E326: What Happens When AI Starts Replacing Analysts?

You built model and model to solve your own problems within your family office.

What were those problems?

So when Hans and I, who's my brother, we sold the second company, we

were still pretty young.

Hans was, I don't know, 21, 22.

I was maybe like 26, 20, 27.

We made a bit of money and we decided that we wanted to invest our money for a while.

So we decided on a few kind of asset classes, strategies behind a few people to

predominantly do the investing.

And then we spent a lot of our day writing software to make that process better.

Before we knew it, so that was maybe 21 coming into 2022, you know,

2023 coming in 2024, as LLM started to really be useful in these sort of

environments, frankly, the product just got better and better and better.

And before we knew it, we just had too many people asking us whether they could

use it and they're willing to pay for it.

So the story goes that we call my mom asked my mom if we could build a third

start up and she said, no, and so we started it the next day.

Give me a specific, low-hing fruit that investors are using, model, and

now in order to solve their everyday problems.

A classic is just like reporting and monitoring in general, right?

It's the classic problem.

You know, you're, you know, even when I think about this back in the family office,

you know, we were making, you know, on the bench or start-up size,

maybe like 25 investments a year, right?

And we would receive updates in like every single format, you know,

sometimes even now it's like a website link.

You know, it's like, you got a website link, you got a notion page,

you've got just a bunch of documents, you've got it in the body of the email,

you've got an Excel file with multiple times, et cetera.

And really what you want to do is you want to consolidate that down and bring

that into your systems in your format.

It's it's somewhat baffling to me that, you know, a lot of these tasks are still

being done manually, frankly.

Um, you know, that is a classic example of something that should be, you know,

automated.

We're not in the world of like 100% automation.

I think we'll get there, you know, probably 12 sort of 24 months away and

of a single task being close to 100%.

You know, anything above 60% automation, we really focus on.

So, you know, it's not about getting, you know, to pixel perfect at the end.

It's really where can AI be most applicable and that's a specific workflow today?

And the classic one is something like your reporting.

And 2025 was supposed to be the year of agente AI.

Now people are saying 2026.

You're one of the only agente AI companies on the planet that are scaled.

You use over trillion tokens with open AI.

Why have you been able to solve agente AI?

In a way that others have not.

We really launched the end of 2024 and we raised about 100 million across a

couple of rounds in our first 12-ish months.

So the business has been growing, right?

And looking back on it, you know, we often think, okay, where did we

differentiate?

And to us, it's quite clear.

These chat type interfaces think, you know,

chat with you and anthropic and others, they're great.

Don't get me wrong.

They're absolutely fantastic.

Um, they're very much going to change the world.

But if you think about the complexity of work that actually goes on in,

you know, these types of organizations, they're going to have a ceiling.

So if you think of any relatively complex workflow that you've done, you know,

in the last few months, can you solve that in a chat or Q&A type environment?

You know, if that's resulting in a 200 page PowerPoint or a 50 tab wide

at Excel workbook, are you going to be able to solve that as of today in a chat

interface?

The answer is almost certainly no, right?

And we kind of knew that up from.

And so we came into the market with a slightly different perspective where it's

like, look, the chat type interface is great and we have that.

It's useful.

But really, I think what firms want is they want true workflow automation, right?

But also, it's much easier for us to sell to them and easier for them to procure as

well, right?

Because they can actually say, okay, well, here are the three or four things that

we want to automate.

Can you automate those things?

Well, if we can, yes, and it's very clear, the difficulty with the chat type

interfaces, it's, it's very hard to quantify, you know, whether that's delivering

additional insight or productivity, it's very different.

It's quantified with workflow automations.

It's just a lot easier.

How should investors, GPs or LPs think about agentech AI and where could they

apply it in their day to day?

The important thing is, is where are we today?

But where is this headache, right?

So today, let's make no mistake about it.

And you know, these application layer products, whether that's in finance,

legal health, get whatever, you know, they are productivity tools for the most

they're giving you, you know, an additional layer of efficiency in your

businesses, right?

The question is though, particularly if we think about this from an investing

standpoint, is when is this going to be able to deliver a level of insight

that wasn't possible pre-AI?

That's really the question, you know, because at the end of the day, productivity

is great, but, but it's all about that alpha from an insight perspective.

And in our view, there is absolutely no doubt that 2026 is going to be that

year. We think 2025 is the productivity year, 2026 is the year where we are

actually going to start to see the systems deliver insight that wasn't possible

pre-AI. Now, if we're going to play that back a little bit, that's not going

to be today, no insight, tomorrow, insight.

