– English Lyrics

Lyrics & Translation

[English]

This idea of moes is so pervasive and so

important.

It is interesting how moes have just

become much more discussed by aspiring

startup founders now than they were pre-

AI.

What is going to prevent you from being

basically subject to infinite

competition?

Like a mode is inherently a defensive

thing and you have to have something to

defend otherwise like

if you got nothing to defend, don't

worry about your mode.

[Music]

Welcome back to another episode of the

Light Cone. Today we're going to talk

about Moes. So, in your head you might

be thinking about barbarians storming

your gate. You've got this little

startup and you've got every other

company out there who wants to come and

eat your lunch. Uh, and you know, right

outside your castle is a moat that keeps

them away. Jared, when you were going to

college campuses, this isn't sort of

this trivial thing that people are

thinking about. It's actually uh

something that keeps them from starting

companies right now.

Yeah, this is a question that we got

from a lot of very smart college

students on on our on our our recent

scholarships. And basically, their

question is like they don't see how

these new AI agent companies like a lot

of the ones that we've talked about on

on this podcast could have moes. um it

plays into this meme of like the chat

GBT rapper that like all of these

companies could be easily cloned and so

they can see how you could build a

business that makes some amount of

revenue, but they don't really see how

you can build a long enduring business.

And so I think it's actually not true. I

actually think these businesses do have

quite deep and interesting modes, but

they're not totally obvious what they

would be. So I think this is an

interesting topic for us to to explore.

At our recent AI startup school

backstage, I had this exchange with Sam

Alman that I thought was kind of funny.

You know, we spend a lot of time

thinking about, you know, make something

people want. Very simple maxims that are

sort of anti- business school. And yet

this idea of Moes is so pervasive and so

important. We sort of remarked how funny

it is that uh one of the more important

books to read these days is actually

business school fodder. um this book

called the seven powers. So today we

thought that we would actually go

through those seven powers. What are

they? What are some concrete examples

and ways that a startup founder who's

just starting out uh could or should be

thinking about these things from real

world examples that we've seen.

So Diana, can you tell us a bit about

this book?

This book was written by Hamilton Helmer

who taught at Stanford Economics School

and was published in 2016. And the book

title was the seven powers the

foundations of business strategies. And

a lot of the examples are more with the

era of uh internet companies from the

2000s. So a lot of the examples are like

Oracle, Facebook,

Netflix, which is a older generation. So

we want to do a bit of a reboot right

now how he applies now 2025 with AI.

I think it's a little bit confusing the

way he uses the terminology in the book.

It's called the seven powers, but it

would make a lot more sense if he just

called the thing the seven moes because

that's really what he's talking about.

He's really talking about seven

categories of moes that a business can

have. And I it's true that the examples

are out of date, but I think the

framework is actually pretty timeless.

Like it turns out there's just only so

many kinds of modes that a business can

have and they don't really change. And

so like even though the specific like

versions of these modes are different in

the AI agent world, like the categories

haven't changed. Thankfully, we live in

a world where there's markets and

there's free markets, where there's lots

and lots of competition. And these moes

in a lot of ways are the only way if

you're running a business, you can sort

of fight against all of the other people

who might want to do exactly what you're

doing. And um you know, famously Peter

Teal talks about uh competition is for

losers. And so the profound view there

is that given infinite competition, what

is going to prevent you from being

basically subject to infinite

competition and then as a result uh you

know your margins, how much you can

actually profit off of what you're

selling goes down to zero. And what that

means is like actually your business

will die and so you know having a moat

is uh relatively existential eventually.

You made a great point earlier, Gary,

that like this is actually like you kind

of have to worry about this at the right

time of of a startup. Do you want to

talk about like how like early stage

founders should think about Moes?

I mean, this is sort of why we generally

tell people to go find a person with a

real problem and then go solve that

problem first. It's um what's funny

about the world uh that's a little

surprising is that you can go almost

anywhere and find some painoint, some

problem that could be solved with

software and especially with AI that

frankly just isn't being solved. And if

that and they're they're so numerous and

so severe that if you find that thing

and solve it, you literally can mint a

billion dollar or 10 billion or even

hundred or hundreds of billions of

dollars uh market cap business and it's

just lying in plain sight. That's really

the first thing that people should do.

Like you should just find a problem and

go solve it. And then along the way you

will probably as you work with customers

as as you build the product itself and

engineer it and figure out what data you

need for it and all of these things like

you will stumble upon these seven

powers.

Yeah. The moes come later like it would

be like pretty dumb for somebody to

decide not to work on a startup idea

because they can't see what the

long-term modes of that idea could be.

