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 is more fragility. It is like the slop problem, coding slop in my actual production codebase. I think the broader problem that a new company could solve is that nobody knows how to manage the issue of human attention to engineering. I think it is open season around this really big problem.
The SaaS-polcalypse Discussion
Hi listeners, welcome back to No Priors. Markets are melting down about the end of software.
Today, Alotad and I are hanging out and asking: Is SaaS actually dying, or are people just projecting five-person startup behavior onto the Fortune 100?
We will talk about what is real: incredible revenue growth, collapsing token costs, and faster turnover of vendors. What is just hype, and how to size the opportunity. We also discuss the changing bottlenecks in building a software company and some parallels to the internet and cloud eras. Let's get into it. It is good to hang. The market is freaking out around us.
So, in all that noise, what are you thinking about?
Oh, you mean the SaaS apocalypse?
The SaaS apocalypse. The end of software.
Yeah.
Yeah. It is kind of interesting. I feel like there are some meta-trends that people are getting right, and then a lot of specific companies that people are getting wrong. I guess the basic premise is that SaaS software and purchased software will no longer exist, and everything is going to be replaced by AI and everything is just going to be commoditized. So, why would you pay X dollars for a Salesforce instance when you can just code it internally?
All of 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 are lots and lots of companies that are quite durable. I think an interesting example of that, where I am still a shareholder, is Samsara. Nobody is going to code a fleet management app that will then get distributed through enterprise sales or something. You are going to build an in-cab camera sensor that everybody will install in these fleets, and then you are going to support them using agents or something. It is just very overstated. So, I feel like it is one of those things where there is 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 companies like Samsara are examples where you are moving from per-seat software to basically utilization-based customer support related agents. That is a real shift that may impact some of the prior wave of sort of per-seat software companies, but this is not going to be every single SaaS company. So, 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 enterprise sales is hilarious. We have portfolio companies with hundreds of millions of dollars of revenue who are very committed to as much token usage as we can, and as few great people as we can have.
And today, they have less than 50 engineers. They went from zero to close to 100 salespeople very quickly.
And so, it is just a view from the growing AI natives that enterprise sales is not happening. Oh yeah, enterprise sales is definitely never happening anytime soon. And so, it is just again, all this, it just seems like a very strong market reaction and market correction. It seems like it is very overstated, especially relative to a handful of companies that you are just like, "Why? How will you displace this company with coding?" And in the fleet example, you are not going to have the fleet managers writing their own apps to do all this giant surface area of stuff. It is just not going to happen. In the short run, I think a lot of it is actually driven by some assumptions that engineers and builders are making about the rest of the world. Because there is this implied belief that everyone will want to make their own software.
Is software eating the world? Is that what you are trying to say?
I am not. I think we are still time to build.
I do not 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. If you think about a good example of this, engineers sometimes have a personal labor-focused picture of the world. So, should you build Jira? In most engineering organizations, is that a good use of your time if you are focused on product?
I mean, the other piece of it is the examples that people use. "Oh, my five-person startup built our own CRM, coded 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 did not have to code anything. And so, for very limited niche applications where it is a technical team doing something really quick because it is useful and custom and bespoke, amazing.
AI Change Management in Large vs. Small Companies
Of course, that is 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 is also extrapolating or projecting behavior of very small technical startups onto the world's biggest enterprises. And that is the second thing people are getting wrong: they are misunderstanding the moment. And I think the internal software stuff that people are building is amazing.
It is not like it isn't impressive that you can do that. It is incredibly impressive. It is just extrapolating that behavior so aggressively, so early, just does not make that much sense right now.
I think, to your point about the five-person company versus the very large enterprise, if you ask that same engineer who is pissed about paying $10 a seat for Jira, if you asked him or her, "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 probably not.
“Is Software Eating the World?”
And so, I think it is focused on. I actually think the idea that the actual production of code becomes not the bottleneck for if you know what the spec is, is incredibly interesting. But I do think it overstates how much of the overall software vendor problem that is.
Yeah.
I think people also misunderstand how much demand exists for software products. By software products, I mean everything. I mean AI. Is software eating the world? Is AI eating the world?
