# AI Engineering, SaaS Disruption, and Global Market Dynamics

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-11-04

## Transcript

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 Shao, 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 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'll also talk a little bit about the origin story of Latent 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.
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.
and then replicating it independently.
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, 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.
They're such good founders.
They live in the wrong country.
If they live in the US...
They'll kill Amazon.
They'll kill 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.
So thank you for joining me for this show.
So I think a good...
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 July 4th about your essay on your relationship with the Silicon Valley.
You move 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 and that this time you bring the AI 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.
It's like Hanhua and like Leitu and Meng Ying and those folks as stand-up 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.
US and China, Chinese AI in general, you know, whatever we can do to surface like high quality conversations, I think that AI 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, your house is in where?
Oh, nice Chinese.
Oh, cool.
And I don't know.
And that's how it feels like to be Chinese diaspora.
We don't know what our origins are because it's just mixed and nobody kept any records.
Nobody even knows our birthdays sometimes.
But fifth generation of Singapore, I grew and raised up in Singapore, and I went to the US for University and did my first career in finance.
I was in trading and investment banking and hedge funds for 10 years, sorry, eight years, and then switched to tech.
So I worked in Amazon and a few startups.
And then four years ago, became independent, wrote Leighton 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're doing the Latent Space podcast, you're writing newsletters, and also I find out that you have different roles in several startups like Connection, et cetera.
Yes.
Yeah, just wonder, you know, what your current titles or roles in different roles and...
Yeah, so it's very funny because I recently had to go over my taxes.
I figured out like...
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 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.
you know, a global conference organization, then I had to be CEO.
I couldn't be part-time employee part-time here.
Colonization is very big.
Fireworks is very big.
Modal is very big.
Superbase.
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 talked with different researchers online and then become the early conversation of latent space.
Probably just talking about the past days, how to 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.
Right.
If I know something and I tell you that that truth loses value.
Right.
In tech, 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.
They share their code.
They screw up.
They share that they screwed up.
In finance, no.
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 like, 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, 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 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 choose, you like 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 is before ChatGPT.
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 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 did like 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, Imad Mostak 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 that work was basically me learning AI the past five years of just grinding 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.
especially in Asian cultures, there's a lot of emphasis on credentialism.
Like, you need that piece of paper.
You need some qualification.
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 you get the job?
You must have a degree.
It's just like an 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 worked 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 judgment.
They really did work on substantial things rather than insubstantial things.
And that's generally what happens with learning public.
So yeah, that started Lanespace.
And then very, 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 SWIX writing about AI.
That's it.
But we happened to host some events, like some 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 hackathons.
So we hosted a hackathon.
And at the hackathon, that's where the idea for AI Engineer came about, which is the realization.
It actually was this woman that came up 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 my career, 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 GRPO, and you do manifold hyper-connections, which is what DeepSeek 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 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 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 side story in China is that at the time when you just mentioned the event 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, Confi Anonymous.
We've had him on the podcast.
Yeah, yeah, yeah.
And in China as well.
Do they use ConfiUI?
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 was on the LARC.
So everybody contributed their knowledge, shared their use cases about stable diffusion, comfy UI, and that became 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 word 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.
People were 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 Clare.
Is it James Clare that wrote Atomic Habits?
I think so.
I forget the authorship relationship, but I think it's James Clare.
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's 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?
while you give them a job title that pays money.
Like, yes, then they will be loyal to you.
Yeah, right.
So, yeah, 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.
Right now you mentioned hardness, a lot of developments happen in the 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 thought?
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 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 one of them, or AI architect.
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 a company does AI.
Yeah, that's AI architect.
So and then there's all the other 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, my timeline to AGI is like two years away.
You know, all of you are going to have 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, other conferences I've seen in my career, like GDC and KubeCon and JSConf and PyCon and all these other conferences that I've spoken at.
where basically 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...
the first AIE Summit that you had organized 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 small event to such a big scale event this year?
AI WorldSphere is basically a similar industry flagship conference similar to the ones that every other industry already has.
So gaming has GDC, 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 like 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 Claude 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 50 other labs that are not OpenAI and Anthropic that also have something interesting.
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.
Yeah.
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?
Yes.
They cannot officially take meetings with you.
Yes.
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.
Right.
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, you know, we're aiming for 6,000 people this year.
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 to 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.
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 it whatever.
Oh, are you actually creating a CLI based on top of that?
Everything is an MCP.
MCP.
Oh, nice.
Everything has been transcribed.
It's available as a data set.
If you want to train your LLM on it, go ahead.
Have you published yet?
Yeah, it's on AI.engineer.com.
Oh, nice.
Check it out.
