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AI Fact or Fiction: The Fable Ban, Tokenmaxxing, Saaspocolypse — With Ara Kharazian
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- [00:00]
A
How much are companies actually spending per employee on AI? Is AI winner take all? Is SAS dead and is anthropic screwed? After the fable 5 dust up with the White House let's get to the bottom of these questions, separating fact and fiction with Ramp lead economist Eric Krazian right after this. Depending on who you ask, between 80 and 95% of enterprise AI projects fail to get AI to work for you. You don't need more tokens, you need better people. A board pairs powerful proprietary tools with senior engineers who've seen it all. That combination means your project doesn't stall, doesn't drift and doesn't fall. It ships. Whether you're a startup that needs to get to market or an enterprise with complex legacy challenges, Aboard delivers exactly what your business needs Fast Aboard is your partner for AI transformation. Visit aboard.com and let's build something together. Welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of the tech world and beyond. There are a lot of rumors flying in the AI industry, a lot of narratives flying in the AI industry and what better way to attack these than to look at the actual data and separate the fact from fiction? Well, we're going to do it today. We're going to talk of course about how much companies are spending per employee on AI and whether token maxing is a thing. But we're also going to get into the news of the week which is whether the White House's Fable 5 ban effectively putting export controls on on Anthropic's model, we'll have a long term damage. Looking at what happened the last time Anthropic had a dust up with the Department of Defense. We're joined by Arik Arazian. He is the lead economist at Ramp. He's publishing some great stuff at the Ramp Economics lab. I've been a reader of his work for a long time and I'm thrilled to welcome him to the show. Eric, great to see you.
- [01:45]
B
Great to talk to you again. Great to be on the show for the first time.
- [01:49]
A
Yes, first of many, I'm sure. So let's talk right off the bat about what we can anticipate the impact of the government's Fable 5 export controls on Anthropic's business. To be right, because we've seen a version of this before with the Department of Defense naming Anthropic a supply chain risk. This is obviously on a bigger scale and even if they go back on the ban, there might be some impact. I keep calling it a ban. Go back on the export controls, which is effectively a ban, there may be some impact here. So what can we expect?
- [02:24]
B
Well, you're right to look at the Department of Defense decision from earlier this spring as the closest recent example that we might use to inform how it's going to affect Anthropic's business or business adoption going forward. I mean, let's go back to the spring when the Department of Defense labeled Anthropic as supply chain risk. Usually in most industries, for most software vendors, when you are labeled as such, businesses are not likely to continue to use that vendor going forward. So if you're just going to think about this from first principles, we would have expected businesses to shut off their Anthropic subscriptions for new businesses to not want to sign up, for businesses to explicitly not want to use Anthropic going forward, both because of a true security concern that the government is citing and because they might want to engage with the government on government contracts. That's not what happened earlier this spring. If anything, this spring was when we saw Anthropic's adoption really accelerate with businesses. It was coming off the heels of the successful launches of cloud code last year, finally starting to move into a more popular posture with non technical users. And just this past month we showed that Anthropic is now the most popular AI model used by US Businesses, according to ramp data. So several of those assumptions didn't come to be, I think two main reasons why. One is that most businesses didn't really seem to take the Department of Defense's label very seriously. It was kind of like, okay, yeah, the Department of Defense is saying this, but this was one of the best models available. Still is the best model available. It's popular with businesses. Second reason is that Department of Defense lost a lot of credibility over the continuing weeks when it acknowledged that it would be issuing exceptions both internally and externally for businesses that wanted to use Anthropic anyway. So if you're going to go back to the example from earlier this spring, if anything, it's probably the case that the Department of Defense's supply chain risk labeling accelerated Anthropic's adoption with businesses. And this label now anything puts it on a very interesting competitive standpoint with OpenAI. Who has the better model? Probably the one that the federal government has suggested is so powerful it must be controlled and there's brand strength in that.
