[State of Evals] LMArena's $1.7B Vision — Anastasios Angelopoulos, LMArena

Light in space like 2025. Recaps. Light in space like 2025. All right, we're here with Anastasios from Arena. I actually don't actually know your... Stars even aids, like very-

Angelopoulos.

Yeah. Every-

Yeah. There you go.

Um, congrats on all the success. You got the Arena handle.

Yeah, we did. We got the Arena handle. Thank you. Big branding moment.

Uh-

Really

... I mean, I, I think X is, like, being more commercial, so obviously you bought it.

Yes.

But, like, at least you, like-

Yes

... have a place to go to where you can be like, "Hey," like, "we really like this." But I do think, like, dropping LM-

Yeah

... has changed the, the, the, the, the feel of it. I don't-

Interesting

... know how you feel. Yeah.

Yeah. I don't know. I mean, the reason we kept the LM at the beginning is because we were, like, we started as LMSIST, right? Out of the LMSIST sort of con- you know, uh, conglomerate at Berkeley. So we decided to-

You know, those language models, yeah.

Exactly.

So.

So we wanted to maybe broaden a little bit. Yeah.

Yeah.

And, and we were the first Arena, so we feel like let's kinda try to own that.

Yeah.

So.

Last time you, we had you guys on, w- you hadn't really spun out yet, and we-

Right

... I had a call with Alessio, and I was like, "These guys are gonna start a company." And I didn't know, I think you actually were already started at the time.

I don't, I don't remember.

Uh-

Maybe

... 'cause Anj, I had a, I, I chatted with Anj.

Mm-hmm.

And he said he was your founding CEO.

He was indeed.

Uh-

Yeah

... which, like, people don't know.

Yeah.

The, the- Anj- Anj is a very interesting character. We, we have a podcast scheduled with him.

Yeah, yeah.

He does a lot more than normal VCs.

He does. He's been incredible to us.

You, you wanna shout out some stuff that he did?

Yeah, absolutely. So you know, the way the company started was as an incubation by Anj.

Yeah.

So what he did is he kinda, like, found us at Berkeley and picked us out of the basement and was like, "Hey, these guys seem like they're onto something," and started working with us really early, gave u- you know, gave us some grants.

He was not, you know, a16 was not the only one to do this. We also had a great grant from Sequoia. But, um, Anj was in particular quite, quite supportive of us and, you know, gave us some resources in order to continue building out Arena before we even were committed to s- to starting a business.

And in that capacity, he sort of like, you know, formed an entity for us and this and that. And you know, he was like, "Hey, you guys can walk away at any time if you guys don't wanna start a business."

I mean, it was really incredible, very aggressive investment move by him. Right? Because, uh, of course any money that he spent-

This is his business, yeah

... at the end of the day, like, we could walk away and give, leave him with nothing. But I think he knew wisely that the right thing for Arena was to start a company out of it. It was the only way that we could scale and that, you know, Weiling Yan and I would, would ultimately see that and be excited about doing it ourselves, which is, which, which ended up being the case.

Was there a moment for you where you were, uh... I'm sure you were debating it yourself.

Mm-hmm.

You had other opportunities. What was the deciding factor for you?

It became clear that the only way to scale what we were building was to build a company out of it, that the world really needed something like Arena, Arena being really a place to sort of measure, understand, and, and advance the frontier AI capabilities in, on real-world users, on real-world usage based on organic feedback.

And that in order to achieve the, the scale and, you know, distribution necessary and the quality, of course, of the platform necessary to, to do this effectively, we would need to start a company out of it. You know, we considered other options.

Are we gonna keep doing this as an academic project? Are we gonna do it as a nonprofit? Blah, blah, blah. But ultimately, under those c- constructs, we didn't feel like we'd have the resources necessary to accomplish our mission.

So you raised 100 million or-

Yep

... 80? 100.

100.

$100 million. That's a lot of resources.

It's great.

What-

Yeah

... what's it for?

Well, you know, obviously-

Asking on behalf of, like, everyone-

Of course

... who are like-

Yeah, everybody

... "Dude is in Arena."

How are you gonna spend your money?

Yeah.

Yeah. It's great.

Yeah.

Yeah. I-

Please tell.

First of all, we don't necessarily need to spend all that money, right? The, the purpose of money at a company is to give you cards to flip. It's to say, "Hey, you, you have, uh, enough resources necessary" and it's so that if your first bet fails, you can make another bet and another bet, of course.

