Unpopular Ideas That Became Billion-Dollar Businesses

If you only want to work on things that

are hot, you're going to find yourself

working on derivative ideas that end up

being obvious, that end up having 5, 10

100 competitors. It's great for that

number one, number two, but guess what?

Like number three through number 98 of

all the people in that market, their

startups are going to die. Nine out of

10 people might tell you you're stupid

or crazy, but then one out of 10 people

might be exactly the person who believes

what you believe. run out and try to

find things that humans really

desperately want and need and then

you'll figure out the rest.

Welcome back to another episode of the

light cone. As Peter Teal says

competition is for losers. And as we

look at all the AI startups we're

working with, increasingly AI

competition is back. So how do you

actually deal with it? You know, well, I

think if we go back to 01 again, uh what

we would say is we're looking for how do

you think from first principles, how do

you actually deal with that competition

by being contrarian and right? Har, how

do you think about it? Yeah, something

I've been thinking about recently is um

probably just over a year ago, we talked

about how we were finding it um easier

than ever to fund companies that were

looking for a startup idea and that they

could pivot and find an idea. And it

felt like the two causes of that were

one there was just so much green field

like AI was new. Um there were so many

verticals to go after that hadn't been

picked over yet. And the models

themselves were changing like there was

such a there was a step function

increase in new models every few months

that just caused the idea space to

expand. So you could both there was

green field and you could always count

on a ch big change coming that would

shake things up um and create more

ideas. And I think clearly we've seen

the benefit of that like there are so

many vertical AI agent companies that

are doing tremendously well. But I kind

of feel like the vibe is shifting a

little bit now where when I'm talking to

founders doing office hours trying to

help them find ideas, it's not as easy

as hey like go figure out like a a

vertical where there's like a workflow

to automate like insurance or banking

because there's multiple startups in

each of these verticals now. Um and

there actually hasn't been like a model

that's shaken things up um for a while

now. And so I think it's becoming more

important to think about what's your

actual like unique insight that is going

to enable you to find a good idea and

what's like the contrarian bet you're

going to make um so that you can

actually stand out from all of the

competition.

>> So, how, do, you, actually, discover, a, secret

if you will, you know, something that

you know and believe that nobody else

believes yet?

>> Maybe, we, could, do, some, case, studies., So

maybe like why don't we talk about some

of the companies we've seen um that made

a contrarian bet when they were coming

up with their idea initially and like

maybe that will shake out some things

people can learn from. I think a helpful

comparison to the previous technology

shifts that we've had in in the world.

Like if you look at the history of when

we invented the internet, when we

invented uh the smartphone, each time

there's a, you know, roughly two-year

window where it was really easy. There's

there's essentially like a like a modern

day gold rush, right? Where like the a

whole new class of startup ideas like

just opened up. Everybody rushed in

launched all the obvious ideas and then

you know roughly two years after that

the obvious ideas begin to have been

picked over and you have to like look

deeper for a secret.

>> I, mean, I, think, it's, deeper, than, merely

like is it obvious or non-obvious.

Non-obvious sounds like in your body

might feel like you know neutral but

actually non-obvious feels dangerous and

scary like I could devote my life to

this waste 10 years on this and have no

outcome. And you know, I guess the way

that manifests, I think, is that um

there's just like ways of thinking that

are out there that we pick up in media

or in like sort of the conversations we

have with the people around us that um

we don't examine. So, you know, I was

doing office hours recently with uh

someone in the marketing space and they

came to me and said, you know what, like

nobody

has ever made a company large enough

doing like just what we were doing. The

weird thing was you we have AI now. Like

this is sort of the perfect moment where

no one's doing it. That means there's no

competition.

