Mercor CEO on Why Application Layer Companies Have No Defensibility & Token Spend Exceeds Salaries

Building defensibility in the software layer on top of the models is going to be incredibly difficult.

We have the demand to double overnight. We just don't have the capacity. Joining me in the hot

seat today we have Brandon Fudy, co-founder and CEO of McCore. One of the fastest growing AI

companies valued at over $10 billion today, doing over a billion dollars in revenue over the last

two years. Everyone has increasingly realized that the model is the product. I think we're seeing in

a real time that services are getting automated. This is the most revealing interview that Brandon has

ever done. Discussing is revenue really revenue in this business? What does that look like moving

forward? Would he rather invest in open AI or anthropic? Right now we're spending more on tokens

for our internal agents than we are on employee headcount. How much does it cost to hire a high-quality

AI researcher? Oftentimes it would be in the tons of millions of stock per year. Ready to go?

Brandon Ed is so good to have you in the studio, dude. Thank you so much for joining me in person.

Super excited to be here. Thanks for having me, Harry. So I was thinking about how we're going to

structure this. I was like, you know what, there's quite a lot of myths or rumors around McCore.

And given it's our second time, I thought I could break the ice and just go straight for them.

So myth number one that we're going to tackle is there was a hack or a leak or whatever. I don't know

how you can tell me what do you call it, but a hack. And revenue has been flat. What's really happening

with McCore? Chua Fals. So there was an incident. All of the other parts are false and that we

obviously handled it very quickly. We were in touch with customers. We moved incredibly fast at

engaging Mandean and a bunch of other security consulting firms and the company's been crushing it

ever since. We've expanded our relationships with all of the frontier labs and added 300 million

and net new ARR in the last 60 days. 300 million and 60 days. Fuck me. That's been pretty crazy.

Yeah. Keep you as busy. I'm sorry, I just have to ask, where were you when you found out about the

hack? And what did you do? Well, it was a Saturday. So it was in the office. I was talking with our

engineering team. And I think the initial thing is of course like, you know, how are we communicating

this to customers and trying to be very proactive about understanding exactly what happened,

what was accessed, etc. And then how do we communicate this to the experts and just moving

containing it, moving quickly on the comms. And then from there, of course, making sure that we put

in place all the right things so that it never happens again. You know, there's a brilliant poem

and poet Roger Kippling who said, you know, kind of essentially you have to keep your head when all

about you are losing theirs. That is a time when everyone is losing theirs. Definitely.

I did by naming it one of your patch and I think we're both young, you're younger than me.

What do you do to say, calm when that is an oh shit, man? Well, it's interesting because I feel like

throughout the lifetime of the business, I have been through a lot of very stressful moments. That

was definitely stressful, but it definitely wasn't close to the most stressful one. Seriously. Yeah,

I mean, there's some times when I'm freaking out about making sure we get something right with

a customer or whatever it is, but I think part of it is that there was this broad perception on

Twitter that was much more exaggerated than what actually happened within the business. And so

having a thorough understanding of what actually happened and having really strong relationships

with customers gave us a lot of confidence that we would get out through get through it,

be on the other side even stronger. And we used to have six values as a company, but we had it a

seventh value of security to make sure it's very ingrained in the culture. But I think that

yeah, just that confidence that we know what's going on and that there's sort of this echo chamber

on X that we need to hedge against a little bit. And to find us need to pay attention to it.

I mean, I think founders need to pay attention to it. Like we had an all hands with a company where

we just laid out, here's exactly what's happening. Here's the trajectory of the business. And I think

that was very helpful to the entire team. But it's just definitely annoying that there were all of these

people saying things that didn't actually happen. And we couldn't quite speak out against them to

explicitly. Otherwise, there's going to be the Twitter mob circling and all these recommendations

from lawyers, etc. The hard thing is there are often a lot of people with economic incentives behind

the scenes. Totally. Who will absolutely trounce you and be very negative because they are aligned to

a competitor or we're in a YC company that's been through a lot of shit in the last few days.

And their competitor has a lot of people behind them through various different means. And

the alignment is not obvious, but it really sounds out on Twitter. That is exactly what happened.

Like I can even think of one person that's very prominent who's invested in multiple competitors

and just made this tweet about how all of our data was getting access by China when it was totally

untrue. You mentioned adding security as a third or seventh pillar there. We've seen so many hacks.

It's almost become normalized as awful as that sounds. Are we about to enter a golden age of cyber

given the new threats awakened by AI? I think so. I mean, we're even seeing this on the customer side

where our customers obviously are very focused on how do we improve the model cyber defensive

capabilities so that we can have the best AI security engineer that is able to defend every

enterprise from all of these attacks. Because in our incident, it was the attacker that used a swarm

of coding agents to help get access to the system as it's happening in a lot of these. And so I think

there's going to be an enormous boom in AI security engineering tools and various forms of defense

that are able to help protect companies against all of the increasing waves of cyber incidents that

are just going to start. Can I just be very naive and dumb here? How do the swarms of coding agents

make for such dangerous and malicious actors? How does that actually work?

So the reason is that when a normal attacker is trying to find vulnerabilities, they can only review

so much code and go through a certain portion of it at a human speed bound by the amount of people

in their team versus a when they're using swarms of agents, they're able to be very exhaustive

in reviewing the entire code base, looking at the entire front end, all the different things that

they've accessed. And so that has allowed a lot of these attackers to just move much more quickly.

And so we've been exploring various collaborations with customers and how we can strengthen

their cyber defensive capabilities to hedge against exactly this type of attack as well.

Got you. In terms of these various customers,

jewelfuls, you lost open AI and matters customers in the hack.

Boss, our relationship with open AI is stronger than ever. Obviously, I can't speak too much to

specific customer relationships though. Can I push on matter? Of course. I mean, I think that meta,

like currently the relationship is still paused. Every other one of the frontier labs has grown.

Their relationship with us since and the company has been crushing it, but they're the only one that

is- And it would be possible just because of the security. Well, there's other things happening there.

Obviously, I think that meta is a unique customer because of the scale acquisition.

And so naturally, they're going to work with scale more, but I don't want to speak too much to

the specifics of a customer. Because I thought when you saw hand shapes,

revenue just like power roll, it was just like meta shifting spend from you to them.

That's not true. Interesting. What is that? I probably shouldn't speak to a

Gradula, or to lead to that. Totally cool. Okay, but okay, so we have-

We'll speak to everything except customers, but we have lost open AI. Got you.

Cool. Stronger than ever. Because I got told by many of you before the day.

Definitely. Have I? Great. Good. Thank you. You're wrong.

