a16z Podcast - Why AI Isn’t Killing SaaS Yet

Episode Date: May 25, 2026

Originally aired on MTS segment, Monetary Matters, Jack Farley and Max Wiethe speak with Ara Kharazian, Lead Economist at Ramp, about what real business spending data says about AI adoption, why the ���SaaSpocalypse” narrative is overblown, and how companies are actually buying and deploying AI tools. They also discuss Anthropic overtaking OpenAI in Ramp’s AI Index, token-based pricing, AI productivity gains, and why many legacy software firms may be more resilient than people expect.   Resources: Follow Ara on X: https://x.com/arakharazian Follow Jack on X: https://x.com/JackFarley96 Follow Max on X: https://x.com/maxwiethe Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 ramp data. Cursor did that with GitHub copilot. My main take is that Caspacalyptus is a pronouncement has come way too soon and is typically not informed by actual business behavior. You're saying that Sespacalypsepocalypse is not in.

the data. 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, 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 seat-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 Wheathe speak with Ara Karazian, lead economist at Ramp. We have the lead economist from Ramp economics. Ara Karazen, thank you so much for joining us today. Are 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? Yeah, I mean, I think that's what's so important about this kind of work is that 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 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 SaaS firms. Are we seeing any declines in the, in adoption for those traditional SaaS companies? Are we seeing any changes in how people are buying SaaS? And that's really where I defied SaaS poplips into two different categories, right? There's one that's, hey, are people shifting away from traditional SaaS over to, over to competitors provided by the model companies? And then number two is the way people are buying software changing are people shifting to a new model where you buy like agentic, you know, capabilities where you buy tokens instead of paying a SaaS platform fee or 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 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 projection 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 vendors' 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. Maybe there will be some down-the-line usage by an AI agent that you can't charge a seat in the traditional way. So more companies are starting to offer that. But whether or not that's actually playing out today as far as how businesses are buying SaaS, right now it's still a half a percent of actual spend. So for that to really take off, I mean, In theory, you would only offer that as a company if you believe it's going to allow you to take market share or increase usage right among like within an org.

So is somebody going to have to go after everybody else's lunch with a token base model to force that upon the rest of the market? Like what scenario would people fully switch? Would we see that take over? Well, no, you got a really good point too, that all of this is going to be the result of a series of competitive responses by all of the very thoughtful well-capable. FAParized firms in this space. I mean, you look at Anthropic versus Figma, both of them now offering a design product. There's nothing inherent about Anthropic

that makes it more capable of reaching designers where they're at or producing the software that designers need. You know, it has the model, it has the velocity, product velocity to continuously make improvements. But Figma is a very popular product, which has access to the same models that Anthropic produces in cells.

And so I haven't really understood from SaaSpocalypse 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 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 ramp data. Cursor did that with GitHub co-pilot. So I think it's something worth keeping track of.

But my main take is that's a Spacocalypse 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. If we could share this chart, 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. And nor has it killed off, at least the companies that are frequently mentioned. And you 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. I've seen 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 some of that even being like flowing through to these supposed competitors versus it replacing a seat-based 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 like seat-based thing on top of it. 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. Anthropic, surpassing Open AI. I think everybody is kind of familiar with that story at this point.

What about some of the other models? What are the trends that you're seeing in terms of spend on models that maybe people aren't nearly as aware of? Yeah. Well, in my post about Anthropic, I actually was a little bit hedging, if you would say it,

about Anthropics prospects going forward. I think when we put out, because we started putting out this metric last year called Ramp AI Index, which is tracking the sort of business adoption rates for Anthropic versus Open AI. For a long time, Open Eye was the leader. Anthropic just took the first place spot.

And what I found over the course of putting out this kind of research is that people will often interpret my findings as me saying, oh, it's over, open eyes the leader, or Anthropics the leader. That's typically not, First, this is not my intention, but it's also not how I see this market playing out. In increasing share of firms on our platform are using more than one model in some deployed way across workers, whether that's a subscription-based deployment or if it's something that they're building into their AI native product.