In fact, everyone else who has been in

the space uh you know prior to the AI

revolution has failed. So there just so

many dead bodies stacked up around this

idea. You how do you know that you're

not the right person to go and actually

blow this wide open? Especially because

there is still this giant new

capability. You know, we have age of

intelligence. The rocks can talk, they

can think, and they can actually do the

work of real human beings. You know, you

might be one of the only people who has

the guts to go into that space and

actually apply this. And his numbers are

going up. The customers are sort of

pulling it out of him already. Um

sometimes I'll talk to people about his

startup and people say, "Oh, I need that

tomorrow." So, you know, this is like

the perfect example of, you know

sometimes the market will be giving you

indications of product market fit, but

then you'll be using these mental models

from, you know, are people on X talking

about it or is Techrunch talking about

it or, you know, what will my friends

say at a party? They'll say like, oh

you're working on a tarpit idea. So, it

sort of interacts with this idea that

like competition is back and competition

is bad. It's that like the obvious

things uh you know you're just going to

pile up 10 20 like you know some of

these spaces seem like they're like five

or six or maybe a dozen or like two

dozen different startups all of which

have a real shot at it and then now's

sort of the moment to again focus on the

secret again. Yeah, Jared, to your

point, the like new tech platforms

create this like two-year window. And

so, we were talking about this earlier

like both mobile, like iPhone launches

Android comes soon after it. Um, in the

immediate like year or two before that

like there were a lot of like sort of

obvious ideas to go after like photos

Instagram sort of grew out of that era.

But like the actual big winners turned

out to be companies like Uber, Door

Dash, and Instacart.

>> Those, were, so, nonobvious., It's, like

folks who weren't around then don't

remember how nonobvious that was. Like

when the iPhone came out, there was like

a million articles, a million like

social media posts about like what kind

of companies you could build with the

iPhone. And like I don't think a single

person thought that like Uber would be

the consequence. I think the one in this

context is really interesting to talk

about actually might be Door Dash

because um sort of essentially what

we're saying is right now competition is

back. There is more competition to find

a good idea AI idea than ever. Door Dash

entered a very crowded space. Like food

delivery, food delivery in general had

been around for a while. Mobile was

probably like um a catalyst for even

more food delivery apps. So by the time

Door Dash launched, you already had

Postmates. Um iterations of it. I was

just talking and Seamless were huge

companies.

>> There's, actually, a, YC, company,, Order

Ahead, that Gary and I worked with that

was actually doing quite well at the

time. Um wasn't actually delivery. It

was um let you pick food up from the

restaurant which at the time actually

pick up.

>> It, seemed, like, a, bigger, market, maybe

than food delivery at the time.

>> They, were, farther, ahead, like, they, had

way more locations for instance.

>> Yeah.

>> I, mean, interestingly, like, it, I, think, it

happened even before that with uh it was

uh Lyft before and they were called

Zimride before and then funny enough YC

had a company called Ridejoy

>> that, was, exactly, a, competitor, against

Zimride

>> and, they, were, like, neck, and, neck.

>> Neck, and, neck., Yeah., And, um, Zimride

decided to try to do uh peer-to-peer

local uh rides. You know, Zimride and uh

Ride Joy were sort of both picking off

people from Craigslist. And the idea

there was like, oh, I'm driving to LA

this weekend. You know, this platform

would match you with other people and

help you get, you know, a ride to Tahoe

from SF or LA. Uh and then suddenly

Zimride realized well wait a second like

what if we actually did this at a much

smaller scale because we have smart

everyone you know 70 80% of people out

there started having smartphones. So

instead of having this like long

drownout email back and forth to like

meet at this time so we could get a ride

and then you know I'll pay up for gas

money. It became something that could be

used every single day uh for short hall

rides. And that was like sort of the

first moment that people thought, well

maybe you could have a lot of people

more or less like a a mobile workforce

um entirely driven by the phone. And

then I think that's that happened like

sort of contemporaneously to Uber with

their black cars. But the funny thing

was, and maybe this rhymes with uh the

lot often the office hours that we have

to do all the time. It's like I remember

meeting with the Ridejoy guys. I was

like, "Hey, this Zimrite thing seems

like it's working pretty well. We're

seeing um a lot of other companies that

you know I think we had uh just met a

poor Vamea at Instacart and he was doing

it for grocery delivery. Basically the

market was sort of pulling it out of all

of these startups and that was exactly

the moment where um interesting

interestingly I think those founders

said we're worried about uh the laws. It

seems illegal and we don't want to do

anything illegal.