So that's not to say that we're gonna spend all of it. Of course, you wanna spend things responsibly. Having said that, the platform's actually quite expensive to run. We fund all of the inference on the platform. You know, the way, the way that it works is the platform-

Like, you pay market rates? They don't give you discounts?

No, no, we get discounts, but they're, but they are, um, standard enterprise discounts.

Okay.

The same that would be given to any other customer of the LLMs.

And if you disclose any, uh, I don't know any numbers. I, I'll just, I see numbers of votes.

Uh-huh.

But, like, what's that in, like, monthly tokens or... I don't know.

I don't know about tokens.

Whatever you see.

You know, I'd have to back, back that out. But we have, like, you know, let's say, again, this is off the cuff, but I can safely say we have more than 5 million now, 5, 6 million. We have probably 250 million conversations that happen over the course of the platform.

We're on the order of, you know, mid 10s of millions of conversations every month that are happening on the platform. Um, it's actually quite a large, you know, it's one of the largest consumer platforms for LLMs. Of course, nothing is really comparing to ChatGPT, but- You know?

But no, still, I mean-

I think-

... the, the largest scaled ones. I would-

The benefit of this is that, like, it's actually quite a diverse population, so 25% of the people on our platform, for example, do software for a living still at this scale.

How do you know?

Because we, like, do all sorts of th- inst- we, we either survey them or we'll, like, analyze the prompt distribution that's coming to the platform. I'd be happy also to share more. We've done something called Expert Arena, which is, like, trying to understand the distribution of experts that are coming to the platform.

A lot of that can be, like, unauthenticated or whatever usage.

It is, but about half of our users now are logged in.

Yeah.

And so we have some ability to understand them, and, and we also have surveys that we run on the platform that tell us a bit more about who are the actual users.

Yeah.

Of course, there's always, like, response bias in surveys, so you have to take it with a grain of salt, but nonetheless.

And if you don't know Anastasios' background, like he's, he's the guy to correct for response bias.

Yeah. Well, okay.

Yeah.

There's a lot of, there's a lot of guys like that, and girls, so.

Guys and girls. Um, you're not the only player. Uh, there, there are this Artificial Analysis started-

Yep

... Arena. Yup AI.

Yep.

It's, like, some crypto people that, uh, started this.

Yep. Yep. Yep.

I don't know if you have a conversation with them on like, "Well, hey, this is our thing," or like, "Let's work together on something." I, I, I don't know if you've, uh, what the initial-

No

... was like.

You know, so I've talked to the Artificial... I've actually talked to both groups.

Yeah.

Both seem, you know, great people.

Am, am I missing any major players? It, it's just those two, right?

No.

Okay.

I think those, I don't know Yeah, I don't think so.

Okay.

I think those are, like, some of course large, depending on how you define the term.

Yeah.

Artificial analysis obviously has, like, huge, like, market mind share-

Yeah

... um, around the analysis of, of different AI systems.

Yeah, they told me they were just like, "We, we are gonna be Gartner of AI."

Yeah. That's, that's kind of their goal.

Right.

And I think they're going after that consulting market and so on.

Yeah.

But, uh, Artificial Analysis from what I understand is, like, a team of consultants that are doing this.

Yeah.

Seem like really, really nice guys, um, going after that, that particular market. It's different from our platform in the sense that their analysis is based on sort of, like, aggregating public benchmarks and turning those into analytics.

I- independently rerunning.

And independently rerunning.

Which matters.

Yeah, yeah, yeah. Which matters, yes. Um, and, and, and, and using those in order to compile sort of reports and so on that, that educate the field on the performance of all these different models.

But they also have arenas.

They have arenas, but the arenas are not based on organic usage. Like, the, the thing that distinguishes our platform versus theirs is that the users are actually inputting their own use case. They're actually asking their own question, and that gives a, a level of realism-

Yeah

... that platform doesn't have.

Yeah.

Of course, they ha- they specialize in a slightly different thing, but I see those platforms kind of diverging in that sense.

Yeah.

Um-

Yeah. A- and sometimes it's like, it's the only way to do this. So like for example, for AA, their video arena is pre-generated videos. You can't enter in your own video prompts.

That's correct, but we're doing it organically.

Yeah.

