AI Is Crossing the Frontier of Human Knowledge - Kevin Weil on AI for Science

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- You have no excuse if you got an interesting idea.

You can now create anything that you can think of.

The models can now solve problems that humans have never solved before, going beyond the frontier of human knowledge.

And that's how AI, I think, and AGI will really change our lives.

Why not try and accelerate science?

Bring about the science of 2050, but in 2030 instead.

- Most people think of AI as a productivity tool.

Kevin Wheel thinks the biggest impact may be somewhere else entirely.

Formerly CPO and Vice President of Science at OpenAI, Wheel is focused on a future where AI doesn't just help people write documents or generate code, but contributes directly to scientific discovery itself.

The idea is ambitious.

Use AI to accelerate breakthroughs in mathematics, medicine, materials science, and other fields that shape the future of human progress.

In this conversation, Kevin discusses frontier science, robotic labs, AI reasoning, startup opportunities, and why he believes some of the most important consequences of AI may come from expanding humanity's ability to discover new knowledge.

- All right, this is an incredible time to be alive, I think.

Now you helped build and scale some of the most important technology companies of the last decade, Facebook, Instagram, and Twitter, and now you're doing that at OpenAI.

I did ask chatgbt what it thinks about you.

And so, as you would expect, it was very complimentary, but you're also a very accomplished guy.

So Kevin is thoughtful, low ego, and unusually grounded for someone who's been at the center of so many high stakes products.

So how did you, as you looked at all those four opportunities plus many others that were amazing, what gave you the confidence that this is the type of company, this is the team I should be working with?

- Yeah, number one advice, marry up.

It was my wife originally, actually.

I was in grad school doing a physics degree, and I met my now wife who was a Mayfield fellow at Stanford and actually worked at Andreessen for a little while.

And she was the one that kind of opened my eyes to everything, startups in the Valley and all that.

I grew up in Seattle, my dad was an engineer at Microsoft for a long time, so I grew up programming, but still was just like math and physics, math and physics as I went through grad school.

And it was my wife, Elizabeth, she introduced me to Twitter back in the day, because she and Jessica Verrilli knew each other from Stanford.

And after seven years at Twitter as it grew, she and Kevin Systrom were also Mayfield fellows together at Stanford.

So that's how that connection happened.

And so, you know, a bunch of these things were just my wife, me just following the coattails of my wife.

I used to call Sam periodically before, whenever I would be thinking about doing something new.

Sam and I didn't know each other super well, but we knew each other well enough to do a quick phone call.

And 'cause he always has his hands in lots of different things, he's like doing fusion startups and all of this stuff.

I remember talking to him in 2020 or something, and he was like, "AI will not replace blue collar jobs first, it'll replace white collar jobs first.

Coding is going to be one of the big things for AI."

This was 2020.

And none of us used AI particularly much, at least not outside of like classical ML models that, you know, ranking your feet and stuff.

And I just remember being like, "Yeah, whatever, dude.

Sure, but let's talk about something."

So anyways, the opening I think happened because I called him and this time he was like, "Actually, you know what?

We have this role open, you should come talk to us."

Soon as I did, I was just like, "I don't care."

Like all work for free.

I don't, just, this is the most interesting thing in the world.

And if you give me an offer, I'm coming.

So fortunately he did.

- And then the original mandate as CPO with Sam was to what, and what are you sort of most happy with what you accomplished during that time?

- Yeah, originally it was CPO, so leading our consumer products, B2B products, developer products, et cetera.

And I mean, man, I think it's, we grew like a weed.

I've never seen anything grow that quickly in my entire life.

And I think brought, you know, kind of brought AI to a whole bunch of the world.

So very proud of that work.

A few months ago, it was getting clear that our models could not just be great inside chat GPT or inside Codex, which I think is an incredible product, but we're at the level that they could start to answer frontier scientific problems.

Like the models can now solve problems that humans have never solved before.

So a lot of people like the criticism of AI is, oh, well, it's just, it's just bringing together different ideas from different places and summarizing them for you to give you an answer.

I can't actually do novel thinking, but we've now seen there've been, I don't know, what, 10 or 12, just in January, 10 or 12 open mathematics problems solved, mostly by GPT 5.2, now a few recently by Gemini.

Models are going beyond the frontier of human knowledge.

And I wouldn't claim yet that they are solving problems that humans can't.