Which also 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 will you have a profile and we'll basically try to make the wikipedia of ai nice and just one last question to follow up the ai is that when did you actually bring the aie or today to global like different markets i know yeah now this year we have singapore but i think A few years ago, Paris is the first one.
So how was that journey in?
Mostly that is basically exactly following the JSConf model.
JSConf is the number one JS conference.
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 was the first to apply.
Okay.
Very fun fact, the organizers of AIE Paris got acquired into Mistral.
Oh.
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.
Right.
Yeah.
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 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 there's three things, right?
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 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 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.
Are you Singapore?
Yeah.
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 SuperAI.
And 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 knew Lei very well, and I asked him that, hey, you came to Singapore for the first time.
When do you go to Shanghai or China?
And he told me that he is working with you.
Yeah, so China is the tricky one, right?
Because I'm 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, OK.
There's lots of franchises.
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.
Great.
Yeah, that's great sharing.
And I guess next topic is something, the contest you just shared 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.
OK, you will only care if you own a business.
Because most developers do not own businesses, they're not that interested.
But if you aspire to own a business someday, you will be better care.
And also, because SaaS was a very hot topic several years ago.
Oh, yeah, SaaS is dying.
And dying and being discussed a lot this year.
Yeah, yeah, yeah.
And so I found out that this This show is very interesting.
OK, 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 less like 10% of my budget.
That's like 10% of my margin that I could be getting back.
That is through just 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 paying 1 million a year in just like SaaS.
Like, you want this, you want that to do your work.
Okay, 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 worker.
Yeah, I'm going to say yes.
But like, when it all adds up, like...
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.
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, OK, 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, Claude, Devin, to just customize it, whatever, right?
And we did it.
We, you know, 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 a stop.
Like, I didn't give money for that.
But we have 69 good ones.
And most of them were like usable, about 40, 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...
They just turn away.
They just turn away.
In one year, your business is gone.
You went from earning $20 million a year to nothing.
Nothing, yeah.
Yeah, that's...
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 like, people who do software B2B, who own this like mid-tier SaaS, Salesforce is fine.
Like, nobody's worried about Salesforce.
But like, the mid-tier SaaS, where like, you know, a lot of small SMB, do you guys call it SMB?
SMB.
SMBs, they're gonna 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 are the 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 US 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 follow a question because you already answered my first question.
You already gave some advice to those.
I'm more interested.
So 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 vibe code your own sense.
And like my customers are happy, my employees are happy, I'm happy.
It's a cool story to tell.
Yeah, you know, SMB is, I think it's a 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 timeline.
Buy your codex, buy your cloud and...
Yeah, yeah, yeah.
I mean, like, you know, $40,000 worth of Claude is a lot of Claude.
Yeah, it's a lot of Claude.
I can spend $10,000 and, like, have a lot of money to, like, do whatever I want.
Yes.
So I think that's why I call this is a business owner problem, not a developer problem.
A developer problem would say, like, oh, $200 is a lot.
You know, like, I'm a business owner.
I understand the productivity benefits of having...
10 of my employees work together well.
Like 10 of my employees, I'm paying 1.5 million for them, right?
Like if they work together 10% better, there's 150K.
Potentially, it's not 10% better.
It's like five times better, right?
That's like 7.5 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 philosophy is that once you create so many software yourself, would that the maintenance, the work will be something, you know, difficult?
Yeah, it's real, right?
It's not zero.
It's not zero, but we have coding agents.
Oh, yeah.
Self-maintaining.
Yeah, and a lot of it is, this is why I would say like mid-tier SaaS.
You are not Salesforce.
You are not Google.
You are not AWS.
You're not this like mission critical 99.999 uptime thing, right?
You're just like a database where people type in their thing and they get something out.
Yes.
So yeah, OK, it goes down for a day, like whatever.
And you can fix it in a day.
Right.
Right.
And there's a lot of coding agents where, like, I think I would argue I would take that downtime in exchange for like, I can customize.
I can customize anything.
It's amazing.
You know, and so my team is so much more productive.
You know, I actually put this in the episode in Kill My SaaS.
One of the favorite features, let's say I have, so for example, for AIE Code, my next conference next month, I have 1,000 submissions and I have 25 seats.
Okay.
I have to go to 1,000 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, click.
And so in the Kill My Sess 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 sess.
In order to get this view, we have to request for six months.
And they just clomate it and talk to mice.
You just request whatever you want.
And like, now I can do it.
I can see everything in one go.
Yeah.
Okay.
That's so quick.
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 tool.
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 with like, you know, that looks vaguely technical to, hey, if I quote this SaaS for me, please, maybe they're not going to do so well because they don't have any training.
But just give it one ear.
Watch a lot of AIE videos.
And then you'll become 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.
Yeah.
And one last thing to follow up, because you just said in the podcast that you have 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.