>> And, they, weren't, wrong., I, remember

talking to the Lyft founders um the week

before they launched Lyft and they were

extremely worried that they would go to

jail and they decided to like roll the

dice and launch this thing anyway. I

think a big reason why other people

didn't launch Lyft and Uber before was

that it's like it was basically illegal

to do that and they were worried that

they would go to jail.

>> What's, funny, now, is, uh, it, does, seem, like

the world will sort of change the laws

as it were if it turns out that um the

end user, the end consumer wins by that

much.

>> Totally., I, mean,, a, lot, of, great, startup

ideas are sort of in this gray area of

like the law is not totally clear. It's

a little bit murky whether it's legal or

illegal. Even open AI is like that

right? I mean, they they crawled the

entire web without permission. You could

argue that that's fair use or you could

argue it's like massive copyright theft.

I guess all of these sort of buttress

this idea that like non-obvious is not

merely like, you know, purely

intellectually, you know, not clear that

this is going to work. It actually is a

little bit more subtle than that. It's

like, oh, I feel like that's might be a

little dangerous. Like there might be

something that, you know, I don't feel

comfortable doing. Um, and then really

really great founders sort of sense that

as actually uh signal.

>> Coinbase, was, like, this, as, well., Were, the

Coinbase folks like I mean that's always

operated in a gray area of legality.

>> Coinbase, to, me, feels, a, little, bit, less, I

don't feel like crypto was well

understood enough for it to be clearly

legal. actually they they actually

couldn't take that approach because they

actually needed to get a banking partner

um in order to even be like have the

service launched, right?

>> I, would, say, that, uh, Brian, started, off

recognizing that a lot of the use cases

maybe way at the beginning part you had

um cipher punks like people who were

like radically into like libertarianism

and this idea that you should not have

centralized banking. Uh but they took it

to an extreme where they wanted totally

anonymous identities. I mean it was more

you know Bitcoin really very early on

was a lot more Silk Road than what it is

today. So I actually think that Brian

Armstrong early was like the exact

opposite of

>> you, I, mean, he, was, contrarian, in, another

way. Like if you hung out with a lot of

people who were really really into

Bitcoin uh in, you know, 2010, 2011

2012, the majority of people you ran

across were cipher punks who said, "F

the state, uh, you know, f the laws and

we're going to, you know, have sort of

this radical freedom through Bitcoin."

>> And, there's, Brian, Armstrong, like, doing

deals with banking companies and working

with regulators.

>> Yeah., That's, what, his, contrarian, bet

was. it was that it's worth doing all of

this like extra work at a for a time

where it wasn't clear the market even

wanted it. Like it was very clear the

cyber punks and like Silk Road all

wanted like crypto and anonymous

payments. It wasn't clear that regular

people would. And so why it didn't seem

like a valuable thing at the time to go

and talk to a bank and do a partnership

and go through all the like KYC and AML

laws because like you'd only do that if

you thought that regular people would

want to trade crypto at some point.

>> And, those, things, actually, make, your

product worse, right? like like forcing

people to go through KYC like greatly

increases the friction

>> and, beyond, that, they're, like, they're, the

exact opposite of what the current

market what like the current market

could not have been more enraged about

like Brian and Coinbase's approach to

crypto at the time.

>> Yeah., I, mean,, you're, just, going, to, run

across people who say like it'll never

work. But when markets are brand new and

nent like, you know, often when you're

that early, the thing that passes as

you know, obvious is uh clearly wrong

actually. So that might might have been

like a very profound version of that.

before anyone cancels us for telling

saying that uh you know founders might

get something out of doing illegal

things like I actually don't think that

people should by default go out and try

to do illegal things like I think the

through line is not that you should do

illegal things or you know it's that you

should think about from first principles

what are the things that markets and

people need

>> totally

>> and, then, I, think, basically, ina, in, the

case of you know Uberx and uh it became

very clear within like sort of the first

few months of that uh coming out

especially in San Francisco where

literally we have some of the worst taxi

cab like sort of infrastructure and

really difficult transit. I mean

basically like that came out of

necessity. And then the quality of life

inside San Francisco went from here to

here the second that you could actually

just freely move around very easily

without the uncertainty of like call a

taxi cab and they would just literally

not show up

>> not, show, up, like, half, the, time., I, know.