Right. It is interesting how moes have

just become um much more discussed by

aspiring startup founders now than they

were pre- AI. Seems like the main reason

for that presumably is just that big the

original chat GBT rapper meme and that

the the moat that most people are

worried about is moat against the big

model companies and how like are you not

going to get crushed by one of the big

labs when they decide the product you're

working on is really valuable and they

want to own it too. And I think Varun uh

from Windsorf who we hosted some time

ago he said it himself the early stages

at the beginning the only model that

startups have is really just speed. Once

you pass that and build something that

people want then you figure out and go

deeper into these type of modes that

we're going to discuss.

I really like Verun's point that the

only moat is speed. That is not one of

the seven powers in the book, but I

think it probably should be.

I think it also comes with a lot of the

essays from PD because one of the

tenants really at the beginning is yes,

your big company, let's say OpenAI at

this OpenAI is the new Google. It's like

sure OpenAI or Anthropic could build all

these features let's say like cloud code

and then compete directly let's say with

cursor or etc. And for a startup like

cursor to really win even in the

beginning is they had relentless

execution because a larger company like

a Google or Anthropic it they just have

a lot of uh more craft that they need to

do in order to ship a product. They just

have all these product managers all the

operations. It needs to go through a PRD

some spec dog and it takes mo a lot more

time to ship a feature as opposed to

cursor. the incredible story about

cursor. When we hosted Michael Truel to

come talk to the badge, he was sharing

how his product development cycle for

shipping features and sprint cycles were

one day

one day. So one day sprint

in the at the beginning during a 2023

2024 around era they would start the

every day would restart the clock and

try to ship things every day. I mean

that's like insane speed. Like there's

no big company that could ship something

at that speed

at most weeks, couple weeks and maybe

like larger companies. I don't know your

Google maybe like multiple months or

sometimes years. I mean they had Google

Bard or Gemini a long time ago that took

years to get out, right?

I think Kurser and Windsor are great

examples of when you should start

thinking about the modes because for the

first few years I don't think it really

matter that much. They just had to like

they proved out that hey like codegen is

going to be a really valuable

application of AI. The development

environment is going to be very very

important to own. they like got rapid

growth and then it's only when they're

at scale that you know like they have to

start thinking about how are we going to

defend against like clawed code or

codeex or all the other things coming in

and sort of like the mental model that's

really stuck with me is when we spoke to

Bob Mcgru a couple of weeks ago um and

how I think Jared you brought this up

actually was one way you could think

about it is that sort of all of these

startups are kind of forward deployed

engineering teams like for for the labs

maybe and so like early on actually

because this is all green field we don't

actually know what the valuable

verticals and products to build are. So

in a sense you don't you step one is

just figure out what that is and it

wasn't actually even two years ago it

wasn't actually clear it was codegen or

um the IDE once you figure that out and

you find and you sort of struggle then

you keep digging that's when you have to

probably assume at some point you're

going to get more competition because

people are going to realize oh this is

really valuable there's lots of money to

be made here and then you have to start

like defending like the treasure you

found. So, I mean, all the things that

we're about to cover aside from speed

are sort of 1 to a billion, 1 to 10

billion, 1 to a 100red billion, one to a

trillion dollar sort of problems and

then uh the real stupid thing that

people might do is watch this and look

for this as a reason to not even get to

one.

Yes. So that would be

they try to use it to like pick between

two different startup ideas because

they're like trying to forecast five

years in the future which one will have

a greater moat

which just isn't how it works. I mean

literally you shouldn't do that.

Like a moat is inherently a defensive

thing and you have to have something to

defend otherwise like

maybe you have nothing.

Yeah. Hey nothing to defend. Don't worry

about your moat.

Yeah. Otherwise it's just like a puddle

in a field.

Yeah. Exactly.

Let's assume that someone has found

something that's valuable that is worth

defending. Should we talk through what

some of the modes they they could think

about are?

Yeah. So process power again like the

terminology is kind of funky but like

basically it means you built something

that's like you built a very complicated

business with a lot of stuff that's just

hard for people to replicate just

because you like built all this stuff.

Um and so the example that he uses in

his book is like the Toyota assembly

line. And I think the AI version, the AI

agent version of this is just a really

complicated AI agent that's been like

finely honed over like multiple years to

work really well under real world

conditions. We've we've talked about a

bunch of these on this podcast like Jake

Heler with um Case Text is like the

original example. A couple other ones I

was thinking about from more recent

companies. We have like a couple

companies that sell AI agents to banks.

We have Greenlight who worked with Tom.

They do KYC for banks. And we have Casa

which like does loan origination for

banks. So it is essentially tells banks

like which loans they should give. And I

think these are interesting examples

because for all of these AI agents you

could build a version of green light or

CASA or case text like a like a demo

version in like a weekend hackathon. And

I think when college students are

thinking about these AI agents I think

what they have in their mind is like the

weekend hackathon version of the product

and they're like like I could build that

in a week. Like how could that be

defensible? And like the reason is like

the the version you build in a hackathon

isn't useful to anyone. It's like like

like like if Casca or or Greenlight fail

like the the banks will lose millions of

dollars. This is like missionritical

infrastructure. So it's it's more like a

self-driving car.

