After SaaS

Hey Reid, great to be here today.

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Always fun.

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So this week we're actually going to talk about a theme across all of our questions today, and we're going to talk about software or more specifically, Whether or not SaaS software as a service is dead. Mark Cuban recently argued that software is dead because everyone is going to customize everything they do to their own unique utilization. Just a few weeks ago, a tweet about Claude Code drove SaaS stocks down over 5%. No one is arguing that coded products are dead, but some people do believe that software may stop showing up as a fixed product and instead become something more adaptive, generated and specific to each individual company's use case. I think some people are really excited about that. They're, they've been annoyed that they're getting a one size fits all software. So this new era of customization is pretty exciting. So when people say SaaS is dead, what do they, what does that mean to you and do you agree?

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I think there's one sense in which I agree and then there's a typical inference that people make where I disagree. So the, the thing where I agree is that the exact model for the last 20 years of SaaS companies is no longer, you know, sustainable. And the way that model was is that you build out a SaaS category, you make it detailed and ornate enough that it's like, you know, table stakes to play in. It is a billion dollars of software development and then you go out and you've acquired a bunch of the market and you're growing as you're, as you're going. And the problem is that closes the door to a startup competitor. You can charge pretty good margins on it because there's relatively little competition. You know, you need to have this like, it's like a, you know, kind of turns into a natural oligopoly, by the way. And you know, people are like, oh, is that terrible? It's like, well, that's what happens with the auto industry and you know, a bunch of other things. It's like turning into a competitive oligopoly, you know, still ends up, you know, generating a pretty good consumer and societal, you know, output. And that's kind of where, where a lot of like SaaS categories were kind of heading towards, you know, the reason why you couldn't, you, you would pay the margins, eg, pay for the software. It's not just like pay for it as a unit, but like at a 40% margin or 50% margin as, as kind of a, as a way of doing this is because that was the only thing that made enough of your requirements and was stable in terms of going into the future. And so the reason why people say, well, that business model is now gone is because they go, well, they're imagining that you go to Claude Code, Microsoft copilot, you know, OpenAI, Codex, etc. And you go, make me an HR system, make me a CRM system, make me a accounts payable system, you know, and it just does it right, which is foolishness and even foolishness. As they, as the models get substantially better now they say, well, but you know, the AI will get so good, it'll figure out all the different details and what are the things that need to be running for me. And you know, da, da, da. And you're like, yeah, there's a bunch of stuff it does there. But I'm not trying to make my company the expert at doing HR systems or even HR systems or finance systems or accounting systems or CRM systems, you know, all the Saskatchewan systems. For me, what happens is, I think, and this is the implications, I think, oh, all these SaaS companies go away, there's no longer sale of software. I think that's incorrect. But the sale of software now has to be AI like, like you have to have the, that AI generativity that you get from these coding agents as part of what you're doing. And so then when it gets to what, what is the place where there's an interesting, you know, kind of economic model for this, it has to be within that loop. It isn't just like, oh, it takes a billion dollars of human software time to build this so competitors can't enter. It's got to be the, okay, what's the things that, that make it work? Maybe it's an understanding of all the requirements. Maybe it's the, you know, that the AI systems have been really tuned to work well like when you buy a CRM system and CRM system comes with a, a set of really well constructed AI, you know, kind of agents that help tune iteratively your CRM system for you and that there's a new way that this system now works. Both having powerful sets of libraries and tools back end. Because by the way, part of the way the coding agents work is they figure out, figure out, you know, which, which systems build. And I think there will be a lot of like deep software libraries and different things including how do you make this a whole lot better for doing it. And then there will be new lacuna. Now is it a, like, for example, one of the more Subtle points when you begin to get into this, you know, is kind of like is it a SaaS model or is it a different kind of model? Right, like it is a seat model, but so it's. So I think SaaS is dead. Insofar as if I came along to you and said I'm going to build a seat driven new CRM piece of software and everyone's going to pay me for seats on this. You're like, okay, you know, not so much. But if you said, hey, I think the CRM industry is being disrupted and here's a new economic model, a new way of providing it and the way, the way that customers just like when you went from CRM on prem to CRM in the cloud, now there's a new thing. Well, that's a new thing.

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Yeah, I mean it's like people say like anthropic is still using workday. Like there are gonna be, you wanna focus on your core competency, you don't wanna be distracted by all those other things. And so amid this obviously we're gonna have to see new business models, you know, new market entrants. How does the new landscape affect the engineers and the builders? Like does AI make them more power? The kind of technical talent that is needed within these SaaS companies. How should they think about it?

