Chapter 1: What is the main topic discussed in this episode?
This is one of the most dynamic markets we've seen, particularly for buying software, where month over month you will see large incumbents be replaced by the newcomers. Anthropic just did that with OpenAI, now the most popular model used by businesses, according to Rampdata. Cursor did that with GitHub Copilot.
My main take is that Setaspocalypse as a pronouncement has come way too soon and is typically not informed by actual business behavior. You're saying that Sasspocalypse is not in the data? I'd say quantitatively, neither aspect of Sasspocalypse is supported by actual business spend. Neither aspect as in it's going to change the way we buy it.
Currently, it has not in any meaningful way, nor has it killed off at least the companies that are frequently mentioned.
This episode originally aired on the MTS segment, Monetary Matters. For the last two years, the dominant story in software has been that AI will wipe out SaaS companies, collapse seed-based pricing, and centralize everything around a handful of frontier model providers. But when you look at actual business spending data, that story becomes much harder to defend.
Many of the fastest-growing AI companies are not model labs themselves, but the infrastructure, workflow, and application layers forming around them. At the same time, businesses are increasingly using multiple models, becoming more cost-conscious, and experimenting with AI in ways that don't neatly fit the prevailing narrative around automation and labor replacement.
Jack Farley and Max Wheathey speak with Ara Karazian, lead economist at RAMP.
We have the lead economist from Ramp Economics, Ara Karazin. Thank you so much for joining us today. Thank you, guys. You've written a number of pieces looking at this very topic. Are people changing their spending patterns? What are you guys seeing at Ramp?
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Chapter 2: How is the 'SaaSpocalypse' narrative challenged?
Yeah, I mean, I think that's what's so important about this kind of work is that, you know, we live in this world, particularly in tech specifically, where everyone wants to make these big pronouncements about where the market's going to go. Everyone seems like a pundit. No one's really armed with any data to inform them about what's actually happening in the market.
So I have this unique job at Ramp where, you know, we see the spend data from 50,000 businesses, $100 billion of annual spend. And so we did set out to research what is actually happening in the market
new ai companies are coming out with competitor products to a lot of prominent sas firms are we seeing any declines in the uh in adoption for those traditional sas companies are we seeing any changes in how people are buying sas and that's really where i defied sas apocalypse into two different categories right there's one that's hey are people shifting away from traditional sas over to um
over to competitors provided by the model companies. And then number two is the way people are buying software changing or people shifting to a new model where you buy like agentic capabilities, where you buy tokens instead of paying a SaaS platform for your seats. And right now we see that neither of those trends are happening. It's not happening in any meaningful way.
Seat-based contracts are still about 65%, 75% of spend. Flat platform fees about 20%, 30%. Even amongst the companies that have offered some kind of token-based pricing for their SaaS tools, we're only seeing uptake about half a percent of spend on those platforms. That's companies like HubSpot and Adobe, both of them offer pricing. some kinds of token-based offering for their SaaS offerings.
And then you want to talk about whether or not people are shifting away from SaaS providers. You can look at Figma, where Cloud Design came out. Everyone was just talking about Figma's strong performance and earnings. But we'd reported in our data set already a month ago that Figma was one of the fastest growing vendors on our platform, that businesses were continuing to buy from it.
And so I think it just goes to show you that a lot of these pronouncements, they're coming from people's projects about how products may develop, but they're not often informed by the actual changes happening with respect to business spend.
So it sounds like less has changed than people are talking about. Where would you say the real change is happening? What is changing on the margin?
I think it's definitely true that an increasing number of vendor software companies are starting to offer some kind of token-based product. Adobe, which I just mentioned, is one of them, which knows that its product is increasingly going to be used by people who are using the AI capabilities that has a marginal cost associated with it.
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Chapter 3: What do business spending data reveal about AI adoption?
And so I haven't really understood from SaaS apocalypse people what the intuition is to suggest that, hey, we're going to see all these SaaS companies die off. Now, I do think that there's going to be competition, right? Like Anthropic is a worthy new entrant into a lot of different markets.
But that's a very different claim than saying that, hey, we're immediately going to start to see businesses shift over to to these new players. And especially because business spend can be pretty sticky.
Now, the counterpoint to that, though, is that this is one of the most dynamic markets we've seen, particularly for buying software, where month over month, you will see large incumbents be replaced by the newcomers. Anthropic just did that with OpenAI, now the most popular model used by businesses, according to RampData. Cursor did that with GitHub Copilot.
So I think it's, you know, something worth keeping track of. But my main take is that SaaSpocalypse as a pronouncement has come way too soon and is typically not informed by actual business behavior. You're saying that SaaSpocalypse is not in the data? If we could share this chart. I'd say quantitatively, neither aspect of SaaSpocalypse is supported by actual business spend.
Neither aspect as in it's going to change the way we buy it. Currently, it has not in any meaningful way. And nor has it killed off at least the companies that are frequently mentioned. And even look at Perplexity. People talk about Perplexity being an at-risk company. More in the pure play model space than a Figma.