One way to look at it is way better

engineering

uh is actually that's like the most

profound form of process power. Like one

example might be Plaid which you know

the surface area of the number of uh

financial institutions that they have to

support is so giant it's you know

probably thou on the order of thousands

to tens of thousands of different

different websites different crawlers

and then all of the different you know

can you imagine like Plaid's CI/CD

structure and then you know this is pure

speculation but if I were uh Zach

running plaid like I you know know that

I would want to be using codegen tool,

the latest codegen tools to be able to

uh you know basically add every new

financial institution on the planet

quicker than anyone else. Like that's

sort of a very profound form of process

power uh in the modern AI age.

I think this is probably the main form

of defensibility for the existing SAS

companies. Like if you look one

generation before the AI agent companies

like why is Stripe or Rippling or Gusto

defensible? I think it's mostly this

right. It's just like they've just built

a lot of software and it'd be really

expensive and hard to replicate all of

it and like you can't just copy it from

their landing page. Like there's like

like the backend logic is like super

deep.

There's also I feel like kind of a shle

blindness aspect to this going on too

where like the the hackathon version of

any AI tool is like quicker than ever to

get to. But actually the last like 10%

of getting it to work reliably across

like tens of thousands of KYC requests

like per day is sort of like a

particular type of painstaking

drudgery work in a way that I think like

lots of engineers just not excited to

do. And then that is also kind of like

the teams at OpenAI are going to

experience this too, right? Like if

you're if you're working in one of the

big model labs and there's teams of

people trying to invent AGI, um it's

going to be hard to get jazzed about

nailing the like final 5% consistency on

your like KYC tool.

Yeah. And so I I think this is

especially true for like verticals like

KYC that are require specialized

knowledge to even know what to even to

know what the edge cases are. Like if if

we had to pick from the seven powers

like I think speed and this these are

probably like the two dominant ones that

come up the most often

and those are most related to execution

is where uh the hardcore builders win.

having really good product taste and

building the best product really matters

and I think it comes to a lot of the

point maybe the the misconception is I

think a lot of these products you can

probably build the 80% solution with 20%

of the effort but for these solutions

and products to work you need the 99%

accuracy one which then takes like 10

times or even sometimes 100 times the

amount of effort right it's sort of that

parto principle type of thing what about

uh the the other power for corner for

resources.

I think the classic view is they're just

coveted assets or things that uh you

know they're not arbitrageable. Um they

must be independently valuable and then

I sometimes they offer preferential

access with you know rates that are way

lower. So uh the classic example that

you know you could look at is you know

pharma companies have these patents that

are very hard to get. Um they have to

come up with them and then prove them

and get through regulatory approval. And

the sheer fact that they have a patent

plus you know uh getting through FDA

approval is something that can be very

durable and it's uh you know so powerful

that patents have a uh limited lifespan

because you know you don't want people

to have that forever. A more modern

example I think you know on the

regulatory side might be you know scale

AI is doing a ton of work with the DoD

um you know Palunteer as well. Uh, in

order to even get there, it's, you know,

a painstaking process. You've got to

hire the right people. You've got to

spend a lot of time in DC and Langley or

wherever, you know, you're trying to

sell to. And, uh, you've got to

literally build um, uh, skiffs, like

these like sort of, you know, special

data uh, centers where, you know, it's

at great pain and expense. Um, you have

to get embedded with the government. But

then when you do, like, well, you've got

it. you know the corner resource in some

sense is even the brain space in people

who work in the government like you

right now if you're working with AI like

you've got to go through a palunteer or

a scale and that's like literally

written into their uh public documents

around like how they're thinking about

the nature of warfare and the nature of

uh you know everything that they want to

do having to do with AI moving forward.

So, you know, the corner resource

doesn't have to be a diamond mind. It

could be the diamond mind in your

customer's heads. Those examples are

sort of uh closer to like being way up

in the sky having this like insane

decacorn like worth hundreds of billions

of dollars sort of situation. But what's

relevant for startups that I think all

of us uh sort of see every day is sort

of what you were mentioning with uh this

forward deployed engineer you know FTE

forward deployed engineer model that uh

that is what a lot of startups that are

extremely successful today are literally

doing like they're going out and getting

a cornered resource in the form of real

data and real workflows um literally

sitting with a customer who normally

would never get access to good software

and then spotting Okay. Uh this is sort

of the tailored time in motion. You

know, first the uh you know a request

comes in by email. Then we take this and

we enrich it in this way. Uh sometimes

we have to have a call center call this

person like you know actually

understanding what um might be a very

boring process um and then translating

that into your own prompts, your own

evals, eventually your own uh data sets

to tune your own models. Like those are

all things that are incredibly valuable.

