From SaaS to AI-First: How Companies Are Reshaping Innovation

Summary

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No Priors Episode Summary: "From SaaS to AI-First: How Companies Are Reshaping Innovation"

Podcast: No Priors: Artificial Intelligence | Technology | Startups
Hosts: Sarah Guo & Elad Gil
Date: February 19, 2026

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Main Theme & Purpose

This episode tackles the anxiety and hype around the supposed “SaaS apocalypse”—the belief that traditional software companies, especially SaaS businesses, face existential risk as AI-native startups rapidly transform what’s possible in enterprise software. Elad and Sarah critically assess whether these fears are justified, discuss how AI is altering company structures and software bottlenecks, compare this shift to past technological eras, and surface practical insights for founders and investors navigating unprecedented growth, competition, and market volatility.

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Key Discussion Points & Insights

1. Hype Versus Reality: “The SaaS Apocalypse”

• Market Overreaction:
  • Media and market participants are prematurely calling for the end of SaaS, overstating AI’s near-term impact on established software businesses.
  • Elad: “It just seems like a very strong market reaction and market correction…especially relative to a handful of companies that you're just like, why, like how will you displace this company with coding?” [03:19]
• AI Replacing Everything? Not Yet:
  • Not every enterprise is ready—or willing—to “vibe code” replacements for major systems (like CRMs or fleet management).
  • Sarah: “Vibe sales is not happening, right?” [03:19]

2. Misreading Startups and Enterprises

• Small vs. Large Company Behavior:
  • AI-native, engineering-heavy startups may build custom tools, but this doesn’t scale to Fortune 100 companies with complex needs, security, and change management.
  • Elad: “...projecting behavior of very small technical startups onto the world's biggest enterprises...just doesn’t make that much sense right now.” [04:38]
• Not Everyone Wants to Build Their Own Software:
  • The belief that everyone will replace vendors with homegrown solutions is misplaced.
  • Sarah: “I don't think that everybody wants to make their own software.” [04:13]

3. Changing Bottlenecks and Job Satisfaction

• Abundant Code, New Bottlenecks:

  • While AI makes code generation abundant, production is no longer the main bottleneck—the challenge shifts to maintaining code quality, managing complexity, and ensuring human attention.
  • Sarah: “The anxiety that I see is if you can generate an enormous amount of code and no one is reading it…there's more fragility…it's like open season around this really, really big problem.” [10:08 & 00:00]

• Engineering Identity & Enjoyment:

  • Some engineers thrive on bespoke, artisanal code; others see code as a practical means to an end. As AI accelerates utility coding, “craftsperson” engineers may be less satisfied in large orgs.
  • Elad: “I think a subset of those people are going to be less happy in the new world…it goes against their approach of how they like working and what they enjoy out of the work.” [06:45]

4. Market and Revenue Trends: Insane Growth and Pricing Collapse

• Fastest Revenue Acceleration in History:
  • AI labs are scaling from $1B to $10B in revenue dramatically faster than previous generations: from 20 years (Adobe) or 8 years (Salesforce) to now just about a year.
  • Elad: “We're seeing the fastest time to real massive revenue that we've ever seen in the history of software.” [15:26]
• Collapsing Token Costs:
  • The cost of running AI models (token pricing) is dropping by orders of magnitude—GPT-4 equivalent models from $37 to $0.25 per million tokens in under two years.
  • “Pricing dropped by 150x in 21 months…and then for .01 equivalent models…another 88x drop in 11 months.” [15:26]
• Inference Load Exploding:
  • Demand for AI computation (inference) is rising 1000x as efficiency improves, but remains far less energy-efficient than the human brain.
  • Sarah: “The inference clouds are growing 1000x in terms of consumption…and then they're getting more efficient. So revenue grows at some lower rate than that. But it's wild.” [19:43]