- [04:55]
A
That's right. So we talk on this show all the time about whether Anthropic's positioning around safety and the fact that it's taken this steadfast approach when the federal government has asked it to do something. Uh, and that's led to some of these actions by the federal government, whether that's marketing. Um, let's put the intent aside though. Uh, it could end up being a boost for its business, you know, whether it wanted this to happen or not. I keep thinking about the fact that at least we're recording Monday. And I keep thinking about the fact that, you know, when you're in Claude, it says, hey, Fable is not available right now. Right. And you're seeing all these posts on X about people who are like, oh, those five minutes with Fable really give me a glimpse into what this could be. And you'd imagine that when it gets turned back on, that forbidden fruit effect just kind of drives the interest in Fable, you know, the model too powerful for the government to let you let. Let you use the interest will probably just go through the roof.
- [06:01]
B
Well, let's also note that part of Anthropic's the product experience of using Anthropic models, at least over the last six months, has been getting really comfortable with models being down all the time because Anthropic has so many compute constraints that it is very common to be a user of Anthropic models and to be hit with a crash notice or something that just says, hey, the models are down right now or try again later, an API error. And yet we would normally expect that that would drive a lot of users over to OpenAI models. OpenAI models are. OpenAI doesn't have nearly as much of the compute issues that Anthropic does. And yet we haven't seen that kind of switching behavior yet over the past couple months. Maybe some marginal switching happening from people who are using CLAUDE code now switching over to OpenAI's codecs. But if anything, it makes me wonder, both as a researcher and user of these models, how long is this forbidden fruit effect of Anthropic's models going to last before it starts to turn users off who really just need to get to work and use the models that they are paying for and have them work as expected?
- [07:18]
A
Yeah, I mean, you're the economist. I don't know. Is there an economic theory that explains why people would stick with the vendor that has constant interruptions, even if there is another one with just as good or on par capabilities that doesn't have the turbulence?
- [07:35]
B
Well, what we found is that these models are a little bit stickier than we thought they would be. We talk about them being these commodities that you can just switch between one model and the other. And maybe at the model level you really can think of things like that. But in terms of how many employees at firms are actually using AI, better models don't necessarily create the switching for employees and users. It's about the product experience around those models. Claude Code was so successful not because it was powered by the models of Claude, but because it was integrated into your workflows in a very effective and agentic way. That it was the first model and first experience that allowed a engineer to execute on multi step tasks without babying a chatbot the entire time, such that incremental improvements of the model are important and helpful. But they weren't the whole story for the actual growth of Claude code. And so you can imagine that to be driving some of the stickiness between Claude code and codex, OpenAI and anthropic is that people get really used to the tools, the software that they're in. They like the experience that one provides over the other. I also do think there is going to be a sort of branding effect where Anthropic's AI safety posturing. You know, love it or hate it, there are people who really do like that they're the company that at least postures itself as being thoughtful about the effects of AI, whether or not they are the right guardians of that.
- [09:23]
A
Yeah, it's possible that the federal government is sort of, you know, by disrupting Anthropic, maybe giving it a helping hand by sort of making its safety messaging seem more legitimate and again giving it that forbidden fruit effect. All right, one more economics style, you know, thought question for you, then we can get into your data. You know, there have been recent reports that OpenAI is looking to drop prices drastically and we talked about it on the Friday show that my thought was basically they are looking at lifetime value of potential customers and if they were to drop their prices, they could get people used to using, let's say a codex and then keep them with the stickiness that you're talking about. And so therefore it would be a good move on their end to say we're going to drop prices to win people over and then hopefully they'll stick with us. Your thoughts?
- [10:19]
B
Part of it I think is very natural. This is an extremely competitive market where it's very common to see in a matter of months a newcomer take the lead over a relatively popular provider. You know, we've seen this in software before, but it's especially true in acute in AI. I mean, we saw this with cursor versus GitHub copilot. You know, when coding agents came out, at least When AI code autocomplete came out two, three, four years ago, GitHub Copilot was the enterprise tool. That's what everybody used. Unsurprisingly, it was also backed by Microsoft. Cursor comes out and within about a year it has the majority of the market. Then, by the way, quad Code comes out and then now quad Code has majority of the market and then. So we saw a similar story with OpenAI anthropic in RAMP's data. We have been tracking using our flagship research Ramp AI Index, the share of firms in the United States that are using AI, at least paying for it through subscriptions or tokens directly. And then we break it out by which model they're paying for. And for most of AI's commercial existence, 2023 onwards, OpenAI was clearly the dominant player somewhere hovering between 20 to 30, 40% of businesses in the US were actively paying for OpenAI. And it wasn't really budging much, it was just a very gradual increase, particularly 2023, 2024 onwards, no one was really thinking about Anthropic, which was popular with technical users, but otherwise wasn't this broadly understood competitor in the market. Second half of 2025, we see month over month percentage point increases in the share of firms that are using Anthropic's models coming to the forefront last month when anthropic overtook OpenAI and actual business adoption. So now Anthropic sits at about 41% of US firms are using anthropic, 39.5% of firms are using OpenAI. Anthropic is still growing. OpenAI is relatively flat. And then even in the sectors that are early adopters of AI, we're seeing that growth continue to grow and accelerate while OpenAI holds relatively flat.