And then uh clearly there are examples

you know earlier uh we're saying like

character AI for instance um you know

took LLMs you know obviously built some

of the first LLMs then took many of them

and then fine-tuned them in a way so

that they could bring down the cost of

uh serving those models by 10x and so

you know that itself is also a form of a

cornered resource. the best cornered

resource to have is your own model that

can like do the specific work. Yeah.

Better, right?

And for a while, people thought that

that was the only mode that you could

have in this space. If you didn't have

your own model, like you were totally

hosed. Turns out that's not true. Turns

out there's just one of the possible

modes.

Partly that is a threat people are

worried about in in the big picture. The

10,000 foot scary thing is if the labs

at some point decide to treat their

models as a cornered resource and they

restrict access. I guess the interesting

thing right now is like it may well be

true that the you know platonic ideal

perfect manifestation of an AI system

will require a lot of both you know

maybe uh pre-training post-training RHF

like just so many different things that

you have to throw at it to get it to

like chat GPT level but we're also so

early in the revolution that um you know

even if just context engineering gets

you 80 or 90% of the way there. That's

plenty. That's actually all people need

to do for like the first 2 years of

their startup almost always. You know,

Cursor didn't start out by doing, you

know, full parameter fine-tunes of GPD5,

which they probably have access to now.

Um, they started just by making

something people want. You earlier we

were saying like don't use these

frameworks to count yourself out

prematurely. And this is a very profound

version of that.

So the third power we're going to

discuss is switching costs. That is uh

the concept where you get a mode when

your customers

are kind of trapped because it becomes

very expensive for them to find a other

solution. Even if the other solution

might be like a little bit better, it's

just very painful for them to switch

financially or in terms of the

operations times or effort because they

just have so much of it in the current

solution. And examples that are given in

the book are um like databases like

Oracle. When you have all of your system

of record and all your data in Oracle,

it becomes incredibly hard to migrate.

like database migration is something

that people don't do. Other example

given is a Salesforce and because once

you have all your customer records in

Salesforce you build all these workflows

the UI and it's just a lot to retrain a

lot of your sales team to use like a new

software you need to like migrate all

the data and then at that point for the

company to switch to a new CRM is

probably going to take I don't know lose

like a whole year of productivity or

something even if the new solution is a

little bit better. I think how AI

companies are building mode with this

has to do with a version of what Gary

mentioned with the forward deploy

engineer. We've given examples of this

with Happy Robot or Salient where they

start with specific workflows that are

very customized per company and they

work uh with large enterprises and part

of it is actually with the forward

deploy engineer they may have actually

very long pilot pilot periods which

might last like six months to a year but

if they succeed these convert into seven

figure contracts and the reason why

these pilots are so long is because

They're very much building custom

software for the specific operations in

these companies. And the examples for uh

Happy Robot, they got customers like DHL

where they went deep into integrating

into a lot of the workflows for how all

their logistic operations are done which

is very accustomed to the DHL operation

or the example for salient who's

building AI voice agent for the

financial industry. They integrate with

banks and a lot of the banks have very

different workflows on how they do a lot

of the loan

consilation, how do you do the debt

recovery,

how they do a lot of the fraud

monitoring

and risk and compliance and it's all a

little bit different because all these

companies have built kind of internal

tools and the whole part of u being an

AI company that builds these workflows

they build custom workflows then that

work with them. But as a result, the

trade-off is you do have very long pilot

cycles, but the pot of gold is worth it

because you end up with this big

contract and once you're in, you're kind

of minted and the big enterprise is not

going to do another bake off because

it's gonna it's going to be a huge waste

of time for them to let's try the other

whatever cool AI voice agent company. At

that point, it's like we just want to

get the benefits. So that's how these AI

companies are winning. I think it's like

at once a moat and it's also uh in it's

interesting in the age of AI that uh

simultaneously you could how see how AI

brings down the cost of switching by a

lot and that's you know sort of another

lever that a startup could use like if

you can write um use codegen to

basically extract data out of old oified

systems or your competitors then you you

know there are things that might have

really relied on switching costs that

you could potentially bring it down to

zero.

Yeah, there's actually two different

flavors of switching costs, right?

