The anxiety that I see is if you can

generate an enormous amount of code and

no one is reading it, you don't know the

quality of the code, nobody deeply

understands the codebase and there's

more fragility, right? It's like the

slop problem vibe coding slop in my

actual production codebase. But I think

the broader problem that new company

could go solve is like nobody knows how

to manage that issue of human attention

to engineering. I [music] think it's

like open season around this really

really big problem.

>> Hi listeners, [music]

welcome back to No Priors. Markets are

melting down about the end of software.

Today a lotad and I are hanging out and

asking is SAS actually dying or are

people just projecting five person

startup behavior onto the Fortune 100?

We'll talk about what's real. Incredible

revenue growth, collapsing token costs

and faster turnover of vendors. what's

just hype and how to size the

opportunity. We also discussed the

changing bottlenecks in building a

software company and some parallels to

the internet and cloud eras. Let's get

into it. [music] It's good to hang. The

the market is freaking out around us.

So, in all that noise, what are you

thinking about?

>> Oh, you mean the SAS the SAS apocalypse?

>> The SAS apocalypse. The end of software.

>> Yeah. Yeah. It's kind of interesting. I

feel like there's some meta trends that

people are getting right and then a lot

of specific companies that people are

getting wrong. And so, you know, I think

I guess the basic premise is that SAS

software and proceed software will no

longer exist and everything's going to

be replaced by AI and everything's just

going to get viodated. So, why would you

pay X dollars for a Salesforce instance

when you can just vibe coded internally?

And all that stuff strikes me as

incredibly shortsighted in the near

term. over the long run, who knows what

happens in 20 years or whatever, but

there's lots and lots of companies that

are quite durable. I think an

interesting example of that where I'm

still a shareholder is Samsara, where,

you know, nobody's going to vibe code a

fleet management app that will then get

distributed through like what Vibe sales

vibe, you know, enterprise sales or

something [laughter]

and you're going to build a Vibe like

incab camera sensor that everybody will

install in these fleets and then you're

going to support them using Vibe agents

or something. It's just it's just very

overstated. So I feel like it's one of

those things where there's a massive

market correction around something that

in the long run has a lot of truth to it

and maybe in the short run for certain

types of companies has a lot of truth.

Right? Ultimately I think that and

Sierra are examples of companies where

you're moving from per seat software to

basically utilizationbased customer

support related agents. Right? That is a

real shift that may impact some of the

prior wave of sort of per seat software

companies but this isn't going to be

every single SAS company. So, I I I view

it as very short-term, overstated. In

the long run, who knows? How about you?

How do you think about it?

>> I mean, I think the idea of Vibe

Enterprise sales is hilarious. Um,

because I we have portfolio companies

with, you know, hundreds of millions of

dollars of revenue who are very

committed to as much token usage as we

can, as few great people as we can have.

And today, you know, they've less than

50 engineers. and they went from zero

[clears throat] to like let's say close

to 100 salespeople very quickly, right?

And so it's just a view from the growing

AI natives that like vibe sales is not

happening, right? Like

>> oh yeah, vibe sales is definitely never

it's not happening anytime soon. And so

it's just again all this it just seems

like a very strong market reaction and

market correction. And it it seems like

it's very overstated, especially

relative to a handful of companies that

you're just like why? like how will you

displace this company with uh coding and

you know in the fleet example you're not

going to have the fleet managers like

writing their own apps to do all this

giant surface area of stuff it just

doesn't it's just not going to happen in

the short run

>> I think a lot of it is actually driven

by um some assumptions that you know

persona close to my heart but engineers

and builders are making about like the

rest of the world right I because

there's this there's this implied belief

that like everyone will want to make

their own software and I think it's

software is eating the world. Is that

what you're trying to say?

>> I I am not I I think like we're we're

still

>> time to build Sarah. Time to build.

>> I don't think that everybody wants to

make their own software. I think some

set of people will want to make it and

others will want other people to do it

for them. And like sometime like what's

a what's a like if you think about a

good example of this engineers sometimes

have a like my personal labor focused

picture of the world. So if you like

should you build Jira in most

engineering organizations like is that a

>> yeah it's not a it's not the best use of

your time if you're focused on product.

I mean the other piece of it is um the

examples that people use. Oh my five

person startup built our own CRM vcoded

it blah blah blah. Yeah of course I mean

before that you just did it all on a

spreadsheet and that was fine too. You

didn't have to v code anything. And so

for very limited niche applications

where it's a technical team doing

something really quick because it's

useful and custom and bespoke, amazing.