- [12:41]
A
Right.
- [12:42]
B
So it's an extremely dynamic market where you could expect all of the involved players want to compete with each other. But I actually think that right now the focus is on OpenAI anthropic, but there are a lot of players that are underrated. Google, I think, is extremely underrated and I think might end up being one of the big winners here that no one's talking about.
- [13:01]
A
Okay, but then briefly, the price war or the price undercuts, do you think that's enough to dislodge the stickiness? I know you don't have the data on it, but there has to be some formula out there about the price plays into usage.
- [13:17]
B
Well, so I Do think it's going to be enough to. I think, look, we're going to come to a head at some point where we know businesses keep demanding some better control over AI spend. We went through a sort of token maxing era. Everyone was talking about, okay, we need to spend as much as possible on tokens. And then now we're in this sort of new era where businesses are saying, hey, we need to actually rein in token spend or at least understand where it should be can continue to rise, but we at least need some control over what it is. Neither OpenAI nor Anthropic have built products that allow firms to actually manage their token spend, nor have they built products that that incentivize firms to keep those costs under control. If anything, Open Anthropic are incentivized to have firms spend as much as possible. So so far, you know, they can compete on price reductions, but really what firms are asking for is some degree of control. You know, maybe that means, hey, build us something that allows us to smart route tasks over to the most performant but also most efficient model for that task. Other competitors are offering that. It's not usually OpenAI and Anthropic though.
- [14:36]
A
So your data shows that again, like you said, anthropic has overtaken OpenAI with business spend. Very briefly, Ara, the criticism of the RAMP economics lab, whether well placed or not, has been that yeah, you're looking at companies with RAMP cards using ramp which tend to lean towards sort of startups and tech forward companies and it's not representative of the full economy. Your thoughts?
- [15:07]
B
The way I normally think about this is that so we have actually a pretty good distribution in our data set across sectors. However, no matter the sector, the businesses on our platform are inherently more tech forward and that they're using something like RAMP to manage their spend. In general, I do see that as a strength for a couple reasons. One, AI is this very new in nation technology and one that is not effectively tracked by other data sets. It's not effectively tracked by government data sets either, which have their own set of criticisms as far as how they are surveying firms about AI spend and also the firms they are surveying themselves being a little bit self selected. AI spend, if anything is skewed over toward these tech forward businesses such that if you do want to understand how businesses are spending and operating on AI, it actually behooves you to look at these very forward thinking businesses that have been leading this charge. They are more likely to be early adopters of this technology and whether or not they are representative of the average firm in the United States. We know they are not. It's more likely than not that the average firm will look more like these firms in a couple years then they look like the average from today. So I think if you want to be forward looking, you want to look into the future a little bit, you probably want to use this kind of data set for what it's worth. I actually think that in many ways we underestimate adoption.
- [16:31]
A
Okay, all right, we'll get into more methodology later. But you know, given that these are the forward looking companies, let's get into some more of your data because I think that there have been some narratives about token waste, that your data has a little bit of a different perspective on that. I think we should, we should just discuss. So you've talked about what is the cost of being an AI pilled company? It's $7,449 per employee per month. So you said the top 1% of firms spend that much unemployed per month and the top 10% spend $611 per employee per month and the median firm spends just $11. Like the cost of an enterprise seat on employee on Enterprise ChatGPT or a cloud subscription. Now $7,400 a month is pretty high on a technology. Like for a single person to spend that much on a technology seat or license is somewhat unheard of. However, when you think about the headlines that we've been seeing, a company left clawed on and spent a half billion dollars in a month, your data actually presents somewhat of a different picture that companies are aren't sort of spending unrestrained right now. They seem to be, you know, sort of dipping their toe in the water as opposed to going all the way in and you know, spending tokens like they're going out of business.