It's going to be slightly more incremental than that, right?

I'll give you a quite a specific example.

One of our middle market private equity clients, it's a European client,

they're absolutely fantastic.

They've pretty much automated 80% of their IC memo, so their IC paper, right?

A lot of that is going to different data sources and really just data retrieval,

a bit of reasoning and producing that in a format that they're used to digesting,

graph tables, charts, logo in the same formats that they would digest the

information for, you know, going to the data room and going to cap IQ,

I got a pitch book and so on, all the areas of information that you would normally go

to. But there's two or three pages in that now that are not just generated by

AI, but it's kind of like the AI's opinion, right?

And these are things like, you know, the AI's opinion on the overall

management team based on your historical investments.

We've noticed that there's a lack of experience over here.

There's a lot of experience here, for example.

Now, as of today, they glance over that in their IC meeting.

It's kind of like, this is interesting.

We spend five minutes on it and we move on.

But one of the things that's clear to them and it relates to us is the importance

of those two or three pages is only going one way.

In other words, the AI opinion is only becoming stronger and stronger and stronger.

And so I think that's why it's super important.

That firms, you know, it might not be perfect today, right?

But you've got to embed this into your culture and these systems into the way

that you think as soon as possible because that future insights only going to be

unlocked by doing this today.

Tell me about that.

Why do you have to prepare today for insights in the future?

I was thinking coming into this conversation.

What would I advise, you know, irrespective of what we do?

What would I advise firms to think about today?

I would really think about data.

I think things like trying to transcribe calls is a great example of like, you know,

if you think of what these systems are going to need in future, the more data that they have,

particularly now because they are a sort of data structure agnostic,

whether they're calls, emails, files and folders, structured data, it doesn't matter, right?

You want to try and capture as much data as you possibly can as part of the investing process.

I think that's important.

The second part of that question is it, you know, this is more of a cultural shift than anything else.

I think as we thought about so, so I should say, you know, our customers are about a third,

you know, say asset management in general, a third and, you know, the largest, you know,

consulting firms who are on a third and backing, there are their vows is, you know,

there's a few other vows in there now, but there are their vows.

Now, the consistency across all of them, right?

So not just on the bicep system across all of them is this is clearly not a technology problem

anymore, right?

This is becoming more and more of a cultural change and a structural change, you know,

as to how you think about the organization, how you think about AI from a cultural perspective,

but that takes time and, and, and, you know, I really would encourage firms to just start

and not overthink the initial process and, and start.

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It's interesting because a lot of people use this analogy of data as the new oil,

but no one goes upstream of that and thinks about how do we capture more data?

You drill it, you drill in the ground to get more oil and you have to capture the data.

If it's truly oil, why aren't you capturing more of it and why aren't you creating the schema,

the processes and the cultural aspects of becoming a data driven organization?

The reason I always use in that example, sort of cool transcriptions is because it's kind of

in a way obvious, but less obvious, right?

You know, these models, they're incredible at interpreting videos,

incredible advertising, audio, text, it doesn't matter, right?

And so it's almost like regardless of how you're thinking about AI in a short,

made a long term, start capturing that data.

We did some analysis around, you know, what actually goes into the decision making

process and where that information comes from.

It's something like 70% of all information is coming from calls and meetings, right?

So you want to try and capture as much as that as you possibly can.

Again, this is less relevant to us.

It's more just our view on like a cake outside of adopting a vertical application,

which clearly seems like something that firms should do.

What else can they start thinking about doing, sort of agnostic of that choice?

And it's, it's just capturing more data where they can.

Well, last time we chatted, you gave me the shocking statistic that you believe

that companies will experience 55% productivity gains in the next 12 to 18 months

on the backs of AI.

Where are these productivity gains specifically going to come from?

So I was saying, well, what even qualifies us for making that statement?

A lot of what we're doing as we're selling into firms is we're building business cases, right?

So we have to deeply understand where AI is applicable today.

The big thing that really no one saw is the progress that happened with reasoning models.

No, reasoning is it is effectively a technique.

It's very difficult to forecast progress in underlying techniques.

Now, forecasting progress in the underlying models, you know, is theoretically,

you know, that's a lot more predictable, but techniques are more difficult to predict clearly.

And so as we look at that and we look at all the data and really

looking at where the time is going more importantly, particularly the more junior members of the team,

you know, we're looking at a 50% efficiency.

Certainly before the end of 2027, but that could be a considerable amount of time before that.