Of course that's going to happen. Does

that mean that a Fortune 100 company is

going to displace their CRM with some

internal thing that got backed over the

weekend? Probably not. And so I think

it's also extrapolating or projecting

behavior of very small technical

startups onto the world's biggest

enterprises. And that's the second thing

people are getting wrong is they're

misunderstanding the the moment. And I

think the internal software stuff that

people are building is amazing, right?

It's not like it isn't impressive that

you can do that. It's incredibly

impressive. It's just extrapolating that

behavior so aggressively so early just

doesn't make that much sense right now.

I think to your point of like the five

person company versus the very large

enterprise. If you ask that same

engineer who's like pissed about paying

$10 a seat for Jira,

>> like if you asked him or her like do you

want to do the change management in Bank

of America of getting everybody to do

this the way you think is right

>> and then dealing with all the security

considerations and managing other

people's opinions about potential

changes to the story management workflow

and then maintaining the system, the

answer is like probably not, you know.

Um and so I I think it's it is focused

on um I actually think the idea that

actual production of code becomes not

the bottleneck for um if you know what

the spec is not the bottleneck is like

incredibly interesting but I I I do

think it overstates like how much

>> uh of the overall software vendor

problem that is.

>> Yeah. I think people also misunderstand

how much demand exists for software

products and by software products I mean

everything. I mean AI I mean

>> is software eating the world. Is AI

eating the world?

>> AI is AI is eating the world. So I think

that that is actually true and I think

Mark's post on that was really um

thoughtful and forward thinking on it

all. I think that fundamentally um you

know there's there's so much demand for

software and there's so little supply of

engineering in reality relative to that

demand that as you add this enormous

boost of productivity to software

engineers um it just gets sucked up

right because there's so much more stuff

to build and to do and I don't see teams

you know startup teams continue to hire

engineers for a reason you know I think

the nature of the work is shifting and I

think some people are going to have real

issues with that shift because

fundamentally you're shifting from you

know in some cases you know there's the

there's a few different types of of

mindsets around engineers and one of the

mindsets is the really bespoke

craftsmanship

you know I'm going to make I I'm going

to do the aesthetics of the thing that

I'm doing really well and I care about

the code quality and you know and um the

the artisal version of what I'm doing

and then there's people who write code

because it's a utility that allows them

to build product. There's some people

who really like aspects of the math or

you know there's lots of different

motivators for people to write code and

I think a subset of those people are

going to be uh less happy in the new

world. It's kind of like the indie game

developers who'd make these handcrafted

individual games for themselves and then

for their friends and then they launch

them on the Apple store or whatever. Um

versus the people who' work at EA and

they each had their own version of

craftsmanship but it was just a

different type of thing. I think we're

going to see a lot of these really great

engineers who care about the the bespoke

craftsmanship of everything they do.

They're going to be unhappy working at

larger companies as these coding tools

get even more accelerant because it goes

against their approach of of how they

like working and what they enjoy out of

the work. And for other people who are

really focused on the utility of just

building product, it's going to be

freeing in some ways. So I think there's

also like a variance in terms of the

reactions to this stuff depending on the

type of uh utility function that you

have relative to the work you're doing.

>> Yeah. I I think related to that the um

one thing I've seen is that if you have

an engineering identity that's based on

that like a value based ranking of

difficulty or skill like the the

specific types of engineering that are

considered, you know, impressive or high

status can actually be like less hard

for agents, right? So I think there's an

enjoyability like element and then an

identity element. Um, and actually one

of your founders, um, from applied

intuition wrote a good blog post where

there is a, um, an essay where he says

like keep your identity small. I think

that's like wonderful overall advice for

this period of time, right? You're like

more adaptable if it's true.

>> Mhm.

>> But I I think your overall view of there

are a lot of unsolved problems and like

making an abundance of software can

better address that I I strongly agree

with. Then one one thing that um

actually is near and dear to the

audience that is really unsolved is like

we've broadly been thinking about what

happens if you have abundant code

generation and in like I think in all of

our teams

agent first engineering management and

thinking about code quality is an

unsolved problem.