Like-

Exactly

... and so I think-

But, uh, as a voter, it does help in terms of I don't have to wait.

It does-

You know

... but also, why would you go? Do you actually care about, like, other people's videos?

To, to inform your own intuition.

Maybe, yeah.

Yes.

Maybe you're, like, interested in comparing-

Like, I'm a shitty prompter, right?

Are you?

So-

I don't believe that.

I'm a, a terrible prompter.

No, no, no.

I learn by example.

Don't denigrate yourself. Don't denigrate yourself.

Um- There, there, there are many prompters much better than me. Let's say that, right? That's, that's, that's a fact.

People have all sorts of cool ways of prompting LLMs.

So, yeah, yeah.

Yeah, it's educational to see.

The, the only way to learn is by, like, well, look at other people's prompts and see the results and go like, "Oh, I didn't know you could do that," and that's how I-

Totally.

Yeah.

Yeah, yeah, yeah.

Okay. So let's come back to arena. Oh, one thing I do wanna say is the number one use of funds is getting off Gradio. Uh-

Oh, yeah. Yes. We... Well, listen. Gradio, incredible platform. Gradio scaled us to a million MAL.

Yeah.

That's incredible. And of course-

Did you tell the Hugging Face folks that?

Of course.

Yeah.

Yeah. We're really, really grateful to Gradio for taking us so far. Eventually, you know, it became time for us to move off of that and-

I mean, it's-

... be able to react sort of-

I, I'm sure Hugging Face would've loved you to stay on.

They would have, I'm sure.

Um-

Yeah, yeah.

Was there a technical, like, reason you, you, you just couldn't get the performance?

Yeah. It just became hard to develop, and there were all these tools that we wanted in React and, you know, to do all the fancy things that you can do in React became kind of like 100 times-

Give us one example of that. I don't know. What, what's a feature that you really wanted?

Let's say we wanted to create, like, our own custom, like, loading icons for video with notifications.

Okay.

How are we gonna do that in, in React? It's hard.

Uh, yeah. I mean, we'll make a-

I'm sure the-

... a custom component in React

... I'm sure the Hugging Face guys are gonna come in and say like, "Hey, you can do that in Gradio"-

Yeah

... which maybe you can but-

Of course they're gonna say that

... also fewer developers know. How are we gonna hire for that? We have to reskill them. The people are less familiar with that stack. It would-

Yeah.

You know. Anyway.

So it's full React Next.js, all this, all this-

Yes. Yeah, yeah, all that.

Okay, cool. Are there use of funds that are, that might, that might be interesting? Like, uh, you know, basically-

No, that's basically it

... how you deploy the resources. Okay.

Yeah. It's, it's, it's on primarily inference that funds the free usage of the platform and, and then also, uh, uh, hiring of course, headcount.

Yeah.

We have an office, you know.

That's us.

It's in SF.

I'll tackle one of the major things this year, which I'm sure you're tired of thinking about, but I-- for people who are not in the loop, this is, uh, gonna be news to them, uh, the leaderboard illusion, the whole thing with Cohere.

Let's summarize the, what they said and then your response.

Mm-hmm. So leaderboard illusion's a paper that critiques LMArena, and the main-

Pretty, like-

Yeah

... brutally .

Well, you know, I would say unscientifically. Um-

And let's be clear, Cohere wasn't doing that well on the leaderboard.

I think, uh, Cohere was, like, 74. It's all good. You know, it's actually not... It's a, it's a respectable place that they had on the leaderboard. I don't even think it was really Cohere people, like the Cohere model developers doing this.

It was more their research side. But in any case, what does the, what does the leaderboard illusion say? It says that LMArena was what... Their-- the claim is that LMArena was doing this undisclosed, quote-unquote, private testing on our platform, that model providers will send us pre-release models, and we'll expose them and so on and so forth, and that this creates so-called inequities in the leaderboard, you know, due to that pre-release testing.

For example, they will s- they cited that Meta at some point tested-

Llama 4

... some amount of models with us.

Yeah.

Of course, we can't disclose all of the details of how all that was done, but that is, that is the main claim of the paper. Now, our response to that paper, it's online. You can find it on Response to Leaderboard Illusion, and our response to that paper is essentially pointing out a series of factual mistakes in the paper that, that question the validity of the claims.