There's the the old school ones from the

SAS era, all the system of records like

Salesforce, but also ATS's like like

Lever and Ashb where the switching cost

was the painfulness of migrating data

from one system to another. And I agree

with Gary. LLMs might significantly

reduce the switching cost because the

LMS can figure out how to like morph the

data from the old schema into the new

schema. You use brower like use browser

automation on both sides to like solve

issues where like people don't let you

export the data. But then there's this

new form of switching costs that I think

is pretty native to the AI era like

you're talking about to Tayiana which is

like this these these lengthy onboarding

processes that lead to like deep

customizations of the logic of the agent

not just the data that didn't really

exist in the SAS era. Like I guess you'd

like customize your like your Zenesk

implementation a little bit but like not

that much.

Yeah. I mean and then for AI companies

on the consumer side, I mean this is all

very nent, but like I think memory is

already becoming a bit of a switching

cost for me. Like it actually blew me

away that Claude was so behind on memory

and then you know uh my relationship

with Chetch I feel like has evolved very

significantly in the last year where I'm

like oh I actually just generally it

seems to know you know what I'm into and

what I care about. So you know that

switching cost I think over time will

only become greater and greater and so

personalization for consumer is actually

a huge piece of that.

What about counterpositioning the other

moat on the book?

The definition of counterpositioning is

doing something that is difficult for

the incumbent that you are competing

with to copy because it would

cannibalize their business. I think

there's a couple of ways that this plays

out. In every category, there is a

Darwinian competition between the

existing SAS incumbents building their

own AI agents and the new AI native

companies building AI agents on top of

the existing SAS companies. So like for

customer support, the existing SAS

incumbents like Zenesk and uh Intercom

and front are all building their own AI

agents. But then we have like a new wave

of companies that grew up in the last

couple years that are building AI agents

that interface with with those systems.

I think it's like I don't know this

could be a topic of a whole like Lite

Cone episode which like who will win in

in in each of these fights I think is

really interesting. Um

unstoppable force meets the movable

object.

One way where this is playing out in the

counterpositioning is that all almost

all these companies their pricing model

is they charge per seat i.e. per

employee. And this is I think a very big

Achilles heel that they have

strategically which is that if their AI

agents do a good job and actually work

those companies will need fewer

employees doing this work because

they're like the work will be automated

by AI agents and in a and in a

simplistic way that will just actually

reduce the more successful they are the

more they will reduce the revenue. My

guess is like some of them will be able

to navigate this like especially if

they're still founder controlled. I

think like intercom for example like the

I think the founder controlled versions

of these companies are smart enough to

recognize that this is existential and

they may be able to cannibalize

themselves. I think the ones that are

not founder controlled I don't have a

lot of hope for it's super hard to

cannibalize your own revenue.

The alternative as we're seeing is so

much of the startups um pricing models

are around sort of like work delivered

or tasks completed. I think it's it's

exactly what you said, but it's also

that then switches the product towards

having to actually be able to complete

the work. And um something I actually

repeat at the last YC batch um at the

end as closing advice is that I wish the

founders in a batch could just somehow

go spend a month at some of the latest

stage companies. Um uh cuz the top thing

we hear from the founders running those

companies is how hard a time they're

having sort of resetting the engineering

culture in their org to actually embrace

AI to use the tools to want to do like

context engineering and prompt

engineering and and the the net result

of these teams not actually being able

to be AI native one of a better term uh

is that they just can't deliver the

products that work right and so like

they both don't want to switch from

Percy pricing because like that's what

they're used to um uh and in a world of

AI being able to do the work, there's

going to be less seats to sell to, but

they also just cannot deliver on

products that can do the work. And so

they they wouldn't that that pricing

model is not going to make any sense for

them either.

Yeah, it's it's like the process

engineering part. They're not good at

the process engineering part for this

new kind of engineering.

I mean something sort of uh emerging

that's very interesting in a bunch of YC

startups like uh Aoka for instance,

they're doing customer support software

kind of like Service Titan but for um

HVAC. So literally like people who help

you with heating and uh air conditioning

and uh you know I think service titan

has something like 1% wallet share 1% of

the gross transaction value of like a

given HVAC company um which is very

small right I mean people don't spend

that much money on software because

these are relatively low margin service

businesses but the wild thing that Aoka

discovered is that you know they can

come in as software but then over time

they're actually getting a bigger and

bigger chunk of the wallet share because

they can get the HVAC people to pay them

uh actually for the customer support

piece which is not 1% of their spend but

four to 10% of their spend. So what you

may well find is that uh this new breed

of AI startup will actually have more

growth uh and uh higher wallet share.

So, you know, actually, we may well be

all uh undervaluing how powerful and how

big the vertical SAS uh AI companies

will actually be because you're not like

1% of wallet share. You can get to 10.

