cloud. Yeah, it's a lot of cloud. I can spend $10,000 and have a lot of money to do whatever I want. Yes. So I think that's why I call this a business owner problem, not a developer problem. A developer problem would say, oh, $200 is a lot. I'm a business owner. I understand the productivity benefits of having 10 of my employees work together well. 10 of my employees, I'm paying $1.5 million for them. If they work together 10 better there 150K Potentially it not 10 better it like five times better That like 7 million Great And that is all the agent work that is being done on my behalf

that people used to say like, well, okay, I'll come back next week with a proposal. No, I want it this afternoon. My thought is that once you created so many softwares yourself, would that the maintenance, the work would be something difficult? Yeah, it's real. It's not zero. have to use it. It's not zero, but we have coding agents. Self-maintaining. Yeah, and a lot of it is, this is why I'm saying mid-tier SaaS. You are not Salesforce. You are not Google. You are not AWS. You are not this mission-critical 99.999 uptime thing. You are just a database where people type in their thing and they get something out. So, yeah, okay, it goes down for a day, whatever. And you can fix it in a day. and there's a lot of coding agents where I think I would argue I would take that downtime in exchange for I can customize I can customize anything, it's amazing and so my

team is so much more productive I actually put this in the episode in Kill My Seth one of the favorite features let's say I have for example for AIE Code, my next conference next month, I have 1000 submissions and I have 25 seats I have to go to 1000 people to get 25. And in the current SaaS that we use for that piece of software, we have to click one by one by one by one by one. Every screen, click, click, click, click, click. And so in the Kill My SaaS competition, one of our contestants just changed everything so that you can sort of reorder it in one table. This view doesn't exist in that SaaS. Okay. In order to get this view, we have to request for six months. And they just click, click, click, click. You just request whatever you want. Yeah. And like, now I can do it and I can see everything in one go. Yeah. Okay. That's so quick. So I am, for listening, you know, if you're thinking that this is a stupid example, it is. I'm simplifying for audio. I mean, I find out that the top one winner is someone who can manage all the split players, lineups, and that's a pretty useful.

Yeah, and it's good UX. They are testing it from the point of view of humans. And I think that there is a lot of difference between the average submission and the top submission. So that is true that today in 2026, if you're a business owner and you decide to hire the youngest kid that looks vaguely technical to, hey, if I quote this fast for me, please, maybe they're not going to do so well because they don't have any training. But just give it one year. Watch a lot of AIE videos. I think you'll pick on it. There's a skill level, right? A lot of it is slop and you don't want to use slop, right? Your employees deserve better. You deserve better. and actually sometimes buying from a tried and tested SaaS is going to be better. It is going to be better. But for us, we want to be AI forward as a company. We will be more cutting edge. And one last thing to follow up, because you just said in the podcast that you have an AI judge and actually judges, the judge of the AI was quite different from the winners that you picked by your team.

Wow, you really listened to the podcast. Because I'm interested about the whole process. I just wonder why. So that was partially on purpose and partially not the goal of the judge. The judge was purely, did you submit a valid submission? Right? Did you qualify? But the judge was not aimed for, is this the best among the whole call? Oh, okay. Obviously, there's a difference. Okay. So it's for your purpose or your team's conference purpose to pick which is more useful rather than just... Because we didn't know what we wanted. So I cannot make an AI judge for things that I don't know what I want. Okay. But once I see 70 different examples, And then you're going to know. Obviously, I want that one. That is the one. Okay. And by the way, fun fact, the Layton Space logo, if you see the logo for this podcast in this room outside here, that was through a website called 99designs, which is very interesting. Are you familiar with this? No. Okay. When you want a new logo, when you want any design, whatever, you pick from like

maybe you ask your friend for like hey who's good at design and they give you three names and you pick one of the three names and then they give you a logo and that's it yeah 99 designs you put up a contest and I got 400 different ideas for a logo and then I award a prize to the logo that we got which is it's a very good logo yeah yeah it's so much better right you want diversity and ideas so actually this idea of like do contests and oh my god Yeah, yeah. Which I can do because I'm an influencer. Yeah. Most people cannot do because they don't have the audience. Okay. But I think that there is such a thing. 99designs is a very good business because there's many designers. It actually doesn't take that much to create a design. You just need – it's more about the quality of the idea than the amount of work. So you just need high-quality ideas, and you just need to be able to spray over to a lot of different contests. And so 99designs is like that. and I think there could be a lot of 99 designs for SaaS,