- [17:57]
B
Well, look, AI spend is the fastest growing spend category we've ever observed in our data set. Probably one of the fastest growing spend categories for businesses ever. Depending on how far back you go into what a business is defined as
- [18:14]
A
in prehistoric times, I couldn't imagine any spend ramping faster than this. Yeah, what could it be?
- [18:21]
B
Exactly. And so since January 2025 through May 2026, so last month per business spend on AI tokens is up 15x. And that's amongst firms that were already spending on AI at the same time, AI spend itself isn't really that meaningfully large for most businesses. So it's grown a lot. But for the top quartile of firms that are spending on AI, top 25%, it's only about 2% of business spend, excluding payroll, maybe at 1% if you were to include payroll. So it's grown a lot. That's why we get all these concerns from company executives about how do I manage this growth. But as far as its actual level, it's relatively small. So you'll notice when people talk about firms pulling back on AI spend, they might be pulling back on AI spend in some parts of the firm. They might be more mindful about which models they're using or making sure that teams don't have uncontrolled budgets. But if you actually look at firms spend on AI in the last couple months, just last month, it still increased 14% month over month. So there's clear evidence on our platform too that firms are making more cost disciplined decisions. You know, we've seen an increase in AI spend being routed away from OpenAI and anthropic and over to these sort of open source platforms. Last month, DeepSeq was one of the fastest growing vendors on Ramp. And yet it's still a very small share of AI spend that is actually going through those rails. It's a relatively small share of businesses that are using open source platforms in general. And the vast majority of spend happening is still rising. So those cost discipline measures are important, but they're really just occurring on the margin and they're not happening fast enough to pull back the rising slope of AI spend. Yeah.
- [20:25]
A
Do you think that there's going to be a moment where some firms start to spend more on AI than they do spend, you know, on. They spend more on AI per employee than they spend on employee, for instance. I don't know how accurate this was, but I think like we're actually trending in this direction where someone figured out how long, how much it would cost to run the Fable or the Mythos API, and they found out it was something like $600 an hour where if you like multiply that over a year, it's 1.2 million. So what do you think when you think about the trajectory, do you think we're going to get to a place where people end up spending more on AI per employee than they spend on employees themselves? I imagine some will.
- [21:13]
B
Yeah. For some firms, I imagine it makes a lot of sense. Right. But for the typical firm, there's really, it's really hard to benchmark where you should be. And so the top 1% of firms spend $7,500 per person per month on AI, but that's the top 1%. So you can imagine that's a pretty tech heavy group that actually may include a lot of firms that are ultimately using AI, not just for the employees usage, but also for the underlying infrastructure of the firm. Maybe they've built a bunch of internal tools, right. So it all gets balanced out.
- [21:45]
A
And software engineers are double that, typically 15,000 or close to 16,000amonth.
- [21:50]
B
Well again would depend on the firm because you look at the typical firm on our platform and again this is ramp, so relatively tech forward platform. Right. And the median firm is only spending about $11 per employee per month.
- [22:04]
A
Right.