You've been- I read this article. You've been trying to poach micro one team members with

signing packages in the millions. We have not extended a single offer to someone for micro one.

To no millions. No millions. Bucker. Why does that come back? Because I've read this article.

So the reason for the article was that someone on our team sent an outbound to some people at

micro one saying that we were hiring a variety of people with these very high signing bonuses. I

think one of them said $500,000 as a potential signing bonus. They took first meetings,

but we didn't move forward with offers in any one. Obviously, the way that gets framed to the

presses, oh, these are offers that are going out with a giant distinction from one of our employees.

I dig a message to one of their employees versus actually sending out a legal offer letter.

Love it. Press is a wonderful thing. Totally. Okay, next, but I'm enjoying this.

You show mythbusters. You might get uncomfortable with this one. I had a rumor that Amazon tried

to acquire you for $13 billion. True or false? That one is false. I obviously can't speak too much

to other acquisition kind of stuff. So I'll reserve any comments on future acquisition questions.

Would you sell for $30 billion? No, I wouldn't. I mean, ultimately, we've gotten a lot of acquisition

interest. We could walk away with, like, I could walk away with billions of dollars in cash.

The thing is, that's just not what motivates me. I'm very motivated by how do we solve this incredibly

important problem in the world of how humans fit into the economy. I feel like we have the opportunity

to build a legendary company and creating this new category of work and our probability of executing

on that vision wouldn't be as high if we weren't an independent company. How humans fit into the economy?

Fascinating. When we look at the news, we see intuals of 16,000, metal,

a list of 8,000 4M linked in the 1,000 coinbase that will click up now 22% going.

It's hard for people to see how humans are going to fit into that new economy.

Totally. Do you share that concern? I think to some extent, I believe there's certainly going to be

many more jobs in 10 years than there are today. But there's also going to be a lot of job

displacement along the way. And amidst all of these layoffs, I think the most important question is

understanding what jobs is AI able to do and what jobs is AI not able to do. And so we're building

a ton of initiatives such as the AI productivity index or APEX that are becoming the industry standard

and answering that question of measuring across all the different popular job categories that people

talking about ranging from consultants to investment bankers to lawyers to software engineers. What are the

actual tasks within those that AI can automate and what are the tasks that it can't? With the greatest

of respect, does that not change so quickly? When you spoke, when you saw André Capathy talk about

how he uses coding agents, it was like, oh, I use it for 20% of the work. And then it's like,

oh, it does 80% and I do the final 20% within a six months period. Definitely. Well, even another

example on that is on APEX, the frontier model right now is that about 40%. And 12 months ago,

the frontier model was 01, which was scoring 1%. And so that's with the progress of the last 12

months. And obviously, we expect it to continue and be fairly significant. But I think that the key thing

is that everyone underestimates the elasticity for demand and increased productivity in the economy.

Like, ultimately, over the last 250 years, we've increased productivity by 25X equivalent to

automating about 96% of someone's job. And during every technology revolution, ranging from

the agricultural revolution to the industrial revolution to the computer revolution, people feared

that there would be this enormous job displacement because of the lump of labor fallacy, where people

assume that there was a fixed amount of things that had to be done. And when we made people more

productive, that would all of a sudden mean that there were fewer jobs. Yet, 250 years later, there's

more jobs than ever before. And it's because we have no shortage of problems to solve this society,

right? We still need to solve climate change and cure cancer and do all of these other new things.

And so I buy that completely. What I don't buy is the speed of transition. And what I mean by that

is when you look at industrial revolution, agricultural revolution, it took multi-decade cycles to implement

and train new technologies to do what humans did. Now with Nanobonon Pro, I can get rid of

all designers in my media company pretty much overnight. Well, the thing I agree with you is about

displacement. I agree there's going to be a very significant amount of displacement. But I also think

that the economy is becoming much more effective at creating new job categories and allocating new

labor. Like a great example is what we do in that now we're paying out over $3 million a day and the

fastest job category ever created in history. And I expect that's going to continue growing exponentially

from here. And I think that there's going to be so many new job categories created across everything

within AI, such as training agents for deployed engineering, building data centers, all the way to all

of the problems that we otherwise wouldn't have been able to address as a society. Like how do we

build solutions to climate change? How do we have more people working on rockets,

ticks for space, etc. Totally get used. It's 3 million per day paid out. What is that in 12

months time? And 12 months time, that's probably about triple that. 9 million. Do you think you're being

ambitious enough? Maybe it's quadruple that. We have internal projections that are always much more

aggressive than our external projections. But we almost doubled our projections last year. What new role

will we have in 5 years that does not exist today? One of the largest things that people underestimate

both in the context of AI labs as well as within the enterprise is how significant of a job category

it is going to be to train agents. Like what we're seeing is that all knowledge work is converging

on training agents because it is structurally more efficient to do something once. Instead of having

a customer support representative that is redundantly responding to hundreds of tickets,

they're going to train an agent how to do that once. Instead of having a lawyer that is redundantly

doing dozens of similar red lines on commercial contracts, they're going to train an agent how to

automate that. And even probably when you're playing around with cloud, you see that there's so many

repetitive workflows of how you prepare for a meeting or draft emails or whatever it is where

it's just much more efficient for you to train the agent how to do that activity so that you can

amortize that over the entire useful life cycle rather than doing it redundantly yourself. And so I

think that there's going to be this enormous paradigm shift as agents enter the workforce and everyone

begins to manage them. Can I see when we think about that enterprise adoption? I think one of the

biggest problems that we have is data structures and data clandiness. I interviewed a guest the other

day and they said we'll have data cleaner as one of the most important jobs in the next five years.

Is data structure and data clandiness the biggest barrier to enterprise adoption?

Well, I agree in part. I think that certainly the models need to have access to data to

perform their jobs effectively. But the caveat is that they'll be able to clean the data themselves

fairly effectively as reasoning capabilities go up. The thing that humans will need to contribute to is

all of the tacit knowledge within the organization that isn't written down because I found that when I

try to get agents to do all of these workflows throughout Mercor, there's just an enormous amount of

context that lives in people's heads that the agents need to have access to to perform effectively.

And so much of that is going to be the new job of employees of how do we codify all this knowledge?

