The AI Code Slop: Risk or Opportunity?

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

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

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

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

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

or something. It's just it's just very overstated. So I feel like it's one of

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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,

that. Just this pace of shift of cost, of revenue, of utilization, of everything. And this is back to like I'm

everything. And this is back to like I'm incredibly bullish on everything that's happening. Um and so it's more

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.

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

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

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

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

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

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,

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

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

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

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,

>> 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

larger, right? Um than uh than the conventional wisdom.

>> I think we've already believed that though. Like I think um everybody

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.

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

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,

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

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

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

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.

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

era? I don't I don't think it changes in this era. I think that it depends what

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

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

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

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

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

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

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

12-month window. Your company's the most valuable it will ever be and then it crashes out. For a very small handful of

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

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

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

>> 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

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

grew explosively. I it got into the hundreds of millions of revenue like really really fast. And this was the 80s. Yeah. Right.

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

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

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

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

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

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

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,

exploration. Not true in cloudland, right? Just more replacement market and

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

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

turbulent. It's because the technology is shifting so so dramatically so quickly. And that's just part of scaling

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

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

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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