Firms that were early adopters are the most likely to use multiple models and add more AI vendors over time. So if you want to consider the early adopters of sign of where the rest of the markets are going to go, and that has tended to be how AI adoption is developed in our dataset, then you can assume that the average business is also most likely going to have a couple onboarded models. And then with that, there are signs that companies are becoming increasingly cost conscious as far as how much they're spending on their models. For the typical business that spends on tokens, so APIs and then also like the very high usage agent to coding, token cost for that business have increased 13x just over the last year.

It's still about for those high-intensity spenders, maybe about 2% of business spend excluding payroll. So it's a very small share of actual spend that's happening. But you project out that 13x and you get to an extremely unsustainable spend path. Most businesses can't do that and probably shouldn't. And it's those firms specifically that are increasingly shifting some of their AI spend over to platforms like open router. So anything that would allow you to

select between multiple models and ideally take advantage of free and open source models that are priced a little bit cheaper when they're deployed. Even that is still a very small share of spent. They've got like 3% of spend our platform goes directly through an open router

versus an anthropic, the AI spend on our platform. But increasing and particularly concentrated amongst those firms that are most incentivized to use platforms like open router, that ones that spend the most on AI. So they're finding the best value there.

And I think it's likely that those practices will start to move down the chain and be adopted by the more mainstream firm to start to opt for models outside of open eye and anthropics. Or use open eyes models and anthropics models, but use something that routes them over to the cheaper options when that makes most sense for the task. So those are the main headwinds I see on the model companies. cost and competition, particularly from arguably themselves in terms of the cheaper models that are available and then also the open source versions available. I mean, that sounds like a pretty big bare case just for spend on AI.

To what extent do you think people are figuring out that, oh, these tasks, now that we're getting better at creating and training our own agents, that they just don't need the frontier models. Like 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 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 are coming up. And I'm relatively good about applying best practices, but I don't think that 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. Opener 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 Open AI, 80% of their business revenue is token-based. So it's directly tied to usage and incentivizing you to do that.

Have we seen a software plugin 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. 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 bulk case for something like cursor, which can compete on the developer experience in that way in a way that Open Eye Anthropic in the absence of Cursor or not incentivized to do.

They can offer a cheaper, more performant model or better routing as part of the product experience. Hey, all right. So in this great chart you've got, we've got Anthropic now leading Open AI, then Google, then XAI. Way, way, way at the bottom is the blue line. People can barely see it is DeepSeek. What happened, era? I thought, you know, five months ago, we were hearing about how 80% of venture capital-backed tech startups were actually using DeepSeek because DeepSeek was so cheap. If that's the case, how come I can barely see this blue line on my chart? What's going on? I don't think 80% of venture back companies are using deep seek. We did. Look, when Deepseek first came onto the scene, now about a year ago, maybe a little bit over, we did see a spike in adoption for Deepseek, but it never hit even 1% of firms on the platform. Now, we keep it in this tracker because I think it's helpful context for people, though there was a case to say we should remove it at this point.

But Deepseek is not the only model frontier. Not the, not frontier. Deepseek's not the only open source or cheaper model. available out there. So in future versions of Ramp AI index, we're going to expand, though, to track adoption of a bunch of other cheaper models that are available on the market. And even if it's open source, you know, you might still be paying for it in some way in the cloud implementation of it. That's something exactly what you might get from like an open router, for example. My next question. So I don't, so deep seek, deep take is a tricky case, though, because it may be cheap,

but even if you are, no one wants to use deep seek for security purposes, even if you're a local, hosting it, there's just this like perception around it, particularly if you're building a product that appeals to businesses or consumers. And there are other options available at this point. So I don't, I don't really see Deepseek gaining adoption very quickly ever. Yeah, thank you. And what about Google's Gemini model? I mean, the yellow, the orange line quite low on this chart, is today's news going to change that, you think? Google's underrated, I'll admit, because let's, I'll point out one of the features of our research here is that we only track paid adoption. So there are a lot of firms that are using Google,