That's what we talked in that episode

where vertical AI SAS agents will be 10

times at least 10 times bigger than SAS

because it's really to your point Gary

tapping into a whole different part of

the spend of the companies is not the

wallet of software where you're kind of

at this point I suppose is a bit of a a

finite budget but is really new space

where with things that were not possible

and it was mostly workflows from from

people

and I you know I know that people are

like pretty sensitive about uh workforce

displacement but you know customer

support for an HVAC services company is

not a fun job and you can tell because

all of these customer support jobs

actually have like 50 80% annual

attrition rates. like they're just such

torturous, not fun jobs that uh the

companies themselves and the call

centers themselves spend almost all of

their time trying to vet and bring in

more people to work on these terrible

jobs. And so when you have better

software, what's sort of happening is

that instead of like people aren't

losing their jobs, these people are

quitting their jobs anyway because it's

terrible job. And then if anything uh

what Avoka has told me is that many of

the people who were in those customer

support uh you sort of roles uh now

they're actually having more fun jobs

because instead of like managing a whole

set of people who don't want to be there

uh they're actually managing AI agents

and then handling the interesting weird

cases. The coolest part of it is like

they actually can go in and sometimes

alter the prompts and sometimes you

actually have an imp direct impact on uh

both the experience of the customer but

then also their own day-to-day and that

immediately is like a 10 times more

interesting job like wrangling a bunch

of AI agents and making uh the support

process better and better over time.

Like that's you know as knowledge work

goes like way more interesting than

follow this script and read what the

computer says.

So Harj you you had a really interesting

point about a second form of

counterpositioning. this space has moved

so quickly that in every vertical um or

many verticals there's sort of early on

emerged one company that's seen as the

early winner in the space and often it's

actually like the second movers at least

within the YC context we have seen over

and over again that like there's

advantage to being the second mover in a

space like stripe came after uh Brainree

and authorized.net then a bunch of

things and was able to like actually win

by just building a better product. Door

Dash came after Grubhub, Postmates,

various other delivery services and

eventually went on to win. And so I

think it's interesting to sort of just

consider about if you're entering a

vertical where it's already feels

competitive or there are already there's

already seem to be like a early winner

in the space. How do you counter

position against them? One thing I think

is really interesting here is Legora

versus Harvey. Lagora is obviously uh

both in the legal AI space. Harvey was

the early winner. The counterpositioning

that I see from Lorraa is Harvey came in

early and maybe got early sales um but

focused a lot on fine-tuning and sort of

like their product differentiation when

over time it's seen that that was

probably not the right move. You wanted

to actually focus in on the application

layer and actually just sort of building

a better product and and Lora has

focused on that. That's what their

branding and positioning is and it seems

to be working really well for them as a

second mover into the space. A company

that I've worked more closely with, Giga

ML, enter the customer service space and

they're competing with Sierra and

Deacon, like really well-known customer

support companies and from having seen

their sales motion, how they've been

able to sign up some big customers.

They're I think their counterpositioning

is their product fundamentally just

works better out of the box and as a

result they can have a much faster sales

and onboarding process. So it's like

their counterpitching is if you want to

sort of get your customer support

working as quickly as possible um you

should go through like the Gig ML

onboarding process versus like the

decong and I think that's actually

worked quite well for them.

Yeah, Giga ML is an interesting example

of how to your point about like hybrid

displacement.

It's clear that an AI agent can do this

job not just as well as a human but

actually much better than a human. like

the Door Dashers that the Giga ML agents

are talking to, a lot of them don't

speak very good English. They speak all

kinds of languages. You can't hire a

customer support person who's fluent in

200 languages. Um but

but LMS are actually out of box.

Out of the box. Um and they're

infinitely patient if like there's a bad

connection or so that's pretty

interesting.

I think you have other example where to

your point of superhuman abilities is

where the AI version of the product

actually works. I think Hargie you had

the example of a Dualingo versus speak.

Dualingo is obviously the biggest

language learning app I think um most

consumers know. The emerging criticism

of it I would say is that um what it's

actually just sort of like a gaming app

versus a language learning app that like

the way the app works is orthogonal to

learning a true language. And then you

have speak um which is a uses LLM like

uses voice to actually like help you

practice and actually learn the

language. Um, and that

counterpositioning is working really

well for them, right? And sort of

speakers has got explosive growth and

it's not trying to compete with Dualingo

on the we're we've got like lots of

gamification and points and sort of like

a great game mechanic. It's competing on

hey, we're actually just a place you

should come if you want to learn the

language by speaking it. I think the

counterpositioning mode is very um sort

of close and overlaps with the branding

mode idea. I think in the book he talks

about you know like brand is it's

essentially a mode when you become so

well known that even if you have an

equivalent product um consumers will

still choose you um because the the

brand effects and I think the the

example uses like Coca-Cola in the AI

context I think it's probably harder to

apply brand as a moat directly to

startups it just takes time to acquire

brand um but you can certainly see its

effects like the thing that still stuns

me is open AI chat GBT has more

consumers is using it per day than

Google's Gemini. I think anyone who

understands the models and uses them um

daily would say that Gemini Pro 2.5 and

Gemini Flash 2.5 are like equivalent

models

and Google also had all the users like

basically everyone in the world is a

user of Google.