AI is eating the world. So, I think that that is actually true. And I think Mark's post on that was really thoughtful and forward-thinking on it all. I think that fundamentally, there is so much demand for software, and there is so little supply of engineering in reality relative to that demand, that as you add this enormous boost of productivity to software engineers, it just gets sucked up because there is so much more stuff to build and to do. And I do not see teams, startup teams continue to hire engineers for a reason. 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 are shifting from, in some cases, there are a few different types of mindsets around engineers. One of the mindsets is the really bespoke craftsmanship: "I am going to make the aesthetics of the thing that I am doing really well. I care about the code quality." And then there are people who write code because it is a utility that allows them to build product. There are some people who really like aspects of the math, or there are lots of different motivators for people to write code. And I think a subset of those people are going to be less happy in the new world. It is kind of like the indie game developers who made these handcrafted individual games for themselves and then for their friends, and then they launch them on the Apple Store or whatever, 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 are going to see a lot of these really great engineers who care about the bespoke craftsmanship of everything they do.
They are going to be unhappy working at larger companies as these coding tools get even more accelerant because it goes against their approach 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 is going to be freeing in some ways. So, I think there is also a variance in terms of the reactions to this stuff, depending on the type of utility function that you have relative to the work you are doing.
Addressing the Unsolved Problems
Yeah.
I think related to that, one thing I have seen is that if you have an engineering identity that is based on that, like a value-based ranking of difficulty or skill, the specific types of engineering that are considered impressive or high status can actually be less hard for agents. So, I think there is an enjoyability element and then an identity element. And actually, one of your founders from Applied Intuition wrote a good blog post where there is an essay where he says, "Keep your identity small." I think that is wonderful overall advice for this period of time. You are more adaptable if it is true.
Mhm.
But I think your overall view that there are a lot of unsolved problems and that making an abundance of software can better address that, I strongly agree with. One thing that is actually near and dear to the audience that is really unsolved is, we have broadly been thinking about what happens if you have abundant code generation. In all of our teams, agent-first engineering management and thinking about code quality is an unsolved problem.
Yeah.
And we will get there. It will be your work, and we will get there. What do you view as the major problems? Well, the anxiety that I see is if you can generate an enormous amount of code and no one is reading it, you do not know the quality of the code, nobody deeply understands the codebase, and there is more fragility. It is like the slop problem, but instead of it being coding slop for random websites for non-technical people, it is coding slop in my actual production codebase for every lazy engineer, which is every engineer. I think people are looking at some problems. I actually think ticketing systems are at risk. But I think the broader problem that Jira could go solve or a new company could go solve is that nobody knows how to manage that issue of human attention to engineering. And there are a bunch of ideas like testing and smart review, just let agents do it, formal verification. But I think it is open season around this really, really big problem. I think the one other thing people are bringing up that I do not quite buy is that agents are already making big decisions for vendor purchases and things like that. And I think somebody near and dear to your heart posted about that. And I think the statement was, "Oh, agents are increasingly making decisions about what software people are using." And really, what that is is you have a partnership with your cognition or your Claude or whoever, 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 has always happened. If you are using Airtable and they are on AWS, you are spinning up an AWS instance without knowing about it 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 is true. But then you get into all sorts of agentic 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 are in a little bit of a noisy moment where people are kind of potentially, and I am somebody who is very pro-AI progress and a believer in all the changes that have happened and are coming, but I think we are having a lot of overstatement now of what is actually happening in the world. And part of that is a SaaS apocalypse and this giant recreation, and part of it is extrapolating that the future is here already when, in many cases, it is just say we did a BD deal or whatever. So, I just think people kind of need to, or you know, the Mooltipool stuff where you are like, "Yeah, that seems human-generated," in terms of the emergent behavior. So, I do not know. We are in this odd moment where I feel like this was the month of hype in a way that we have not 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 mainstream media and others are like, "Oh my gosh, look at this behavior of these agents trying to cut out humans from their forum," where it is Reddit-like and blah blah. And you are like, "Okay, maybe you should see where the posts are coming from in some cases." And it is exciting, by the way. Do not get me wrong. I think it was very exciting behavior that is happening. I just think a subset of it was planted for marketing purposes. Yes, certainly. I think people are also figuring out that there are things that tap into deep emotional reactions that people have to their view of things that feel very human, from a marketing perspective. And that is clearly one of the things that has happened around the Mooltipool stuff. I also think that one of the things that I actually think happened was the idea that demos are different from the reality of the full software that you need has not quite arrived in many of the equity research people's desks. And so, I am like, "Guys, your whole job was to think about the structural advantage of your businesses and what is going to compound, and the theory of competitive advantage did not just poof disappear." 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 feel like that has been missed a little bit.