>> And, it's, like, how, do, you, actually, like

live in a city and get around like you

know that I I think that you know these

services literally made San Francisco

10x more livable in a lot of ways. So

you know I I think it's like don't just

run out and do illegal things like

that's actually clearly net bad like

it's more what are things that uh people

users want and then thinking from first

principles also does actually involve

thinking about well what are the

downsides right um and how do we

mitigate those things

>> I, think, I, think, the, through, line, is, that

the laws were dumb and didn't make sense

and if you thought about it from first

principles you'd realize that these laws

were actually holding these particular

laws were actually holding back society

rather than helping it because they

ridden in an era before the smartphone

when like illegal taxi services were

actually kind of a scourge of society

because you'd have like random people

driving around like claiming to be a

taxi but then they'd like kidnap people

and like there was no accountability and

like tracking. But now that everyone is

in like a smartphone based system, there

actually is accountability and tracking

and Uber is was like is actually able to

operate it safely and the taxi medallion

system that like ruled the country just

didn't make any sense anymore in a

smartphone era.

>> Yeah,, I, think, that's, the, key, point., The

key point is, yeah, don't go out and do

things that the law explicitly says you

cannot do. But finding laws that were

written in a time before some big tech

shift that changes everything and just

don't reflect reality can be really

valuable. I mean, again, going back to

crypto, crypto is a prime example of

this. Like you have lots of securities

laws that were really well-intentioned

and designed to like protect consumers

from scams and um and buying things they

didn't understand. And so like a big

part of the SEC rule is like you have to

break things up into like brokers and

intermediaries and clearing houses, but

a lot of the rules in the crypto world

just don't make sense like um and so

they almost have to be like rewritten

why there's a lot of legislation going

through now. But I think that's if you

can find things where like the laws

don't really cover the world as it

exists today and you sort of brave

enough to see what happens if you try

that can be

>> that's, our, ground., The, reality, is, like

uh actually like that's the role of

government like this is why we have a

legislature. You know one of the things

we're trying to fight for right now is

for instance open banking. So, you know

I think all of us sitting here and most

of the people watching, I think, would

take things like Plaid for granted.

Like, of course, I want Plaid. Like, you

know, that's my data. That's my money.

And so, you know, what do you mean like

a giant bank has the right to charge

exorbitant fees uh to get access to that

data. But, you know, that's being

adjudicated right now. like the Trump

administration actually has to make some

decisions and is an open comment period

right now around uh whether or not you

know a giant bank can prevent tiny

startups like you know YC startups or

the startups that people are going to

start here. Can they charge a crazy

amount or use their terms of service to

actually block access to this stuff? And

so my hope is that you know a

functioning government will sort of

think about all these things. you know

what the banks might be saying in order

to get regulatory capture right now is

that oh it's not safe we're thinking

about the safety of the consumer but

when you really dig into it it's like

they just don't want people to be able

to switch off of their banks you know

they they don't want the money to go to

a lower fee place like this is their

moat right regulatory capture absolutely

is one and then the thing that is cool

about um a modern democracy I think

people would debate whether we're we

have that on the one hand. On the other

hand, like you know, I think on in

individual cases, that might be, you

know, the wrong thing happens here and

there. But in aggregate, like over a

long enough time period, if you can get

your product and your service into the

hands of enough people, you know, we do

still live in a dem democratic society

where, you know, representatives will

vote and will like change the laws and

that's very much good news. So first

principles plus democracy equals open

markets and freedom which like what

that's what we're fighting for but it

does happen.

>> I, guess, when, different, question, is, what

are some of the current contrarian

things that founders should be looking

at right now since what we're saying is

there's more competition now as opposed

to a year ago. There hasn't been to your

point harsh a big model improvement or

one preview has been all about a year or

so. That was the big stepping function

for model capabilities and there hasn't

been anything thus far. I mean there's

been incremental things with 03 and all

that but nothing like that that makes it

harder for startups to find new new

ideas. What are the new things that are

also in this gray area that founders can

look into

>> potentially, one, framework, for, finding

them at least is not like a prescription

for exactly what is to look at what are

like emerging playbooks for ways to

build startups that might be wrong or it

might be time to flip them. Like to give

you a concrete example like Door Dash I

think was a prime example of this

because when Door Dash started there was

actually another YC company Spoon Rocket

um doing food delivery where they would

actually like cook the food um in these

kitchens that were spread around the

city.