How do we train agents so that they're able to perform these tasks effectively across every function

in the organization? I'm sorry for digging down, but you said reasoning capabilities will

enterprise is to clean data more efficiently. Why? Well, the reason is that if a model is able to,

for example, read through every message written in Slack of the last six months, the model can presumably

structure a table of, you know, hear all the different customer conversations that happened in

this year, and et cetera. And so I don't expect humans to be doing like that type of stuff of how do

we structure data? How do we classify it, et cetera? But I do think that humans will do the things that

models inherently can't do such as the tacit knowledge. When we look at the market for being a data

provided some of the largest models in the world, it's such a large market that you've seen the

unbundling of it in today, such verticals. I met the other day a medical real world medical data

provider to them where basically they have surgeons that kind of I don't have video cameras on and

they record all the real world data. Do we see the mass unbundling of the data providing market?

Is that how it plays out? It's interesting. We're doing a ton of data collection in the physical world

as well, especially across scale domains where you have electricians and mechanics and scientists

dropping cameras to their head to record things. I think that there's always going to be some degree

of value in some of these like niche vendors that are able to go really deep in a specific vertical.

But what we're finding is that there's enormous value to aggregation and economies of scale. And that

when we have this talent network of over 5 million people that are able to refer their friends,

it's just so much easier for us to find the marginal doctor because we have that enormous talent network

that can refer us to their friends. And even more importantly, that the kind of data shapes that we

would build for a lawyer are often very similar to the kinds of data shapes that we would build for a doctor.

And so all of the tooling that we build is very, very cross applicable. And that's the way that

most labs have been scaling out their data quite horizontally. And so for that reason, we are finding

that the labs tend to prefer partnering with a very horizontally capable vendor that is able to

flux across all of the different verticals and scale extremely quickly rather than working with

100 different vendors that they have to train for the same data shape and 100 different domains.

Do you think we'll go through your period of consolidation? Because there are a huge amount of the way

you'll actually end up buying the medical data product because it's a really important part.

Definitely. Do you think you will have that period of consolidation?

I think there will. I think in most markets when the markets are so frothy and anyone can get funding

and run negative margins, of course, there's going to be this proliferation of companies that pop up.

And when markets come back to earth and there are natural corrections, that's when there's periods

of consolidation. And so we view having over 500 million in cash in a super profitable business as

a significant asset and allowing us to be prepared for when there is a market correction to make sure

that we consolidate market share. You're profitable today. Very profitable. How long have you been

profitable? We've never really burnt cash. We burnt half a million dollars after our seed round.

Then from there, we've pretty much been profitable ever since. We have more cash than we've ever raised.

It's just because the business has grown so quickly that we obviously try to redeploy capital as fast

as we can to invest in growth. But the business has grown so fast that we haven't been able to

redeploy capital. Commence rate with that. Can I ask you myth buster one?

After we had a dache on the show, I think first time, people were like, the revenue is not real

revenue. It's GMV. When we understand your revenue, what's the revenue say? I can't show the exact

revenue number, but it's dramatically higher than whatever has been posted publicly. Let's give a

bull photo just because my simple number size genuinely, I'm not like a billion. It's much more than

that. Let's say a billion because it's easy for my brand. So we have a billion. Is that like sales for

Airbnb and then they get 20% of that? So the revenue is between a 30 and 40% gross margin, but the

key distinction and why it's not GMV, but as revenue is that the experts are actually only one part

of the broader value chain that we deliver to customers. So when a customer comes to us, they're

generally buying tasks where they would say, hey, they'll pay $1,000 for this task that delivers

model improvement. Then we do the end-to-end process associated with how do we find the experts,

how do we hire the experts, how do we build the platform that the experts work on so the experts

can do the work, how do we have our AI project manager manage the experts to automate all the

coordination of helping to produce this data, how do we have automated quality checks, etc, to

produce the end product of the task that we're delivering for our customer. So that's the large

distinction of how we're powered by a talent network in the same way that Uber is powered by a

driver network, but that's not the end product in the same way as some of those marketplace businesses.

What's so interesting for me and you can tell me if this is bullshit or not, it's like you've

seen the evolution of this business from like, hey, we provide raw data back to the largest models in

the world, like how it started. And now it's like end-to-end, we provide it fully and then we send it to

you. We make sure everything's ready and it's full stack exactly very vertically integrated.

Well, because so many parts of the downstream signal and form the upstream signal, right? Like,

we can use the quality checks on how high calibers each of the individual data points to understand

exactly what are the types of experts that we should be onboarding to achieve the data that drives

the most model improvement. And there's oftentimes this very power law nature of data that drives

model improvement in that out of a data set of 10,000 tasks, the top 2,000 tasks will create

majority of the value. And so it allows vendors that are extremely high quality to be super differentiated

in so far as pricing power because quality is the X factor that becomes dramatically more valuable

than any other dimension. What tosses super high value? Is it like the medical the financial modeling

style? It corresponds extremely closely to economic value. So think if you go through the top 5

demands that we serve it would be software engineering, it would be finance, medicine, law,

consulting, etc. And the super long horizon tasks within those. And so think we're moving away from

the paradigm of how do we get a investment banker to prepare a financial model and moving towards the

paradigm of how do we get a banker that can talk with five different colleagues and wait to hear

back their responses and prepare an entire slide deck with a deliverable that includes the financial

model, the analysis in a multi week long project. Those are the kinds of tasks that we need to be

building to push the frontier of research and evaluation so that those are the capabilities that

people are able to use in the models in six to 12 months. Can I ask which segment are we underserved

in in terms of model capabilities? In terms of like we don't have enough medical data, we don't have

enough financial modeling data, we don't have a is there a segment where like you know what if we

would require a company in this space to plug a hole in our data supply? Well, I would say maybe I'll

give it from my course perspective and then I'll give it from the labs perspective. Like we tend to

be now so good at mobilizing experts that were able to access pretty much any domain. There's always

going to be some degree of like these niche pockets of oncologists or whatever it is that have a

particular background, but generally we can fill those fairly quickly and it's more about people that

actually are very acclimated to the frontier of AI because it's the people that understood that both

have the expertise and oncology, but also our power users of chat GPT or cloud that are able to

find where the model makes mistakes and help the model learn from those mistakes. And so that's from

the more core perspective from the perspective of the labs. It seems like it's all encompassing. It's

just like the barrier to automating everything that you can do and say Google workspace is how do we

cover the full distribution of all of the context i messages, slacks, slides, excel sheets and all

of the tasks, prompts and outputs that correspond to everything that you do in your job. And that applies

to every individual and every domain throughout the economy. And so there's this enormous mobilization

of hundreds of thousands and so many of people to build out the full distribution of everything that

you could pass into Google workspace and everything that you could want out on the other side

in every job category throughout the economy. Can I ask you before we dive into a tweet that you did

which slightly terrified me to be quite honest. So you said about 30 to 40% is kind of how we think

about like our revenues from that. Generally up. Okay, so if we take the rounds that we've raised,

which round felt most uncomfortably high? Good question. We'll see. I'll talk through the

valuations of each of the revenue. So at our seed round did Fountain not fly you in the chopper?