>> Yeah. And we'll get there. It'll be your

work and we'll get there. What do you

view as the major problems? Um well the

the anxiety that I see um is like if you

can generate an enormous amount of code

and no one is reading it, you don't know

the quality of the code, nobody deeply

understands the codebase and there's

more fragility, right? It's like the

slop problem, but instead of it being

like vibe coding slop for random

websites for nontechnical people, it's

vibe coding slop in my actual production

codebase for every lazy engineer, which

is every engineer. I think people are

like looking at some problems of

actually do think ticketing ticketing

systems are are like at risk. But I

think the broader problem that Jira

could go solve or new company go could

go solve is like nobody knows how to

manage that issue of human attention to

engineering and there's a bunch of ideas

like testing and like you know smart

review just let agents do it formal

verification but I think it's like open

season around this really really big

problem. I think the one other thing

people are bringing up that I don't

quite buy is that um agents are already

making like big decisions for vendor

purchases and things like that. And I

think somebody near and dear to your

heart posted about that and um uh I

think that uh there the the statement

was oh agents are increasingly making

decisions about what software people are

using and really what that is is well

you have a partnership your cognition or

your claude or whoever and you have a

partnership and as part of that

partnership you spin up a superbase

instance and you use very specific tools

because you have a partnership to do

that and that's always happened right if

you're using air tableable and they're

on AWS like you're spinning up an AWS

instance without knowing about it right

in the background. So I also think that

whole notion that in the short run

agents are making these choices is also

overstated. I think in the long run it's

true, but then you get into all sorts of

agenic commerce decisions and do they

understand your persona and what you

actually want and need and all this

stuff. So I just feel like we're in a

little bit of a noisy moment where

people are kind of potentially and I'm

somebody who's very pro AI progress and

a believer in all the changes that have

happened and are coming, but I think

we're having a lot of overstatement now

of what's actually happening in the

world. And part of that is a sass

apocalypse and this giant recreation and

part of it is um you know extrapolating

that the future is here already when in

many cases it's just say we did a BD

deal or whatever. So I just think people

kind of need to or you know the mult

book stuff where you're like yeah that

seems human generated you know in terms

of the emergent behavior. So, I don't

know. We're we're we're in this odd

moment where I feel like this was the

month of hype in a way that we haven't

seen in a while where a bunch of stuff

got overstated in all sorts of ways and

people believed it. And by people, I

mean like mainstream media and others

are like, "Oh my gosh, look at this

behavior of, you know, these agents

trying to cut out humans from their

forum where it's Reddit like and blah

blah." And you're like, "Okay, like

maybe you should see where the posts are

coming from in some cases." And it's

exciting, by the way. Don't get me

wrong. I think it was very exciting

behavior that's happening. I just think,

you know, a subset of it was planted for

marketing purposes. Yes, certainly. I

think people are also figuring out like

there there are things that tap into um

deep emotional reactions that people

have to their view of like

>> things that feel very human, right? Um

from a marketing perspective and like

that's [clears throat] clearly one of

the things that's happened around them,

the mold book stuff. I also think that

like one of the things I actually think

happened was like the idea that demos

are different from the reality of the

full software that you need like has not

quite arrived in many of the equity

research people's desks, right? And so

like I'm like guys like your whole job

was to think about these like the

structural advantage of your businesses

and what is going to compound and the

theory of competitive advantage didn't

just like poof disappear, right? like

>> software markets have been a fight about

how to do things and how to distribute

to customers

>> as well as a battle of how to produce

code for a long time. So I um I feel

like that has been missed a little bit.

But I I do think long run the the

fundamental thing that the bottleneck on

production of you know expensive to

produce software uh being loosened is

really cool, right? It just means like

if you think of there's a lot of

embedded points of view in software on

how to solve a problem, right? you know,

if it's engineering or uh enterprise

sales, not a very software problem or or

general productivity, right? Like notion

is a way to do things. It's a building

block system, but it's definitely got a

point of view. And so, if you reduce the

cost to express that point of view in

software, I think it's cool that we're

going to like see a lot more ideas.

>> Oh, that's amazing. And again, I think

it's a revolution. So I don't get me

wrong, I'm I'm I've been involved with

coding companies really early on and um

I'm very excited about everything that's

happening and I think it's

transformational and I think it's

revolutionary and I think it's really

important. I just think we had a month

of kind of hype.

>> Okay. So if we ignore the noise of the

last month where people got a little

like frantic, what do you think is a

signal that people are not paying

attention to enough in such a noisy

landscape? you were telling me that like

growth growth pace is like of the of the

biggest companies is is still under

underpriced.