>> Yeah., They, operated, ghost, kitchens, all

over

>> SF., Uh, Sprig, was, another, one, right, and

actually in that moment those companies

I think came out of this meme of like a

full stack startup. There was this

period where it was seen as just

building software is not ambitious

enough and like the big opportunities

are, going to, be, in, going, full, stack, like

don't just build a food delivery app

actually have the kitchen and make the

food and that's where all the

opportunities were and I think that was

becoming more established as like the

playbook around 2014ish era and so in a

way that like Door Dash is contrarian

bet was actually say we're just going to

do deliver we're just actually going to

have like an app and a marketplace and

we're not trying to be a full stack

startup which was obviously the right

bet in hindsight and So what might be

some of the the sort of consensus

playbooks for building AI companies that

have emerged over the last year that

like might be wrong might be worth

taking the other side of?

>> I, guess, when, I, can, think, of, that, has

come up there's the compound startup

notion that Parker Conrad from Ripling

really made it popular. I think it

hasn't been as adopted. I think people

try but it's actually in practice really

hard. I think it's one of those that for

certain AI startups, it is actually

possible to execute on it and not have

to wait two years to ship the product

which is what I think Ribbling roughly

took for the first version.

>> Maybe, an, example, of, a, startup, is, uh

Campfire. This is a YC company that's

building basically a AI native version

for CFOs to compete versus Netswuite.

And it turns out that Netswuite is a

pretty big piece of software. It's very

hard to compete. can't just do like um

kind, of a, point, solution, in, order, to, be

adopted. So one thing to reevaluate

instead of doing the standard SAS

approach of let's just build the best

point solution that's actually built the

whole thing which is not something

typically done as an advice that we give

for early stage startup because you just

end up not shipping for a long time and

not getting customer feedback. maybe in

cases like this is the right answer and

campfire they've been closing a lot of

now big accounts and it's taking a bit

of time to get there but it seems to be

working.

>> Yeah,, they're, killing, netswuite, which, is

wild like how can a startup that you

know has a dozen people kill Netswuite.

This is like hard to believe on the one

hand on the other hand like this is the

timeline we're on. So the other thing

that's cool about campfire that we're

seeing in a lot of enterprise startups

is uh you know with codegen increasingly

you can actually bring the switching

cost closer to zero and that was

actually the you that's how you can how

you know how can campfire switch someone

off of Netswuite on a time frame that

matters like even with the idea of a

forward deployed engineer like before

codegen like it'd be like six weeks of

like writing custom like scripts to like

convert one data a schema to another and

you know often very specific like if if

it's dynamic schema on one end that's a

lot of custom work that's painstaking

and if you get something wrong like the

end result doesn't actually work and

then you have a churned customer. So

that's a a very like that's some good

news for people especially doing very

complex enterprise projects with like

conversion like we're seeing more and

more examples of enterprise sales that

just wouldn't work or it would be you

know 6 months to actually get someone to

say yes and sign and then another 6

months to get a data uh a data

conversion or data integration done. If

the demos are very good, like that six

months could be like two weeks. And then

if you can codegen and get very very

good at a suite of tools to convert like

data from the schema to yours, you could

have like time to value in like less

than a month when it used to take a

year. So that's like very good news for

startups out there that are doing

enterprise.

>> I, think, for, deployed, engineers,, we've

spoken about a lot, but like that might

be one that's worth wondering if that's

going to shift back as well. Like if you

think like Palunteer obviously was like

invented it was an incredibly contrarian

thing at the time that they did it. This

sort of like blur the line between

consulting and and software. It's now

certainly become the adopted playbook.