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26 is beginning the year where we begin to see the both the quality of the models that kind of got there in November, December and also the stampede to using them where the. I think the fastest changeover in doing a job we're going to see is in software development. Because they're technically sophisticated already. They know these tools like as you and I know, when we go to other people and say hey you should be using Claude go. I don't even know how to start right. Like for non software engineers, even though you can. The thing that I think will happen already now and is happening from software builders is that they're starting to use it intensely now. Here's an interesting current milestone. Software groups that are starting from scratch are seeing a huge acceleration from current AI tools. Software groups that are going, I have 500 million, you know, or even a hundred, but you know, like let's call it real 500 million code base and I'm refactoring. That's actually much slower. Like AI does not like just plug it in, do it, et cetera. Like you go here I've got this bug in this software, fix it and put as much compute behind as you and it still doesn't kind of really get there. It isn't to say that it won't. It's to say that this is a current milestone in terms of how this is operating. And how it gets there is interesting. Like, for example, part of what this is, is like, okay, so construction from scratch. You know, if you're not doing an orchestration of a whole bunch of AI agents, you're like, kind of like. It's like you're, you're using your punch cards. But if you're doing the kind of remodel refactor. Yeah. You're still struggling your way through it. You're figuring out what are the, what are the right deployments and where is the AI building to now. One of the things that'll get to be interesting in this is what does it mean? Because part of what happens with code is not just you build it, but you maintain it. Well, what's AI going to look like for that? Are we going to have a bunch of, like, you know, what's the role between, you know, kind of human and AIs in doing that? And I, you know, people tend to think, oh, because it's going so fast and exponentiating, eventually the only role for humans will be metacognition and awareness and kind of orchestration. That's, by the way, possible. It's also possible that in, you know, kind of planning this larger context space, that there is also, you know, kind of various forms of human ingenuity in it and also in goal setting and like, what is the real understanding of what goal you should be trying to do? Like, if you're actually doing this stuff, the people who I know who are like, living in this go frequently, they have to go unblock it because it doesn't understand, hey, if you shifted this goal a little bit and you called this library and did this, all of a sudden you'd be doing a lot better. And it can get caught in that. And that's with, you know, millions of dollars of compute going into the tokens, kind of realizing it. And so there's still this kind of role for that. But that's the reason why to engage, engage primarily as a director and orchestrator.

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And so I think that's the question that people have. We, we talked about this, that at the top companies or the companies who sort of started post AI, their developers aren't writing code. It's the AI that is writing, you know, 70%, 80%, 90% of the code that's coming out of these places. And so their question is, like, what are you Talking about like then what have, then what engineers do, then what do they do all day? And so you were talking about goal setting. Is it also deciding what should be built? Is there a trust component, like when to actually ship? Like, what are the other things that you think are going to become more important for software developers now that they're not spending most of their time writing code?

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So here's kind of like the detail. It's almost like what's happened is a lot of code before was management of this very specific set of like detailed set of tools, like how to call the API, how it worked, you know, writing the code and the right syntax and language and you know, kind of blocking all, all that together and that stuff, you know, AI is already great at. There's ways in which it's still not like fully in the kind of human code or capability, a large code maintenance. But that's the reason why people go to. Well actually in fact it's, it's strategy, it's taste, it's metacognition, it's, it's. And by the way, now that you have these tools, like. Well, you say, well previously if I had to write a connector to a different kind of computer system, I'd have to research, understand a whole lot that would take me weeks. Now that's probably like an hour. And it's an hour with a, you know, like, okay, I set a bunch of, of of long, you know, kind of co pilot codex, you know, Claude tasks going. It comes back, I inter, you know, I look at certain things. The trust thing that you mentioned, because you know, it's like trust but verify. And by the way, that's how human coding works too. That's part of the reason why we have like Agile programming, pair programming, etc. You know, there's kinds of ways of doing this because the understanding that verification is an important thing, it's an important thing even before we had AI. And so, you know, and maybe with AI we'll get to, you know, more cracking of codes of, of verification coding like it's verified to work, which has been too expensive for, you know, most human systems in terms of doing it. But maybe there's, there's, there's ways of doing that with AI. So that's why I'm saying that the surface opens up. So yes, I think there will be the kind of metacognition, the strategy, the understanding goals, the trust in what's happening. But also like for example, when I started with a connector, it's like, well, maybe what we should do is figure out how to have, you know, not just the connector, but what are the other things that should actually really go with the connector? Should there be a monitoring system? Should there be a fail system? Should there be a modularity to doing multiple connectors? Like, like when you do this because the, the cost of doing certain kinds of tasks gone so far down, it now opens up the space of what you're doing. So not just do I build a connector, but I also then say, okay, we're gonna have a monitor, it's gonna play in. Oh, we have a whole monitoring system. This is how the monitor is. Oh, well, this is the way that we should be actually checking safety and cybersecurity in this. This is why the Jevons paradox is when there's infinite demand for stuff, it massively expands. And in software there's tons of areas where there's infinite demand. Now to back to your original thing, it's like, well, what should you be doing? It's like, well, you should be, you know, at the forefront of using and deploying and leveraging these tools because that's how software is going to be constructed. It's like, if I think I'm going to be a modern mechanic for cars and I show up and I've got my wrench, you know, like, okay, you

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know, like, you're not ready for the 21st century.