5. Shifts in Economic Power and Competitive Dynamics

• Tech’s Share of GDP & Market Cap Keeps Growing:
  • Tech now exceeds 50% of the S&P’s value, rising from 4% to 12% of U.S. GDP since 2005, with projections up to 30% by 2035 as AI converts services and jobs into software spend.
  • Elad: “[Tech companies] make up well over 50% of the S and P...they went from basically 4% of GDP in 2005 to about 12%...You can end up with anywhere between 15, 20% of GDP to 30% of GDP in 2035.” [21:50]
• Valuations and the Investment Landscape:
  • The scale possible for successful startups is far beyond the SaaS era, raising questions about what “late-stage” really means.
  • Sarah: “A billion dollars is no longer late stage.” [24:33]
  • Elad: “You suddenly have trillion dollar market caps and that means there's a lot more companies also worth a hundred billion than there used to be.” [25:18]
• Platform Forward Integration and the Startup Fail Rate:
  • Just as Microsoft bundled Excel, and Google built vertical services, AI labs are expected to bundle and cannibalize lucrative software verticals.
  • Elad: “Every single platform shift has seen a commiserate forward integration of that platform into the most important vertical application.” [25:18]

6. Historical Parallels: Internet, Cloud, and Now AI

• More Like the Internet Bubble than the SaaS Revolution:
  • Speed, disruption, and potential for sudden, massive winners (and losers) echo the 1990s internet era much more than the slower-growing, stickier SaaS decade.
  • Elad: “…in the Internet wave … maybe a dozen to two dozen [companies] are still relevant. Everything else roughly died or got bought … The same thing is going to happen for a number of companies of this era.” [30:10]
• Durability and Exit Timing:
  • Most startups have a window of peak value, then risk being wiped out by competitors or platform shifts—founders should regularly, and unemotionally, reevaluate whether to exit.
  • Elad: “Pre-schedule once or twice a year, the board meeting where you talk about exits. That way it becomes non-emotional…” [30:10]

7. Product Strategies and Defensibility

• Defensive Moats: Go Beyond "One Thing Well":
  • Bundles, multi-product platforms, networks, hardware, and ecosystems (not just feature sets) provide greater resilience against rapid displacement.
  • Elad: “The best way to defend against this is to build a bundle…cross sell multiple things into the same organization and you become a default part of the workflow…” [37:08]
• AI Era Demands a New Founder Mindset:
  • If a “year is a decade,” founders must stay nimble and intellectually honest about market position and control points.
  • Sarah: “You have to think about…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.” [39:30]

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Notable Quotes & Memorable Moments

• On hype versus reality:
  • Elad [03:19]: “It just seems like a very strong market reaction and market correction...especially relative to a handful of companies that you're just like, why, like how will you displace this company with coding?”
• AI’s impact on engineering identity:
  • Elad [06:45]: “…subset of those people are going to be less happy in the new world. It goes against their approach of how they like working and what they enjoy out of the work.”
• Abundance of code/inference:
  • Sarah [10:08]: “If you can generate an enormous amount of code and no one is reading it, you don't know the quality of the code...it's like open season around this really, really big problem.”
• Collapse of token pricing:
  • Elad [15:26]: “Pricing dropped by 150x in 21 months…and then for .01 equivalent models…another 88x drop in 11 months…”
• On defending against disruption:
  • Elad [37:08]: “The best way to defend against this is to build a bundle...so that you cross sell multiple things into the same organization and you become a default part of the workflow…”

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Timestamps for Key Segments

| Time | Topic/Quote | |----------|-------------------------------------------------------------------------------------------------------------| | 00:00 | Anxiety over code abundance, quality, and the "vibe coding" phenomenon | | 01:19 | Beginning of "SaaS apocalypse" discussion; skepticism on total AI-led disruption of SaaS | | 03:48 | AI-native startups vs large enterprises; why “build your own” is not for everyone | | 06:41 | Software and AI “eating the world”—new bottlenecks, new job satisfaction dynamics | | 10:08 | Code quality and the open challenges it poses in an “agent-first” world | | 15:26 | Revenue growth acceleration and token cost collapse in AI, with historical revenue scaling data | | 19:43 | Power consumption: AI inference scale compared to human brain, efficiency issues | | 21:50 | Tech’s growing share of S&P 500 and GDP; implications for valuations and competition | | 25:18 | Strategic investment questions, platform forward integration, startup fail rates | | 30:10 | Lessons from internet and SaaS eras: durability, exits, and founder/board decision-making | | 37:08 | Moats in the AI era: bundles, platforms, ecosystems versus single-feature companies | | 39:30 | Concluding insights: be honest about position and control points, don’t overreact, but adapt strategy |