I think if you took enough people and, you know, applied enough mathematicians towards some of these problems, they would have figured it out, but they had not figured it out yet.

The model went beyond what we had ever done as humans.

And that's pretty cool.

And that's today, right?

If there's one thing I've learned over the last few years, it's that you go very quickly from, models could never do this thing.

It is beyond the capability of AI today.

To models could just barely do this thing and it kind of sucks at it.

And like, it's wrong most of the time, but you get these glimmers of like, ooh, they can almost, you know, do this.

Maybe it only works five or 10% of the time.

And then six to 12 months later, it's like, models are great at this thing.

And I would never, I would always use AI anytime I ever do that again.

Like in eval language, you go very quickly from like zero to five or 10% to like 60, 80%, you know?

We are clearly in that middle phase with frontier science and AI, where you have all these glimmers of like, wow, it can do something that we never thought AI could do.

So, you know, what more interesting place to apply it than science?

It may be the most tangible way that we all feel the impact of AGI.

If we dropped like GPT-9 inside of chat GPT for you today, I'm sure it would be awesome.

But maybe even more awesome would be that we have all of these new materials and we have superconductivity and we understand the nature of the universe and we have personalized medicine.

And you know, like that's how AI, I think an AGI will really change our lives.

So- - And fusion power.

- Man, like why not try and, you know, accelerate science and bring about the science of 2050, but in 2030 instead, and that's our goal.

- You know, when did you have your Claude weekend moment or your Codex weekend moment where you're like, wait a second, the world has fundamentally shifted and everything that I've done before is going to be very different going forward.

- Yeah, so I have a very different take on this.

I think it's awesome and exciting.

Like when you take something that has historically been the craft of a relatively small number of people, there aren't that many people in the world that know how to program, I don't know, like 30 million people maybe.

And you expand it by a couple of hours of magnitude, you get an explosion of creativity because lots of people have ideas and sometimes they didn't have any route to actually implement those ideas.

And you go everything from like thinking about people that can start companies now that didn't used to be able to start them, all the way through to like, I remember sitting and talking with, this was like a little bit post COVID with this city official somewhere, like small city.

And he was telling me about the kinds of programs that they wish they had been able to run and operate, just like basic kind of information awareness things for the people that lived in this small city.

It's like, I just didn't have any way to do it.

The data was sitting there.

I could draw what I wanted it to look like, but I had no way of getting it done because he wasn't an expert and they didn't have, you know, however many thousands of dollars to hire a person to do it for the city.

Now what would you do?

You would like enter a prompt into Codex and it would be done.

So I think these kinds of things are awesome.

I'll say personally, we just launched Prism, like what a week ago.

Anybody see that use Prism tried it.

It's like an AI native environment for scientists to do scientific writing and collaboration.

So if you're using LaTeX, things like that, because it's a small product and a small team, I've been spending a bunch of time like writing code, fixing bugs, et cetera, which I haven't done in a bunch of years.

And it's super fun.

It also, I went through this transition in the middle of this, you know, 'cause most of my day is meetings and you know, we've got a bunch of stuff going on.

I went through this transition where I was sitting in, I don't know, I remember I think it was like Fiji, a meeting with Fiji and I closed my laptop 'cause we're all trying to be better about not multitasking 'cause that's one of the worst habits that people at OpenAI have, everyone multitasks all the time.

I had not gotten a Codex job running before I closed my laptop.

And I was like, shit, I just wasted an hour.

Like not because of the meeting, but just because I could have been multitasking during that hour, my Codex agent could have been fixing a bug or implementing a feature or doing something for me.

And now I have to sit in this meeting just like a farmer being in a meeting, come on.

And so, the same thing before you go to bed at night, you're like, okay, what like really hard task can I give Codex and just let it chunk away for like 10 hours?

So like that to me is a different world.

And if you're really good at it, you are not just juggling one job, you've got three or four things running in parallel across different work trees and like, I don't know, what a cool world.

- Yeah, the creativity that's gonna be unleashed is just incredible.

And I think about it as like if you were a furniture, handcraft furniture maker, like 100 years ago or 150 years ago, industrial revolution hits, all of a sudden there's factory mass produced furniture that's better than yours or at least equivalent, right?

And you're saying, wait a second, my whole world has changed, but now you can design something that can be used by so many more people all at once.

But like it's this disorientation that people feel because it's so disruptive and so quick at the same time.