And I don't think that's because of the technology.

I think that's because of the adoption part of your business processes.

You go into organizations and you look for productivity gains that they could have using agente AI.

What's the exercise that you go through?

How can an organization figure out where they have the most productivity gains?

We do an entirely free, you don't have to sign up, discovery phase.

Right. So, so, you know, we will work with a customer and we will identify where we think AI is most applicable today

beyond just a, you know, chat-based interface, you know, as I said, work for automation.

So, so we do that over a two, three or four week period and really come the end of that period.

You know, we're targeting a number of, and we don't really look at workflows unless it's a 60% efficiency gain on a single workflow level.

The reason we do that is because we really want to focus on sort of short-term ROI to deliver value, you know, quickly.

But we do that now with, with all of our customers.

Perhaps this is a dumb question, but is this a software process that's running on machines?

Are these virtual machines?

How do you actually implement these systems?

The user just logs in, presses a button and the report is generated.

How does that mechanically work?

So, if you kind of picture the way I would work is we have an Excel type interface.

So, think of, you know, just Excel, but powered by AI.

You upload a document and what we're doing is we're deconstructing that document into its individual components.

And we're making, you know, the model is making the best guess is to where you would get that information from,

from documents, from fax set, from local filings, from, you know, wherever you may normally get that information from.

The user would come in and confirm they would click save and then that workflow would exist, right?

And they can deploy that workflow to just themselves, to the rest of the team, to the whole group.

And then they can obviously, as you go, you can make alterations and then someone can make a copy.

And, you know, adapt that workflow to them as a specific user if they want to.

Is it fair to compare you as an enterprise open cloth of sorts?

And how do you look at open source projects like open cloth,

we're competing with what you're doing?

Quite important.

That has become very clear in the legal AI space, I think.

You know, I think it's like 80, 90% of the top 100 law firms now use either Lego or Harvey.

And I think finance is about 12 months behind.

So, I think we can learn a lot from what happened in that market.

And if you look what happened there, it's really in sync with us is the entire product.

You know, that's the bottom and the agents system through to the UI is designed with the customer

might, right?

And that last mile delivery to the customer, really from what we're seeing is where the impact is.

So, it's not what the agents are necessarily going and gathering.

It's how that's presented the customer.

And it's very specific to that customer base.

Exactly.

And it's just, it's the design of the overall system, right?

So, it's like, you know, if you think about the agents, it's what they do integrate with.

The way that the agents communicate, what language do they communicate?

And do they understand financial concepts?

Well, whereas the context coming from, so this, you know, from an agents perspective,

there's also finance specific things that go on that are really, really important.

Again, you've seen that in the legal AI world.

But then it's the UI.

How is a user interacting with these systems?

You know, coming back to this point of, can you build a 200 page,

you know, PowerPoint presentation exactly as you would done before,

graph, tables, charts, logos?

You know, think about the complexity that goes into these types of outputs,

think, FDDs and CDDs and so on, right?

Can you do that on your phone and chat GPT or in Claude?

Well, no.

So, you have to, it's the agent system all the way through to the UI

and with the user interacting with these systems that becomes really, really important.

You mentioned it.

Legal tech.

Really pioneered this professional use of agentech AI.

What can you learn from the Harvies, Lagores of the World?

And how do you apply that to the Fintech?

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Use case.

That last model delivery we talk about

is really really important.

So when I talk about last model delivery,

I'm sure you've heard of this concept of FDEs

for deployed engineers that's like booming.

That's the concept of having people literally work

in the offices or from the offices of your customers

for a period of time to not just drive adoption

but to deal with onboarding

and also to innovate really, really quickly.

I think that you saw a lot of that.

And I think that is just as if not more important in finance.

Again, because it's not about just the adoption

and the driving, the cultural change,

it's those really, really quick feedback.

One of the things that we've become very well known for

if we look at the competitive landscape is

if you look at our nearest competitors,

we're maybe built about a product in a quarter of the time

with a tent of the capital.

Now, I know that we've raised a lot recently,

but before that, we're the tent of the capital.

I think that's really really important

because if you look at the legally eye world and our world,

our customers are not betting on our product today.

They're betting on where our product is in 12 months time.

So, for all intents and purposes,

they're betting on the team

and the team's ability to ship the best of the best

and be at the forefront consistently.

And so, I think with this forward-to-point model

that you saw in the lead guys,

based a lot, we're doing what's actually happening there

is we're taking feedback in real time

and we are altering and approving the product in real time.