That was serious. So our seed round was in September of 2023, we were at called a million in revenue run

rate or just shy of that. And I initially didn't want to raise because I wanted to bootstrap the company,

but a dorshan series condition on dropping out was that we needed to raise buddy. And so we met general

catalyst 8 a.m. on his Sunday morning. They gave us a term sheet within 36 hours for $2.3

billion at a $23 million post money valuation. So that was that was pretty reasonable.

And so far as Max and him on this was Max and Eko. And then at our series A, we the business that

didn't grow that much from the seed to the series A, but we found the market was a key differentiation.

And we met Victor when we were at one and a half million in revenue run rate in May of 2024.

And Victor got super excited. Initially I refused to take a second meeting, but then he said,

oh, have you ever been in a helicopter and so Peter took us on the helicopter flight. And then

benchmark really one to work with us. And so we were by the time that they gave us a term sheet,

we were at call it 2.5 million in revenue and they gave us $250 million post money valuation.

And then just four months. They don't feel uncomfortable because that's a big jump to you know,

two 23 post to 250. So keep in mind at the time, this sounds crazy because we were at 2.5 million in

revenue. But I was projecting 50 million in revenue run rate by the end of the year and 500 million by

the end of next year. And so it felt like a bargain. And then did you know who found this project?

Right? But we didn't have a project. Just don't happen often.

Yeah. And then four months later we met Felisa's and we never like would make a slide deck or take

investor meetings. And so Felisa sent us an email saying, hey, we know your co-founder, Surya,

really likes for us. So do you want to go racing for us with us? And then I replied and I said,

you caught my eye, tell me more. And they said, we'll meet at the airport in Heyward and go on

items private jet to Las Vegas to race for our ease around the F1 track. And so I was like, we're

available in three weeks on a Sunday. And so we do this. We race for our ease, we're at 20 million

in revenue. They ask us what valuation do we think makes most sense? And I say one to two

billion dollars. So they give us a term sheet at a two billion dollar valuation. And at the time,

you know, that's a hundred times revenue. And ever think that that's a high valuation. Meanwhile,

it was an incredible investment. So I'm going to be honest, this is when I interviewed a

Darshan at that time. And at the end of the year, I was like, dude, I would love to invest. Please

let me invest. And you very kindly let me put a small check in. And I then spoke to several of the

biggest and most in the world. And no offense. They like chuckled at me like, dude, that's such a high price.

He is such a high price. Well, so here's the thing going. We'd been growing 50%

month of our month for the prior six months. And I think what none of them really realized was that

it would continue, continue for the subsequent, you know, 12 plus months. Yeah. And so then that

compounded more and more by September. We were at a September of 2025 or say October. We were at

called 400 million and revenue run rate. And then Felicia's was like, we want to invest more.

And so they gave us a term sheet of $10 billion valuation. We didn't really want to spend much time

on a financing because the business was growing 50% month over month. And so we were very preoccupied.

And so that was about 25 times. And you know, the business has almost four Xs and so review which one

felt most uncomfortable. If you were to choose any, if I had to choose any, I would say that the

series B priced in the most like the furthest ahead of our growth. Or that or the series A, I think it

was probably the series B. Two billion. Because both were 100 times revenue. But it's very different to

be 100 times revenue when you're at 2.5 million and revenue versus 20 million revenue. Yeah. So that

that was probably the largest one. But obviously both were great investments and hindsight.

What is the next round done? We'll see. Probably a much higher valuation. We're getting a lot of

offers at meaningfully higher valuations. But the company is fairly profitable. And so we're

taking our time to see where the right partner is. We're also just going through modes of transport

on me. We had the chopper. We had the for a raise. We've had. We had to get observation exactly.

I totally agree. I've never been on a warship before. But that's a lot of fun. There you go.

So we'll line up the warship. The next 12 months will be dramatically better for infrastructure

companies upstream of Anthropic and OpenAI than for application layer companies downstream of them.

This was your tweet. Why do you believe that? The reason I believe that is that the application layer

companies businesses are not far removed from the foundation model companies businesses. Like it

is not a far leap for cloud co-work to add capabilities across medical and legal. Obviously they did it

with software engineering and do that can do that across finance. And so I feel like building

defensibility in the software layer on top of the models is going to be incredibly difficult.

Whereas on the other side of things and the infrastructure side, it feels like there are

meaningful modes that are getting built. We're compounding enormous network effects in the

business and a pretty significant data mode as we build out the inventory for our customers.

Compute companies obviously are able to build modes through these very long R&D cycles.

And so I think that there are going to be high margins that get achieved at the infrastructure

layer in sustainable profitable businesses in a way that it's less immediately clear at the

application layer. I mean you told me that I didn't know if you saw this but they increased

that pricing by 30%. I didn't know. Of course support. We'll have absolutely no impact on demand.

That's insane. Isn't that absolutely not so you increased price by 30% zero impact on demand?

It's probably the same for us honestly. We have the demand to double overnight. We just don't have

the capacity. And so it's mainly a question of how effectively can we scale to mobilize people to

build out these environments much more quickly? You do pricing elasticity tests because if you can

double price and double the business. We maybe can't double prices. We could double capacity.

We could probably increase prices by 30% without much of an impact. But the other thing you need to

consider is that pricing is not merely a question of optimizing for the next six months. It's

optimizing for a structure that wins the market over the next decade. And for that reason,

we're very focused on how do we do what's best for customers? How do we do what's best for experts?

And how do we build a sustainable business while we're doing it? But make sure that we're not

leaving oxygen in the market because high margins and by competition.

OK. I am an investor in several application layer companies downstream like a LaGora which you

mentioned there. We see the LaGora versus Harvey battle. I think everyone actually is coming

around to the fact that they shouldn't be fighting each other. They should be wary of anthropic to

your point. Totally. But then I look at it and go there is incredible defensibility. It's a very

deep product specifically suited to the workflows of lawyers. Anthropic would have to build out

whole separate product teams divisions to come after them. They'd have to build that GTM teams,

customer success teams, adoption teams. It's a different fricking company. The defensibility is there.