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Closing Takeaways

• The SaaS “apocalypse” is overstated—hype outstrips reality in the short term, though profound, rapid change is underway.
• AI’s impact is real and is compressing cycles of disruption, growth, and displacement to unprecedented speeds.
• The most resilient companies will invest in broad, defensive moats and regularly, unemotionally review their strategies and exit options.
• Founders and investors must adapt to a world where a “year is a decade” and not assume that previously “safe” moats or metrics still apply.

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For further insights and charts referenced in the episode, visit no-priors.com.

Transcript

[A]

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 code base. And there's more fragility, right? It's like the slop problem, vibe coding slop in my actual production code base. 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 think it's like open season around this really, really big problem. Hi listeners, welcome back to no Priors. Markets are melting down about the end of software today a lot 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'll talk about what's real, incredible revenue growth, collapsing token costs and faster turnover of vendors. What's just hype and how to size the opportunity. We also discussed the changing bottlenecks in building a software company and some parallels to the Internet and cloud eras. Let's get into it. It's good to hang. The market is freaking out around us. So in all that noise, what are you thinking about?

[B]

Oh, you mean the SaaS apocalypse?

[A]

The SaaS apocalypse, the end of software.

[B]

Yeah. That'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 guess the basic premise is that SaaS software and proceed software will no longer exist and everything is going to be replaced by AI and everything's just going to get Vibe coded. So why would you pay X dollars for a salesforce instance when you can just vibe code it internally and all that stuff strikes me as incredibly short sighted in the near term, over the long run, who knows what happens in 20 years or whatever. But there's lots and lots of companies that are quite durable. I think an interesting example of that where I'm still a shareholder is Samsara, where you know, nobody's going to Vibe code a fleet management app that will then get distributed through like what Vibe sales, Vibe, you know, enterprise sales or something. And you're going to build a Vibe like in cab camera sensor that everybody will install in these fleets and then you're going to support them using Vibe agents or something. It's just, it's just very overstated. So I feel like it's one of those things where there's a massive market correction around something that in the long run has a lot of truth to it and Maybe in the short run for certain types of companies has a lot of truth. Right. Ultimately, I think decagon and Sierra are examples of companies where you're moving from proceed software to basically utilization based customer support related agents. Right. That is a real shift that may impact some of the prior wave of sort of proceed software companies. But this isn't 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?

[A]

I mean, I think the idea of Vibe Enterprise sales is hilarious because we have portfolio companies with 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 they've less than 50 engineers and they went from zero to, let's say close to 100 salespeople very quickly. And so it's just a view from the growing AI natives that like Vibe sales is not happening, right?

[B]

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 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 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.

[A]

I think a lot of it's actually driven by some assumptions that Persona close to my heart. But engineers and builders are making about the rest of the world because there's this implied belief that everyone will want to make their own software.

[B]

And I think it's probably software is eating the world. Is that what you're trying to say?

[A]

I am not. I think we're still.

[B]

Time to build, Sarah. Time to build.

[A]

I don't think that everybody wants to make their own software. I think of people will want to make it and others will want other people to do it for them. And like sometimes, 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?

[B]

Like, is that a. Yeah, it's not the best use of your time if you're 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, Vibe 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'd have to Vibe 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 by Coded over the weekend? Probably not. And so I think it's also extrapolating or projecting behavior of very small technical startups onto the world's biggest enterprises. And that's the second thing people are getting wrong is they're misunderstanding the 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.

[A]

I think to your point of the five person company versus the very large enterprise, if you ask that same engineer who's like pissed about paying $10 a seat for Jira, like if you asked him or her, like, do you want to do the change management in bank of America of getting everybody to do this the way you think is right, and then dealing with all the security considerations and managing other people's opinions about potential changes to the story management workflow and then maintaining the system, the answer is like, probably not, you know, and so I think it is focused on, I actually think the idea that actual production of code becomes not the bottleneck for if you know what the spec is not the bottleneck is incredibly interesting. But I do think it overstates how much of the overall software vendor problem that is.