but they're using it through Google workspace, which integrates Gemini for the free. That's what we do. Yep. Exactly. So you can draw the line about what counts as AI adoption and how significant that adoption needs to be. Maybe you need some amount of paid adoption to really see productivity gains across a firm. Google's offering right now, I think most researchers in Econ are skeptical that chat subscriptions on their own will be driving the kinds of productivity gains we want to see in the economy. We might, so 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 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, what is going to make a model? Obviously, huge gap between anthropic, open AI, 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, I'll suggest actually that from a economic productivity measurement standpoint, the main interest is not to see, hey, which model company is it had 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 a 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 that 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? Well, it's really early. I'll say that no paper has answered it effectively because what you really want is you want a dataset that shows, hey, these firms adopted did AI and here's how their head counts change. I can tell you my in my my early sense of it two things are going on one there has been a decoupling from of revenue growth to had from head count growth particularly at software companies where there is not an obvious there is a less less obvious

connection between growing your headcount and growing your revenue well that's one the second thing though the counteracts is that firms that adopt they are typically quite fast growing and they have a lot of opportunity ahead of them. And that is in part because they're adopting AI. It's in part because of features of that firm already. But my instinct is that the firms that are adopting AI particularly well probably have a lot of work for people to do as well. Doesn't mean that we're ever going to get rid of layoffs completely in the world. You know, there's always some reasoning for that in some world in some way. Hell yeah. But I'm not a, I'm not a, I'm not a, I'm I'm not one of those like doomers about AI.

I would love to see some research about why the model companies keep saying that it's going to destroy all jobs because I don't see why that is helpful to them. By the way, that's not even the position of most economists. So that's where I would like to see research efforts. Well, it's tam posting. I mean, what's the tam of labor, right? Like, that's what they're doing. They're, they're basic.

Everyone tells them to stop doing that. And then it sounds weird and scary. I feel like that's what everyone's saying. But 99.99% of people think it's weird and scary, but what if the 0.01% of people are the people who are funding it and making these decisions? Maybe they're the audience.

Yeah, we're not the audience. The college grads aren't the audience. It's fees 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 group think.

It's more... Because I agree. No, I mean, we're content creators, right? Like, sometimes you'll do, 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 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, 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 that you think people should be paying attention to as it relates to the SaaSpocalypse? Well, a lot of the growth that's happening, most 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, who are extremely concerned about a SaaS. Pockups, not to say there won't be transformations in the market, but to say that there are underrated parts of the SaaS market that are growing in a large part because of AI. AEO as a category, so answer engine optimization, I guess. If SEO is how you show up in Google results,

AEO is the software that firms use to track their performance and AI models and whether or not they're being recommended. Huge growth. I mean, that sector did not exist. The companies that are making that software are not the same companies that were selling the SES. SEO software. I mean, SEO ones are getting into it, but the very fast growing ones in AEO are new. Profound is a new one, growing extremely quickly. So again, not a product offered directly by the model companies, and arguably the model companies could never offer that product because Anthropic can offer it for Anthropic, but they wouldn't be able to offer it across all of the different models in any effective way. I mean, it's arguably something that should happen externally by some

other vendor. And so there's a lot of growth in this sector that people just ignore or it doesn't make the discourse because it's not about the jockeying between, you know, two large players. But it certainly counters sask apocalypse in my mind. Well, but it is interesting that like the other example we talked about was keeping track of what model your tasks should go to. Like the biggest areas of growth that we all see are sort of just making sense of AI, right? making sense of what's happening in the AI ecosystem, when you look at the growth in SaaS, is there anywhere where it's not tied to AI

that you're seeing growth, where there isn't some connection? Well, it depends where you draw the line on what is AI, right? Like software that uses AI features to improve an existing legacy software product. Sure, we can call that AI native, AI-driven, but that's a different narrative

from SaaSpocalypse, right? Like Adio is this extremely fast-growing London-based CRM. It's like a couple single digits of market share, but it's growing really quickly. Salesforce is 80% of the market, so it's not something that's going to be unseated very quickly. But Adio is like the AI native alternative.