- I don't know, you think we'll end up with like, 'cause now you still, people still value custom made furniture and they will end up with like bespoke websites.

- Yeah.

- Like this was done by a human.

- Exactly, exactly.

Same as like, you know, people will be like, I drive cars like as a hobby, right?

- Right.

- Right.

- And so, you're like, get off the road, you're driving a race.

- Right, right.

Yeah, stay over there in like your little, you know, little area for human drivers.

With things moving so quickly, like you gave an example of like how you stay up with things like you're, you know, doing work with your team, you're staying close to your team.

Like, do you have any other ways that you're both able to lead the team, lead the strategy, but also stay close to all the developments that are happening within your company, within other companies?

I feel that feels like more than a full-time job in its own right.

- Yeah, I mean, the industry is just moving insanely fast, right, I've never seen anything like it.

It's exhilarating and it's fun and it's also, it's a lot to keep up with.

But I think that, I just think this moment kind of selects for people who are high agency, because you can now create anything that you can think of and you have no excuse if you've got an interesting idea not to like get, you know, Codex thinking about it while you do something else, whatever you were originally gonna do in the morning, keep doing that, but have Codex working on your idea in parallel and sometimes you'll wake up in the morning, have an idea and have a thing implemented by the time you're done with the day, in addition to doing what you thought you were gonna do during the day.

So like people that are high agency, people that are really curious, people that learn quickly, those skills are more valuable than ever in this moment.

And you know, kind of whatever the future holds, I think those skills are going to see us through.

- Yep, I've heard you talk about a vision of both the experiment design, but also then the experimentation itself and the validation that's very, you know, was very captivated to me, maybe we could share it here a little bit more.

- This OpenAI for Science group is only a few months old, although in some sense, of course, OpenAI has always cared deeply about science.

So I feel like even though we're a smallish team, we kind of have the might of all of OpenAI research at our back because every researcher at OpenAI cares about science and scientific data has been one of the ways that we, you know, have improved our models for a long time.

But so we started thinking about math, physics, theoretical computer science, because you can do everything in Silico, you have like closed loop systems that you can optimize and you know, part of this is teaching the models to answer really hard scientific problems, teaching them to think, not for like 10 minutes or maybe the hour that you can get GPT-5 Pro to think if you ask it a really hard question, but teaching models to stay on track for a day, two days, a week, two months at a time to answer even harder problems.

'Cause just like you and me, like if you could give me problems that I couldn't solve in 20 minutes, but I could given, you know, two hours.

Same is true of the models, the more time they think, the more impressive problems they can solve.

So you start with things like math and physics, but then, you know, some of the biggest ways, the most important ways that accelerating science is gonna feed back into all of our lives in a positive way is through stuff that we can feel in real life.

It's like, you know, one of our relatives survives cancer because we've made advances in medicine.

You have new devices and materials because we've been able to make advances in material science.

And those things require labs.

You can't do those just in silico.

Although I think the importance of simulation is going to go up pretty meaningfully because you will be able to apply huge amounts of compute to these problems, but then you're still gonna need experimental validation.

You're gonna need to try things in the real world.

I think it's gonna be a while before we have a model that can, you know, first principles go from like a quark all the way through a model of a cell all the way through human biology.

Like experiment matters.

So, you know, you start to think about how you do that at scale.

There's lots of opportunity to partner with existing labs and we will, but I think there are really interesting, you know, the science of the future will definitely involve robotic labs and reinforcement learning loops that go through the real world where the model is thinking, maybe running a simulation, thinking some more, refining the experiment that it can run using the best possible parameters and then sending that to a bunch of robotic labs, which by the way you can scale horizontally, having the experiments run in real life, the results come back to the model, the model thinks, runs more simulations, thinks, and you have this, you have like multiple loops.

You have tight loops with the model thinking and the simulation.

You have longer loops, you know, that go through the real world.

That's how a lot of science is gonna be done in the future.

And you know, that is its own form of acceleration.

You think you have robotic labs that you can scale horizontally that can run 24 hours a day.

You know, they're not grad students pipetting things that need to take breaks and sleep and things.

So, and then the grad students can do things that are much more, you know, leveraging of what makes us human than pipetting things.

- Right.

- So I'm quite optimistic about where this goes and about our ability as a society to accelerate the pace of science very meaningfully.

- How far away do you think we are from that?