- [22:04]
B
So you know, that's bare, that's a chat subscription, that's like one of the low level OpenAI and anthropic subscriptions, maybe a little bit more on the margin. So it's another reminder of how early we are, right, in that the vast majority of firms. And this has transformed our research approach because for a long time, the last year and a half, my research is focused on trying to estimate the economic impact of AI. But if there's no way to find that in productivity statistics, if people are still not sure what the ultimate gains of AI is going to be, then really the only way to start is, hey, how many firms are using AI? And so our original version of ramp AI index is just that, it's hey, what is the share of firms in the US that are even buying it and buying it month over month to try to get some way of at least approaching the question, hey, is this valuable? Now more than 50% of firms are using AI, 54% of firms are using AI in some way or at least paying for it. So our question gets to transform a little bit. Not just who's using AI, is it valuable, but how are they using it? How much are they spending on it? What does it mean to be an effective user of these models and to deploy it effectively through your organization? Because that's what's really interesting about AI too is that it is unevenly distributed in that certain sectors are more likely to adopt it than others. The products themselves are also unevenly designed and distributed. And that some of the best, most advanced usages of AI are designed for certain job categories, coding agents. It's like the most obvious commercially advanced way that you could use AI to be productivity enhancing. And yet that doesn't exist for most other firms. Maybe there's productivity enhancements you can find in a lot of other jobs, but the products themselves are not well developed to make that clear to the average user. So AI is unevenly distributed and so it's ultimately going to be difficult as a firm to identify and benchmark against what good usage of AI means, especially because the effects of AI, the productivity gains of AI are not likely to show up in your first couple months. There's clearly a learning curve to implementing AI throughout your organization and even through your own personal workflow as an employee. And then beyond the learning curve, there's also this sort of minimum threshold of adoption where I don't think anyone really expects you to get massive economic gains from everyone having a chatbot, but the idea of everyone having their own cloud code for their job is much more compelling. But you wouldn't know that if you're just using a chatbot for like a month or two and then you write off AI because it's like, you know, what's the point of this?
- [24:53]
A
But you look at the curves that you have in your research, right? And it's just a number. Just like three hockey sticks, right? If you look at the spend per employee per month of the top 1% of companies using AI, the top 10%, and the median company using AI, it's like legitimately, like a, like a lightly sloping line. And then all of a sudden it shoots up in all three of those categories.
- [25:17]
B
And
- [25:19]
A
so you're trying to, you said you're trying to get to the answer of, you know, how is, is this technology valuable? And so I'd love to hear your perspective on what these numbers mean, even though it's more, I guess, quantitative or qualitative. And quantitative. Right. Do the fact that we're seeing these spend increases mean that companies are seeing an ROI on AI, or is it still potentially in the sort of FOMO stage?
- [25:46]
B
Well, I really am of the school of thought that businesses have no reason to be spending this much money just out of a sense of obligation. And fomo, I get it if we're talking about people buying stocks, but companies making fairly large investments in software, that you can just talk about it externally without making not only the large investments in software, but month over month increases in how much they are spending. So I'm generally of the school of thought that if firms are doing this, they must be finding some value out of it. But there are some places that we look quantitatively for that evidence and also informs our thinking that this is different from most software markets. So one is that the most advanced spenders on AI don't lock in with one vendor. This is fundamentally different from how we typically think of software, where it's like, hey, if you're using a CRM, you're going to use one CRM. Maybe you'll like, experiment with a couple providers, but ultimately you're going to sign with 1. That's not the case with AI. The top 1% of spenders on AI use 8 vendors on average, whereas the median maybe uses 2. And that's vendors fairly narrowly defined as LLM providers and maybe some AI infrastructure companies. Now, it's also not just experimentation. There's some amount of it. That's always this continuing experimentation where it's like, okay, the AI models come out, but they also change so much, so frequently that if you are an organization that is using and implementing AI effectively, you probably want to have paid access to all the major model companies so that you can switch to the most effective model for whatever task makes sense. Or when a new model comes out, you can see if it makes sense for this workflow or that workflow. That's what being a good AI user often means to these firms in the top 1%. But that is an unfamiliar idea for many businesses and frankly, people at firms who are buying AI in charge of procurement. I often get the question when we report OpenAI versus anthropic adoption rates, should I buy OpenAI or anthropic? And then when you, which if you're someone who uses these models day to day, it's like a very surprising question because you would just say, well, you should try both. You should probably have access to both. It's not that expensive to have access to both. And so the question itself doesn't really register as a sensible question. But if you are applying the typical common practices of software procurement to AI, you will find yourself asking that. So I think it's just a fundamentally different market that the people are not used to, and that's what ends up driving a lot of this spend. But I don't think firms would move along that advanced AI adoption curve if they weren't getting some benefit. You know, you're not going to keep signing up for new vendors. You're also probably not going to keep renewing vendors. And if anything, in our data set, we see that renewal rates increase year over year with firms that are more advanced, so they're more likely to stick to the vendors that they've been using as opposed to switch so frequently.
- [29:04]
A
One question about that, it's kind of remarkable, right? If you look at the graph of revenue that you're seeing from OpenAI and Anthropic, it follows that, I mean, you would imagine, right, it was going to follow that hockey stick as well. That type of curve shape. Isn't it interesting that even as companies spread their spend across two to eight vendors, that those two have been able to grow the way that they have?