[B]

Yeah, I think people also misunderstand how much demand exists for, for software products. And by software products, I mean everything, I mean AI.

[A]

I mean, is software eating the world? Is AI eating the world?

[B]

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, you know, there's, there's so much demand for software and there's so little supply of engineering. In reality, we're all under that demand that as you add this enormous boost of productivity to software engineers, it just gets soaked up because there's so much more stuff to build and to do. And I don't see 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're shifting from. In some cases, there's a few different types of mindsets around engineers. And one of the mindsets is the really bespoke craftsmanship. You know, I'm going to make. 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 the artisanal 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. There's 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's kind of like the indie game developers who make these handcrafted individual games for themselves and then for their friends and then they launched them on the Apple Store or whatever versus the people who'd 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 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 how they like working and what they enjoy out of the work. And for other people who are really focused on the utility of just building product, it's going to be freeing in some ways. So I think there's also 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're doing.

[A]

Yeah, I think related to that, one thing I've seen is that if you have an engineering identity that's based on a value based ranking of difficulty or skill, 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. And actually one of your founders from Applied Intuition wrote a good blog post where there is a, 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. But I think your overall view of there are a lot of unsolved problems and making an abundance of software can better address that. I strongly agree with. And one thing that actually is near and dear to the audience that is really unsolved is we've broadly been thinking about what happens if you have abundant code generation. And I think in all of our teams, agent first, engineering, management and thinking about code quality as an unsolved problem.

[B]

Yeah, and we'll get there and it'll be your coworker and we'll get there. What do you view as the major problems?

[A]

Well, the anxiety that I see 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 code base. And there's more fragility. Right. It's like the slop problem, but instead of it being like vibe coding slop for random, random websites for non technical people, it's vibe coding slop. In my actual production code base for every lazy engineer, which is every engineer, I think people are like looking at some problems of actually, do you think ticketing. Ticketing systems are at risk. But I think the broader problem that JIRA could go solve or new company 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.

[B]

I think the one other thing people are bringing up that I don't quite buy is that agents are already making like big decisions for vendor purchases and things like that. I think somebody near and dear to your heart posted about that and I think that there the statement was, oh, agents are increasingly making decisions about what software people are using. And really what that is is while you have a partnership, your cognition or your cloud or whoever, and you have a partnership and as part of that partnership you spin up a Supabase 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 Airtable 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 were making these choices is also overstated. I think in the long run it's true. But then you get into all sorts of agent commerce decisions and do they understand your Persona and what you actually want and need and all this stuff. So I just feel like we're in a little bit of a noisy moment where people are kind of potentially. And I'm somebody who's very pro AI progress and a believer in all the changes that have happened and are coming. But I think we're having a lot of overstatement now of what's actually happening in the world. And part of that is a SaaS apocalypse and this giant reconnection. And part of it is extrapolating that the future is here already, when in many cases it's just, hey, we did a BD deal or whatever. So I just think people kind of need to. Or the multiple stuff where you're like, yeah, a lot of that seems human generated in terms of the emergent behavior. So I don't know, we're in this odd moment where I feel like this was the month of hype in a way that we haven't seen in a while, where a bunch of stuff got overstated in all sorts of ways and people believed it. And by people, I mean, like, mainstream media and others are like, oh my gosh, look at this behavior of, you know, these agents trying to cut out humans from their forum where it's Reddit, like, and blah, blah. And you're like, okay, like, maybe you should see where the posts are coming from in some cases. And it's exciting, by the way, don't get me wrong, I think it was very exciting behavior that's happening. I just think, you know, a subset of it was planted for marketing purposes.