>> Yeah. One thing that um Jared on my team

put together that I thought was super

interesting was um he pulled data from

uh Capital IQ where they just like

predicted some projections on OpenAI and

Enthropic and they looked at um and then

he sort of graphed out and maybe we can

share these graphs as part of this

episode. He graphed out um how long it

took different companies in years to go

from a billion in revenue to 10 billion

dollars of revenue. So for example, ADP

took 20ome years to grow from a billion

to 10 billion in revenue. And then the

next wave of companies like Adobe took

about 20 years to go from 1 to 10 and

then you fast forward in time and you

have things like Salesforce or SAP sort

of an even more modern cohort and they

took eight or nine years. Microsoft

took, you know, sevenish, eight years.

Google and Meta and AWS took a couple

years, you know, three, four, five

years, but the AI labs did it in roughly

a year, right? And then if you look at

the projections that

>> it's a wild chart and so we should we

should add it, right? But you just see

it go from like 20some years with Adobe

to like a year for the AI labs. And then

if you look at the projections that are

sort of the public projections, they

aren't necessarily the company driven

data, but the public projections on

where the labs will end up or how long

it'll take them from to go from 10 to

100 billion in revenue. For Microsoft,

that was something like uh 27 years. For

Google, it was over a decade. Same with

AWS, roughly the same for Meta. And then

for the AI labs, it's like 3, four, five

years. You know, it's very fast. And so

we're seeing the fastest time to real

massive revenue that we've ever seen in

the history of software. There just

these insane curves and again we should

post them. Part of that I think is just

the internet has created this global

pool of liquidity and suddenly your

customers online. It's much easier to

distribute than it's ever been. So

that's one piece of it. There's more

people with access. There's higher GDP.

There's lots of drivers for that. But

then simultaneously you're just creating

enormous um business and user value at

massive scale simultaneously. and these

capabilities are so rich that you're

seeing this take off in terms of revenue

and so it's it's it's unprecedented.

It's really impressive and I think

people are ignoring the revenue and

usage side of the equation. Um the other

thing that we actually put together was

the collapse in token pricing for

equivalent models. I think this was done

initially by David who worked for me and

then Shan and so for example we looked

at the cost of a GPT4 level or

equivalent model. Uh we looked at that a

year or two ago and basically in 21

months it went from like 37 bucks for a

million tokens to 25 cents. And so you

know pricing dropped by 150x in 21

months and then we tried to accelerate

that curve but obviously people aren't

really using GPD4 level models anymore

even though you know they're 2 three

years old. And so we looked at 01

equivalent models and the cost of a

million tokens on an 01 equivalent model

in December of 24 was about 26 bucks.

And then in November of 25 it was 30

cents. So we saw another 88x drop, not

88% or 88, you know, 88 88 times cheaper

in 11 months for that next generation of

model. So we're having pricing collapse

on the token side while we're having

revenue ramp insanely on the usage side.

And so that's insane if you think about

that. Just this pace of shift of cost,

of revenue, of utilization, of

everything. And this is back to like I'm

incredibly bullish on everything that's

happening. Um and so it's more

dismodulating it against this you know

this odd overextrapolation of what's

actually happening or actual

capabilities or you know what these

things are really doing. Yeah, I I think

one thing that people miss in the like

bare case and all this stuff is as you

said like revenue numbers which is hard

to miss um but but and then um uh just

like actual um like token inference

count right if you look at one if you

look where's the inference happening

it's either happening in inference

clouds right base 10 mobile fireworks or

it's happening at the pro like the very

large model providers and it's happening

in a lot which is still much more two

magnitudes more humanity in general. And

humanity in general. Yeah. Yeah, it's

true. In terms of power utilization, a

human brain is really impressive. What

is it like tens of watts? 20 watts. How

much like what's the power utilization

of a human brain?

>> I don't look it up right now. It is. It

is two magnitudes.

>> It's like 10 or 20 watts. I thought

>> I think to the point of like real data,

the inference clouds are going a 1000x

in terms of consumption, right? And then

they're getting more efficient. So

revenue grows at some lower rate than

that. But it's wild.

>> It's 12 to 20 watts of power, which is

comparable to a dim light bulb or a

computer monitor in sleep mode. It's not

even like a computer. It's when your

monitor is sleeping, that's the amount

of energy that your brain is consuming

as it does all these crazy calculations.

>> It's one blade of one GPU fan in one of

these data centers. That's

>> nuts. I feel like No Shazir's brain

though is probably consuming like a

thousand watts.

>> Well, I think that's great. I think like

we have a lot of efficiency work to go.

>> [laughter]

>> I I kind of meant the opposite. You

know, he's so smart, but he's probably

consuming more energy. But to your

point, maybe he's more energy efficient.

>> Oh,

>> maybe he's at like one watt and I'm like

at 1,000 watts or something.

>> I meant for the computers

>> like get the algorithms going.