The Noise of the Last Month vs. Excitement
But I do think long-term, the fundamental thing that the bottleneck on production of expensive-to-produce software being loosened is really cool. It just means that if you think of there being a lot of embedded points of view in software on how to solve a problem, if it is engineering or enterprise sales, not a very software problem, or general productivity, Notion is a way to do things. It is a building block system, but it definitely has a point of view. And so, if you reduce the cost to express that point of view in software, I think it is cool that we are going to see a lot more ideas.
Oh, that is amazing. And again, I think it is a revolution. So, do not get me wrong. I have been involved with coding companies really early on, and I am very excited about everything that is happening. I think it is transformational, and I think it is revolutionary, and I think it is 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 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 growth pace of the biggest companies is still underpriced.
Yeah.
One thing that Jared on my team put together that I thought was super interesting was he pulled data from Capital IQ where they just predicted some projections on OpenAI and Anthropic. And they looked at, and then he sort of graphed out, and maybe we can share these graphs as part of this episode. He graphed out how long it took different companies in years to go from a billion in revenue to $10 billion of revenue. So, for example, ADP took 20-some 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 of years, you know, three, four, five years. But the AI labs did it in roughly a year. And then if you look at the projections, it is a wild chart, and so we should add it. But you just see it go from like 20-some 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 are not necessarily the company-driven data, but the public projections on where the labs will end up or how long it will take them to go from $10 billion to $100 billion in revenue. For Microsoft, that was something like 27 years. For Google, it was over a decade. Same with AWS, roughly the same for Meta. And then for the AI labs, it is like three, four, five years. It is very fast. And so, we are seeing the fastest time to real massive revenue that we have ever seen in the history of software. There are 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 are online. It is much easier to distribute than it has ever been. So, that is one piece of it. There are more people with access. There is higher GDP.
There are lots of drivers for that. But then simultaneously, you are just creating enormous business and user value at massive scale simultaneously. And these capabilities are so rich that you are seeing this take off in terms of revenue. And so, it is unprecedented.
It is really impressive, and I think people are ignoring the revenue and usage side of the equation. 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 GPT-4 level or equivalent model. We looked at that a year or two ago, and basically, in 21 months, it went from like $37 for a million tokens to 25 cents. And so, pricing dropped by 150x in 21 months. And then we tried to accelerate that curve, but obviously, people are not really using GPT-4 level models anymore, even though they are two or three years old. And so, we looked at Llama 2 equivalent models, and the cost of a million tokens on an Llama 2 equivalent model in December of 2024 was about $26.
And then in November of 2025, it was 30 cents. So, we saw another 88x drop, not 88%, or 88, you know, 88 times cheaper in 11 months for that next generation of model. So, we are having pricing collapse on the token side while we are having revenue ramp insanely on the usage side.
And so, that is insane if you think about that, just this pace of shift of cost, of revenue, of utilization, of everything. And this is back to, I am incredibly bullish on everything that is happening. And so, it is more dismodulating it against this odd overextrapolation of what is actually happening or actual capabilities or what these things are really doing. Yeah, I think one thing that people miss in the bare case of all this stuff is, as you said, revenue numbers, which is hard to miss, but also just actual token inference count. If you look at where the inference is happening, it is either happening in inference clouds, like BaseTen, Mobile, Fireworks, or it is happening at the pro, the very large model providers. And it is happening in a lot, which is still much more two magnitudes more humanity in general.
Yeah. Yeah, it is true. In terms of power utilization, a human brain is really impressive. What is it, like tens of watts? 20 watts. How much power utilization does a human brain have?
I do not know. Look it up right now. It is two magnitudes.
It is like 10 or 20 watts. I thought I think to the point of real data, the inference clouds are going 1000x in terms of consumption, right? And then they are getting more efficient. So, revenue grows at some lower rate than that. But it is wild.
It is 12 to 20 watts of power, which is comparable to a dim light bulb or a computer monitor in sleep mode. It is not even like a computer. It is when your monitor is sleeping, that is the amount of energy that your brain is consuming as it does all these crazy calculations.