- [29:34]
B
Yeah, well, it's because adoption we measure at the firm level, but then within the firm, there's a lot more heterogeneity around who's using AI and how. And then within the person, there's even more. I should stop using economics terms like heterogeneity when I'm on this podcast. Variation.
- [29:55]
A
Yes.
- [29:56]
B
You know, you have different teams that are still on different parts of the adoption curve, and then you have individual people within those teams that for different tasks, they're on different parts of the adoption curve because people are still figuring out how to onboard. This is what I mean about the learning curve. The firm is on a learning curve, the teams are on a learning curve, and then the individual itself is on a learning curve for their specific tasks, trying to figure out, hey, can this task actually be done effectively? Have I even tried this task or not? Not to say that everything can be done by AI. I don't think everything can be. But I do think the more that you experiment with it, you will find what it is good at, what it's not good at, and then if it's somewhat good at something, you kind of get better at understanding how do I modify my workflow so that actually, maybe I take out this part of it, maybe I take out that part of it. This part's not really necessary anymore. Oh, now AI can actually do 80% of the job, but you don't figure that out without experimentation. So I think that's why you see rising spend over time. We're clearly not at the point at which people have found their benchmark level of AI spend. And if anything, if you want to look at our charts of AI spend per person, you don't see a token maxing era. There's no point at which, oh, it went up and then it went down. It's still going up again. Median firm is only spending $11 a month, so there's a lot of room to grow. But even for the 1% still going up.
- [31:29]
A
Yep. All right, let's take a quick break. When we come back, I want to talk a little bit more about what you're seeing with deep Seek, because I think a lot of people expected that that deep seq moment, you know, sort of happened in February, January, February 2025, and then dissipated. But it is growing once again. So we'll talk about that. We'll talk about Model orchestration and then we'll talk about sas, right? Whether this has apocalypse is actually being borne out in the data. So we'll do that right after this. And we're back here on big technology podcast with RAMP lead economist Ara Kharazian. Eric, great to see you. Thank you again for being here. Let's just talk a little bit about the deep SEQ growth that you're seeing. You're saying basically what you found in the data is that there is an increased reliance on these cheap open source models. Deepseek, when it comes to your list of trending models is number one. What do you think that says about the way that AI is adopting? Is it being adopted? Is it that, as you know, some people have said the future is going to be that you have this like maybe super smart foundational model orchestrator that makes your big decisions for you like an Opus 4.8 or a GPT 5.5 or 6, and then you just sort of deliver the more straightforward work to these smaller agents with the open source models. And I'm curious if you think that that is already being borne out in the data.
- [32:55]
B
When I talk to businesses, the single most important factor they, they list for why they have not adopted AI comprehensively throughout their organization is a concern around the cost. Not just the cost, but not really knowing what the cost is and knowing and hearing all these stories that hey, once you adopt AI, it's really hard to control the costs. And I think that is an indictment of OpenAI and anthropic who have not developed predictable pricing structures for their products. If you adopt OpenAI as an enterprise or Anthropic as an enterprise, you are more or less at the behest of your employees as far as how much they spend. In tokens, there are very few controls available to admins. And so that is ultimately I think been driving this growth and demand for open source platforms or open source models or what you're describing. These sort of like routing models where you know, you instead of sending your queries directly through OpenAI and Anthropic, you send it through this like middle layer which then decides, hey, actually this very simple task, I can send it to a pretty cheap model or a cheaper model even through openair and Anthropic. And I'll go through that. So that's one of the really popular ways to reduce spend. What I want people to know is that yes, there's evidence that this is happening on the margin, it's a little bit overrated. So 5% of firms on our platform are even using these kinds of open source platforms last year is 1%. So it's 5x growth and it's actually growing faster than the growth that's happening for OpenAI and Anthropic. But it's a relatively small percentage of companies