And yet Perplexity is also one of the fastest growing vendors on ramp, seeing a lot of uptake specifically because it's offering products that the model companies have not competed on yet.
So you talked about as well, like the increase in token based spend with some of these software companies. To what extent is that adding AI capabilities to the existing sort of stack of products and many some of that even being like flowing through to these supposed competitors? Yeah. versus it replacing a seat-based consumption, right?
It's adding AI capabilities to their suite of products, and that you might pay token-based, but you still have some sort of seat-based thing on top of that.
It is additive, but it is still so small. Again, we're talking about at the companies that are doing it for the first time, but doing it in an earnest way, less than a percent of the actual spend that's happening on their platform. So it's hard to measure whether or not it is truly something additive to their profits.
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Chapter 4: Are companies shifting from traditional SaaS to AI models?
how much of our work that we do doesn't require a frontier model. And up to this point, it's always just been like, let's just throw compute at the problem and that will solve it. And that it's actually just going to be like thinking a little harder about what the process should be.
Yeah, you don't need the frontier model all the time. I mean, oftentimes it's worse for what you want because it's expensive and slow. You know, I find myself in my day to day oftentimes automatically routing things to the most expensive model, even though it's slower. And I would actually prefer it just use Haiku or Sonnet when it's necessary.
But you can't expect the typical worker to make that decision. I mean, my job is to be informed about AI trends, so I know all the new models that are coming out, and I'm relatively good about applying best practices, but that comes with a lot of tracking of the market. I don't think that you can expect most American office workers or workers in general to do that. And they shouldn't have to.
And the market hasn't caught up to that use case yet. Open and Anthropic have no incentive to offer an auto routing product that allows you to lower your AI spend because they make money on tokens. And by the way, they make more money on tokens than other AI frontier labs and firms like Google, makes money from a lot of stuff. They don't need you to spend money on tokens explicitly.
Anthropic and OpenAI, 80% of their business revenue is token based. So it's directly tied to usage and incentivizing you to do that.
Have we seen like a software plugin that that basically determines what sort of model your task should go to? I mean, Cursor does that.
Cursor will automatically, I mean, I think the model companies will have to respond to that kind of competitive pressure increasingly because, especially Anthropic, right, people are increasingly hitting these compute limits. Mm. And so it behooves them to make that change first so that at least more people can use it most effectively. They certainly have the demand for it.
But I think it's going to come from whether the first step is going to be whether or not other firms that are in this space start offering it first and end up being that competitive pressure.
And by the way, that's the bull case for something like Cursor, which can compete on the developer experience in that way, in a way that OpenAI and Anthropic, in the absence of Cursor, are not incentivized to do. They can offer a cheaper, more performant model or better routing as part of the product experience. Hey, all right.
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Chapter 5: What trends are emerging in token-based pricing for AI tools?
chat subscriptions on their own will be driving the kinds of productivity gains we want to see in the economy. We'll probably need something more comprehensive than just the kind of usage you get out of Google Workspace, at least most people do. But Google is definitely underrated. It certainly has a distributional advantage in that it's at all businesses that use Google Workspace.
So on the Ramp AI Index, you know, in public markets, Index Inclusion Day is a big deal. People speculate, what's the company that's going to be added to the S&P 500? It's going to create all this. What is going to make a model? Obviously, there's a huge gap between Anthropic, OpenAI, and everybody else. Who's on the short list to be added to the Ramp AI Index?
And what are the sort of criteria that you're looking at to say, hey, this is a company that deserves to be, you know, on the list with the big boys?
Well, I mean, I'll suggest actually that from a economic productivity measurement standpoint, the main interest is not to see, hey, which model company is ahead versus number two versus number three. Adoption is going to trend toward multiple large players.
the next stage for our research is to measure not just business adoption, but the intensity of that adoption and try to come up with some definition of, hey, what does it look like to be a firm that adopts AI particularly well? And what is the path to that? And that's not an easy question to answer, you know, because The instinct is, well, let's look at the firms that spend the most on AI.
But spend does not translate to productivity gains. If you ask people a year ago, they would say, well, the price of AI is going to go to zero, so there's no point in measuring spend. So there's a lot of really cool research happening, though, about kind of productivity gains that a firm might get and trying to come up with the... outcomes that we would measure.
Head count, of course, is a big one. How is it going to affect jobs? Which jobs go up? Which jobs pull back? In engineering specifically, software engineering, there's some really interesting research about quantity of PRs, some quality measurement of PRs too. So I think that's really the next stage of our research.
What have you seen in this early stages of this research? There's been speculation a lot of these layoffs that CEOs are attributing to AI adoption are really just regular old layoffs. There was overhiring and it's a convenient way to say this is why we're laying off 10% of the workforce.
Are you really seeing that productivity come through from these firms that are saying it's because of AI adoption?
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Chapter 6: How are legacy software firms responding to AI competition?
It's VCs and banks and the controllers of capital.
Are you guys suggesting that there might be some element of groupthink in the tech sector?
Not necessarily. I don't know. Not groupthink. It's more... Because I agree. No, I mean, we're content creators, right? Like, sometimes you'll have somebody on and they're talking about something and, you know, it's just very institutional, right? Like, it's for a very small group of people.