And I think it would be a mistake to discount the growth of these kinds of firms just because they have AI features and say that, oh, it's entirely because of its entire, tied up to the performance of the model companies. These are very different unique products that model companies can't really compete on or haven't yet. And so

AI is going to touch a lot of these developments that are happening, but it's also going to grow a market outside of the explicit model company's business segment. I wonder the integrations that Anthropic recently announced,

okay, you can access Morning Star via Anthropic. You can access, and you can access Anthropic via Morningstar. Same with Veris for an insurance sector or the London Stock Exchange, refinitive. Is that good for the software companies or bad? Why? Well, you can also access ramp data now through the cloud connector and the Open AI.

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.

Is it good for a business like refinative 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 do data 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 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, you know, perhaps venture capital backed and probably, you know, I hadn't heard of them, they probably

are private. But then what about the large behemoths, you know, the service now is 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 know, 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 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. 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're We all have access to the same information there.

So disruption can take a lot of different forms. It can take the form of a company overtaking one of their legacy competitors. They can also take the form of sort of like defensive M&A. It tends to depend on like how quickly those legacy players are recognizing the speed of growth. I'm sure you have people coming to you guys trying to get this data to understand how in danger their positions are. Are these legacy players like in the conversations you're having about your data, How concerned are they about these upstarts and do you think we're going to see more defensive M&A

or sort of legacy players getting left in the dust? Well, look, I think part of it is that a lot of legacy players aren't really sold on AI being that transformative. Or maybe they believe it'll be transformed, but they don't necessarily think it's something they need to be extremely responsive to. There's a really good example, actually, because it was in a Wall Street Journal like a month or two ago.

Deloitte sponsors like a note in the Wall Street Journal. It's called like CFO Journal. And so I read it because it's normally just like pretty good write-ups. Like it's not necessarily, it's not an ad. It's just like sponsored by Deloid about what CFOs are thinking about. And so the headline was something about how like this is how the big four, all the big four accounting firms are doing AI. Like, okay, that's an interesting thing for Deloitte, the sponsor.

And it goes, talks about how KPMGD is implementing agents for this task. and P-Y, PWC is doing this thing, and EY is doing all these experiments where they're giving their employees access to this and that thing. And then it said, Deloitte is actually taking a much more hedged approach. We think it will be something that improves our workers and augments them but not replaces them.

And it was fascinating because I actually wasn't sure until I got to the Deloitte part why they were doing this advertisement for the other accounting firms. And then I realized, oh, this is an explicit effort by Deloitte to position itself as not anti-AI, but certainly a little bit more conservative about its implementation of AI. And it's directly counter how the other accounting firms are positioning themselves. And by the way, how a lot of companies are positioning themselves

as firms that are actively adopting it and trying to sell to their clients and let firms, their customers know that, hey, we're sort of on top of this new technology and we're going to offer you something that accounts for it. And it's going to make us better accountants at the end of day, something like that. Now, I'm on the side of, I generally airs this side of, hey, technological adoption is good, more from Shoddop. AI, obviously do it intelligently. Don't, you know, unleash a product that doesn't make sense or that, you know, messes up your customer's 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 the New York Times, a lot of newspapers which are doing deals directly with the model companies to sort of license their work.

Are they, 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?

Like, send me the free link. Like, why is this paywalled? And if some of that is now being captured by these large companies that are paying a fee, like it's not the same as if all of those people who were stealing the content are paying, you know, a full subscription price, but at least they're capturing some of that.

And then also there's the aspect of like the AI has not like unlimited time to read, but as like human beings. is like our attention spans are going the opposite direction. I don't think AI necessarily has that problem. It can consume a large article very quickly. Like, do you think it has the potential to actually improve the quality of reporting? Because it's not being, you're not competing for competition, in the attention economy for human eyeballs anymore because the humans are consuming it

through something that kind of like already parses the long form of thing for human attention spans. I'll say this way. I think AI is pretty bad at most writing. I think it's really bad at writing blogs or anything opinionated. I think it comes out with results that are, I mean, people have talked about this extensively. Like, I know when I'm reading something that's written by an AI. It's not just that.