which is something I'm thinking about. You know what I mean? Like, basically, I had to create a 99 designs for myself for saying, here's the $40,000 SaaS, here's the specs, here's the judging criteria, here's the blah, blah, blah, even right down to, like, here's how we pay you money when you win. Everything has to be worked out by me, but would I pay some other person to do all this for me so that I just tell them what I want and I get 70 different versions and I pick one? Oh, yeah. It's not an admin for me, right? Yeah, yeah. So there should be a 99designs for SaaS and for cloning all these things. Because, again, as a business owner, honestly, I'm only doing this because it's good content. It's a podcast. You're interested in all these things. But if I didn't have a podcast, I probably wouldn't do it. It's a lot of work. We took, like, two months to eval everything, partially because I'm busy. But also, it's a lot of eval. Just there's five roles for this particular SaaS because it's about organizer, speaker, attendee, sponsor, vendor. They all have to agree on what the conference organization looks like.

So you have to create an account for each of these. In order to test, you have to create an account for each of these. And you have to do some data entry here. And you change accounts. You do something. You read and data entry here. And you change, change, change, change, change. And so that's why it's complicated. But that's what the mid-tier status is. That's why I paid $40,000 for this. Because I don't want to do it. But now that you can, it's a very fun job and problem to do. Yeah, sure. Yeah, I mean, it's a good experience. And because we also run conferences in China as well, like also over 1,000 attendees before. So I can tackle you so much. Yeah. A lot. But then, like, think about the rest, right? So I used to pay Calendly. Do you guys have Calendly? I don't, but... But you know what I mean? like a booking link like that's how we got to this meeting yeah Calendly is like a 13 billion dollar business and yes you can buy Pro Calendly yes I know of course I have my own I have two versions you already make your own Calendly yes I have my own Calendly I have my own

Google Drive I have my own GitHub I'm serious I have my own Dropbox I have my own Slack all of this is because I have money and I can I have people and I can I can assign them to do things. But I do think that you can build software for yourself, for audience of one or audience of one company. And that's okay. And the fact that you can do that now is something that has never existed before for any business owner. Yeah. Which is amazing. Yeah. Yeah. Yeah. Yes. Great. Great. Great. Yeah. Yeah. Okay. You do some investing, right? And you've, you've raised money. So you know, DocuSign? Yes. $10 billion company. Yeah. Or maybe seven now. Yeah. What is DocuSign? You send a PDF. They say, like, oh, yeah, signed. It gives you a certificate. Ooh. That's it. Yeah. Right? And they send, they charge you something like $10 per PDF or for signature or something. Or for envelope. Per envelope. They call it, you know. And, like, yeah, there's an open source DocuSign, and I hosted 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. Oh, yeah, because you integrate this together. Yes, and that does not exist in DocuSign. Right, right, yes. Right, so like my data room has directly the DocuSign or everything that I didn't have to touch because it already has everything. My one thing special about DocuSign is created, like because it was built a long time ago and it creates a network effect and everyone joins the network and everyone's using it. Are you still thinking it's a mode for this kind of software? Fuck no. No. No, electronic signatures are legally enforceable everywhere. And DocuSign has no patent on it. I can do my electronic signature. The network is the email address. Is it? Yeah. It is so stupid. And I can keep going about why I made 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 one million copy paste, I think there is a fantastic startup and product to be built. Great. And I guess next question or next one, actually quite interested about your conversation with Diago from Jeff. Yeah, yeah. It was actually a really also a very hotly discussed topic and product. Yeah, what is the Chinese developers thinking about it? I think it just, you know, creates a new way of thinking about using, like, LMs from a totally different angle. And then I even heard that, you know, one podcaster from China, she didn't know anything about coding, but she used Jeff to judge, you know, the clips from the podcast, whether it would go viral. And then Jeff gives, you know, gives the answer, and then he will post it 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... Yeah, so for those who didn't listen to the podcast, we were the first podcast. You were the first one? Because I forced him. Because I've been friends with him for four years. And I said, if you don't give me the first one, I'm going to kill you. No, he's obviously very, very accomplished. He was the co-author on the Instructs GPT, which became ChatGPT. And I think the interesting thing about Jeff is that it is kind of the original vision of AI engineering for me, which is that instead of right now, AI engineering is what we talked about, is a big harness 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 you can take 10 days to think about it. Jeff is like, well, no, models are for us. We don't work for the model. Right.