It is one blade of one GPU fan in one of these data centers. That is nuts. I feel like No Shazir's brain, though, is probably consuming like a thousand watts.
Well, I think that is great. I think we have a lot of efficiency work to go.
[laughter] I kind of meant the opposite. You know, he is so smart, but he is probably consuming more energy. But to your point, maybe he is more energy efficient.
Oh, maybe he is at like one watt and I am at like 1,000 watts or something.
I meant for the computers, like get the algorithms going.
We are all stuck without the brain-computer interface work improving, but I am just interested in how much efficiency we can get out of the models.
Yeah, it is probably obviously, just based on the human brain, there is a lot of room. You know, one thing I do think about, I was talking to a friend who leads a bunch of purchasing at a traditional large enterprise this morning, and he was like, "Oh, well, the incumbents can, this whole thing is overstated. We are so committed to all these big enterprise vendors, whatever."
A lot of things that we have been talking about here. And his other view was that the incumbents have the money to buy and go fight back on these dimensions. I immediately thought of just like reflexivity in markets is such a good concept. And here it is like, well, they do, unless they do not have the market cap to do it, right? With these companies that, to your point, first the labs, but then a series of the very best application companies, if they are growing to a billion of run rate rapidly and valuations grow in concert with that, then I do think there is a question on whether or not you have the currency to compete too.
What Proportion of GDP is Tech?
Yeah, I am already seeing that in the SF housing market, right? Where SF housing is starting to rise again, in part due to I am 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, there is this new sort of influx of cash in the ecosystem. So, and there is also Nvidia going from, you know, tens of billions or $100 billion to trillions in market cap. Like, there is just this shift happening right now in terms of scale. And there is an interesting question, actually, where this is one other thing that we looked at as a team, and maybe I should just publish all these slides. We basically asked, what proportion of GDP is tech, in just the US economy at least, and how has that grown over time? And also, what has that meant in terms of market caps? And so, if you look back to 2005, Google was worth $100 billion, and Exxon was the world's most valuable company, or $100 billion market cap. And then it took until 2018, 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. Before that, it was say, 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.
Market Cap Shifts
At the same time, they went from basically 4% of GDP in 2005 to about 12% of GDP today. And so, then the question is, how much proportion of GDP eventually just becomes tech? And AI is a driver of this, right? Because you are taking services and you are taking certain types of jobs, and you are augmenting them with AI, and you are 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 30% of GDP in 2035.
But that means that the market caps of these tech companies get even bigger.
You know, it is 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 is the other lens that people are not really thinking enough about in terms of what are some of these terminal values, 10 years from now, how much more can things grow, and what are your assumptions around that basis for growth? You know, and this is back to that ramp-up into revenue. So, it is a very interesting kind of set of questions that we have been asking on my side, just in terms of these meta things, you know, like 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 structural questions about how to invest based on this for you, because, you know, asking for a friend, my funds are small. I think there are good implications and bad implications based on what you said. One might be, if everything is going to get a lot bigger, a billion dollars is no longer late-stage, right? As just, you know, take a marker on valuation that it is like, even now, it is not late-stage because people are raising at a billion-dollar valuation with two million of revenue, right? Well, you can decide. At least one company like that, you can decide whether that is a smart idea or not, right? But, you know, the point we would absolutely agree on, I think, is just, you know, the runway for some of these foundational companies is just much larger than the conventional wisdom.
As a Company, When Should You Sell?
I think we have already believed that, though. Like, I think 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 was basically once every couple of years, a company would actually get to that and stick with it. Because this is back to, you know, 10-15 years ago, the biggest market caps were in the hundreds of billions at most, and low hundreds of billions. And then we saw everything grow 10x over the last 15 years. You suddenly have trillion-dollar market caps, and that means there are a lot more companies also worth $100 billion than there used to be in tech. So, I think in general, we have seen these shifts happening already. And 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, and relatedly, if everything gets pulled up, how do you think about how you invest over the lifetime of a company in general? Or how do you think about that as a founder in terms of the end state? And then also, there is a related question of what is 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. Every single platform shift has seen a commensurate forward integration of that platform into the most important vertical applications. So, as an example, you know, Microsoft very famously on its OS, forward integrated into the Office suite, Excel and PowerPoint and Word.