So you can go look at the first version of the paper on Archive yourself, and you'll see the claims. I think most scientists would view that as, um-

Oh, they've corrected it.

They've cor-... Of course-

Oh

... because we... I mean, but they didn't correct everything. They just corrected-

Okay

... some aspects that were just blatantly unscientific and false. But you know, they, for example, um, said that we were, that we only sampled, like, 9% open source models and, like, you know, 60%, like, closed source models, and this created an gap between open and closed source.

But rea- in reality, we're actually really supportive of open source models, and, uh, it was more like 60/40. And, uh, so that was, you know, one of the examples of, of a, of an er- a c- an error in the claims.

Another example is that they were claiming that there was some sort of bias introduced by this pre-release testing, and that it was undisclosed. In reality, as you probably know, we've been doing this pre-release testing for a long time.

Our community loves it. They loved basically getting like-

The secret code names

... yeah, the secret code-

And it's like-

Like NanoBanana.

Yeah.

All that. So NanoBanana, by the way-

It started on you

... started on us.

Yeah, yeah.

Right? And people loved it. Went like global sensation.

Yeah.

Like non- non-zero fraction of the global population using NanoBanana.

Did you talk to Laina about naming it Banana or was that-

No

... her decision?

It was, it was their decision, I believe.

Okay.

But it was sort of this randomly generated thing.

Ah.

And it just went-

No, no, so apparently, uh, Naina, who's a PM-

Yeah, yeah

... is named after her because her nickname's Naina.

Oh. Oh, that's sweet.

Yeah.

I didn't know that.

It was Naina-

Yeah, yeah

... put Banana in it, yeah.

Oh, that's sweet.

And that's the whole thing.

Yeah, I didn't know that, didn't know the origin story. Yeah, I mean, to us it just looked like sort of a random thing. And then it went-

But it went like clearly heads and shoulders above-

It's huge

... which, like, before that, there was Reeve Image, remember?

Yeah, I do. Of course, yeah.

And also, uh, BFL and, uh, all those AI models.

Yeah, I mean, all those models are also great.

Yeah.

Um, and I think those teams are also improving quite quickly.

Yeah, yeah.

But NanoBanana was a sensation.

Yeah.

I mean, m- that moment alone changed Google's, like-

Roadmap

... yeah, market share.

Yeah.

Seriously.

Yeah, yeah.

I mean, Google's stock, billions of dollars-

Yeah

... are moving because of NanoBanana.

And now there's like an OpenAI Code Red and everything.

I, I-

Yeah

... I don't, I don't know about that, but yes-

Yeah

... the Information reported this.

I, I would say, like, the, uh, image generation, I would say, has been like this weird part of AI in- overall, 'cause it's not strictly AGI-critical. Like, it's not reasoning. It's, it's, it's, it's, it's not like feeding more context into the model.

It is the model generating a visual, uh, representation. And so it's basically like, uh, I, I always think like, well, you know, Gemini used to get a lot of complaints for generating like racist, racist images or whatever.

Mm-hmm.

Um-

Yeah, that was a hilarious moment

... and, and ChatGPT also had, had it i- in the past. And I'm like, "Well, can we just get rid of this? Like, do we have to do image generation?"

Mm-hmm.

Because let's just focus the, the positive reputation of AI in general on language models and coding and, you know, the other stuff.

Yeah.

Um, but I, I, I, I'm wrong. I'm- I'm wrong. I'm such a huge NanoBanana Pro show.

Yeah, I-

Uh

... totally agree.

It's good.

I was also kinda wrong about this. I didn't see the positive benefits. But actually I think that these, like, multimodal models are gonna become some of the most economically valuable aspects of AI.

Yeah. AC.

Both in, both in consumer and also in enterprise, because one of the fastest-growing segments, market segments in AI adoption is marketing and in, in market- marketing and design.

Yeah, ads and-

Yeah.

And so, so, uh, I'm a content creator, right?

Yeah, of course.

I need-

I'm sure you're using it all the time

... infinite supplies of diagrams and explainers and-

Totally

... infographics.

Yeah. Soon we're not gonna be even making the product papers.

YouTube thumbnails, yeah.

We're- they're just gonna be our paper figures are gonna be made by AI.

Actually, yes, yes. I, I do think that it actually one-shots. So, so I, uh, DeepSeek came out with, uh, V3.2 recently. I took their explanations, which are very wordy. Uh, they like very concise papers. It's 23 pages long, but it's very dense.