I'll give back. Maybe I would say two things. First is that I think over the last two years,

everyone has increasingly realized that the model is the product. That we can build so many of

these different abstractions of trying to stitch together API calls and having all this patchwork

logic where people use to have all these drag and drop agent builders. And then they just realize that

if we give the model the end goal and we train it to accomplish that end goal, it has outperformed

every other solution in almost every case that we go after. And that votes incredibly well for those

that are training models and the second thing to consider is that software layers are able to get

recreated very quickly now. We're building out an evil set that measures how effectively agents

can build end to end SaaS applications where 2025 was the year of how do you get a model to make a

PR on a code base and 2026 is the year of how do you get the model to clone Slack end end. And those

capabilities are going to exist in the models in the next 12 months. And so that means

very significant things for companies that are betting on software modes sustaining their businesses.

If we take that extrapolated further, how effectively can we build Slack internally agent led

entirely? That would very much concur with the SaaS's dead because if you're a large company

maybe small customizations, integrations, say you're a real estate company and you need very

specific integrations to pricing providers, you'd build your own. I generally agree. I think that

the caveat is when those companies have network effects, there's probably a significant mode that

isn't being priced in fully. For example, Salesforce has tons of companies that are building integrations

on top of their platform that creates this almost marketplace and network effect around it or

Slack has Slack Connect. And I think even Cart is another great example of this whole network

effect of the people that use it and one use the same platform across all of their companies.

I think that the companies that have network effects will be able to in some ways generate more value

because they can iterate 10 times faster while leveraging those network effects to create more

value for their customers and therefore build more valuable products, charge more money, etc. and

increase revenue. The companies that don't have network effects are going to struggle very significantly

because then there's not really a defensible mode in the pure software associated with the products

that they build. And so to me, that is the litmus test that determines whether this company is going to

become worthless or whether this company is going to gain dramatic value from their ability to

10X product velocity. You said we're learning more and more that the models are the product.

What if I push back and say the go-to-market is the product? When you're selling to law firms,

it's about being in the room with, you name your biggest law firms, your coolees, your goob winds,

your widening case, your Clifford chance, building the relationship with the buyer, and then the CS

and the adoption. And it's actually in the go-to-market, not in the product.

So I agree with this in part, but the caveat I would give is I think it's arguably more the forward

deployed motion rather than the go-to-market. And for deployed motion being the post sales, go-to-market

being the pre-sales because ultimately, say you're just really good at sales and then you provide

a SaaS product and you have a savvy customer who's spending a million dollars a year on the SaaS

product and they realize they could just like teleclod to copy it and they'll get the same exact

thing. It feels very difficult to maintain your pricing power even if you're the best in the world

at sales. Whereas on the other hand, if you have a great forward deployed motion where you're going

deep with a customer, you're training the agents based on all of this tacit knowledge within the

companies so that it understands how to perform effectively, that feels incredibly differentiated

and hard to recreate. And that's also the reason that we see obviously the labs,

open-air and in-thropic investing so much in this forward deployed motion. And so I think that

the Sequoia article that services are the new software resonated a lot in that these software

modes are whittling away. And it's the ability to layer services on top of software to meet the

customer where they're at and go the last mile. That is creating stronger defensibility.

You buy this new sexy category. I mean, Adventure Invest is a wonderful people, but this new

sexy category, but AI-enabled services is the future gold mine. I think in a large way I do.

I think the key thing is that you need to make sure that they're actually going to leverage AI.

Like, I think there are a lot of companies that are just building services and not gaining a

significant competitive advantage from AI and using that. That's the thing you've got to be

careful about, but I think it's very rational. Like, I'll give an example in the context of Mercourt,

which is that we within this process of turning human time from the talent network into building

these super rich environments that mere everything that people could do in their jobs. There is a lot

of human coordination of how do we answer people's questions, how do we track the KPIs of the project

and manage it effectively? How do we build the bespoke tooling for that project? We have about

100 people, or called 150 people in our delivery organization that do that for deployed work of

helping to go the last mile for the customer. Now we have an AI project manager that just completed

its first project managing that entire thing end to end, where it's able to hire the experts. It's

able to answer their questions. It's able to build the annotation tool using its coding tools

within our platform and produce the end data type. The experts all had a really good experience on the

project reporting to the AI project manager that was running it. I think we're seeing in a real time

that services are getting automated, and that that is going to be this extraordinary transformation

in the economy. One thing that powers obviously the agents that we use is the tokens that power them.

And I thought the whole point was that we have increased token efficiency and token costs come down.

Token costs are rising for everyone. How do we understand how you see token costs changing in the

next six to 18 months and why that is? Well, it's a faster-in-case study in Javan's paradox.

So, we were talking about in the context of making humans more efficient leading to more jobs,

when we make models improved by 10x year-over-year that has just been causing the total consumption

of the models to go up and up and up as the costs per performance go down. I think insofar as how it's

going to develop is that this trend is going to continue very, very significantly before we start seeing

any leveling off of token consumption within the enterprise. Right now, we're spending more on tokens

for our internal agents than we are on employee headcount. And I think most businesses are going to

look like that. Were you spending more on tokens for agents than you on headcount? Exactly.

Your token spend on agents is more than salaries. That's correct. It's pretty incredible. And so,

the way we manage it is that we have a variety of these key workflows throughout the company where we have

an AI project manager as I was describing that manages operations. We have our interview question

agent that where we've done over five million interviews and asked all the questions in the interviews.

We have our interview ranking or the broader candidate ranking where it helps to assess all

the candidates and figure out who we should be hiring. We have agents for accounting automation,

we have agents for fraud detection, etc. And corresponding to each of these agents, we have an

e-vail that tells us which model is best to use for this given use case and what is the

prediffrent tier of price performance for that specific use case. And that e-vail allows us to make

the decisions around where should we be allocating our inference spend, what provider should we be using,

etc. And I believe that over time, this is going to develop to look very similar across every

Fortune 500 where they'll need to have this system of record for evaluating and specifying agent

behavior across every workflow in their business. And they're going to use that to commoditize the model

layer because they want to enable perfect competition for the models having zero switching costs.