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 is not surprising that the labs will forward integrate into the most interesting applications on top of them. You are already seeing that partially with code, but what else is coming there? And then what implication does that have for people running startups? Like, which of those verticals are durable and defensible, and which of those are going to get eaten by the labs? And so, you can make arguments in both directions in terms of will more of overall GDP aggregate into a smaller number of companies, which is already what is happening, just ignoring the labs, even right? That is 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 happening simultaneously? We also have a lot more startups that are worth more because there is just so much more market cap to go around. But also, the internet continues to provide this global liquidity.
To me, I think the tail dominates because the surface area of what you can address with technology is just increasing more rapidly. But maybe to add more nuance to like a billion dollars is, is that true? So, if you actually look at market cap, it is very much a power law. It is the head and torso aggregate almost all the value.
That is actually true of customers too, although people tend to misunderstand that. 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 would add up Google's ad revenue, and you are like, "Actually, it is all the head and torso." And so, I feel like there are these head and torso effects that keep getting ignored. It is like Paul Graham's power law on startups, right?
Most of the value of YC is probably five companies, 80% of it. I am making it up, right? But it is really concentrated.
And so, why would that change in this era? I do not 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 are a lot more. It does not mean there are fewer hundred-billion-dollar businesses. Actually, there are more because the surface area is growing. And at the same time, the distribution of how much is in the head is probably the same, and those are even bigger.
Multi-Product Bundle Defense
Yeah.
It is possible. Yeah, it is an interesting question. Do you think for investing, there is a thing that is good for me and then perhaps bad for me, or just a question for the continued growth stage investors? The time to market leadership and to revenue scale, I think, is compressing. I mean, it is not. I think this is happening. We have a large handful of companies that have gone from zero to $100 million-plus run rate faster than SaaS companies that we have seen 10 years ago. And so, valuations have grown with that. I think some set of companies that look like this, they are durable, and some leadership can still flip, right? Like a question might be, is it you, or is it Anthropic, or is it OpenAI over time? To your point of actually, you could grow to a billion dollars of revenue and still face that question. And that is, I think, a risk that maybe some of the growth ecosystem would find as a new thing versus category leadership at a certain scale felt unassalable 10 years ago.
Yeah.
And I think there are 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 were say, 1,000 to 2,000 companies that 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. In social networking, people thought Friendster and then MySpace were unassailable, and Facebook won. In payments, I remember when I invested in Stripe, everybody said, "Why are you doing this? BrainTree exists, and PayPal exists, and all these things exist." And so, why would you ever invest in another payments company? And of course, that ended up being the winner, or one of the winners. Payments is so big, it is a fragmented igopoly. But I just feel we have kind of seen this story before. And so, as a founder, it is 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 are going to exit? Because often for companies, there is about a 12-month window where your company is 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 are going to hit a point of maximal value, 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 to pre-schedule once or twice a year the board meeting where you talk about exits. And that way, it becomes non-emotional.
Conclusion
It is not about, "We are going to exit." It is not like, "We should exit." This has actually been Horace's advice, I think, from when he was running Opsware.
You just set up a non-emotional meeting once or twice a year. You are 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 five years."
"Now's the time to do it." Right? And I think it is 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 is 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 an interesting point about the comparison, especially to like the internet age versus the SaaS, I don't know what you call it, the cloud age from the last decade, as being more similar. Because there were, I was not around for this era, but from my research and from working with a bunch of people in that period, you are not old enough for this era either. AOL was the internet for a moment. Yahoo was the web's front page. Netscape was the browser. Internet Explorer was the web runtime. eBay was the market. Like, I think there are a number of these. And AOL exited at the exact right moment to Time Warner, at their peak, their peak valuation. And I do think that people, founders and investors, may over-rotate on the SaaS era, where like it did feel like at a certain scale, like the internet era, there was a period of time where growth was the default, growth at a wild speed. That was not true in SaaS land. And so, it was more like incremental, and beyond a certain scale, it felt very protected.
But I think this probably does look more like the internet era, where the question is, does that growth compound to a control point where you are a very special company, or 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 do not know if you remember this company.
I have implemented Lotus 1-2-3 at an enterprise business as an intern.
Yeah.