It's what? It's not this. It's that. Yeah. Yeah. The quiet part out loud. Yeah. But I will say it's pretty good at certain kinds of writing.

Anything like highly structured. If you just tell it like, hey, just like give me straight up bullet points, summarize this thing that happened, and that's essentially what granola and not taking apps do, it's pretty good at that. It doesn't add the weird, you know, vicissitudes of how it talks. It does a pretty good job being comprehensive. Yeah. Yeah, for like stylization. Yeah.

And a lot of reporting, you know, let's say a reporter does their job and they talk to it. to a bunch of sources and they, you know, they go on the ground. They literally talk to people in person. The AI robots will never do that. They're not going to go to an event and interview people. They don't have that ability. But, you know, if as a reporter, you can sort of ask, you can do your sort of research process, the actual reporting work,

and then have an AI write the straight summary of your reporting. That's not unreasonable. I mean, can it write a profile of someone? Maybe not. Can it do the investigative journalism aspect of having, to go in person and talk to the county clerk and get some documents that are sealed. No, it can't do that.

But it can write out what you found. So I think there is a world in which some parts of writing are done by AI models. I was less talking about the writing itself and more the reading of it, right? If you were to say before AI, there's only so many people who were actually reading the 10-page deep dive article

and then it gets like telephoned out through social media and by the time it reaches the majority of people like a lot of the context has been lost the facts have slightly changed if instead of the writing being consumed by like a small number of people and then miscommunicated out like it's that the AI is reading and communicating the key details

uniformly to everyone and thus like it incentivizes like you're kind of disincentivized to go deep and do good writing because by the time it's reaching everybody So I'm saying, like, it's the fact that AI is doing the reading is maybe going to incentivize, like, higher quality journalism. I mean, I don't know.

I don't know if AI is actually going to, it's interesting to see if AI has actually lowered the, sort of the viewership on, like, a New York Times typical article, for example, right? Like, I think that the people who subscribe to newspapers are probably going to keep reading newspapers as they do. Maybe on the margin there will be some reduction, but I don't know. I'm talking about the everyone. else. Like if there's everyone else

and they're not being monetized and now they're at least being monetized via the big model company is paying something to get that. And instead of like your third degree connection on Twitter being where you get your news or like a fake news bot, like I'm just going to go to Claude or I'm going to go to Gemini

and say like is this story true? I mean at rock, right? Is this true? But I do want to ask actually about XAI. A.I. sort of a closing question here. You know, going back to your chart of the index, like XAI dangerously low on that graph.

Yeah. Do you think that is going to change? Why is XAI so low? And obviously it's such a huge part of the SpaceX IPO. Yeah. Wouldn't have combined the companies if he didn't think it was important. Like, what's going on there? XI has not seen the rise in business.

I mean, first of all, XAI was a relatively late entrant. into this market. I mean, the fact that it even grew up to, I think, 3% adoption within a couple months of launch is a pretty significant feat. However, it has not been able to translate that into sort of vertical growth that Anthropics saw over the last six, 12 months. But I don't think that these are unreported or underreported, certainly. I think if their acquisition of Cursor goes through, well, actually, then we're going to have to start integrating Cursor into X-AI's market share. But I I think there was a good reason for that acquisition as well. I mean, when you look at what XI really had available to it,

which is this incredible compute power, also informed it's, there's sort of a new agreement with Anthropic. Acquiring something like cursor to actually drive some kind of model adoption, I think will be a really good move for XAI. All right. Well, Aura, thank you so much for joining us. Everybody can find your stuff at,

it looks like it's on substack, econlab.substack.com. I'm on substack. It's my name on Twitter, Aura Karasi. It's on nowadays I'm on LinkedIn and Instagram too so find me on all platforms. Out now on all plats, Auraq Harazin. Thank you so much for going on. Thanks for listening to this episode of the A16Z podcast.

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