We talked about it with Bob Mcgru a few

episodes ago. Um

>> and, he, was, actually, fairly, skeptical, of

it himself even though he's one of the

people who invented it which is

interesting., remind, us, how, say so, when

we see

>> well, he, was, saying, essentially, that, like

he thinks it's like being greatly

overused and he would like thinks it

should be used very sparingly and very

unusual situations as opposed to being

like a default playbook which is

interesting.

>> It, has, certainly, become, the, default

playbook. It is actually working

incredibly well. I think we're seeing

like the companies are growing at like

aggressive growth rates because they're

employing the forward deployed engineer

model. But yeah, if if I had to if I had

to guess one thing that if I were trying

to just purely pick what's like the

contra like what's the one thing to be

contrarian about like judged by just

what's become the most entrenched

default playbook it might be the forward

deployed engineer approach

>> actually, yeah, you, have, a, good, company

that is flipping it on its head this gig

ML

>> yeah, I, think, gigger, is, taking, the

concept of the forward deployed engineer

which is everything you said that you

you want to basically transform sort of

like your customer schema and business

logic into sort of your schema and and

logic.

>> It's, like, consulting, work, basically,

>> right?, But, instead, of, a, human, forward

deployed engineer they they have they're

just using codegen to do it actually

they they've, built, their, own, AI, forward

deployed engineer and a big part of the

reason they can win deals faster than

their competitors is that the human

deployed engineer still takes weeks to

get the thing going which is like really

fast compared to like historic

enterprise consulting arrangements but

like the AI FD can do it in minutes and

so I think that's like the kind of the

flipping that plate like it's not really

an FD at all really it's actually just

product um just like your customer

inputting in specs and you like deliver

them a product instantly. But I think

that could be an example of a contrarian

bet that will pay off really well.

>> So, I'm, hearing, a, request, for, startup

which is uh AI for AI FD

>> maybe

>> meta.

It's just abstractions all the way down.

>> Flock, safety, could, be, a, really

interesting one to talk about.

>> So, the, story, of, flock, safety, I, remember

I was still at initialized. We were

looking at companies at demo day and

then funny enough that morning um all

the cars on my street in Noi Valley were

broken into and you know it was a a

professional crew. They came in and uh

broke into every car on our street. They

took all the bags out the back of the

car and then you know my house at the

time had like a little al cove that was

dark and so they brought all the bags

into our driveway and then rummaged

through them all and I you know had to

cancel my meetings that morning to talk

to the police and you know I had a Nest

camera that was like captured the whole

thing and I was like you can't do

anything like it was a pro crew like

they were three people it was like with

mechanical like military style precision

and the police basically said sorry you

know unless you have a license plate.

uh we can't do anything and so uh that

made the first principles investment

very easy for me because uh you know

Garrett Langley came in he's founder

from Atlanta he had a successful exit

previously uh but this was hardware so

they were selling a uh camera about this

basically had a Raspberry Pi with a

camera in it and then a solar array and

uh you know computer vision had you know

with imageet had been around long enough

that you could run it at the edge in the

device and solar had gotten progressed

just to a point where it was just good

enough that you could run these like you

know sort of in perpetuity. And so that

was his pitch like we're going to go to

neighborhood groups like neighborhood

associations and you know they already

had started I think in Piedmont and in

Atlanta in the Atlanta larger greater

Atlanta area. There's a lot of things

that um from a VC VC standpoint like VCs

didn't like hardware. Uh they don't like

things that you know sell to potentially

small markets. I mean I think in the

investment memo I put together like I

just called that out directly. It was

like, well, if you multiply out the

number of neighborhood groups and the

ACV for those ne neighborhood groups

uh, there's no way that I mean, maybe

the max was like 50 or $60 million a

year or something

>> like, that, the, actual, TAM, of, this, thing

was like only $50 million a year.

>> I, mean,, speaking, as, a, VC,, like, maybe, the

best exhortation I can give to the

people listening is like do not use that

like you know, it's merely uh an

indicator. It's like useful to know, but

like neither investors nor founders

should use that as like cross it off the

list right?

>> And, he, was, being, run, out, of, Atlanta,

Georgia. Right.