>> We're all stuck without the um you know

uh brain computer interface work

improving, but I'm I'm just interested

in how much efficiency we can get out of

the models.

>> Yeah, it's probably obviously just based

on the human brain there's a lot of

room. You know, one thing I do think

about, I was talking to uh a friend who

leaves a bunch of purchasing at a

traditional large enterprise this

morning and he was like, "Oh, well, the

like incumbents can this whole thing is

overstated. We're so committed to all

these big enterprise vendors, whatever.

A lot of things that we've been talking

about here." Um, and his other view was

that the incumbents have the money to

buy and go like fight back on these

dimensions. I one thing I immediately

thought of was just like

like reflexivity in markets is such a

good concept and here it's like well

they they do unless they don't have the

market cap to do it right with these

companies that to your point you know

first the labs but then a series of the

very best application companies if

they're growing to a billion of run rate

rapidly and valuations grow in concert

with that then I I do think there's a

there's a question on whether or not you

you um have the currency to compete too.

>> Yeah, I'm already seeing that in the SF

housing market, right? Where um SF

housing is starting to rise again in

part due to um I'm assuming outcomes

from the lab tenders and things like

that because suddenly you have these

companies that are worth hundreds of

billions of dollars out of nowhere in a

few years and as employees are selling

into tenders um there's this new sort of

influx of cash in the ecosystem. So, and

there's also Nvidia going from, you

know, tens of billions or 100 billion to

trillions in market cap. Like there's

just this shift happening right now in

terms of scale. And there's an

interesting question actually where um

this is one other thing that we looked

at as a team and maybe I should just

publish all these slides. We basically

asked um what proportion of GDP is tech

right in in just the US economy at least

and how has that grown over time and

also like what has that meant in terms

of market caps right and so if you look

back to uh 2005

Google was worth hundred billion and

Exxon was the world's most valuable

company or hundred billion market cap

and then um it took until 2018 18, Apple

was the first company with a trillion

dollar

market cap, right? Ever. Everybody was

shocked that anything could get to a

trillion. And at the time, tech

represented about 30% of the S&P. Um,

before that, it was say, you know, uh,

10%ish back in 2005. And now, the top

eight tech companies are about 23

trillion of market cap, and they make up

well over 50% of the S&P in terms of

value.

At the same time, um, they went from

basically 4% of GDP in 2005 to about 12%

of GDP today. And so then the question

is how how what proportion of GDP

eventually just becomes tech. And AI is

a driver of this, right? Because you're

taking services and you're taking uh

certain types of jobs and you're

augmenting them with AI and you're

converting them into effectively

software spend or tech spend. And you

can make different assumptions about

growth rates. And then based on that,

you know, you can end up with anywhere

between 15 20% of GDP to, you know, 30%

of GDP in 2035.

But that means that the market caps of

these tech companies get even bigger.

You know, it's kind of a metric for how

big can these things actually get as

they sort of aggregate up portions of

GDP. So I think that's the other lens

that people aren't really thinking

enough about in terms of what are what

are some of these terminal values 10

years from now like how much more can

things grow and what are your

assumptions around that basis for growth

you know and this is back to like that

ramp up into revenue. So it's a very

interesting kind of set of questions

that we we've been asking on my side

just in terms of like these meta things

you know like what are the what are the

bigger trends that people may not be

paying attention to that may be super

interesting. Okay. Well, then I have a

set of uh structural questions about how

to invest based on this for for you

because you know asking for a friend, my

funds are small. Um I think there's like

good implications and bad implications

based on what you said like one might be

if everything's going to get a lot

bigger. Uh a billion dollars is no

longer late stage, right? As like just

you know take a marker on valuation that

it's like

>> even now it's not late stage because

people are raising at a billion dollar

valuation with two two million of

revenue,

>> right? Well, you can decide. you know,

at least one company like that,

>> you can decide whether that's a like a

smart idea or not, right? But um but you

know, the the point we would absolutely

agree on, I think, is just, you know,

the the runway for some of these

foundational companies is just much

larger, right? Um than uh than the

conventional wisdom.

>> I think we've already believed that

though. Like I think um everybody

shifted I remember I wrote a blog post

like 15 years ago or something 10 years

ago that basically talked about how hard

it is to get to a sustainable $5 billion

market cap. Mhm.