[A]

Yes, certainly. I think people are also figuring out, like, there are, there are things that tap into deep emotional reactions that people have to their view of, like, things that feel very human. Right. From a marketing perspective. And like, that's clearly one of the things that's happened around the Malt 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 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 feel like that has been missed a little bit. But I, I Do think long run. The fundamental thing that the bottleneck on production of expensive to produce software being loosened is really cool. Right. It just means if you think of there's a lot of embedded points of view in software on how to solve a problem. Right. If it's engineering or enterprise sales, not a very software problem or general productivity 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 see a lot more ideas.

[B]

That's amazing. And again, I think it's a revolution. Don't get me wrong, I've been involved with coding companies really early on and I'm very excited about everything that's happening and I think it's transformational and I think it's revolutionary and I think it's really important. I just think we had a month of kind of bullshit hype.

[A]

Okay. So if we ignore the noise of the last month where people got a little like frantic, what do you think is a signal that people are not paying attention to enough in such a noisy landscape? You were telling me that like growth, growth pace is like of the of the biggest companies is still under underpriced.

[B]

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 something 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 one 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, 7ish 8 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, it's a wild chart. It's a wild chart and so we should add it. Right. But you just see it go from like 20 something 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 to go from 10 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's like three, four, five years. 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. 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 it's something every customer is 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 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 unprecedented, it's 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 Shiran. And so for example, we looked at the cost of a GPT4 level or equivalent model. We looked at that a year or two ago and basically in 21 months it went from 37 bucks 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 aren't really using GPT4 level models anymore, even though they're 2, 3 years old. And so we looked at.01 equivalent models and the cost of a million tokens on an O1 equivalent model in December of 24 was about 26 bucks and then in November of 25 it was 30 cents. So we saw another 88x drop, not 88% or 88 times cheaper in 11 months for that next generation of models. So we're having pricing collapse on the token side while we're having revenue ramp insanely on the usage side. And so that's insane if you think about that. Just this pace of shift of cost of revenue, of utilization of everything. And this is back to like I'm incredibly bullish on everything that's happening and so it's more dismodulating it against this odd over extrapolation of what's actually happening or actual capabilities or what these things are really doing.

[A]

Yeah, I think one thing that people miss in the bear case and all this stuff is as you said, revenue numbers, which is hard to miss. And then just like actual 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 mold of fireworks, or it's happening at the very large model provider. And it's happening in Lod Spring, which is still much more. Two magnitudes more expensive.

[B]

Humanity in general and humanity in general. Yeah, yeah, it's true. In terms of power utilization, 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?

[A]

Let's look it up right now. It is two magnitudes, something like that.

[B]

It's like 10 or 20 watts.

[A]

I thought, I think to the point of real data, the inference clouds are growing 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.

[B]

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 mod. It's when your monitor's sleeping. That's the amount of energy that your brain is consuming as it does all these crazy calculations.

[A]

It's one blade of one GPU fan in one of these data centers.

[B]

That's how I think of it. That's nuts. I feel like Noam Shazir's brain though, is probably consuming like a thousand watts.

[A]

Well, I think that's great. I think we have a lot of efficiency work to go.

[B]

I kind of meant it the opposite. He's so smart, he's probably consuming more energy. But to your point, maybe he's more energy efficient. Maybe he's at 1 watt and I'm at a thousand watts or something.

[A]

I meant for the computers.

[B]

Get the algorithms going.

[A]

We're all ST without the, you know, brain computer interface work improving. But I'm just interested in how much efficiency we can get out of the models.

[B]

Yeah, it's probably obviously just based on the human brain. There's a lot of room.

[A]

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 like incumbents can this whole thing is overstated. We're so committed to all these big enterprise vendors, whatever. A lot of things that we've been talking about here. And his other view was that the incumbents have the money to buy and go fight back on these dimensions. One thing I immediately thought of was just like reflexivity in markets is such a good concept. And here it's like, well, they do, unless they don't 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're growing to a billion of run rate rapidly and valuations grow in concert with that, then I do think there's a question on whether or not you have the currency to compete too.