So 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?
Did you qualify?
But the judge was not aimed for, is this the best among the whole cohort?
Obviously, there's a difference.
So is it 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.
But once I see 70 different examples...
And then you're going to...
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 for 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.
99designs, you put up a contest and got 400 different ideas for a logo.
And then I award a prize to the logo that we got, which is a very good logo.
It's so much better, right?
You want diversity and ideas.
So actually this idea of like, do contests and...
Oh, hackathon.
Contests, yeah.
Which I can do because I'm an influencer.
Most people cannot do because they don't have the audience.
But I think that...
There is such a thing, but 99designs is a very good business because there's many designers.
It actually doesn't take that much to create a design.
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 99designs for SaaS, which is something I'm thinking about.
You know what I mean?
Basically, I had to create a 99design 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 a lot of admin for me, right?
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 just, it's a lot of eval.
Just there's five roles for this particular SaaS, because it's about like, 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 the things, and you have to do some data entry here, and you change accounts, and 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 SaaS is.
That's why I pay $40,000 for this, because I don't want to do it.
But now that you can, it's very, fun job and problem to do.
Yeah, sure.
Yeah.
I mean, it's a good experiment.
And because we also run a conference in China as well, like also over a thousand attendees before.
So I can echo you so much.
Yeah.
But then 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, that's how we got to this meeting.
Yeah, Calendly is like a $13 billion business.
And yes, you can buy ProCalendly.
Yes, I know.
Of course.
I have my own.
I have two versions.
You already made your own Calendly all day?
Yes, yes, 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.
Oh my God.
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're, 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, sign.
I give 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.
For envelope.
Per, per.
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 it together.
Yes, and that does not exist in the DocuSign.
Right, right, yes.
Right, so like my data room has directly the DocuSign, so 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 that everyone joins the network, everyone 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.
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, 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 show, I'm actually quite interested in about your conversation with Diago from Jeff.
Because 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, just, you know, creates a new way of thinking about using like LMs from a totally different angle.
Yes.
And then.
I even heard that one podcaster from China, she didn't know anything about coding, but she used Jeff to judge the clips from the podcast, whether it would go viral.
And then Jeff gives the answer, 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...
Yeah, so for those who didn't listen to the podcast, we were the first podcast with Jeff.
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...
Instruct GPT, which became ChatGPT.
And I think the interesting thing about Jev 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.
Jev is like, well, no, models are For us, we don't work for the model.
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 a chatbot.
That is basically what everything is.
Or you look like, Codex or ChatGPT or Cloud 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, your 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 researchers and developers or AI engineers.
What do you think?
Why was Diago that made the job?
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 Stanford PhD and all the kinds of things.
So he has that sympathy with the regular developer, but he also worked on ChachiPT.
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.
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 models good.
Right.
faster, cheaper, better, whatever.
But somehow Diogo worked.
One, because he has the credibility of OpenAI.
Two, he made it into a System 1 versus System 2 thing, which I think makes the communication very clear to the point where you can play Doom or whatever.
There's some game demo that he showed in the launch post that was very clear, like, there's no way you will ever use LLM to do this.
And I think that showmanship that he wears the pink jacket, he's so passionate, he's talking.
That showmanship plus the technical capability is, again, similar to the learning public.
I'm a small version of that, right?
I'm technically capable and I can talk.
But for him, he's like 10x on both dimensions.
And I think that really comes through when you talk to him.
And also, I guess my next question is about your roles on advising other startups that just you mentioned.
I'm just very curious, you know, for us advising role, like exactly what kind of devices that you are working with those startups?
I can do, I'm also curious what, can you, 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 that a lot of Chinese-based founders want to go global.
And then I think they were pretty good at building or shipping.
products quickly, but they found a huge gap between, you know, go to market, marketing, and understanding the market.
Okay, if you're a Chinese founder trying to make it in the US, you suck at this.
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.
That's it.
Yeah, yeah, yeah, right.
Yeah, so that's how I actually found out that there's a huge gap between, you know, understanding the local market.
and then ship 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 adversaries to those founders really start to find a little bit there on the right track of BMF, but still lack some talents and help from the go-to-market side.
Yeah, it's interesting.
We also host meetups.
So Diogo actually, before he launched, uh jev uh he was actually here in this in this room oh uh we hold this like sort of um writers meetup thing where people can show their work and i will give commentary on it on it um and so the sort of typesafe manifesto came out of that but i think it's very similar to 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 like 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 like, whatever, 15 years old and worth $200 billion.
And I have my equity.
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 talk to everyone and I understand the different scenarios of like, okay, my advice is not going to 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, right?