What are the key like technologies as a, you know, robotics that need, what needs to happen for us to unlock that fully?

- I mean, some of that piece is robotics.

It's already happening, right?

I was just giving examples of the model solving open math problems.

And there are certainly robotic labs out there already.

It's all kind of in the early adopter phase, but at the pace that we're all moving, I don't think it's long.

And it's so clearly the right thing for many fields that this is not, you know, we are not the only ones to have this idea.

There are a lot of people that have this idea.

There are a lot of interesting startups building things along these lines.

And, you know, the world is just moving so fast.

It can't be long.

- Right, right.

You've shipped products used by hundreds of millions or billions of people.

What was a product decision you were nervous about that ended up being right?

- You know, the fun thing with product decisions at that scale is any example I give, I bet there are people in the audience who were like, no, no, no, no, you got that one wrong.

(both laughing) Very few are unambiguously right.

Probably one was like ranking the Twitter feed, which was extremely controversial back in the day.

Twitter used to be completely real time.

And the thing you saw at the top of your Twitter feed was the thing that was tweeted one second ago.

And the next one was the one that was tweeted four seconds ago.

And if your spouse or your best friend happened to tweet an hour ago, like, yeah, too bad.

You were never gonna see it.

It was gonna get totally buried.

But there were a lot of people that said, this is the magic of Twitter.

How could you possibly do that?

You know, you're becoming Facebook now.

So that was very controversial at the time, although it seemed in some sense like, how could you not want, you know, you do care about different people's stuff more than other people's stuff.

How could you not want ranking if we could do it well?

And if we could bring the right balance of recency and everything else.

So that was one.

And I think Facebook saw the same thing when they originally put out the newsfeed.

You have a bunch of people that are super upset, but then the metrics tell you an incredibly positive story, like double digit positive kind of thing.

And so, you know, and then you just, you can just keep making that better and keep getting wins there.

It was interesting trying to figure out how exactly we would, when we rolled out a one preview, the first reasoning model, what was the right kind of UX paradigm for a model that would not give you an immediate answer?

Like all the other previous chat models, you type in an answer or you type in a question, and you basically get an answer right away.

There aren't a lot of experiences online where you have to wait like that.

So, and the model is doing this interesting thing with its chain of thought in the meantime, which we didn't want to expose completely because we didn't want to, because you can distill that and, you know, basically copy our model, which, you know, for a bunch of geopolitical reasons, we didn't want to have happen, but you want to show some.

So it was very interesting trying to figure out how you, was the model that people would go away and just, you know, pop back in whenever it was done, or were they gonna watch?

And if they were gonna watch, what would we show that, and how do we balance not giving too much information that would, you know, lead to model distillation, but would be interesting.

So that was an interesting experiment.

And it's surprising sometimes building stuff with models, a reasonable analogy for how should I, you know, handle the UX of this current situation is, how would I want a human to behave, you know, in a similar situation?

And like, if you ask me a question that I can immediately answer, you know, GPT-4 style, then I will just immediately answer, right?

If you ask me a question that I need to think about, I don't immediately start babbling my entire chain of thought, right?

I don't just like spew out whatever goes through my mind.

I also don't like completely turn around and go mute and like do nothing until I come back to you with an answer a minute and a half later.

You know, I might say like, huh, okay, that's an interesting question, let me think.

And then you kind of give little like cliff notes as you think, well, it could be this, no, hmm.

And so that ultimately is kind of what the model does, right?

It gives you kind of like periodic updates of what it's thinking about as it's thinking, which is interesting in and of itself.

And then it comes back with the answer.

So we tried to model it a little bit off that.

One of the big debates like in terms of product development is data versus taste.

What's the time when you said, hey, I've got a hunch.

This is the taste.

This is where we're going.

We need a little bit more time for the data to catch up to it.

If you just blindly follow the data, then it will take you, like then you're not in control of where it takes you.

And that's that, I don't think that's where you want to be.

There's always a huge amount of value in anecdotes too, when you get user feedback.

Like the, even though it's usually the case in my experience that if you're getting, the data tells you one thing and you're getting a bunch of user feedback that takes you in a different direction, then what's actually happening is you have some bimodal thing and what's your, the data is like giving you the average of those answers.

And it's actually the case that you have two very different things going on.

And you need to cut your data differently and dig in because you should not dismiss the anecdotes.