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Yes. So it's like you have to be doing that and, and look, it'll be a little vertigo, it'll be dizzying, it'll be changing rapidly. People wish you could just say, oh, it's just like, well, you just have to learn to use, you know, rust. Now. It's like, no, no, no, it's, you know, or, you know, I'll go old school for some of the older people on the thing Java, you know, and so it's like, okay, it's a fast moving and iterative, iterative tool set. But by the way, that's what's going to make it fun too.

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Yeah, absolutely. So talk about something that's a little less fun. It feels like every week we're seeing a new headline about layoffs that are coming and whether it's because of AI. We saw recently that Atlassian said they were cutting 10% of their workforce and a lot of it was due to AI. We also saw reports that Metta may cut 20% of their workforce. Obviously, some of this is AI, some of this is broader political and geopolitical things that are happening in the world right now. Obviously we don't want to see job loss. But the other side of that is people talking about software moats. You said yourself that one of the reasons in the past is you had barrier to entry. People couldn't compete because you had this big company that was so far along that a startup entrant, you know, couldn't compete. When we think about moats, you've obviously talked about network effects for a very long time as one of the best moats around in the new AI world. Are these the same? Are they different? Do the moats disappear because you can have a two person team, you know, taking on an incumbent. Where do you see those sort of lasting and what are the new moats? Post AI?

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Well, the new moats are essentially, you know, in company like as a theory of the game, investing in or you know, co founding, like manas and other things. So it's kind of like a question of like you know, theory of the game in terms of how you're constructing those. And that's not proven yet. I do think it'll be network effects. I do think it'll be your, your position with a significant set of customers, whether those are consumers or enterprises, because that tends to have, there's various ways you can have that as a compounding effect and that gets, you know, you know, all the way back to why investing in models is important or not. Sorry, models, apps, but potentially models too. But apps was what I was thinking about there. And I do think that the notion of the fact that you have to kind of retail retool your workforces for being, you know, you know, kind of AI, you know, kind of native AI heavy, etc. Is, is going to be extremely important. And you know, I think when you begin to get to this question is you begin to go well, what's the cost of tokens? Is one way of looking at us. What, what's the cost of tokens produced by humans? What's the cost of produced by AI? What's the cost of tokens produced by the combination of them? And of course it's the right tokens, right? Like one of the things in this kind of usual token economic discussion is well actually in fact, if you're just producing tokens, well, the monkeys can create Shakespeare. But it's actually, in fact it's the right tokens and it's the right edge and the higher value and that kind of thing. And that's part of what we're going into now that token production is do you have the right unique data sources for producing it? I do Think token production will be part of it. So like, as opposed to like the classic SaaS model of, you know, per seat, I think there will be more of a compute consumption, you know, kind of as, as, as a way of doing this. Now how we're going to work that out exactly, because it, you know, you need to have, you need to be, it's like predictable like. So for example, like part of the reason why you had the previous seat model with SaaS was because it's like, well, okay, I know how to predictably, you know, do my cost line to model it into my business and my revenue line and my margin line and how to be investing in the future and so forth. Well, how am I going to do that if you're charging me on tokens? Like, and by the way, the answer is we will figure out ways, right? You know, a classic old school way is, well, you know, you get a baseline token budget for a subscription and then you get pricing depending on, you know, how much you're like almost like utility or energy. Like you're, you're, you're prepaying for a bunch of tokens or a bunch of tokens per unit, you know, hour, unit, unit day, unit week, unit month, etc. Because you know, part of token production comes from the compute infrastructure, which is, you know, chips and energy and data and all the rest. And so all that comes anyway. So I think there'll be something new around tokens and there'll be an interesting rationalization relative to, you know, kind of human capital and compute capital in this. And I think there'll be a kind of an intersection point between the two of them. And I think we'll begun understanding what compute capital looks like, you know, in addition to human capital, in addition to, you know, kind of energy capital and you know, other kinds of things as, as, as this is playing out now, where does this get to, you know, kind of where the key places where there is, you know, good operating margin and all the rest. And, and I think that's what all of us investors are trying to figure out with our investments right now. I think almost always a bunch of the model, the moats of the past will stay in certain ways, but the game gets really shifted with a platform shift like AI and some of those moats will matter a lot more than others and there will be new ones.

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Awesome. Reid, thanks so much. Appreciate it.

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Always a pleasure. Possible is produced by Palette Media. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tenasi Delos, Katie Sanders, Spencer Strassmore, Emo Zhu, Trent Barboza and Tafadzwa Nimarundwe.

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Special thanks to Surya Yalamanchili, Sayda Sepieva, Ian Alice, Greg Beato, Parth Patil and Ben Rellis.

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