So, wow. So, Lotus built one of the first spreadsheet products, and it grew explosively. It got into the hundreds of millions of revenue, really, really fast. And this was the 80s. Yeah. Right.
And then a couple of 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 killer app on computers for its era.
And then it just died. It did not die. It ended up with a great exit to IBM, but still, 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 is a really hard question, right? Who knows?
But for some companies, you are starting to see cracks, right? Right. And so, for the companies with these cracks, as the market structure shifts, as you see shifts in what the labs are doing, as you see shifts in usage, as you see shifts in differentiation and defensibility and all the rest, it is a good time to ask, "Hey, is this my moment? Are these next six months when I am going to be the most valuable I will ever be, and then I am at real risk?" And if so, you know, you should think seriously about what to do with that. And I view this not just right now. I mean, every six months, there are going to be these shifts that are worth considering. And that is why it is like, pre-schedule the board meeting so it is not emotional. You are not putting something on the agenda, and everybody's like, "Oh my god, do you want to exit? What is going on? Are you upset? Are you worried?" It is more like, "Oh yeah, we booked this six 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, again, in comparison to the internet era, as to why think about it more now, 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, 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 did not lose any money. One of the best all-time financial engineering moments in tech history. That is what made Mark Cuban a billionaire. 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 during that era. But people were thinking about it. I think what most people missed, right?
And like in retrospect, thinking about the flips that made it happen where the ground was moving a lot, is useful, right? Because you have to answer the question, "Am I that company or not?"
Or is my acquirer 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 cloud land, right? Just more replacement market, and then niches that you could cheaply distribute to, new business model. SaaS is amazing. But in AI, it is like, okay, is the next major capability jump from the labs going to screw me and reset the leaderboard? That is an important question to ask yourself. And then also, surface area questions, like agents versus IDE, voice is a default. There are things that change in product experience that also could reallocate power. The best way to defend against this is to build a bundle. So, it is to build a multi-product surface area for your company so that you cross-sell multiple things into the same organization, and you become a default part of the workflow. And that is the best way to defend against this, because then you are being used for five or 10 different aspects of that vertical that you are in, or that application that you are in, versus here is my singular thing that is easy to clone or copy or for people to kind of displace. So, I think the sort of defensive advice on that is do that. Bundles are often seen as offensive, but I actually think they are amazing for defense. And so, I think that is the other thing that people are underdoing a little bit for some of these vertical applications, and that is going to be the way to win long-term or to defend long.
Well, I actually still think, now I sound like I just hate the SaaS era.
I think it is a mistake that people took as conventional wisdom from the SaaS era and apply now without thinking about it, where it was like, "Do one thing well." It was, "Do one thing well, and then people buy you." And then, "Don't go compete with a million things." But, you know, we think that was bad advice. That was always bad advice, though. I mean, it substantiated OKRs companies was bad advice because before that, the prior wave companies were very acquisitive and very multi-product. And it was just the SaaS era where it became this singular thing. I think the other piece of it is the rate of change of velocity and the technology during the SaaS is just slow. It is just like, let's just keep building out the internet. That was kind of the SaaS era, right?
And so, the difference with AI is the velocity of change is so high that what normally would have taken a decade and you would have a normal decade-long displacement cycle is now happening in a year or two. And that is really the reason that these things are so turbulent. It is because the technology is shifting so dramatically, so quickly. And that is just part of scaling laws, and that is part of reasoning, and that is part of all these things that, you know, all the post-training stuff that has been rolled out. So, there has just been so much innovation in such a compressed period of time that that is the reason things are turning over. And things that normally would have taken a decade are happening in a year or two. And that is why we are 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 the changes that are happening. I need to react to them in all sorts of ways."
Yeah.
And so, it is just back to, it is a fun and interesting and exciting time. I think it is going to be an amazing decade of transformation.
Yeah.
I do think maybe one way to think about a lot of the defenses that people did not in the software era are, the last software era are like, "Okay, well, what does not depend on my little feature set just incrementally growing?" Like platforms, ecosystems, networks, bundles, even hardware, like you described with Samsara. That feels like non-trivial control points. And so, maybe the takeaway for me and a lot of the 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 change era, actually think about what the control points are."
Yeah, lots coming. Lots shifting. It is going to be fun.
Okay, have fun.
Yeah, see you later.
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