>> Right., So, it's, like, three, things, that

make it made it like basically

unfundable. But, you know, I mean

that's why like I think I was hanging

out with Brian Singerman at Founders

Fund for a little bit and you know, one

of the things we always talk about is

the more rules you have about investing

uh, the more ways you can basically talk

yourself out of making a lot of money in

ventures. So, I don't know. I mean, to

the extent that that is useful for

founders, like I think that that's true.

Like, you know, we did a whole episode

last episode about how, you know, you

should be aware of the seven powers and

moes, but it would be the stupidest

thing in the world to use that as your

only criteria for whether or not you

should work on a company. And likewise

you know, I think increasingly even in

our office hours, we find ourselves

giving that advice with uh founders.

It's you know it is much better to go

from first principles from uh what does

society need what do users need you know

what ideas look could only happen now

and then the rest like you can kind of

figure out right like the same calculus

you could do for Coinbase very easily

like the you know total market for

Bitcoin you know was not trillions of

dollars it was like probably on the

order of tens to hundreds of millions of

dollars at the moment that Coinbase

really started thinking and the

coalesing around that. Some of this is

the good news. It's that like the people

who actually change everything are the

founders actually. And so if you only

want to work on things that are hot

you're going to find yourself working on

derivative ideas that end up being

obvious that end up having 5, 10, 100

competitors. And then, you know, it's

great for that number one, number two

but guess what? like number three

through number 98 of all the people in

that market are going to die. Like their

startups are going to die. So yeah, this

is just a little bit of a profound

version of that story. I mean, flock

safety today solves 10% of all reported

crime in the United States. It's

>> crazy., I, feel, like, that's, a

generalizable lesson of flock safety

which is um at any time if you have some

startup idea and you go to talk to a

bunch of VCs about it, a lot of them

will give you feedback. And I could

imagine that if Garrett had gone and

talked to a bunch of VCs, they would

have told him like, "Oh, this isn't VC

fundable, selling to local governments

hardware, like you should do B2B SAS."

It's cool that he didn't like he ended

up in a market where like he had very

little competition perhaps because

everybody else like thought it was too

weird.

>> I, think, if, you're, just, really, razor

focused on your customer and the actual

need, like it just became so obvious. I

mean, I literally got a flock safety in

front of my house and even though it did

not end up catching um that crack band

of thieves, I felt way safe when I had

it in front of my house. And um I think

that that's what happened with their

communities, you know, especially like I

remember doing office hours with Garrett

and he's like, "This is insane. We

actually caught um you know, someone who

had kidnapped a kid."

>> Wow.

>> Right., And, so, when, you, look, at, the

actual human impact, I mean, going back

to what we were saying earlier, it's

like don't run out and try to do illegal

things. Run out and try to find things

that humans really desperately want and

need and then you'll figure out the

rest. Honestly, like if you're just

really really focused on people's

problems and how severe those problems

are, like you know, you'll figure out

the rest. It's like you'll figure out

the business model, you'll figure out

the distribution. And like you know

Flux Safety later had to figure out that

these crimes that they were solving

they would be on the evening news. And

all it took was, you know, a media team

that would reach out to the evening news

anchor and say, "Hey, by the way, this

was uh solved by Flock Safety. Here's

some video that you can run that's

B-roll of like, you know, this this

crime was solved by this, you know, new

technology." And then it started

spreading virally. like one town would

have a crime solved often like sometimes

like pretty intense like violent crime

and then literally the neighbor the the

city next door like the police chief

would say like what is this I need it

right now and so you can't learn that

from like reading blog posts or like

going on X and like sort of or even just

like asking Chachi PT about it like you

actually just have to try a bunch of

stuff and then everyone's story is very

very different um but you I Garrett and

the flock team are really unique in that

respect. It's like just like first

principles in terms of what you're

building, first principles in how you

get more customers, first principles in

terms of what your business model should

be. I think that that's just a much

better way to think about it. And then

the the difference is that you can't sit

in your chair sit sitting in front of a

computer and uh divine these things per

se. You actually have to get out of the

house. You actually have to talk to

customers. You know, that's where like I

think the goal setting at YC helps a

lot, right? It wasn't like from uh a VC

meeting. I think Garrett sat and figured

out that he needed to change his go to

market. You know, I think that he

probably looked at basically how do I

work backwards from my growth goal? And

then he realized that uh selling in

neighborhood groups wasn't going to be

good enough. So one way or another, they

really needed to get over to sell to

city government, which sounded

impossible, but they could definitely do

it. And that's that's been like one of

the big engines of their growth.