>> Because at the time there's a basically

once every couple years a company would

actually get to that and stick with it

because this is back to you know 1015

years ago the biggest market caps were

in the hundreds of billions at most and

low hundreds of billions right and then

we saw everything grow 10x over the last

15 years right you suddenly have

trillion dollar market caps and that

means there's a lot more companies also

worth 100 billion than there used to be

in tech so I think in general we've seen

these shifts happening already and that

the reason that we were asking the

question internally about how much

bigger can these things get is because

that has further implications. How many

more trillion dollar companies can be

supported?

Is it two? Is it three? Is it a dozen?

Is it 50? You know, um and relatedly

like if everything gets pulled up, how

do you think about how you invest over

the lifetime of company in general? Or

how do you think about that as a founder

in terms of the the end state? And then

also there's a related question of

what's the actual fail rate

of startups? Should the fail rate go up

or down in that world? And you could

argue it either way. You could argue

that the fail rate should go up because

more and more value is getting

aggregated into platforms like

traditionally happened, right? Every

single platform shift has seen a

commiserate um forward integration of

that platform into the most important

vertical applications. So as an example,

you know, Microsoft very famously on its

OS, Ford integrated into the office

suite, Excel and PowerPoint and Word,

right?

killed or bought companies in those

market segments and that became office

and then they redistributed it alongside

the OS or Google forward integrated into

vertical searches. They had a platform

and then they built out travel and they

built out local and they built out all

these things and so it's not surprising

that the labs will forward integrate

into the most interesting applications

on top of them. You're already seeing

that partially with code but what else

is coming there and then what

implication does that have for people

running startups, right? like which of

those verticals are are durable and

defensible and which of those are going

to get eaten by the labs and so you know

you can make arguments in both

directions in terms of um will more of

overall GDP aggregate into a smaller

number of companies which is already

what's happening right just ignoring the

labs even right that that's kind of what

happened with Amazon and with Google and

all these things

or do you end up with this broader tail

effect as well where things are kind of

happen simultaneously

we also have a lot more startups that

are worth more because there's just so

much more market cap to go around. But

also the internet continues to provide

this global liquidity.

>> To me um uh I think the tail dominates

because uh the surface area of what you

can address with technology is just

increasing more rapidly. But uh maybe to

add more nuance to like a billion

dollars is

>> but is that true? So if you actually

look at um market cap, it's very much

power law, right? It's the head and

torso aggregate almost all the value.

That's actually true of customers too,

although people tend to misunderstand

that. Um, even for things like Google

where they there was I remember the book

that was like the long tail or whatever

of the internet and the claim was the

long tail really matters and then you'd

add up Google's ad revenue and you're

like actually it's all the head and

torso, right? And so I feel like there

are these head and torso effects that

keep getting ignored. It's like Paul

Graham's power law on startups, right?

Most of the value of YC is probably five

companies like 80% of it. I'm making it

up, right? But it's really concentrated.

And so why would that change in this

era? I don't I don't think it changes in

this era. I think that it depends what

your measure was. If your measure is how

many hundred billion dollar businesses

are there I think there's a lot more

right like it it doesn't mean there are

fewer hundred billion dollar businesses

actually there are more because the

surface area is growing and at the same

time like the distribution of how much

is in the head is probably the same and

those are even bigger.

>> Yeah it's possible. Yeah it's an

interesting question. Do you think for

investing like there's a thing that's

good for me and then perhaps like bad

for me or just a question for the for

the uh continued growth stage investors

the time to market leadership and to

revenue scale I think is compressing I

mean it's not I think like this is

happening we have

>> a large handful of companies that have

gone zero to 100 million plus run rate

faster than

>> SAS companies that we'd seen 10 years

ago

>> um and so valuations have grown with

that. I think some set of companies that

look like this um they are durable and

some like leadership can still flip

right like a question might be you know

is it you or is it ant or is it open AI

over time to your point of like actually

you could grow to a billion dollars of

revenue and still face that question

>> and and that is I think a risk that

maybe some of the growth ecosystem would

find as a new thing versus like category

leadership at a certain scale. felt

unassalable like 10 years ago.