and typically the most advanced companies, as is new starters. Companies that are just starting to onboard to using AI are not starting with a Chinese model, they're starting with OpenAI, anthropic. So that's the first thing I'll say. The second thing I'll say is that we've seen the rise of Deep SEQ in our dataset before. In early 2025 when Deep Seq had this really buzzy launch, it spiked in our data set. Then too, it rose to about, I think around half a percent of businesses in our platform for about a month used deepseek and then very quickly fell back to earth through at 0.1% of businesses. And the reason why back then was there was a stressful but also competitive response from the American model companies from OpenAI and Anthropic to offer cheaper but still very performant models that could compete with deepseek. And so they essentially instituted price cuts and businesses therefore had no incentive to be using deepseek anyway. To be clear, there are actual security and reputational concerns for businesses that are transacting directly with DeepSeek. And so you don't want to use Deep SEQ if you don't have to. So last month in our data set, Deep SEQ also had this breakout growth. It's one of the fastest growing vendors on Ramp's platform, but it's growing from a very small base. Only about 0.4% of businesses are using it. Again, that's up from 0.1, so 4x increase, but it's extremely small and I think it's not going to be very durable given that OpenAI and Anthropic are well positioned to respond to that with some price cuts. So I think DeepSeq is a little overrated. I think the open source models in general are a little bit overrated. However, I do think models, companies like Google are very underrated. The main concern here, dynamic here, is that OpenAI and Anthropic are not being responsive, effectively responsive to firms that want some cost control and cost discipline. Openanthropic develop models that incentivize you to spend as much as possible on tokens. And that makes sense for them because 80% of their revenue from businesses is token based. It's not Subscriptions, it's tokens. That's not the case for Google. Google doesn't need firms to spend a lot of money on the tokens and the models, so it actually can offer better routing. It actually can offer these product experiences that give firms a little bit more cost control because they have way more revenue sources available to them. They're also competitively well positioned because Google Workspace is already used by so virtually most businesses have some access to it. And so Gemini is already a fairly popular model. It's just not thought of in this kind of discourse often. And so I think Google is really the best positioned and relatively underrated in these kinds of conversations to take market share away from OpenAI and anthropic.
- [37:47]
A
If Google doesn't need you to spend tokens, then what does Google need you to do? Just to use its model so you don't use the others.
- [37:56]
B
Well, it's not that Google doesn't need you to spend on tokens. They of course produce revenue from tokens, but they are, they are supported by so many more revenue streams as well as a subscription revenue stream. That makes them less dependent and less laser focused on exclusively having you spend more on tokens.
- [38:20]
A
Right.
- [38:20]
B
So that is where they're a little bit better competitively positioned. I mean, if they wanted to, they could also have AI be a little bit of a loss leader. It wouldn't be that big a deal. Right.
- [38:29]
A
I mean, as long as you're using a big deal, as long as you're using Google's cloud. Right. To store your data, for instance, then it's a win for them by making cheap models. They can even have AI be somewhat of a loss leader if it grows cloud. And it has. They've been like 60% a quarter. That's very interesting.
- [38:49]
B
And their cheap models are extremely popular. Flash.
- [38:52]
A
Yeah. And they're also like a more mature company. So you would imagine they won't have these problems with the federal government like an anthropic is having. And you know, the, the sort of. One of the responses has been go open source. But a different response might actually be go Google because you can trust that those services are going to stay up and that they'll.
- [39:16]
B
I wouldn't have bet on that continuity just yet, at least with the kinds of announcements coming from the federal government, but. Well, I get the point that you're making.
- [39:28]
A
Okay, all right, I'll take it. All right. I want to end with the Sasspocalypse. Everyone's been talking about how Sass is dead and certainly it makes sense as a headline. You know, if you're trying to be provocative and you're thinking about what AI can do, but you actually have a post saying the death of SAS has been greatly exaggerated. So talk through what you're seeing there and why the saspocalypse hasn't fully materialized in the way people expected.