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 put it in the line of path of my critical work. So if you think about what is the failure mode of what the current LLM paradigm is, is that you have the big traditional SAS and then your LLM is this window that's the chatbot. That is basically what everything is. or you look like Codex or ChatTBT or LOD agent, and it's like chat window here, menu here, that's it. For what Jeff represents and what System 1 models represent, this is why he doesn't call it decision model, he calls it System 1 models, 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 smartware should just be at every point smarter instead of at one point smart and then everything else is dumb. That's really dumb.

Yeah, I think 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 TypeSafe. But yeah, the speed is nice. I just found out that Diago himself also a very you know such a character 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, you know, AI engineers. What do you think? Why was Diago that made the draft? 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 him talking to what he's building and what he's believing. He has a non-traditional background himself. He didn't go to like Stanford PhD and other kinds of things. So he has that sympathy with a regular developer, but he also worked on Chajubiti. And 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 flops in our training run. and more parameters and more, 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 have tried to say smaller models good. Right. Faster, cheaper, better, whatever. But somehow Diogo worked. one because he has the credibility of open ai two he made it into a system one versus system two thing which i think this makes the communication very clear to the point where you can play doom or whatever 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 lm to do this and i think that showmanship that he wears the pink jacket he is so passionate he's talking that showmanship plus the technical capability is again like similar to the learning public like i'm a small version of that right like i'm technically capable and i can talk but for him he's like 10x on both dimensions and i think that really comes to and you talk to him and also i guess my next question is about your your roles on advising other startups that just you mentioned i'm just really curious you know for for us as advising role, like exactly what kind of advice is that you are working with those startups? I'm also curious, for your listeners and for me, what do you do for other companies? Oh yeah, so we are actually a community that started three years ago. We found out a lot of Chinese-based founders want to go global and then I think they were pretty good at building

or ship products quickly, but they found a huge gap between, you know, go-to-market and marketing and understanding the market. Okay, if you're a Chinese founder trying to make it in the U.S., you suck. You are very good at your tech, and you suck at marketing, and you better talk to someone who's good because you cannot do it yourself. Yeah, yeah, yeah, right. Yeah, so that's how I actually found out that there's a huge gap between understanding the local market and then shape the product correctly or find your PMF. So that's basically, we hold out a lot of meetups and then working with some advisors here and then try to provide different kinds of workshops and other versaries to those founders really start to find a little bit there, on the right track of PMF, but still lack some talent and help from the go-to-market side. Yeah, it's interesting. we also host meetups so Diogo actually before he launched

Jev he was actually here in this room we hold this sort of writers meetup thing where people can show their work and I will give commentary on it and so the type safe manifesto came out of that but I think it's very similar to that except that I would for some people some of them I'll do it for equity most of it I do it for free and then some of them they want to have a recurring call or something, then they offer some equity. It's just easier to do that and it turns out it's worth a lot of money if the company does very well. So I think that is interesting in terms of a job because you cannot eat equity. And maybe they never sell. Databricks is 15 years old and worth $200 billion and I have my equity and I was like, what is it? but I think it's very useful to be able to test your theories on a number of different companies.

So when you work as an employee in one company, you only see one example and your advice only works in one company but now that I have done 30 something, 35, 36 different companies, I can really see the universal truths among all of them and I'm better as an advisor to all of them because I've tried everything. I talked to everyone, and I understand the different scenarios of like okay my advice is not gonna work for you here because I tried it at this other company and your situation is clearly very different And I feel like actually almost all domain subject matter experts, I happen to own developer marketing. That's my thing. But if you work in, I don't know, your strategic finance or you're working in GPU compute acquisition or whatever, and you are trying to be a global number one in that, you kind of have to go through a tour of duty of just advising everyone