>> Yeah. And I think there's two

interesting historical precedents to

this. One is the internet wave where you

know 1999 450 companies went public 2000

and another 450 went public. And so

there was say 1 to 2,000 companies went

public during the internet age and maybe

a dozen to two dozen of them are still

relevant, right? Everything else roughly

died or got bought. And then you fast

forward 10 years and you saw this

assumption of things that people thought

were unassailable, right? In social

networking, people thought Fster and

then MySpace were unassailable in

Facebook one. In payments, I remember

when I invested in Stripe, everybody

said that why are you doing this? You

know, um, Brainree exists and PayPal

exists and all these things exist and

so, you know, why would you ever invest

in another payments company? And of

course, that ended up being the winner

um or one of the winners, right? I mean,

payments is so big, it's a fragmented

igopoly. Um, but I just feel we've kind

of seen this story before. And so as a

founder, it's really useful to be asking

about two things. One is what is the

durability of your business? And number

two is how should you think about when

to exit if you're going to exit? Because

often for companies, there's about a

12-month window. Your company's the most

valuable it will ever be and then it

crashes out. For a very small handful of

companies, the answer is you should

never ever ever sell. For most

companies, the answer is you should sell

when the timing is right. And the

question is, how do you know when the

timing is right? because ultimately

you're going to hit a a point of of

maximal value and then and then it has a

real potential to die even if it got

enormous traction and that was the

internet wave of the 90s and so I think

two people are thinking about this and

one tip for founders

is from a hygiene perspective but also

just a way to make it a non-emotional

discussion is preschedu once or twice a

year the board meeting where you talk

about exits

and that way it becomes non-emotional.

It's not about we're going to exit. it's

not like we should exit. This has

actually been Horus's advice, I think,

um, from when he was running Opsswware.

You just set up a non-emotional meeting

once or twice a year. You're like,

"Nope, still not time to do it." Or you

say, "Oh, you know what? Actually, the

competitive dynamic has shifted

dramatically. Somebody's come to us with

an offer that's higher than anything

we'll achieve over the next 5 years.

Now's the time to do it." Right? And I

think it's useful for you to be

thoughtful about that. And again, the

default for a small number of companies

is never ever do it. For almost

everybody else, it's worth considering

at one point or another because you may

otherwise get stuck with something that

isn't working for a long time or you may

get crushed by a competitor and many

many years of very hard work can just go

down the drain. I think this is uh an

interesting point about the comparison

especially to like the internet age

versus the SAS I don't know what you

call the the like cloud age from the

last decade as being more similar

because there were I was not around for

this era but from from my um research

and from working with a bunch of people

in that period you're not old enough for

this era either like AOL was the

internet for a moment right Yahoo was

the web's front page Netscape was the

browser internet explorer was web

runtime. eBay was the market. Like I I

think there are a number of these

[clears throat]

>> and the AOL exited at the exact right

moment to Time Warner,

>> right?

>> At their peak their peak valuation,

>> right? And I I do I think that people

founders and investors may um over

rotate on the SAS era where like it did

feel like at a certain scale um like

internet era there's a period of time

where like growth was the default,

right? growth at a wild speed. That was

not true in SAS land. And so it was more

like, you know, incremental and beyond a

certain scale, it felt very protected.

But I um I think that this probably does

look more like the internet era where

the question is like does that growth

like does it compound to a control point

where you're a very special company or

like do you actually think about exits

in a different way?

>> Yeah. And if you even go back to the

80s, you know, you had Lotus. I don't

know if you remember this company.

>> I have implemented Lotus 123 at an

enterprise business as an intern.

>> Yeah. So, wow. So, Lotus uh built one of

the first spreadsheet products and it

grew explosively. I it got into the

hundreds of millions of revenue like

really really fast. And this was the

80s. Yeah. Right.

>> And then a couple years later, it

basically collapses into the arms of IBM

and Microsoft launches Excel and takes

the whole market roughly, right? And so

again, it looked like a very durable

business. It was the the the killer app

on on computers, you know, for its era.

And then it just died. It didn't die. It

it ended up with a great exit to IBM,

but still it is it no longer exists,

right, in reality. And so I think the

same thing is going to happen for a

number of companies of this era. And the

question is which companies? That's a

really hard question, right? Who knows?

But for some companies, you're starting

to see cracks,

right? Right. And so the com for the

companies with these cracks, as the

market structure shifts, as you see

shifts in what the labs are doing, as

you see shift in usage, as you see shift

in differentiation and defensibility and

all the rest, it's a good time to ask,

hey, is this my moment? Are these next

six months when I'm going to be the most

valuable I'll ever be and then I'm at

real risk. And if so, you know, you

should think seriously about what to do

with that. And I I view this not just I

mean right now. I mean, every 6 months

there's going to be these shifts that

are worth considering. And that's why

it's like preschedule the board meeting

so it's not emotional. you're not

putting something on the agenda and

everybody's like, "Oh my god, do you

want to exit? What's going on? Are you

upset? Are you worried?" It's more like,

"Oh yeah, we booked this 6 months ago

and we booked it a year ago and we

booked it two years ago." Whatever it

is, this is just when we talk about this

stuff. So, we can just have a very

logical

emotion drained conversation around this

stuff.