>> And, just, fast, forward,, what's, their

current valuation?

>> Yeah,, it's, worth, $7.5, billion, now., And

um they're making way way more than $60

million a year. Um and it actually

required a number of uh business model

pivots. But you know the core of what

they built like you know what they built

on demo day and what they show showed me

was exactly I mean pretty similar like

the like the tech itself is the same. Uh

what they did have to do is figure out

that they couldn't just sell to

neighborhood groups. They still sell to

neighborhood groups but what really

cracked it for them was selling it to um

you know police departments and actually

officially being used for it. Diana, do

you have another example of places to

look for contrarian bats?

>> I, think, one, category, is, sort, of, the

sci-fi founder is really going after

ideas that

most people are scared to build because

they're just so freaking hard. And

sometimes science and physics are laws

may need to be rediscovered to be

possible. One example that really comes

to mind is uh open AI. It wasn't clear

whether AI was going to be a thing when

it got when Sam started out of YC and it

took many years. It seemed to be mostly

a tinkering project for researchers.

They were publishing papers. They were

doing kind of side quests. I mean they

had the uh Rubik's cube solver. They

were solving Dota. And it was unclear

how all these would come together to

what OpenAI became today. And also

something that people forget is that

when open AI launched it got mostly

negative press. There were some people

like a small like group of like techno

optimist people who thought it was

really cool but by and large most people

especially the AI researcher

establishment in academia and in other

companies mostly just like was extremely

negative on the idea that a bunch of

like

>> 20, and, 30, something, Yeah., could, create

AGI. They're like, you know, we're the

experts in this. We've been doing this

for like 50 years. If there was a way to

do it, we would have already done it.

These kids don't know anything. They

haven't published any papers yet.

There's no peer review here. Yeah. Not

publishing papers was a really big

critique. And you know, especially

around the scaling laws like I think

there was this aspect of they're

spending how many millions of dollars on

GPUs on projects that uh will not cause

more papers in the world to arise.

papers were like the thing that uh they

were trying to you know sort of you know

paperclipip optimize for which was

totally the wrong thing like the thing

that uh really great builders optimized

for is like outcomes for customers and

users is the same thing when Elon

started SpaceX. He wasn't the first

billionaire to start a spaceflight

company. And I think he was like the

fifth billionaire to like try to start a

spaceflight company. And so all the

press was like, "Oh, look, another

billionaire like there to squander his

fortune on like rockets."

>> And, the, whole, idea, of, building, reusable

rockets was blasphemous. Right.

>> Yeah.

>> I, think, he, went, and, talked, to, um, rocket

scientists and they were like this is

not possible.

>> Yeah.

>> And, after, many, years, and, many, launches

that didn't work out.

>> Yeah., And, and, then, every, time, a, rocket

blew up like there would be another huge

wave of negative press. So like for both

of those companies like it required the

founders to like stick to their guns in

the face of most people telling them

that they were like stupid or crazy for

like a long time.

>> I, mean, nine, out, of, 10, people, might, tell

you you're stupid or crazy but then one

out of 10 people might be exactly the

person who believes what you believe and

then you're contrarian and you become

right because it's necessary to actually

attract and be a magnet in the world for

all the people who agree with you. Uh I

hope you all take a moment to really

think about how do you know what is real

and correct in the world and re-examine

all the sources of these things and if

it's coming from users coming from your

own personal experience or the

experience that of people who you

directly talk to uh great like that's

probably pretty good verifiable stuff

that you should use as a substrate of

your reality. Uh but if you're doom

scrolling on X, you're you know sort of

listening to famous people like you know

to be frank even including us you know

we're all N equals 1. The only people

that matter are the people who you care

about who have certain problems and your

ability to solve it and your ability to

attract all the other people who want to

solve those problems too. So with that

we'll see you next time.