[B]

Yeah, I'm already seeing that in the SF housing market where SF housing is starting to rise again in part due to, 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, there's this new sort of influx of cash in the ecosystem. And there's also Nvidia going from tens of billions or 100 billion to trillions in market cap. There's just this shift happening right now in terms of scale. There's 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, right? And 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 2005, Google is worth a hundred billion dollars and Exxon was the world's most valuable company at $400 billion in market cap. And then it took until 2018, Apple was the first company with a trillion dollar market cap ever. And everybody was shocked that anything could get to a trillion. And at the time, tech represented about 30% of the S and 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 and P in terms of value. 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 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 certain types of jobs and you're augmenting them with AI and you're converting them into effectively software spend or tech spend. And you can make different assumptions about growth rates. And then based on that, you 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. It's kind of a metric for how big can these things actually get as they aggregate up portions of gdp. So I think that's the other lens that people aren't really thinking enough about in terms of what are 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? And this is back to that ramp up into revenue. So it's a very interesting kind of set of questions that we've been asking on my side, just in terms of these meta things, what are the bigger trends that people may not be paying attention to? That may be super interesting.

[A]

Okay, well then I have a set of structural questions about how to invest based on this for you. Because asking for a friend, my funds are small. I think there's good implications and bad implications. Based on what you said, one might be if everything's going to get a lot bigger, a billion dollars is no longer late stage. Right. It's like just, you know, take a marker on valuation that even now it's

[B]

not late stage because people are raising at a billion dollar valuation with 2 million of revenue.

[A]

Right, well, you can decide that's.

[B]

I know at least one company like that.

[A]

You can decide whether that's like a smart idea or not. Right, but, but 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. Right. Than the conventional wisdom.

[B]

I think we've already believed that though. 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. 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 10, 15 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 a hundred billion than there used to be. In tech. So I think in general we've 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? And relatedly, if everything gets pulled up, how do you think about how you invest it with a 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's a related question of what's the actual fail rate of startups? Should the fail rate go up or down in that world? And you could argue it either way. You could argue that the fail rate should go up because more and more value is getting aggregated into platforms like traditionally has happened. Every single platform shift has seen a commiserate forward integration of that platform into the most important vertical application. So as an example, Microsoft very famously on its OS forward integrated into the Office Suite, Excel and PowerPoint and Word, right? They killed or bought companies in those market segments and that became an office. And then they redistributed it alongside the OS or Google. Ford 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, 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? Which of those verticals are durable and defensible and which of those are going to get eaten by the labs? And so you could make arguments in both directions in terms of will more of overall GDP aggregate into a smaller number of companies, which is already what's happening, just ignoring the labs even, right? 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.

[A]

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.

[B]

But it's not true. So if you actually look at 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. Even for things like Google, where I remember the book that was like the long tail or whatever of the Internet and the claim was the long tail really matters. And then you'd add up Google's ad revenue and you're like, actually it's all the head and torso. Right. And so I feel like there are these head and torso effects that keep getting ignored. It's like Paul Graham's power law on startups. Right. Most of the value of YC is probably five companies, like 80% of it or something. I'm making it up. Right. But it's really concentrated. And so why would that change in this era?

[A]

I don't think it changes in this era. I think that it depends on what your measure was. If your measure is how many hundred billion dollars businesses are there. I think there's a lot more. Right. Like 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. Those are even bigger.

[B]

Yeah, it's possible. Yeah. It's an interesting question.

[A]

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 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 had gone zero to a hundred million plus run rate faster than SaaS companies that we'd seen ten 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 ant 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 unassailable like 10 years ago.

[B]

Yeah. And I think there's two interesting historical precedents to this. One is the Internet wave where 1999, 450 companies went public, 2000, another 450 went public. And so there was say 1 to 2000 companies went public during the Internet age and maybe a dozen to two dozen of them are still relevant. 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 then Facebook won in payments. I remember when I invested in Stripe, everybody said that, 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, right? I mean payments is so big, it's a fragmented oligopoly. But I just feel we've kind of seen this story before. And so as a founder, it's really useful to be asking about two things. One is what is the durability of your business? And number two is how should you think about when to exit if you're going to exit? Because often for companies there's about a 12 month window, your company's the most valuable it will ever be and then it crashes out. For a very small handful of companies, the answer is you should never, ever, ever sell. For most companies, the answer is you should sell when the timing is right. And then the question is, how do you know when the timing is right? Because ultimately you're 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 too few 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 pre schedule 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 is actually Ben 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're like, nope, still not time to do it. Or you say, oh, you know what, Actually the competitive dynamic has shifted dramatically. Somebody's come to us with an offer that's higher than anything we'll achieve over the next five 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.