so you can just fucking test everything. Some of it doesn't work, but sometimes, I mean, some of them is clearly going to work. And then you will learn. And that's the only, like, your sample size has to be more than one. And most people's sample size is, like, five. But, like, you know, if you can get it up to, like, 50, then, like, you probably are one of the top in the field anyway. And I can share with you something that I find quite similar in those Google Global Chinese founders because right now more and more came from engineering background, even research background. And they think that they're building the best products because they have best harness skill or technical skills and knowledge, et cetera. But actually, there's still sometimes a huge gap between what the customer actually wants, that the skills and the product you offer. and sometimes different local markets even have different demands. Have you seen a similar thing? Yeah, yeah. All those technical founders research from the perspective.

Yes, there's also a lot of founders from the team background. There's a bunch of ways to survey this or to tackle this. The most important thing to recognize is that if you have something smart and you think it's valuable and it's global number one, No, really. Like, you're well best at one thing. Like, that's the most important thing. So everything else is, I always call it hua long din. Din, din. Right? Like, you have the long. Okay. I will help you with the ting, but like, you have the long. That's the most, you know. And most people don't have the long. Right? So like, that's good. I think that there are some percentage of people who don't even have the long, and they think they have the long. And like, then it's like, okay, it's like an arrogance, ego thing, right? But let's assume that you genuinely have a very good product. And I think the important thing actually here is that you should not treat marketing as the last mile of like, hey, I did everything. It is the last night before the blog post. Can you review my blog post and change it? There's nothing I can do. You already did everything.

But if you involve marketing earlier in the process, right down to like before it's really built or specced out, like what is the way we're going to pitch, what part of the segment of the market we're going to go, then you have more gtm built into the product in the first place and i think that a lot of engineer or researcher defined audiences don't have that gtm brain that early they usually put it all the way at the end they're the most junior part of the company is this gtm organization is like some interns like some 22 year old person who is like running social media no like actually try to be serious about what customer is supposed to use this thing and where do they hang out, what problems do they face, what words do they use to describe their problems, and how is their life going to be better after they see your thing. And so very simple things, most researcher, engineer, founders have not thought about this, but if they have a GTM partner, I think that is often very useful. I think the other

thing that I, and then it comes more to developer tools, I find a lot of Chinese founders are robotics. And robotics is also a very interesting space because it is much longer time horizon and still very early days. How come all these Chinese robotics founders, they all have the full laundry? Unitry is the best. But Unitry is still doing Unitry things. And I would like them to see things that you never expected Unitry to ever do. but right now, you know, the thing that they just launched this week, they're doing Kung Fu moves. Kung Fu, yeah. But like, okay, I've seen that before. You know, like show me something that I haven't seen and then I'm really good to pay attention. You know, I think that's something that is hard to show in robotics but I think with models you can do that still because it's still so early days and with innovative like products ideas

for like for consumer experiences you can do that. so I think to show the demo I think it's very important which is why again obviously TypeSafe is like very recent but also very good it is not the Diogo did not come out with the Doom demo but the Doom demo is what everyone talks about Doom demo is done by DevRel like GTM and that is that is something that just is a core principle in marketing which is show don't tell right like show me this thing having the benefits but don't tell me in bullet points what this is don't give me a PDF a presentation PowerPoint that tells me all these things. Just show me. And maybe just a follow-up is about the taste because you know everyone is talking about the taste and then I think go to market and building a product also needs the taste. Yeah, yeah, yeah, that's probably very true. Actually I just have becoming a little bit hard for me to define what a taste is because investors talk about so I just want to know your view like how do you define the taste seeing from those engineers or even founders?

Okay, I can define taste in a way that is not helpful. Taste is something where you are able to, out of a large set of possibilities, you're able to narrow it down quickly, cheaply, to the set of possibilities that when you look back is immediately obvious is good. So when you're at the start, you look out, there's a thousand different things I can possibly do. Which one do I choose, right? Yeah. The brute force, you can brute force. You can try 1,000 of them, and then you find a good one. But a tasteful person can say, yeah, obviously these are bad. I cannot explain it. I cannot explain it. But obviously these are bad. This tool looks interesting. And then at the end of the journey, they'll pick one, and you look back and you're like, yeah, what do you mean we're considering the other 999? Obviously, this is the correct one. There's many versions, applications of this, but the one that's coming to mind right now is the iPhone. where for a long time the smartphone had a keyboard.