The anecdotes are almost always valuable.

I think the trick with data is to understand it, not just be like, oh, you know, number go up, like we should implement this thing.

But why does the number go up?

Is it because we're like, because of novelty, which happens a lot, right?

Some new thing that you shipped, people are like, oh, that's interesting, what's that?

And so they click on it once and your numbers look good, but they don't actually come back.

Or is it because they are confused?

That also happens a fair amount.

Either, you know, you didn't get the product right, or if you're, you know, growth hacking, there are sort of negative states of confusion, but they do make the numbers look good.

Or is it like, actually people are retaining on this new thing and they like it, and then you should lean into it.

So you really wanna like interpret the data, try and understand what it means underneath, and then the decision is usually much more clear.

- So a lot of people in this room are building on top of AI.

What's something that you're seeing?

- Is there anyone in this room not building on top of AI?

(laughing) A few, a few, but they're still building with AI for sure.

But when you're building on top of AI, what's a mistake you see startups and founders make?

- One thing that we do internally that people sometimes, I see people not doing is, a lot of things today at least turn out best when you use like an ensemble of models.

If you have a hard question that you're trying to answer, you know, maybe it's customer service or something where there's a bunch of different things going on.

People have different motives for writing in and you need to handle their queries in different ways.

There's a bunch of actions that you need to take.

The models are getting pretty good and now they really can sometimes just completely one shot a tough flow like that.

But you can make your odds even higher if you use models sort of together, or you may have an initial model that's orchestrating and is like putting a plan together and understanding what you should do to answer the question.

And then you have different models.

Maybe some of them are cheaper models that are trying to do one thing really well.

And the orchestration model is, you know, calling the other models and things like, I don't see people doing that enough.

I think we're getting more sophisticated about it as sort of an industry.

And at the same time as we're getting more sophisticated about it, the models are getting better.

And so they need this kind of thing less and less, but that still is an area where like behind the scenes, we use ensembles of models in lots of places, trying to use like small models where you can, bigger models where you need to, and then have them all work together versus just like, oh, let me prompt engineer one giant, you know, prompt, and hope the answer is right.

- Where's a company or a product that's done a really great job of building on top of Open Air?

- Oh man, there are so many.

- Something that surprised you maybe.

- Well, I mean, something that surprised me, what's the new name, Open Claw?

Open Claw built on Codex.

And that's one of the most interesting things that has come out recently, more because it's like a sign of what's to come.

Both, like A, the dude was able to put it together in the span of like three days, right?

Which is, it's just awesome.

Like so many things are now possible in the span of days that would have been months and months of work or just completely impossible before.

But also because it points at like this interesting emergent world of the AIs all working together.

Have people spent any time on moltbook.com?

- Oh yeah.

- So the, it's, you know, you have all these AI, Open Claw agents that, you know, basically have access to somebody's full computer and they can do all sorts of things and you can command them through, you know, messaging apps and stuff like that.

And now there is a social product for them called moltbook where they go and interact with each other and talk about their humans and tell stories.

And it's just fascinating.

I mean, it's all weird.

It's not like most likely the next big startup or anything.

It's just fascinating as a sign of what's to come.

I love stuff like this 'cause like it just gives you a little bit of peek into the future.

- Yeah.

And so the question is how much is emergent behavior versus how much is just novelty, right?

- Yeah, it is a lot of novelty.

There's a lot of humans trolling through, you know, prompting their Open Claw agents.

But there's also just really funny stuff.

- If the product requires you to go out and buy a Mac Mini and you go out and buy it, then you've got product- - Right.

- That fit, right?

- Right.

At least among early adopter nerds like me.

- We'll see where it like, all right.

So you have Open Claw instance.

What are you sharing of your personal information?

- I am being careful.

- You've held all the guidelines, right?

- I am being careful.

But, you know- - The tension there is like, how do you have the full experience without sharing your whole life, right?

- Yeah.

- So.

- Yeah, yeah.

- No easy answer.

- No easy answer yet.

But I mean, these are the things.

When we first started building agents, we were very locked down because the last thing we wanted was there to be, you know, even at low probability, some instance where the agent shared something it wasn't supposed to, like it, you know, read your GitHub repository and then shared private code or something like that.

It's like, once you identify some of these problems that you want to solve, models are getting very good, post-training is getting quite good.