And so we've been growing extremely quickly with the enterprise and helping them to populate

the system of record and building out those e-vails for each of the use cases that they have throughout

their business. Do you think you will see that commoditization at the model layer whereby enterprise

clients are able to really efficiently package the workflows that they do so it does commoditize

the model layer? Because right now it's not commoditized quite. Yeah. So I think the key

distinction is that I think the API layer will get commoditized. You can definitely build stickiness

and workflows that people have on top of those APIs. Like for example, I have all of these routines

running in cloud code and I feel like it would probably be difficult or at least wouldn't put in the

time to move those routines over. And I have a bunch of similar things running in chat CBT. So I think

that there's going to be various ways that people can build stickiness. But for pure API based

products where it's like if we are just spending $10 million a year on a specific workflow,

obviously we're going to have an e-vail for that. And every time a new model comes out we're going

to benchmark that and understand exactly how we should be hot swapping between models and distilling

models. Why does the API layer get commoditized? Because the switching costs are zero. Like when the

switching costs are zero, that means that and there's a new frontier model every two months. That

means that we very quickly are going to swap them out. And ultimately the decisions that we make boil

down to the score on the e-vail corresponding to that workflow. And so it's very easy to compare

model to model one for one in a perfectly like hot swappable way, which is almost the definition

of a commodity. I'm still reeling from your token spend with agents more than headcount. Because

actually not many of them, they spend 300 million on anthropic, which seem like a lot of money. But

actually when you bait it down, it worked out to be about 3.8% of developer salaries is being spent on

anthropic, which actually is much less than one would think. What do you think that is in

24 months time? First sales force. Yeah. I think I don't know about 24 months time, but I would bet

that in five years the average enterprise spends more on compute than headcount. And the reason for that

is that the models are just becoming so capable that it seems like there's just enormous ROI to being able

to have models do something for 100k a year that is going to continue compounding at an exponential

rate in a way that human intelligence is not going to. And so humans will still play an important

role at the things models can't do. But I expect that cost of inference, cost of compute will exceed

that. The reason that that's so interesting to me is that having an e-vail for your specific workflow,

like say we take the case of sales force having an e-vail for how good is a specific model at

code generation in their use case is often a 10x lever on the price performance of that model.

Because they can distill the model, they can have an open source model that is performing as well

if not better for a dramatically lower cost. And so as we see this enormous shift towards compute and

significant inference spend across every workflow in the enterprise, they are going to need to have

e-vails that act as a source of truth for whether those workflows are being done correctly

and whether they're using the right work, rather than using the right models to accomplish that.

With the greatest of respect, e-vails today not relatively unhelpful. It's like how good a you at driving

around the corner for the driving test in a very specific way, but actually that's not how it works in

real world and it's actually not very practical. That's exactly the problem right is that we used to

have this paradigm of all of the academic benchmarks that were totally disconnected from the outcomes

that enterprises actually care about, where people were building everything ranging from GPQA for

PhD level reasoning to IMO for Olympiad Math to humanities last exam for this long tail of academic

problems, no one really cares about. And now they're focused on how do we get the model to do this

end-to-end workflow coordinating with multiple colleagues for a financial model or a slide deck like we

were discussing, how do we get the model to build an entire SaaS application end-to-end? And that's why

there's this enormous build out in pushing the frontier of evaluation as a critical research problem

for the next frontier of model development. Okay, next frontier of model development. If I listened

to everything that you just said, I would draw two conclusions. One, we should just invest all of our

money into open AI and anthropic and then the realization dawned on me that the majority of

startups and you can shoot me down, again, shoot me down, is the majority of startups stay especially on

the West Coast use frontier models to see where they can go and how far they can push them. And then

they use open source often Chinese models to get as close to that as possible at a much better cost

basis in which case, open AI and anthropic are inherently challenged by that much more cost-efficient

open source model right or wrong. I think both are true. Like, there's going to be many

orders of magnitude more demand in five years than there are today, maybe four or five orders of

magnitude more demand, but there's also going to be increased competition with people just distilling

and having fine-tuned open source models that accomplished their workflows.

Ultimately, I think open AI and anthropic are incredible investments and it seems like they're

starting to be consensus around that. It a way that there wasn't just a couple of years ago,

but at the same time, I think that majority of inference in five years is going to be using

a open source or custom fine-tuned or distilled model, not using a frontier model.

Okay, interesting. You said that obviously incredible investments. Where will they be in five years

time? Valuation wise. That's on. Valuation wise. Valuation wise. If we put them both at a trillion

state, it will take. Yeah, this is hard to imagine. This is one I'll play back two and five years

more. We'll both lay back. Either we were very prescient or just completely wrong.

I could definitely see one of them being a $10 trillion company, maybe even significantly higher.

It feels like the opportunity associated with being the frontier model is so large that it will

just eat up so much of the other demand within the economy because that also means that when you

have the frontier model, you can use that as a teacher model to steal your own models, to have the

best small models, etc. I would guess at least one of them is worth more than $10 trillion.

My next assumption was when you talk about orders of magnitude more, when you talk about spending

more on compute than you will on salaries, why didn't we just put all of our money in Nvidia?

I know it sounds supercilious and glib. I think it's not a crazy idea. Nvidia is obviously a phenomenal

business that will continue to execute super well. The only caveat is that it feels like we're starting

to move towards a multi-chip future where obviously Srebrus is executing well. I'm good friends with

the etch guys. Most of the labs are building in house chips. I would guess that in five years,

it doesn't feel like Nvidia has quite the same monopoly, but that's okay because even if they only have

30 or 40% market share in the largest market in the world by far, that is the world's most valuable

company. Speaking of the world's most valuable company, you were seeing this concentration of value

towards the top eight names more than ever before. 84% of the year-to-date rally was driven by the top

10 names. Do you worry about the concentration of value to such a small number of players?

Maybe to some extent, I worry about how do we smooth out the benefits to society? How do we ensure

that every enterprise and every individual is able to reap the full benefits of AI rather than

just a handful of people in San Francisco? Ultimately, I also think that there is some natural dynamic

associated with capital allocation where it is going to be more valuable to give the compute to

an anthropic where they have the marginal demand and can use that right away versus a less successful

company that might not be able to create the most value with that. I think that it's probably good

from a capital allocation and efficiency standpoint. Long as we are able to manage the societal

implications of increasing inequality. As speaking of increasing inequality, you wrote an essay

about, and this is taking from your Twitter, how we should eliminate income tax with the bottom

half of the Americans. Talk to me about that. Well, I believe this very strongly. I actually wrote

this essay as a research paper when I was a freshman in college. It was one of the few productive

things I did in college. Essentially, the thesis of this was that the largest positive externality

in the economy is jobs. People talk about all these economic theory of how we have negative

externalities like carbon or smoking or whatever it is. We should tax those. But on one hand,

the largest positive externality is jobs. Yet on the other hand, the way that most economies

structurally collect income is by disincentivizing jobs, both on the income tax side by taxing the

individuals as well as on the payroll tax side of taxing the companies. As we move towards a world

where there is increased job displacement, increased uncertainty around how many jobs are they're

going to be especially for the bottom half of Americans, I think that this is going to become extremely

problematic. I would suggest that we move towards a paradigm where we instead focus on taxes of

things that aren't necessarily going to have a negative impact on incentives in the economy. One

great example is capital gains, where I'm going to invest money in assets regardless. If there's

higher capital gains tax, it's not like I'm just going to not invest. I think that taxing capital

gains, especially short-term capital gains, which I think is probably not as beneficial for the

economy as long-term capital gains, would probably be structurally much better off than taxing income

with the greatest of respects. If you increase the tax on capital gains, you will disincentivise those

investors to take risk. Why the fuck should I pay more? I'm already taking a risk. I'm already

investing in innovation when other people won't, when banks won't, when all the data tells me not. Now you

want to tax me more for doing that, for taking the risk. Of course, you will disincentivise investment.