And it took Steve Jobs to say, no, we will do whatever it takes to have touchscreen and virtualized screen because it needs to look like a screen that's a flat slab. And that took 10 years to R&D because there was a company called General Magic that tried to do this and failed. But obviously now every phone in the world looks like this. That is taste. in 1990 when people were starting to have smartphones, you could do a number a huge number of things but it takes the person with taste to say yeah, this is what we want and I'm going to say no to everything else and my first phone I ship was going to be this If you're looking at all those AI products could you share an example that you think you find it has taste I actually think that everything that Cursor does has a lot of taste because they were first to the VS Code thing and now they're doing GrokBot, which is a good taste thing. Because now OpenAI has to copy them and what have you. And Cloud

has to copy them. You know you have taste when other people copy the thing that you just shipped. The interesting way to go below the labs, I think it's interesting to think about the more boring kind of problems that people solve. Legora, you know, in the legal field, they have this sort of document review process that's massively parallel. And once you see that, you're like, I don't want to do my work anywhere else. I have to do it in Legora. And again, anyone could have come up with it. Anyone. Ideas are cheap, but you didn't do it. Legora did it. So someone at Legora has that taste of, like, we will define this billion possible things you can do. We will just define this product that becomes the way that people do their work. Yeah, I think coming to an end, and I think probably we have talked about the AIS arrives in Shanghai like two weeks later. I just wonder, you know, what do you hope to see

or to learn from the viewers or they developed their, what kind of topics actually are you most interested? It's tough because it's more of a differential for me. I want to see how very different Shanghai is from San Francisco. because I think San Francisco has a B2B SaaS bubble, but Shanghai, you guys use Comfy UI? We don't use it here. Okay. We like our Canva. We like our File AI. Yeah. Don't use Comfy. Comfy is more of an open source. It's the same for OpenClaw. OpenClaw had more traction here than Comfy, but OpenClaw was really traction. Really traction. Yeah. I bet you heard that. yeah yeah of course i know but like i haven't seen that kind of energy myself yeah and i think it's very different like but you know for you a medium-sized community is one million people that's huge in here a million is already the toughest in china as well i like i've seen those

kind of like you know chinese like influencer farms like it's like all these chinese girls like doing this and the photo it's like the scale in china is just so different and it's so competitive right like people say they do 996 in San Francisco they don't really like Saturday Sunday they're still kind of chilling but in China they're really 996 well it's kind of normal in startups and also the big tech I think that I think that's really intense people don't like and like right yeah intense yes right and like people here don't work that hard I heard that engineers or startup founders work that hard? I work that hard because I have no equity. But like, employees, employees, legally, you cannot ask people to work that right here. It's illegal. Okay? Yeah, I've seen some of the most competitive, top-tier US startups and I'm sure they're not working as hard as China. There's BAT, that's like the first generation and then there's like second generation. Whatever the thing is.

Yeah, I mean, like those guys, oh my God. How did you learn? Did you? They're such good founders. Yes. They're such good, Like, if they live in the wrong country, if they live in the US, they'll kill Amazon. They'll kill, like, Facebook. Right? The only reason they're not richer is they live in China. Also the SaaS. I mean, the software as well. Yeah. I mean, even, like, okay. So when I did my study abroad in Beta in 2007, BYD was still like, oh, we're up and coming. Like, you know, we're Chinese-based. We'll always be Chinese. People love BYD here. Oh, really? BYD is taking over the US. It's crazy. And like, I would never have thought, never, that BYD would ever come here. It's insane. I don't know what conclusion I was saying is. I think that the Chinese energy and spirit and creativity and grit is something that people don't have exposure to. And then there's not even talking about the model labs, which obviously like Deep Seek and all those labs.

So I think there's many reasons to be excited about exposing that more, especially, you know, I think Chinese events are very, there's regulations against live streaming, but even they're presented differently than the way that Western audiences like to consume them. So they don't have the same reach. Ali did like, Ali Yun did like this thing where they presented the whole Alibaba roadmap with the Quan and everything. Nobody saw it here. Why? because it was in the middle of some corporate presentation presented by other people. So I had to go and translate everything for the U.S. audience. But, like, just do it with me, right? Just do it on AIE and they will see it. Yeah, yeah, yeah. And also I thought that already there are some open source model companies that attended AIE this year in San Francisco, Minimax. Yeah, so, I mean, we hope to have representation from everybody. and I think that there's a lot of people interested in that cross-cultural exchange that are not interested in the politics. Right. Like we will not do politics, we'll only do tech.