Yeah.

And so I just took their explanations of the RL environment stuff, and I fed it into NanoBanana Pro, and it fe- spit an image that I he- I used to understand the paper better. And the fact that I can just casually generate like a paper-quality diagram that would usually take a PhD student like a month to, in like in Photoshop or something to do is, is incredible.

Yeah.

Yeah.

It's incredible. It is amazing.

I wanna ask about your principles running Arena. I think you, you manage a giant community, 5 million mile. What have you decided are the core principles, I guess, before becoming a company and now that you're a company? I don't know if there's anything that's changed for you.

I don't think anything has really changed. We want to provide the North Star of the industry and center the use cases of real users, foreground those, so that people know what to target. The goal is to create a benchmark that is constantly fresh, that does not suffer over-fitting because of the fact that we constantly have new data points coming in, that tracks the, you know, all the different new models, um, all the different new use cases of AI, and, um, gives the whole world sort of ground truth, uh, for how real users are using these models and how, how good they are on those use cases.

We continue to do quite a few open source data releases. We've probably released more data than y- basically anybody on the real-world use cases of AI, millions and millions of conversations, real-world conversations from real users that the community's using to study and, and improve on.

Yeah. And then I, I think, like, in terms of what you will build versus what we'll not build-

Mm-hmm

... I guess I, I'm not necessarily caught up on e- everything that you've launched. I, I know recently you've done the, the Dev or Code Arena.

Yeah, Code Arena.

That's, that's the most recent one, right?

Code Arena, Expert Arena.

Yeah, Expert Arena. So basically like what, what is in the critical path for you, let's say i- in- for next year, and what, what have you decided you'll never do?

So let me first talk about things that I'll never do. The platform, integrity comes first to the platform. The, basically the public leaderboard that we show on LMArena, I think of as a charity. It's a loss leader for us.

We don't really make money on the public leaderboard. You can't pay to get on the public leaderboard. It's not like a Gartner in that sense.

Mm.

It's not like any of these like, uh, you know-

Pay-to-play

... pay-to-play systems. Never going to be like that. Models are gonna be listed on the leaderboard whether or not the providers pay and whether or not they're getting a good score. They can't pay to take it off either.

And so what that means-

That's very important

... that- that's very important. And so what that means is that the leaderboard ha- has a certain integrity that will never be compromised, of course.

Yeah, but, but not all preview models will make it onto the-

No, but that's okay

... that's right

... those preview models have never been released.

Yeah, yeah.

Right? Who, who cares about putting unreleased models on a, on the leaderboard? The point is that for every released model, the score that you see on the leaderboard is statistically sound. It reflects the real-world capabilities of the model.

Yeah.

Why? Because millions of people from around the world have voted for it, and that's where that, where that number comes from. All we do to con- compute that number is millions of people are voting, we take those votes, we turn them into a number.

That's always going to remain, you know, a transparent and fair reflection of model performance. Where are we going? Lots of different new categories. I don't know if you recently saw, we expressed- we, um, we exposed, uh, occupational and expert categories.

So now, single-digit percentage of our user base, we're millions, millions to tens of millions of users, right? So single digit percentages means a lot. Single-digit percentage of our user base are in medicine, in legal, in business, you know, finance, accounting, creative, marketing, stuff like this.

And we're able to show the performance of these models on all these different verticals because we have all these users in our, in our user base. Uh, and we're, we're working more towards multimodal, you know, video we're soon to launch on the site at some point, you know, later this year or early next.

So lots of things in the pipeline.

Amazing. Would you expose an API?

We've thought about it, yeah. I think it's a, it's a possibility.

I hope so.

Yeah.

What, what, what are the counter arguments? Like, why, why not?

Well, there's obviously a need for an API. The question is more of focus of our company. Just because we're a startup, and so we really should be doing one thing well.

Arenas.

Yeah, arenas.

Yeah.

So I'm not, I'm not sure that we- how far we wanna sort of splay out and on what timeline we'd wanna do that.

Yeah. Any other sort of like, um, community management tips? You know, m- more broadly, like every AI company like really wants to grow their community. You're obviously-

Mm-hmm

... one of the strongest in the world. What's really wo- really worked?