OpenAI had no users initially.

Google was already one of the biggest

consumer brands on the planet. It was

almost certainly the biggest consumer

brand on the internet and yet somebody

else came along and built the brand as

the consumer AI app and Google is like

playing catch-up.

If someone had try had told me in 2022

that that's how it would play out, I

would have been fairly incredulous.

It's also a perfect example of

counterpositioning. Again, I mean, this

is Google had a uh a business model that

required it to continue to support ads

and an organization that uh they

shipped. And so, you have the greatest

cash cow in the history of man. So, why

would you disrupt it um even at the cost

of setting back uh human access to

knowledge by a few years? Even if that's

like the core stated goal of Google

itself to organize the world's

information,

there's also the untold story of how uh

the origin story of Chachib how it came

to be which is really the original mode

for startups with speed. It shipped very

quickly in a matter of months with a

very small team of a couple engineers. I

mean, it required uh you know, Sam Alman

and YC Research and Greg Brockman to go

uh hire Ilia Suskgiver out of DeepMind

because he was there and you know he all

the people a lot of the people who went

on to help create OpenAI uh they came

from DeepMind like it was already in the

right place. It's just that that place

didn't nurture exactly the thing that

society really needed

for speed.

So there's that mode again speed number

one. Do you want to talk about network

economy Diana? Yeah, on the book a

network economy is described as uh where

the value of the product increases as

more users or customer get and use the

product and everyone deres more value as

a effect of more people using it and

examples that were given in the book are

uh Facebook where as you use it and your

friends use it is more fun for me to use

Facebook because all my friends are in

there as more users come in then is the

social network becomes more valuable.

And this was very much the era of uh the

internet where people talked about uh

network effects that came to be. And the

other example he gives is like visa the

visa network where the more merchants

are using Visa

the more value the consumer gets because

you're can swipe the Visa card in more

places. then that becomes the the moat

because it's harder to then acquire and

amass this number and large number of uh

users or merchants in order to to win.

So that becomes very defensible. In the

current era for AI, the shape of uh

network effects is different. It really

comes into the shape of data. I think a

lot of uh the data that a lot of AI

companies get access to becomes the mode

where the more data they get the custom

models they build become better and the

better models it becomes a better

product for users and there's lots of

examples of these and um besides like

the big foundation

lab companies where they probably use

some of the data I don't know I mean

they probably use some of the data from

the users they probably do

checkpt almost certainly like feeds a

lot of that back because you have a

certain reward function for right

each training run, right?

So all the history of every chat from

chat GBD 1 2 3 4 5 now goes fed into

GPD6 and then so on and so forth helps

create the the next model version. And

there's uh even smaller versions of

this. For example, cursor, they have

probably one of the best uh tap tap

autocomplete because one the the free

version of cursor they actually say it

when you sign up that they they will use

the data and they use that to train it

and the more users they get

I think it's like all the data like I

think it's like quite literally like

every mouse click and every keystroke

that you that you emit when you're using

cursor like is fed into a model which is

like kind of crazy

which then the more developers cursor

the better the product gets and then

they compound a lot of the a lot of the

wins with that. And the version where

this applies to AI startup is when they

go work with enterprises and large

companies they get access to private

data. I mentioned earlier salient or

happy robot when the employees of the

companies where they become customers as

they use their product they have a lot

of that private data that makes a lot of

the workflows better and the way they

improve that which is the second way of

having modes with networks is really

evals we we talked a lot about evals

being the key mode for AI startups is

evals is where you get a lot of the this

workflow work or didn't work and then

take that back and iterate and improve

your context engineering. And that is a

flywheel that you can only achieve when

you get more and more usage of your

product whether being in a consumer or a

or a AI vertical SAS agent. So now the

last mode in the book is uh scale

economies. Jared, do you want to tell us

about it?

Scale economies or economies of scale.

you've invested a lot of money to build

something that's really big and as a

result you have economies of scale and

you can offer the service cheaper than

anybody else. So like the the classic

example would be like UPS or FedEx or

the Amazon delivery network. They built

like massive like physical

infrastructure and as a result they have

like a lower cost per unit um compared

to a smaller competitor. Um I think the

way this has played out in the AI world

I don't think it's actually played out

that much at the application layer. It's

really played at at the model layer,

right? Like training a state-of-the-art

LLM is very capital inensive. Only a few

companies can afford to do it. Once

you've done it, you can afford to like

let people do inference on that model

very inexpensively. This is why the

DeepC announcement was so um was so

earthshattering last year because it

seemed like it might be a lot cheaper

than people previously thought to train

a Frontier LLM which would greatly

diminish the power of this like

economies of scale mode that people

thought the the AI labs had.