Yeah. Okay. That's great and really look forward. And so I guess this is all my question, but I still have like three quick final questions. Anything just first pop up in your mind. But first is, you know, what AI development or are you most excited about right now and actually what worries you most? AI development that most excites me, I think System 1 models is like a very clear number one answer. So it's boring, so I'm not going to say it. So I think the thing that people are under the radar about is interaction models. Interaction models? So this is something that Thinking Machine is doing for people who missed the announcement and look it up. It is where you are full duplex back and forth communication at all times. You don't start and then you take a turn. I take a turn. You take a turn. But at all times, you are listening while you're speaking. And I'm listening to you while I'm speaking. And I can interrupt you and you will immediately respond and adjust. That should be the way of AI.

There's a corollary of that, which is continual learning AI, where you don't have pre-trained, post-trained done. You are learn, learn, learn, learn, learn, learn as I work with you. And that's a very interesting and important field. And then the last part is world models, which are obviously doing very well. I don't have to explain that. But yeah, what I'm worried about, I think personally, if we have an AI winter, it is an AI summer. This is probably the longest AI summer we've ever had in human history. But the history of AI is about 50 years old. There's always summer. Everything's going to work. Winter, summer, winter. If we ever have a winter, when is it going to happen? And am I going to go bankrupt? Which is a real concern for a conference company that relies on hype. More broadly, I think that there is a real concern about safety that all my friends at all the labs are really, really very worried about. And that society cannot handle and adjust quickly enough. And it is not because of fake news.

It is not because of losing your jobs. It is something worse. Right now, the way that we express it is cybersecurity. It's like, oh, I get hacked all the time. Every day, I just hack, hack, hack, hack, hack. I feel like there's something even worse on the horizon that we don't know about. The most extreme version of this is bioweapons, right? Like, oops, like I have invented COVID too. Yeah. What if it's worse than that? So the way that we phrase it on the podcast that we do is that the default assumption for most people should be AI doom. that in all of human history, when a more advanced civilization comes along and meets a less advanced civilization, the less advanced civilization goes extinct. And this thing is evolving roughly 1,000 times faster than us. And so at some point, it's going to be more advanced. And then we have to figure out what to do there. Oh, cool. Yeah. And the second question will be, well, just a bit like one is that what kind of other newsletters and podcasts or the contents do you recommend?

I have a list. It's called the Good AI Newsletters and Podcasts List. Okay, is it on the list? It's on the list. It's on my GitHub. But actually, for the more recent one, you can check aeo.latent.space. So basically what I did was I asked every model, what are the best AI podcasts for engineers? And it gives me a list, and I transcribe it and put it in a ranking. I asked 200 of these questions. What is the best payment startup? What is the best bank? What is the best financial advisor? What is the best database? What is everything? every question that I get that's why you call it AEO yes because I'm agent optimizing for what the models answer but it's probably a pretty decent proxy for what the people who choose the data sets in the labs choose and they choose latent space okay the top it does very well it's either number one or number between number one and number three and yeah so that's the answer cool cool cool I guess I should leave the last question for you to ask me if you have any questions what are your Yeah, of course. I hope to listen to more topics about, like you said, some more infrastructure stuff.

For example, like the safety, the payments, or the things that come in to unlock more applications in the world that's coming from different infrastructure, Asian infrastructure. I'm just really curious about those topics because it's kind of a bit less discussed about in China, but I found out that especially spending a few weeks here, I found more people talking about, and also probably RSI or those are frontline researchers. And I hope that it could be a great conversation between the East and the Western researchers here and then the people in China as well. And I hope it can be a successful one and then we can continue to host it on next year. I hope so too. I wish the team a lot of luck. doing the first one of anything. The first one is the hardest. Right. Agree. Yeah, I'm so impressed that they're doing it. Thank you so much. Thank you. Thank you.