And let me be specific.

We have scenarios where someone will be at our customers office.

They will receive product feedback in real time.

They will write all to the code-base in real time,

make that alteration, do the poor request

and it'll be in production within half an hour.

And some may say, well, that's great,

but is that really needed?

What about the day after?

But the thing is in this world is

these systems are improving so quickly

and the competitive landscape is changing so quickly

that really are moats as a business

as the speed at which we can learn and therefore ship products.

Is that not limit your scale?

How do you go about scaling a services-based?

Maybe this is only now being a little bit English

or maybe this is the y-combinator way kind of installed

in our mind, but our motto here is

fewer, happier customers, right?

We're not a business that's going to over-promise

and under-level, right?

We're a business that when we say we're going to do something

for a customer, we do it.

And if we don't, I think it hurts us emotionally, right?

So I think our view on the market is these demos

that you see all over LinkedIn and so on.

They're great, but the things that we're really manner,

if we fast forward 12 or 24 months time,

it's going to be that sticky revenue.

Sticky revenue is ultimately going to come from

how happy your customers are with your products.

So sure, look, we can't take it to the extreme.

Do things that don't scale, but at the same time,

we just believe that if we deliver to our word in a market

where reputation, frankly, is everything,

is the single most important thing.

Now, you can still scale quickly, you've raised,

you know, about a set about 100 million

in our first 12 months, we're very well capitalized to do so.

You know, the team's gone from 20 people to 100,

I'm sure we're allowed another 100, you know,

in the next few months, so we're scaling

to accommodate for that.

And sure, it might mean in the shorter term,

our margins of society less as a business,

but ultimately, we think if we do well in that first month,

three months, six months, and 12 months,

you know, we can have these customers

for a very, very long time.

Talk to me about the early adopters in the GP and LLP space.

What are the characteristics of these organizations

and where are they adopting it internally?

It's interesting, you say about characteristics,

because I think that comes back to this thing of

a cultural shift, right?

So I think it's really important that

there is top down buy-in, you know, CEO level,

CEO level buy-in of these sorts of products,

or AI in general, which now I think is almost a bit outdated.

I think that statement was relevant maybe six months or so ago.

I don't know of a single firm where this isn't like number one

on the agenda, or maybe number two, but probably number one.

So generally speaking, the best early adopters

that we've seen are where they were,

it's like true kind of top down buy-in.

I think that's the first thing.

The second thing is, we've also noticed it's the firms

that think about how this is going to alter the organization

structurally, because clearly it will.

Like, you know, I don't think it takes a rocket scientist

to figure out, you know, you go on chat, GPT,

and do anything around, you know,

and any of these AI products, you know,

there's a huge efficiency game that's happened and happening,

right? So there has to be a structure change.

So the ones that are really thinking about things structurally

as an overall organization, I think of the ones

of the best early adopters.

Being, if you want me to be specific that,

I really mean, well, if you take your,

you know, more junior members of the team,

and you're seeing, even if we're pessimistic,

and we're seeing a 10, 20% overall possible efficiency game,

well, where is that 10, 20% going?

It needs to be reallocated.

And the best firms and the best leaders,

the ones that are thinking ahead,

and they're saying, okay, well,

we're going to take this 10, the, you know,

this pool of, you know,

it's a 10% of the overall team,

and they're going to become our AI experts.

A lot of time they're self-identified, you know,

we all have friends or, you know,

people that naturally in their roles are like,

super interested, compared to their peers and AI,

they're probably the ones that you want to use,

because you don't need to write code in this environment, right?

So the ones that are actually saying, okay,

we need to move these people on,

and your job is to either assess, or, you know,

AI tools, or build AI workflows in a,

in a product like model ML,

they're the ones that I think are doing incredibly well.

And the last part of your question is, you know,

where do you start, you know,

what areas of the business do you focus on?

I think this is, you know,

can be on a case by case.

And so that's where we've been very value add

in this discovery phase.

So I really think like we do an incredible job

at working closely for, you know,

doing this free discovery phase, helping, you know,

where that's GPs, where everybody, you know,

identify areas that they should immediately be thinking

about applying artificial intelligence.

That's what we've become very good at.

Now, what does it actually look like in terms of use cases?

Well, for now, it's more of those, you know,

inefficient, you know, areas of the business

that you can have these productivity gains.

As I said, I think in future that will become more,

how you're able to drive insight

that wasn't possible pre-AI, but for today it's creativity.

What are we talking about budget wise?