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 take 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 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 Go 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 like best harness skill or you know technical skills and knowledges etc but actually there's still sometimes a huge gap between you know what the customer actually wants that the skills and the product you offer and sometimes like different local markets have even have different you know demands and have you seen a similar thing yeah yeah on those technical founders research from the because there's also a lot of founders came from the same 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 global number one, really, you're world best at one thing.
That's the most important thing.
So everything else is, I always call it, right?
You have the long.
Okay, I will help you with the thing, but you have the long.
And most people don't have the long.
So 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 then it's like, okay, it's like an arrogance equal thing, right?
But let's assume that you are generally 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.
The most junior part of the company is this GTM organization.
It's like some interns, some 22-year-old person who is running social media.
No.
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 to 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, right?
how come all of these Chinese robotics founders, they all fold laundry?
Like, Unitree is the best.
And at least, but Unitree is still doing Unitree things.
And I would like them to see things that you never expected Unitree to ever do.
But right now, the thing that they just launched this week, they're doing kung fu moves.
Kung fu, yeah.
But like, OK, I've seen that before.
Show me something that I haven't seen, and then I'm really going to pay attention.
I think that's something that is hard to show in robotics.
But I think with models, you can do that, because it's still so early days.
And with innovative products ideas 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 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 slide presentation powerpoint that tells me all these things just show me and maybe just a follow up is about the taste because everyone is talking about the taste and then I think go to market and building the product also needs the taste.
Yeah, yeah, yeah.
That's probably very true.
Actually, I just have become a little bit hard for me to define what a taste is because the investors talk about.
So I just wonder in your view, 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 it's good.
So, at the start, you look out, there's a thousand different things I can possibly do.
Which one do I choose?
Right?
The brute force, you can brute force.
You can try one thousand 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 like, 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 Carso 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.
uh, Legora, you know, in, in the legal field, they, uh, they, they have this sort of like document review process that's like massively parallel.
Yeah.
And like, once you see that, you're like, I don't want to do my work anywhere else.
I, I have to do it in Legora.
And like, again, like anyone could have come up with it anyway.
Ideas are cheap, but you didn't do it.
Yeah.
Legora did it.
And so someone at Legora has that taste of like, we will define this billion possible things we can do.
We will just define this products that, 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 AES arriving in Shanghai like two weeks later.
I just wonder, what do you hope to see or to learn from the builders that develops there?
What kind of topics actually are you most interested in?
It's tough because it's more of a differential for me.
I want to see how very different Shanghai is from San Francisco.
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 fail AI.
Yeah.
Don't use Comfy.
Comfy is more of like an open source tool.
Same for OpenClaw.
OpenClaw had more traction here than Comfy.
But open call is really attractive.
Really attractive.
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, you know, for you, a medium-sized community is 1 million people.
That's huge in here.
A million is already the top in China as well.
I've seen those kind of like, you know, Chinese like influencer farms.
Like, it's like all these Chinese girls.
doing this and the phone.
The scale in China is just so different and it's so competitive, right?
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 are really 996.
Well, it's kind of normal in startups and also the big tech.
Yeah, but I think that's really intense.
People don't like, and like, right?
Intense.
Yes.
Right?
And people here don't work that hard.
I heard that engineers or startups just work that hard.
I work that hard because I have no equity.
But employees, legally, you cannot ask people to work that hard.
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 the first generation, then there's the second generation.
Whatever the thing is.
Yeah, I mean, like, those guys, oh my god.
How did you learn?
They're such good founders.
They're such good founders.
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.
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 I would never have thought, never, that BYD would ever come here.
It's insane.
I don't know what conclusion I say it 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 DeepSeek 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, 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 Quen 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 US 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 saw that...
Already there's some open source model companies 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.
Like we will not do politics, we'll only do tech.
Yeah.
Okay.
That's great.
And really, 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, what AI development 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 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 model?
So this is something that Thinking Machine is doing for people who missed the announcement.
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.
No, 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.
I saw the demo.
Bye.
That should be the way of AI.
Right?
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 just 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.
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.
because of fake news.
It is not because of losing your jobs.
It is something worse.
You know, 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.
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 no cool yeah and and the second question will be well just a bit like one is that what kind of other newsletters and podcasts or the contents that you recommend i have a list uh it's called the good ai newsletters and podcast list okay is it on it's on the corner okay it's on my github uh but actually the for the more recent one you can check uh aeo.latent.space so basically what i did was i asked every model what are the best AI podcast for engineers and it gives me a list and I transcribe it and I 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'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.
Okay, the top?
The first one?
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'll just leave the last question for you to ask me if you have any questions.
What are your hopes for AI Shanghai?
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 are coming from different like 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.
So, and I hope that it could be a great conversations between the East and the Western researchers here and then the...
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, Sean.
Thank you.
Thank you.