The key thing about Deepseek was they

figure out and made public this new

unlock for models which is uh how to do

RL. They still built on top of one of

the large foundation models so it's

still expensive. the rail part is

cheaper, but you still need the very

expensive big foundation model. So

that's one of the things that the media

got wrong.

There's a separate question that people

talk about, which is like how will the

foundation model companies be defensible

against each other? And like this is

certainly one way, right? It's just like

it's it's very hard to be a new entrant

into that game now because of this

economies of scale. And we were we were

thinking earlier about like how this had

played out with startups and there's not

that many examples, but I think a couple

of good ones. Well, one one good one is

is a company of yours, EXA. Harge, do

you want to explain what what Exa does?

Yeah, Exa is essentially search for AI

agents. Um, it provides an API for

anyone building AI applications that

wants to search the web.

And the way I I think this is playing

out for Exa is in order to provide that

service, they need to crawl the web. Not

the whole web like Google does, but a

big chunk of it. And that's very

expensive to do. It requires like a

large like fixed capital uh investment.

But then once once you crawl a big chunk

of the web, you can reuse that same

crawl for for many different customers.

I think what's interesting about X the

parallel to the model companies is that

they they had invested in that like sort

of before agents had really taken off

like they were fairly early to this. I

think they were working on this actually

even prehat GBT launching. So they made

the investment early on took a bet same

way that the lab companies took a bet on

like transformers and um uh and scaling

laws.

Yeah. And there are two companies in

just the most recent batch, Channel 3

and Orange Slice, that are both doing

exod.ai like plays where they crawl a

big chunk of the web, have a big like

static crawl on their own servers, and

then have agents that run on top of

those of that crawl. So, I think we're

going to see more and more of this,

especially as the web agents work

better.

You need to mainly focus on uh the first

moat that isn't even in the book, which

is speed. like you know if you're really

breaking your brain about like oh well

are we going to be a cornered resource

or not you're just thinking about it in

the wrong way like you should not start

there you should start with do I have a

specific person who has some sort of

pain point and it's pretty painful it's

not like a oh it'd be nice if I could do

this it's a oh I am not going to get

promoted this year maybe I will get

fired like this is so painful that I

don't want to go to work today Like

that's sort of the type of pain that

you're looking for. And if you can write

software or build things that actually

alleviate that pain, like existential

pain, like the business is going to go

out of business or oh my god, we could

totally take over everything next year.

Like that's sort of the feeling that you

want in your customer. Uh if you can

find things like that, go go Z, you

know, go find that and go zero to one on

that first. With that, see you guys next

time.

Key Vocabulary

Vocabulary	Meanings
startup /ˈstɑːrtʌp/ B2	noun - a new business venture or company that is in the early stages of development
business /ˈbɪznɪs/ A2	noun - an organization or company for commercial activities
competition /ˌkɒmpəˈtɪʃən/ B1	noun - the situation in which people or organizations compete
product /ˈprɒdʌkt/ B1	noun - a thing produced by labor or effort
build /bɪld/ A2	verb - to construct something by putting parts together
defend /dɪˈfɛnd/ B1	verb - to protect against attack or criticism
important /ɪmˈpɔːrtənt/ A1	adjective - having great value or significance
pervasive /pərˈveɪsɪv/ C1	adjective - spreading widely throughout an area or group of people
power /ˈpaʊər/ B1	noun - the ability or capacity to do something or act in a particular way
cost /kɒst/ B1	noun - the amount of money needed to buy or obtain something verb - to require a certain amount of effort or money
speed /spiːd/ B1	noun - the rate at which someone or something moves or operates
scale /skeɪl/ B2	noun - the size or extent of something, especially when compared verb - to change in size, especially to become larger
resource /ˈriːsɔːrs/ B1	noun - a source of supply or support that can be drawn upon
network /ˈnɛtwɜːrk/ B1	noun - a group of people or organizations connected for a common purpose
data /ˈdeɪtə/ B1	noun - facts, statistics, or items of information
market /ˈmɑːrkɪt/ B1	noun - a regular gathering of people for buying and selling goods and services
work /wɜːrk/ A1	verb - to perform a job or task noun - activity involving mental or physical effort done in order to achieve a result
value /ˈvæljuː/ B1	noun - the worth or importance of something verb - to consider something important or beneficial
AI /ˌeɪˈaɪ/ C1	noun - artificial intelligence, the simulation of human intelligence processes by machines
moat /moʊt/ C1	noun - a deep, wide ditch surrounding a castle, as a defense barrier; metaphorically, a competitive advantage