What's has a firm, do you need to be to hire firm

like Madeleine ML?

Or maybe how much do these projects cost?

We as a business don't do many kind of like 5, 10,

20 CETA type deals.

We're really looking at, you know,

in the hundreds of seats at the minimum,

if not the thousands, right?

That's changing and quickly as we're scaling the team,

it's enabling us to work on those smaller deals.

I really, that's just a question of focus, right?

Again, if we wanna make sure we over-deliver

when we're working with our customers,

but at the moment it's in hundreds of seats,

I think that's gonna come down to the 5 and 10s

within the next few months.

Pricing wise, depending on whether you're buying data

through our success, you should really be budgeting

for true AI tools, you know, anything from a hundred bucks a month,

maybe 300 bucks a month per seat,

but again, it just depends on scale.

Chas, I wanted to do a live discovery.

So we have a media side of our business,

we have an asset management side of the business.

So what questions would you typically ask a client

maybe we could role-play with it?

I always ask, this is a question that I think's important is,

I ask people about their own AI journey as an individual, right?

You know, how, what are they using AI for

inside and outside of work, right?

The reason I ask that is I like to understand

their appetite towards AI in general.

I do kind of look like AI as a person.

Sometimes they disappoint, he or she disappoints me

and sometimes it comes through.

I found, so we have on the media side of our business,

we have a lot of our podcasting, the production,

editing, but we also have YouTube and thumbnail

and packaging and all this.

And I have found very specific cases where AI is very good

in certain specific cases where it's extremely bad.

And even worse than it being extremely bad,

it's extremely confidence in those cases.

And whenever I ask it how confident it is,

it gives me like full confidence and then it's completely off.

So that's been kind of my downside of working with AI

where it not only gives me poor work output,

but it also tells me with very high confidence.

If I would say to you areas that frustrate you,

that you feel inefficient,

but you don't necessarily know how and where

to apply artificial intelligence,

what are the things that springs in mind?

I think video production, editing the first part

of our, the podcast.

So we have an editor that does a bunch of fancy stuff,

but just like the pure editing,

the platforms haven't been able to do that.

In terms of thinking strategically,

like mapping guests, mapping outreach, all of that,

that would be a big help.

Also, obviously on the asset management side,

figuring out which companies we would want to,

like what should be on our target list,

mapping the network of how to get to those people,

and how to get to those companies.

These are the things that really take a lot of man hours.

Okay, excited now.

So that's great.

And then what I always ask as well is to what extent

across those are particularly interested

in the kind of more prospecting one,

as you think about the managers,

what tools if a tool ever used to kind of think about

or look at that overall use case?

We're old school.

We talk to a lot of other GPs,

figure out which companies they like.

We don't have a great process for prospecting.

We're very much reactive in terms of the conversations

that we have.

I'd love to be much more proactive.

So the answer is we don't really have a process for that

outside of just gathering information,

old fashioned way, meetings, Zoom meetings,

and person meetings and building relationships

with co-investors.

And then one last question is how do you get the feedback loop

with like the areas or the nuggets of things

that your listeners are interested in,

other than people.

You're asking for my secret sauce.

So I'll give you some secret sauce.

So we get analytics from the audio,

but the richest analytics we actually get from YouTube.

And what I've started to do about two months ago

is we have the retention curves.

I take the retention curve, I upload that to AI,

I take the transcript, I upload that to AI,

and I have a give me feedback in terms of like

where did people drop off?

So we kind of do this recursive improvement.

Yeah, nice, nice.

And then what we try and do if I was working with you

as a customer is like, we try and at least kind of

whiteboard out, it wouldn't be as quick as this,

but we would whiteboard out a couple of use cases

and try and get feedback on them in real time.

Mainly because I think that's what kind of gets

the both the creative juices flowing of what's possible,

but directionally where we think AI is good at.

So the things that was spring into mind

as you were speaking by the way,

things like, okay, well, in the comments on YouTube,

how are we thinking about categorizing those comments, right?

And how are we then thinking about, you know,

the different nuggets of content off the back of that?

So by way of example, have you thought about,

if I said to you, you could have a workflow

that the second that a video has published,

it's monitoring those comments.

So it's actually a workflow that's connected to that video,

and it's in real time, grabbing those comments

and it's categorizing them,

with a view that is consistently updating this output,

sort of summarizing where it fills that thematically,

everyone is interested in,

or all the main areas of interest.

Where my mind went,

the two really leverage points for us on the podcast side,

it's actually the guest.