[A]

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 the cloud age from the last decade as being more similar, because there were. I was not around for this era, but from, from my 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 the web runtime. EBay was the market. Like I think there are a number of these.

[B]

And AOL exited at the exact right moment in Time Warner.

[A]

Right.

[B]

At their peak, their peak valuation.

[A]

Right. And I do, I think that people, founders and investors may over rotate on the SaaS era, where it did feel like at a certain scale, Internet era, there's 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 that this probably does look more like the Internet era, where the question is, 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?

[B]

Yeah. And if you even go back to the 80s, you know, you had Lotus, I don't know if you remember this company.

[A]

I have implemented Lotus 1, 2, 3 at an enterprise business as an intern.

[B]

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, like really, really fast. And this was the 80s, 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 killer app on computers for its era. And then it just died. It didn't die. It ended up with a great exit to IBM, but still it no longer exists 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? 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 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 should think seriously about what to do with that. And I view this not just, I mean right now, I mean every six months there's going to be these shifts that are worth considering. And that's why it's like pre scheduled a 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 six months ago and we booked it a year ago and we booked it two years ago, whatever it is. And this is just when we talk about this stuff so we can just have a very logical, emotion drained conversation around this stuff.

[A]

And maybe I think again in comparison to Internet era as to why think about it more now is well, people

[B]

on the Internet should have thought about it too.

[A]

Sure, sure.

[B]

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 didn't lose any money. It was one of the best all time financial engineering moments in tech history. That's what made Mark Cuban a billionaire was he sold at Yahoo's high water mark 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.

[A]

I think what most people missed, right? And 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 am I acquired that company or not? And in the Internet cycle you had new distribution, new performance, new interfaces, changing user behavior. It was just like everything happening all at once in new exploration. Not true in Cloudland, right? Just more replacement market and then niches that you could cheaply distribute to new business model SaaS is amazing. But in AI it's 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 like surface area questions, right? Like agents vs. IDEs voice as a default. There are things that change in product experience that also could reallocate power.

[B]

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 sell multiple things into the same organization and you become a default part of the workflow. And that's the best way to defend against this because then you're being used for five or 10 different aspects 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 displace. So I think 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 long term or to defend long term.

[A]

Well, I actually still think now I sound like, I just hate like the SaaS era. I think it is a mistake that people like took as conventional wisdom from the SaaS era and like apply now without thinking about it. Whereas like you know, do one thing well. The point it was do one thing well and then people buy you and then like don't go compete with a million things.

[B]

But you know, we, we think that was bad advice. That was always bad advice though. I mean it substantially. Okay, SaaS companies was bad advice because before that the power 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 in velocity and the technology during the SaaS era was just slow. It's just like let's just keep building out the Internet. That was kind of SAS era. And so 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 reason that these things are so turbulent. It's because the technology is shifting so dramatically so quickly and that's just part of scaling laws and that's part of reasoning and that's part of all these things that have all the post training stuff that's been rolled out. So 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 are happening in a year or two. And that's why we're seeing these displacement or potential for displacement cycles. But that also means as a founder, your mindset should shift into this new world framework. You should say, okay, if every two years is 10 years, I need to think really quickly on changes that are happening. I need to react to them in all sorts of ways. And so it's just back to. But it's a fun and interesting and exciting time. I think it's going to be an amazing decade of transformation.

[A]

Yeah, I do think maybe one way to think about a lot of the defenses that people did not in the software era or the last software era are like, okay, well what does not depend on my little feature set? Just incrementally growing 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 hang out today is like, hey, don't over rotate on the last month. But also you have to think about when you know, 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.

[B]

Yeah, lots coming, lots shifting. It's gonna be fun.

[A]

Okay. Have fun.

[B]

Yeah. See you later.

[A]

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