The thing is when investors are taking very high risks, it's generally in an aggregated way

in a portfolio. You would tax the gains on the portfolio overall. Even if you have a portfolio of

like, I know that you don't like to hear the capital gains tax, Harry, but... No, no, no, no, I think I say

this with the nicest respect. It's just wrong because you just move. I agree that the main thing you

need to be careful about is if people would move to other geographies because obviously that

creates problems. But I think the capital gains is one option. I'm so sorry to be addicted and you

can say with draw, that creates problems. Yeah, that's kind of the whole point. You fuck off to

somewhere that doesn't have capital gains and then you lose all the tax revenue completely.

I want, sorry, forgive me, we live in the UK where there's the green party, which is this

idealist movement. It's like, oh, increased. Oh yeah, then we leave and then you have nothing.

I agree. I think that there needs to be sensitivity analysis associated with how does the increased

amount of taxation cause people to just leave and reduce overall government revenue. But I think that

another way of going about it is also taxing consumption of items that probably aren't the best.

It's crazy to me that instead of taxing carbon, we tax the bottom half of Americans. Why don't we

tax carbon? That's a very clear negative externality in the economy, at least in the US, that's not taxed.

And so I feel like there is a lot of low-hanging fruit with respect to things that we could tax without

damaging incentives in a perverse way or causing people to flee the country that would be far

better than taxing the bottom half of Americans. And the other thing is that it's only 3% of government

revenue. Like the fact that it's only 3%, feels like it's a very easy decision for policymakers to

make and the grand scheme of the impact that it would have on people. But would you tax prediction

marketplaces? It's gambling. I probably would. There's probably some value of having good prediction

marketplaces for allowing people to have effective predictions of the future and hedge things within

their lives and investment portfolios. But it's likely okay to tax. The thing on that point of

around taxing the bottom 50% is Jeff Bezos retweeted me, which I was ecstatic about. It's pretty cool.

It was pretty great. Who's the coolest person you've met? I really like Jensen and I really like

Satya. I mean so many incredible people. Obviously Dario and Sam are incredible. But if I had to choose

one person, I mean Jensen's so cool. The jacket is style. He's always on point. So I would say Jensen

is probably one of the coolest. The fascinating one I would love to ask you and you shouldn't give

the answers to this. But I think this and people of us that have been reports right how to answer

is who did you think would be amazing? Who was surprisingly underwhelming? I don't answer that.

I get it. So that would be a really good one. It is an interesting question. I have met a couple

of you. You're like, wow, that gives me confidence that I can do that too. Actually, I will say this one

thing, which is that I remember when I went to Georgetown, I didn't get into Harvard and I was like,

wow, the people at Harvard are probably dramatically smarter than me. I went to this

nonprofit called Praud, where it was a bunch of kids from Harvard and MIT that were all building

startups. They're very smart. Don't get me wrong. But I do think that most of us have this very

equalizing feeling, that majority of people that accomplish extraordinary things. When you spend

more time with them, you realize that they're just a normal person to a significant, not all of them,

but most of them, to a significant extent. I think that makes you feel like when I saw

Ethan Thornton from mock raising $70 million as a 19-year-old. I'm like, wait, Ethan is like a chill guy

and a good friend. Maybe I could do something like that one day. It just gives you the sense of being

able to accomplish so much more. It's so interesting you said that that kind of dispersion effect

from seeing your friends achieve. I think it's one thing it's how'd Europe back in many ways.

You work with some of the largest model providers in the world. How do you feel about Europe's

inability to compete, slash, provide leading models to the world? When you look at the benchmarks,

a Mistral might make an entry at 72. It's like the Eurovision Song Contest. I love it. I'm very

proud of it as you, but, shit, we haven't delivered on the model side. Does Europe improve that?

Does that matter? I think that it's going to be difficult to change because there's just so many strong

network effects around talent, right? When we have the best talent, even I know so many brilliant

friend researchers that go to work at OpenAI and Robbick and DeepMide, right? Because when we have the

best talent at those labs, that's where they all aggregate and then that compounds to them having

more capital, more compute, more impact, et cetera. I expect that to continue and to be one of the

largest, not only economic but geopolitical advantages that the US has. If you were Europe today,

do you just go, you know what, sort it? We've lost that model race, but we can't still be a dominant

energy provider if you're Norway where I'm from. Actually, we do pretty well on Norway providing energy.

Is that what do we just accept that? I would accept that. I think that maybe it's worth having

some post training capabilities because there is going to be value to distillation and some of the work

that happens after foundation models are built. There's definitely going to be some value in

applications, but I don't know if I would lean aggressively into how do we compete, how to

do we compete with the US? Do you buy the sovereignty argument of we need sovereign models because we

don't want our data going to US or China or whatever that is? Maybe in some cases, there is value in

localization. I'll give an example, which is that oftentimes labs will come to us and say they

need their models, not just to be good at American law, but also to be good at British law or good at

French law or whatever the jurisdiction is in the world. I think that that is going to be an

important last mile in making the models useful and whatever jurisdiction that they're operating in.

That said, the labs are just going to hire 10,000 people in France to teach the models how to be

better at French law. I don't think that there is so much that others are going to be able to do to stop

that because the transfer learning capabilities from all of the other domains that they're focusing on

are just so powerful. When you say behind 10,000 people, the thing that's just astonishing is the wave

of cash. For me, I'm sure it opened out as the same, but I've seen it specifically with Anthropic.

I mean, insane levels of comp. How do you compete against that? It's definitely one of the things

that's most top of mind, in particular because the markets for people founding companies are so hot,

we've had three employees that have founded companies worth an excess of $100 million.

I assume you would choose when you do the McCormack theater. Exactly. We're a very young company.

I think that it's difficult for a variety of reasons. A lot of people probably don't have a full

understanding of just how hard it is to build a company as you know, well, Harry. How low the

probability of success is and how fortunate we were and how lucky we got along the way.