>> And maybe I think you know again in

comparison to internet era as to like

why think about it more now is

>> well people in the internet era should

have thought about it too.

>> Sure. Sure.

>> I mean Mark Cuban did this. Mark Cuban's

claim to fame is he sold a company that

that you know let's let's put it this

way it was early in terms of product and

he sold it to Yahoo for a few billion

dollars and then he collared Yahoo stock

so that as the stock dropped he didn't

lose any money one of the best all-time

financial engineering moments in tech

history right that's what made Mark

Cuban a billionaire was he sold at

Yahoo's high watermark and then he kept

all the value as it collapsed in price

that was one of the few people who did

that uh during that era but people were

thinking about it

>> I think what most people missed Right.

Um and like in retrospect like thinking

about the flips that made it happen

where the ground was moving a lot um is

useful, right? Because you have to

answer the question am I that company or

not?

>> Um or is my acquire that company or not?

And like in the internet cycle you had

new distribution, new performance, new

interfaces, changing user behavior. It

was just like

>> everything happening all at once and new

exploration. Not true in cloudland,

right? Just more replacement market and

then like niches that you could cheaply

distribute to new business model. SAS is

amazing. Um, but in AI it's like okay is

is the next major capability jump from

the labs going to screw me and reset the

leaderboard like that is an important

question to ask yourself and then also

>> um like surface area questions right

like agents versus IDE voice is a

default like there there are things that

change in product [snorts] experience

that also could reallocate power

>> the best way to defend against this is

to build a bundle. So, it's to build a

multi-product surface area for your

company so that you cross-ell multiple

things into the same organization and

you become a default part of the

workflow. And that's that's the best way

to defend against this because then

you're being used for five or 10

different aspects of of that vertical

that you're in or that application that

you're in versus here's my singular

thing that's easy to clone or copy or

for people to kind of um displace. So, I

think um the the sort of defensive

advice on that is do that. Yeah,

>> bundles are often seen as offensive, but

I actually think they're amazing for

defense, you know, and so I think that's

the other thing that people are

underdoing a little bit for some of

these vertical applications and that's

going to be the way to win longterm or

to defend long.

>> Well, I actually still think I now I

sound like I just hate like the SAS era.

I think it is a mistake that people like

took as conventional wisdom from the SAS

era and like apply now without thinking

about it where it was like you know do

one thing well. it was do one thing well

and then people buy you and then um like

don't go compete with a million things

but you know we we think

>> that was bad advice that was always bad

advice though I mean it it substantiated

OKS companies was bad advice because

before that the par wave companies were

very acquisitive and very multi-product

and it was just the SAS era where it

became this singular thing I think the

other piece of it is um the rate of

change of velocity and the technology

during the sass is just slow it's just

like let's just keep building out the

internet

>> you That was kind of sass era, right?

And so the the difference with AI is the

velocity of change is so high that what

normally would have taken a decade and

you'd have a normal decade long

displacement cycle is now happening in a

year or two. And that's really the the

reason that these things are so

turbulent. It's because the technology

is shifting so so dramatically so

quickly. And that's just part of scaling

laws and that's part of reasoning and

that's part of all these things that you

know all the post- training stuff that's

been rolled out. So um there's just been

so much innovation in such a compressed

period of time that that's the reason

things are turning over and things that

normally would have taken a decade or

happening in a year or two. And that's

why we're seeing these displacement or

potential for displacement cycles. But

that also means as a founder your

mindset should shift into this new world

framework. You should say okay if every

two years is 10 years I need to think

really quickly on uh changes that are

happening. I need to react to them in

all sorts of ways.

>> Yeah. And so it's just it's just uh back

to you know it's a it's a fun and

interesting and exciting time. I think

it's going to be an amazing decade of

transformation.

>> Yeah. I I do think um maybe one way to

think about like a lot of the defenses

that people did not in the software era

are uh the last software era are like

okay well what does not depend on you

know my little feature set just

incrementally growing like platforms

ecosystems networks bundles even

hardware like you described with Samsara

like that feels like non-trivial control

points and so maybe the takeaway for me

and a lot of hangout today is like hey

Don't over rotate on the last month but

also you have to think about when you

know well be intellectually honest about

the position you have in market and in

the speed of uh change era actually

think about what the control points are.

>> Yeah, lots coming. Lots shifting. It's

going to be fun.

>> Okay, have fun.

>> Yeah, see you later.

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