There's maybe 10,000 people in the world that that piece of content is actually for, but yet it finds its way in front of a bunch of retail investors and they're the ones who are commenting on it. Like, I think that this is in many ways, like, the version of that. Like, these people are not, they're not talking about AI specifically.
to reach the masses and explain to them, like, they're trying to justify a $2 trillion valuation, a $10 trillion valuation. That's what they're shooting towards. And so if you want to justify that, well, you know, the entire labor market is a pretty big total addressable market.
Yep. No, I agree. We'll see if it works out for them.
Yeah, I mean, that's a problem. And you are starting to see regulation, self-regulation. I saw there was something today about, like, AI watermarking, basically, that content has been created by AI. So there is some degree of, like, self-regulation happening here. But I guess as far as, like, other trends that you are interested in tracking at Ramp, what are the other, like,
software-related trends that you think people should be paying attention to as it relates to the SaaSpocalypse?
Well, a lot of the growth that's happening amongst vendors is not, and a lot of the really compelling growth is not necessarily with the model companies themselves. And so that's where I caution people who are extremely concerned about a SaaS apocalypse.
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Chapter 7: What factors influence AI adoption among businesses?
Well, you can also access ramp data now through the Cloud Connector and through OpenAI. Oh, yeah? To be clear, that is, it's the same data that we publish on our website. It's aggregated and anonymized, but it allows you to pull in that data and build your own analyses or get a recommendation for your own business about, hey, what should I buy? What are other firms like me buying?
Something like that. Um, is it good for a business like Refinitiv or is it good for the data selling businesses? I don't know. I mean, I think they kind of have to do it, right? I will say from my perspective, I don't, we don't, Ramp doesn't sell data, so it's all available for free. So we want people to use it and we're in that business.
I would be happy if a lot of data aggregators lost market share because of both the free data that Rant provides and then the ease of access that Anthropic provides to produce data. No shade necessarily to all data aggregators, but I think a lot of that market is unfortunately behind closed doors. The methodologies are not always clear.
And in general, I think information is better when more firms and consumers have access to it. But decision-making tends to be better. And so is it good for the businesses that sell data? Probably not, but it is better for the market overall, I think. And then Aaron, what about the large, the giants of the software business?
You mentioned many software companies that are growing quite quickly, that are doing quite well. Because you said fast growing, that makes me think that they're private companies you know, perhaps venture capital backed and probably, you know, if I hadn't heard of them, they probably are private. But then what about the large behemoths?
You know, the ServiceNow's of the world, the Microsoft's of the world. And then also, there's the private equity backed thing where it's their even later stage, and they're not growing that quickly, but they're producing a lot of cash flow. You know, you've talked about this, you're growing budding world of like kind of new software companies, but what about the more legacy software?
Well, again, it's going to be case by case. And many of them will be disrupted not only by AI, but simply because of demand for their software, which may dry up or change or skyrocket or whatever, right? So, I mean, that's what's going to be interesting about the next couple of years.
There's going to be an instinct to say that, hey, this company failed because of AI, whereas it very well may have just failed because it was not responsive to the needs of its customers, right? Maybe that was because it didn't apply AI, but also maybe that was just because of the natural churn that ends up happening amongst firms that aren't innovating fast enough.
So I think there's a tendency to make proclamations about the entire software market, but AI will certainly be a disruptor, but it's also one that every firm knows it has to respond to in some way. So we all have access to the same information there.
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Chapter 8: What future trends should we watch in the SaaS and AI landscape?
Obviously, do it intelligently. Don't unleash a product that doesn't make sense or that messes up your customers' audits, especially in something like finance and accounting. But I was surprised about Deloitte's positioning. And so I think it goes to show you that not every... firm, legacy or not, is responding to these transformations in the same way.
And many firms are actually quite hesitant to implement AI, at least implementing AI particularly quickly.
I think if you would have asked me like a year ago, do you think Deloitte will be more cautious or less cautious about implementing AI? I think I probably would have gone toward the less cautious side. Who are the companies that you would maybe historically group with Deloitte that are actually at the forefront of adoption?
Like who are the surprise leaders in the adoption race that you're seeing?
It's a good question. I mean, so what comes to top of mind, maybe this is exactly what you're getting at, but a lot of the legacy media companies that are interesting, because they're really going to come out on top of some of the transformations.
I mean, you look at like the New York Times, a lot of newspapers, which are doing deals directly with the model companies to sort of license their work. Does that mean they're going to be well positioned to be ahead on AI and to adopt them effectively as a workforce? There's some signs.
I mean, I talked to a bunch of reporters who have sort of offhand said that despite AI is quite controversial still in the newsroom, but there's also an increasing amount of talk about like, hey, some writing is actually more appropriately done with an AI model and reporters can focus on reporting, something like that.
So there's experimentation happening across sectors, and it's not exactly in the places where you'd expect it.
Well, on that front, I mean, that's very interesting because obviously there's tons of IP theft out there in the journalism world of people like, oh, where's the free version? Send me the free link. Why is this paywalled?
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