If the guest is 80%,

if I have Ben Horowitz, Cliff Asnes,

Bologician of Austin, if they come on the podcast,

yes, the conversation could be good or bad,

but you're more or less know how it's gonna do.

So guest booking,

and how do you get to the mapping the relationship graph,

because I'm connected to pretty much everybody,

just a matter of like how to trace that.

And then the same thing on the company side,

on the investment side,

which company should we be talking to?

So the relationship graph is kind of where I went to,

which is how do we get information

of what the best companies are,

and how do we get who we should have on the podcast,

and then mapping to them through social networks?

Obviously, we regularly a connector,

most of our customers are connecting our product

into that CRM, for example.

So clearly anything in the CRM we can tap into,

but I think there's a lot of work to be done,

your exact point,

it reminds me kind of when the business was started

around one of our first use cases is,

we wanted to understand that relationship instantly.

If there's a mutual connect

with, we're looking at an opportunity,

and there's a mutual connect with the management team,

we're looking at fun, you know, someone knows the GP,

except whatever it might be,

that's very much untapped.

We've actually, we're looking at bringing you

on a product team specifically focused on that area,

and that's many just because our customers

are slowly moving in the direction of, you know,

GP's all these et cetera.

So I think in general,

I think it's going to be quite popular.

When my oldest friend, Johnson Kim,

he started five, nine as a public company,

and he hammered this into me,

he always hammered into me,

you have to be close to the cash.

Companies die because they're not close to the cash,

and when I think, that's why my mind went to,

well, what's driving our revenue?

Advertising and more deals.

So what should we be solving?

Guests and more deals.

That's kind of why my head just immediately went to sourcing,

I guess sourcing on both sides.

One of them, why culminates a motto,

which I think was a motorcycle that said it most,

was, you know, the idea of a growing business

is just don't die,

which basically just means they run out of cash.

So I'm very aligned with that.

If you could go back to when you were just starting

your first company, your first YC back company,

and you could give yourself only one piece

of timeless advice, what would that advice be?

Definitely perseverance.

You know, I've mentioned this,

we were on the YC recently,

and I said this on there,

not blind perseverance, but perseverance.

I think if something makes sense to you,

and you are passionate about it,

and it makes sense in itself,

you should probably continue doing that thing,

and persevere at all costs.

I think across all the companies,

there's ups and downs,

these economic cycles ago.

If you believe in something,

I think the key is to just persevere.

It goes back, I've now interviewed nine billionaires,

hopefully it'll be my 10th billionaire,

you know, let me know in a few years,

and they all have one and a half things in common.

The first thing is they're all compounding something.

None of them are building linear businesses.

You don't have enough life to become a billionaire linearly.

It must be compounding.

Sometimes it's literally network effects.

Sometimes they're compounding brand.

There's different ways to compounding,

but they're all compounding.

This is universal.

And the second thing almost all of them,

I can't actually think of a counter-effectual,

is they're all not only walking in the opposite direction

of the market,

oftentimes they're running in the opposite direction.

I had the CEO of I Capital.

He was a decade earlier to the retail trade.

When he was doing it, people, you know,

no institutional investor wanted to take retail capital,

and he took a bet.

And not only did he say,

I'm gonna do this, you know, hopefully works out.

He was just running there.

And now he's built a $7 billion company or so.

Another example, Ryan Sirhant,

he was doing social media back in 2014.

Every real estate agent was ridiculing him.

You look like a clown.

I think he literally jumped and pulls,

maybe even literally dressed like a clown.

And he didn't care because he had that conviction.

In 2014, he sold, I believe like a $15 million pen house

through YouTube, and that's when he knew kind of,

he had that proof.

So having this conviction,

and if you have this conviction,

you're growing in the opposite direction,

and no one else sees that,

that's basically a sign that there's these signs

that you get in startups.

We had, when we started the podcast three years ago,

every single institutional investor that had to go to,

we had to create a compliance call.

We knew that we were too early.

Thankfully, I was a VC and understood that if you,

if it felt too early, you're probably on time.

Having this contrarian insights where everybody thinks,

most of the time you have a contrarian insight,

everybody thinks that you're wrong, you actually are wrong.

But once in a while, you just keep on going back

to first principles, what am I missing?

What am I missing?

And if you're not missing, you better run

because people are going to catch up.

That's it, pass it in.

Well, Chas, this has been an absolute masterclass.

Thanks so much for taking your time,

and thanks so much for jumping on podcasts.

David, thanks so much.

That's it for today's episode of How Invest.

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