So I think that that's definitely one of the large challenges. Even like there was someone

who's hiring the other day and he had an offer for $20 million in cash per year from TBD. That's

the kind of stuff we run into on a regular basis. TBD met as a super intelligence group. 20 million in cash per

year or it's in stock, but liquid as hot to compete against. It's hard to compete against. Yeah.

Does that change? Does that just continue to escalate? So I think that it'll probably continue to

escalate for the people, for a smaller group of people. But I also suspect that as more people gain

knowledge of how these labs operate and what the capabilities of how to train a frontier

model, that means that there's going to be more supply in the market for people that have

that skill set and thus a little bit more reasonable pricing. So I expect there to be some craziness

that continues. But hopefully, sort of the 99th percent, all at least, within the market will bounce

itself out. What is the hardest role to have for today? Researchers. Just because of supply.

Because of supply and demand, it's just this market where there's 10 times more demand than

there is supply and that makes it very difficult. We've been building out an incredibly strong

research team like Edward who the first author on lawyer on Laura who was previously at OpenAI is

working with us in a bunch of other top researchers. But the market is definitely getting very hot.

How much does it cost to hire a quite quality air researcher? Oftentimes it would

be in the tens of millions of stock per year. For the really good people. I remember when

researchers weren't paid very much. It's like 10, 15 years ago. They were like underpaid but brilliant

people in society. Now I feel like that's totally changed. Is it a harder than ever to run the

company? I don't think so. Like to give a frame of reference, we were 40 people and 50 million in

revenue run rate last year. Since then, we've seven or eight X had count and we've increased the

broader scale of the business by 25, 30X. It's definitely been very stressful to keep up with the

growth along the way. But I think that now we have the supporting functions. We have not really

charged. We have finance and we have legal and we're building out HR. In that, bring some sense

of stability where I don't have to deal with all of these little escalations. I'm able to just spend

my time focusing on building great products, research and time with customers and that I think has made

it easier, significantly easier around the business. I get in a lot of shit for everything. I say these

days, which is wonderful. My team just go, oh no, hi. The job is I don't deliberately rage bait but people

just hate me. It's just the worst thing. But HR, I tweeted after a show with Adam and Abloven,

no great CEO that I've met and it's true. Loves HR. They slow you down, implement policy and

procedure and it's just pain. Do you agree with me? The caveat I'll give is that I think it's really

important. We definitely had challenges and skill and culture when we went from 40 people to

400 people. How does that show up? Extremely quickly. Well, it's so many things ranging from making sure

that we keep a really high talent bar to making sure that people are bought into the mission of the

company to even the tactical things of making sure that managers are communicating to their team

about their performance review and how they're doing so that they're never surprised by performance

review. When we have a young team with a lot of first-time managers that just creates

culture challenges of people that aren't used to giving feedback and maintaining all of the values

and commitment to the mission of the team. I think that to some extent, I agree and I think that

some of the big tech companies probably go too far on empowering HR but I also think that it's

important in one of the large lessons we've had over the last 18 months or so is that it's critical

to really get these foundations in place as you scale headcount otherwise it creates problems. Culture

challenges. Before the show we said that after the show with Adarsh, a couple of people thought that

like 996 was the way that like McCore is run and it's like clock in, clock out. Why is that not true

and how do you think about that? So the reason it's not true is that we've never mandated hours at the

company and obviously I work extremely hard. Adarsh works extremely hard. We work from when we wake

up until we sleep pretty much all the time aside from like maybe working out but I'm still thinking

about working during that time and most of our leadership team of course does as well but at the same

time majority of my leadership team has kids and we want them to be able to like go home and see their

families and all that and so I think that it's some combination of knowing that building a legendary

company requires immense dedication to the mission of the business while also recognizing that

we need to ensure that it's a sustainable environment for the best people in the world to do their

life's work. Are you ready for a quick far out? Of course. Would you like to go public? Definitely. When?

In the next few years I think that all legendary companies eventually go public and so it's an

important part of the journey and maturing and having a much larger company than we have today but

I think that it's not something we're rushing to do this year or next year in part because

we dropped out of college less than three years ago at this point and it's still a very young

business where we want to make sure that we properly actualize everything that we're working on on

the enterprise side especially before going public. Don't laugh. Do you have a light lie in bed at

night and just go like wow it's pretty wild. I'm always pinching myself and I feel extremely grateful for

the team and a darsh and Suria and how all of them made it possible because I could have imagined

100 things that would have gone differently and we'd been a totally different circumstance.

What if you changed your mind on the last 12 months? I used to have some questions around whether the

foundation model labs would be the largest businesses in the world because of the exact things you

asked about in the context of how much those models are going to be able to maintain pricing power

amidst a competitive environment but I think that as we've seen the sheer revenue ramp of these

businesses I've gained immense conviction that they will be the most valuable companies in the world.

You can invest in OpenAI or Anthropic which one? Oh I can't respond to that. I would choose that.

Who do you not have as an investor in the company yet that you have most like to have?

I really admire Jeff Bezos. I think he's so disciplined about the culture of Amazon. That's one of

the things that's always stuck with me. Everyone there just understands the values and

is staring in the same direction as such a strategic business leader. I've never met him but I've

always wanted to. Which competitive do you most respect? I admire that Edwin from Surge has done a

really good job in staying super close to research and it's something that we've obviously been doing

a lot of as well but I think that's probably one of the largest things that differentiates both us

in Surge is our ability to train models to hire some of us researchers in the world and I

admire them for execution on that front. What percent of data providers are just respectfully

transactional talent marketplaces? In terms of volume or number of competitors? Number of competitors.

About half. Half? Yeah. What would you most like to change about your role today? I would say that

there's a decent amount of HR things that get escalated to me and so we're looking for a really strong

head of people that is able to handle a lot of this. Final one for you dude. What's the kind of thing

that anyone's ever done for you? One that really stuck with me is I remember and I'll probably

attribute this to the entire prod community namely especially a couple of people like Rob Walken,

Ben Spector and Richard DeHon but prod was this nonprofit that got started at MIT in Harvard and I

was sort of a blow in because I didn't get into those schools but I went to Georgetown and for the first

year of the business. They would meet with us every week. Ben became a big customer, Richard would

give us tons of money just as to float working capital and Rob gave incredibly valuable advice

and they had nothing in it for them. They took no equity. I tried to give them equity and they

wouldn't accept it and more core wouldn't exist if it weren't for any of those individuals I would say.

And I think that that is something that I'll always be grateful for for the rest of my life.

Dude I have to say I loved having you on the show last time. It was incredible to do this in person.

I'm so thrilled with how this conversation went and you've been amazing. Thanks so much for having me

Harry. Always great to come back.