- [39:57]
B
There's two ways that I think about SaaS apocalypse. One is that traditional SaaS companies are going to lose a significant amount of market share to OpenAI and Anthropic. The second way that I think about SaaS Apocalypse is that every existing SaaS company just needs to rethink its pricing model in that things are increasingly moving to token based spend or usage based spend. SaaS companies are going to become their own little AI companies perhaps. And so the typical way that we think about SaaS pricing being seat based is going to go out the window and every SaaS company needs to rethink its whole product and model. So I found both of those to be a little bit overrated as far as actual business behavior in our data set. So the first part of SaaS apocalypse, whether or not OpenAI and anthropic will eat every other company, we're just not seeing that. I'll use CRM as an example, right, because it's one of those things that just purchased by so many businesses. Look, 80% of the market share for CRMs is just directly going to Salesforce. Firms that are trying to buy CRM buy Salesforce and then some buy HubSpot, whatever. And that's just always, that's just been the case since held that way. And yet in our data set we can actually see month over month growth in small but mighty, if you will, AI native competitors to CRM, to Salesforce and HubSpot. Adeo, that's a London based company, has a very low market share today, but is one of the fastest growing vendors on our platform as well and has a fairly durable rate of growth too. So there's some evidence already to say that hey, first of all OpenAI, Anthropic haven't offered their own CRM. Theoretically someone could vibe code their own CRM. But also the companies that are signing up for Adeo, that's a tech forward company, they know that they could vibe code their own CRM and however they're still buying as opposed to building themselves. And we see that across different kinds of software categories. Of course another one is figma. Right? So a cloud design comes out. Everyone thinks that figma is going to go under figma over the last couple months has continued being one of the fastest growing vendors on our platform, an extremely durable software vendor. Whether or not that's all going to change going forward, who's to say? But what I will say is that there is no indication, at least in our data, that there are even early signs of a slowdown amongst these kinds of SaaS vendors. I think the legacy vendors definitely have some competitive threats, but the competitive Threats aren't just OpenAI and anthropic. They are actually AI native software vendors that are taking market share today. So, and then on pricing, that's the second part where everyone's talking about, oh, we're just going to be paying based on token for everything. That's also not quite happening. Seat based contracts are still the vast majority of spend. For most software it's like 60 to 75%. The rest of it is really just flat platform subscriptions. Metered usage is extremely small, like 5%, less than 5%. And at many traditional SaaS companies that have offered their own sort of metered usage, like in Adobe, you can now pay for Adobe by credits, it's still only like half a percent of their revenue. And then notably, even for the AI companies, they're actually growing on subscription spend faster than they are growing on token spend. So even for them there is still this demand for subscription based spend. So I generally land on saaspocalypse as like, hey, maybe these things will happen. It's generally being made by pronouncements from product leaders. But as far as where the data is on actual business behavior, overrated.
- [43:48]
A
So where do you land on this idea that it won't be necessarily that AI can just vibe code every application, but that the AI becomes effectively a operating system. So you type into codecs like what you need and then it opens up figma and works through figma for you. Could you see that being the future interface? And if that's the case, if effectively, you know, the chatbots are a front end of all software, how do you think that might change pricing?
- [44:23]
B
I think that one makes a lot of sense. I mean I've seen that just as my own user experience that I'm increasingly. If a product has some integration with SaaS that I'm already using, I'm more likely to interact with it through the models than I am through the gui. Now I'll still maybe go into the website and make my own changes for certain things, but as a product experience it's actually pretty good. How that's going to affect spend I mean, look, I do think we'll probably see a steady increase in the kind of spend that is token based and the kind of spend that is agentic, but I think it is overrated. I still think this work tends to be directed by an individual person who is likely going to have an individual seat. I mean, if the AI companies themselves are still seeing this kind of subscription based growth. And I think that's the best evidence.
- [45:12]
A
Any other trends or sort of narrative busts that you've been looking at recently that you think we can share before we go?
- [45:20]
B
We're thinking a lot about the jobs impact of AI. We have a paper coming out about that, most likely in a few weeks. So I'd tell people to keep an eye out for that. And otherwise we write about all of our data@ramp.com data I'm on substack. Follow me on Substack as well. Yeah.
- [45:39]
A
Econlab.substack.com Just give us a quick preview. You don't have to share everything. But is AI taking jobs or is job growth still healthy?
- [45:50]
B
I can't do that yet. Okay. But. But I think it's going to be a really interesting paper.
- [45:57]
A
Okay. Ara, great to see you. Thank you so much for coming on the show.
- [46:01]
B
Thanks for having me. Always. Great.
- [46:03]
A
All right, great speaking with you. All right, everybody, thank you so much for listening and watching and we'll see you next time on Big Technology Podcast.
- [46:25]
C
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