It doesn't mean that we don't make mistakes, but the chances of a mistake are getting lower and lower.

You can build this sort of infrastructure and safeguards around it.

And I think people are much more comfortable now, just, you know, giving more access.

Maybe not all the way to Open Claw yet, but just in terms of like connecting a whole bunch of different MCPs to various, you know, private data stores.

And then, so like, you start with something like Open Claw where people are nervous, but actually, you know, the infrastructure kind of follows behind and patches up a bunch of the stuff you're worried about.

And then you really do get to do the full promise also with good security.

And, you know, again, the pace we're all moving, this will not take long.

- I mean, it could be that Open Claw's legacy will be to accelerate the fully personalized, you know, agents within like the skill players, right?

- Yeah, I mean, one of the cool things about where we are right now is everything is new all the time.

Like today, models can do something that computers have never been able to do in the history of computers, right?

And like in another month, that's gonna happen again.

And in another month, that's gonna happen again.

And we don't know, we don't always know what's coming, right?

Sometimes these things are just emergent capabilities of models as we build them.

Sometimes we do and we're like, okay, we're specifically gonna try and get better at this thing, but other times you're surprised, right?

And when you're surprised, all of a sudden, everyone in the world that uses a model has this new capability that nobody has ever had before.

And then you have like the ingenuity of everybody in this room and everybody outside these walls going, wow, what could you do with this capability?

And like we're all kind of discovering at the same time what you can build if you had these new capabilities.

So one of the reasons I'm so bullish about startups in general right now is there's so many new capabilities and the world doesn't quite know how to, what's possible.

OpenAI doesn't always know what's possible, right?

We're not gonna have all the ideas.

So it just is like the most fertile ground for startups that there has ever been.

Most of these tech shifts that we've seen like in like going back to the dot com era, like the adoption starts with consumers and then goes into enterprise.

So this time around is different with enterprises first and there's not other than like Open Glo, there's not like a lot or a cloud book.

There's not a lot of consumer oriented experiences that are very native or you know, video editing and photo generation.

- Yeah, I was gonna say there's a little bit around like Sora and some of that stuff.

There's not tons, you're right.

- There's not like, where's the eBay, right?

You know, where's the first generation, big consumer place.

Why do you think that is?

And do you think that, do you think that it'll change in the next few years?

- Enterprise like B2B stands out because it's, that is where we do the majority of our economically valuable work and models are getting increasingly good at doing economically valuable work.

So I just, I think from a, where can you show value very quickly?

And also where is their money?

B2B makes a ton of sense because models also cost money to use.

It's not like, and you know, they're in a relative sense much more than like traditional, you know, get a database and pay network costs and stuff like that.

You start having costs right away as a business in a way that maybe you didn't have as much if you were like building a consumer social thing before.

So there's value in having early customers that can help defray some of those costs.

And I don't know, I think it probably just comes back to models being able to do economically valuable things in a way that without, you know, previously, it was only humans that could do these things.

And so you can now build stuff in the enterprise and like take on, you know, save huge amounts of money or time or whatever for businesses.

You can, I mean, we've seen how many, how many different B2B companies have grown, like zero to a hundred million to beyond in a heartbeat.

So there's just like, I think there's a bunch of low hanging fruit there.

What advice would you give to a consumer startup founder that is thinking about distribution, you know, distributing through open AI of now or in the future of Viable Path?

With the apps platform that we built, one of the ways that we thought about it was, or maybe one of the success metrics for it is that you should see new startups being built on top of it that wouldn't have been possible to build before.

So it's not just if you're an existing company, you can use it for distribution.

It's actually, it should enable people to think completely differently about what a business looks like.

Maybe you don't, you can build a business in the future using this apps platform that doesn't have a website or a mobile app and is sort of entirely built around these kinds of new platforms.

So that's where we get, I mean, that's sort of you, if you get there, then you have an interesting platform.

There's still a bunch of work.

We're still pretty early on that, but I'm excited about where it goes, especially as the models get really good at using a huge variety of tools and apps and other things.

Like the value will be bringing all of that together in one interface and allowing you to do increasingly complex things by just, you know, typing into a chat box and letting the agents work for you.

Well, thank you very much.

This was incredible.

You've been a great supporter to us by being here at your company is the reason why many of us are here and also supporting the founders with a lot of great credits and technical support.

So everybody give it up for Kevin.

Thank you.

- Thank you.

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