Well, so first of all, I want to give a shout-out to our community manager, Greg, who is doing an awesome job managing our community, whether that's on Discord, um, or on LMArena. He's really incredible. So I would say hire Greg, but don't-

Don't hire Greg

... hire Greg.

Don't hire Greg.

Don't hire Greg.

Find a Greg.

He's ours.

Find...

Find a Greg.

Find a Greg.

But in general, you know, the question of how do you get to so many users, that is a tough question.

And keep. And keep.

And keep and retain them. That is a tough question because consumer is one of the hardest markets in the world. There's a lot of websites in the world that people can go to, you know. Why should they go to yours?

And the reality is if you want to create a really dominant product, you have to provide people value. And to be frank, I don't think we're all the way there yet. It's not like I have the solution and answer for how to build a, a great consumer product.

If I did, we wouldn't be at 10- tens of millions of users, we'd be at hundreds or, you know, uh, we'd be at a billion users.

Yeah.

We'd be like-

Is there a world you, like, are bigger than ChatGPT or...

I don't know. I don't, I don't know that we need to be.

Yeah.

And I don't know that we ever will be-

Yeah

... because that's a, that's an extraordinary generational product that they built, right? It took a lot of time and, and to some extent, it also involved luck. There's a lot of lightning in the bottle moments, like NanoBruno was for us, where our user base just like goes up by a lot.

But when those users come, they can just as easily leave. So the way I think about it is every user is earned. You have to earn them every single day. They can leave at any moment. They're fickle. And so all the time you have to be thinking about, how do I provide this person value?

Learning, how are they using my website? What more could I give them? And how do I build in all the retention mechanisms so that they stay, and then they're also bringing their friends.

Is there one that's working in terms of retention? Like you said, a lot of people are signing in now, and that's new.

Yeah, sign-in was a big driver of retention.

No, no, but what, what did you give them in order to encourage them to sign in?

Oh, like history. Persistent history.

That's, that's it. That's enough.

Yeah. That's, that's one thing that has had a big impact.

Okay. Yeah, cool. What do you want from people? What, uh, what are you looking for help on, like any call to action?

Yeah, I, uh, we are always looking for people to come and join us. If you are one of the best people in the world in your area, whether that's consumer product, whether that is machine learning, whether that is, you know, B2B, go to market, marketing, all these things, we need you at Arena.

We're building like a high-performance team of real experts in everything that they do and, you know, I'm always looking for excellent people to work with.

Do you need like... What about partnerships, right? Like, let's say I'm in Cognition, I wanna partner with LMArena or just Arena. What works for you? What existing partnerships do you already have that, that's really fruitful?

Yeah. So I mean, we of course, partner with all of the major model labs.

Yeah.

Um-

And that's, that's just straightforward, like, "Hey, we have a new model here. Here you go."

Yep, exactly. So I think the, the most straightforward thing would be for someone like Cognition, it's like let's evaluate Devin.

But that's, that's an agent.

Yeah. But we should be continuing to shape-

You know-

... our... Well, Code Arena's an agent evaluation.

That's true. That's true. That's true.

And in fact, I think-

But it's more focused on... Like, all these a- arenas tend to be focused on the model rather than the harness, so.

But that maybe should change.

Maybe it should change, yeah.

Maybe we should be evolving towards that direction, and I think the Code Arena is a good example of an arena that would support a full-featured harness like a Devin.

Yeah.

And so in my view, I'm-- if, if I'm talking to Cognition, I'm saying, "Hey, let's get Devin on the arena and figure out how to l- you know, loop together the Devin harness-

Yeah, I think that's-

... so that we can..."

Yeah.

I'm sure, I'm sure that there's something that could be really valuable there, especially given Devin. Last week, people talking about Devin dead. Did you see that?

Yeah.

People were saying, "Devin's gone." Devin's not gone.

It's not gone.

Devin's everywhere.

It's doing very, it's doing very well. Uh-huh.

But people... So can we highlight that for people and show them, hey, Devin is actually the best or one of the best in the world at doing what it does. LMArena can actually do that.

Yeah.

And our, our, our place as a central evaluation platform allows, allows that to happen.

Yep. Love it. All right. Thank you for owning the State of Evals.

Thanks so much.

And for congrats on a wonderful year.

Appreciate it. Congrats to you too.

Thank you.

Congrats on all the growing, you know-

Yeah

... momentum in your podcast and in your career.

Thank you.

It's really impressive to see.