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Why AI Isn’t Killing SaaS Yet
00:40:22
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.
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Transcript72 lines
- [00:01]
Ara Kharazian
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 set apocalypse as a pronouncement has come way too soon and is typically not informed by actual business behavior.
- [00:27]
Jack Farley
You're saying it's cesspocalypse is not in the data?
- [00:31]
Ara Kharazian
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.
- [00:48]
Podcast Host (a16z)
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 Maxwith speak with Ara Kharazian, lead economist at Ramp.
- [01:42]
Maxwith
We have the lead economist from Ramp Economics, Ara Kharazan. Thank you so much for joining us today.
- [01:49]
Ara Kharazian
Thank you guys.
- [01:51]
Maxwith
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?
- [02:01]
Ara Kharazian
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. But 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 Apocalypse 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 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% 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 you know, 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, how products may develop, but they're not often informed by the actual changes happening with respect to business spend.
- [04:08]
Maxwith
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?
- [04:15]
Ara Kharazian
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 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, 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.
- [04:56]
Maxwith
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 based model to force that upon the rest of the market? Like what scenario would people fully switch? Would we see that takeover?
- [05:19]
Ara Kharazian
Well, no, you get 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 capitalized firms in this space. I mean, you look at Anthropic vs 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 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 and sells. 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, 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 copilot. So I think it's, you know, something worth keeping track of. But my main take is that saspocalypse as a pronouncement has come way too soon and is typically not informed by actual business behavior.
- [07:00]
Jack Farley
You're saying sasspocalypse is not in the data that if we could share this,
- [07:06]
Ara Kharazian
this chart, I'd say quantitatively Neither aspect of SaaS apocalypse 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, than a figma. And yet perplexity is also one of the fastest growing vendors on Ramp seeing a lot of uptick specifically because it's offering products that the model companies have not competed on yet.
- [07:41]
Maxwith
So, 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 versus it replacing a seat based. A seat based consumption. Right. It's. 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.
- [08:13]
Ara Kharazian
Yeah, 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.
- [08:31]
Maxwith
Anthropic surpassing OpenAI 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?
- [08:45]
Ara Kharazian
Yeah, well I, in my post about Anthropic, I actually was a little bit hedging if you would say it about Anthropic's 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 OpenAI. For a long time, OpenAI 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. This is not my intention, but it's also not how I see this market playing out. An 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 software, 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 data set, 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 decoding token costs 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. Right. 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, you know, platforms like OpenRouter. 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, it's even that is still a very small share of spend. They've got like 3% of spend. Our platform goes directly through an open router versus an anthropic. At least the AI spend on our platform but increasing and particularly concentrated amongst those firms that are most incentivized to use platforms like open router, the 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 open, you know, use OpenAI's 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.
- [11:54]
Maxwith
I mean that sounds like a pretty big bear case for just for spending on AI. To what extent do you think people are figuring out that oh these tasks now that we're getting better at, at creating and, 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 like thinking a little harder about what the, the process should be,
- [12:26]
Ara Kharazian
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, you know, 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 expect the typical worker to make that decision. I mean I'm fairly, 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 I don't think that that, 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. OpenAI 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, no AI 80% of their business revenue is token based. So it's directly tied to usage and incentivizing you to do that.
- [13:40]
Maxwith
Have we seen a, have we seen like a, a software plugin that, that basically determines what sort of model your task should go to?
- [13:49]
Ara Kharazian
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 from it's going to. 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 or not incentivized to do. They can offer a cheaper, more performant model or better routing as part of the product experience.
- [14:40]
Jack Farley
Hey, all right, so in this great chart you've got, you got anthropic now leading OpenAI, then Google, then Xai. Way, way, way. At the bottom is the blue line. People can barely see. It is Deep Seek. 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 Deep Seq because Deep Seq was so cheap. If that's the case, how come I can barely see this blue line on my chart?
- [15:07]
Ara Kharazian
What's going on? I don't think 80% of venture backed companies are using Deep Seq. We did look when, when Deep Seq first came onto the scene. Now about a year ago, maybe a little bit over, we did see a spike in adoption for Deep Seq 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 is a case to say we should remove it at this point. But Deep Seq is not the only model. Frontier. Not Frontier. Deep Seq is 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 might still be paying for it in some way in the cloud implementation of it. That's something. Exactly what you might get from an open router, for example.
- [15:56]
Jack Farley
My next question.
- [15:57]
Ara Kharazian
So Deep Seek. Deep Seq is a tricky case though, because it might be cheap, but even if you are, no one wants to use Deep Seq for security purposes. Even if you're locally 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.
- [16:23]
Jack Farley
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?
- [16:31]
Ara Kharazian
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 free. That's how you can.
- [16:48]
Jack Farley
That's what we do.
- [16:49]
Ara Kharazian
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. But Google is definitely underrated. Certainly has a distributional advantage in that it's at all businesses that use Google Workspace.
- [17:24]
Maxwith
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 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, hey, this is a company that deserves to be, you know, on the list with the big boys.
- [17:53]
Ara Kharazian
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 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. Headcount, 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, you can, 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.
- [19:05]
Maxwith
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.
- [19:15]
Ara Kharazian
Layoffs.
- [19:15]
Maxwith
There was overhiring and it's a convenient way to say this is why we're laying off, you know, 10% of the workforce. Are you really seeing that productivity come through from these firms that are saying it's because of AI adoption?
- [19:29]
Ara Kharazian
Well, it's really early. I'll say that. No paper has answered it effectively because what you really want is you want a data set that shows, hey, these firms adopted AI and then here's how their headcounts change. I can tell you my ins, my, my early sense of it. Two things are going on. One, there has been a decoupling from, of revenue growth to head from headcount 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. That's one. The second thing though, that counteracts it is that firms that adopt AI 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. Oh yeah, but I'm not, 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, that's where I would like to see research efforts.
- [20:55]
Maxwith
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.
- [21:01]
Ara Kharazian
Yeah, but everyone tells them to stop doing that and then it sounds weird and scary. I feel like that's what everyone's saying.
- [21:08]
Jack Farley
But, but you're. 99.99% of people think it's weird and scary, but what if the, you know, the 0.01% of people are the people who are funding it and making these decisions? Maybe they're the, they're the audience.
- [21:19]
Maxwith
Yeah, we're not the audience. The College grads aren't the audience. It's, it's VCS and banks and the controllers of capital.
- [21:26]
Ara Kharazian
Are you, are you guys suggesting that there might be some element of group thinking in the tech sector?
- [21:31]
Maxwith
Not necessarily. I don't know. Not group think it's more because I agree. No, I mean we're, we're content creators. Right. Like sometimes we, 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, they're shooting towards. And so if you want, if you want to justify that. Well, you know, the entire labor market is a pretty big total addressable market.
- [22:18]
Ara Kharazian
Yep. No, agree. We'll see if it works out for them.
- [22:21]
Maxwith
Yeah, I mean that's, 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 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, what are the other like software related trends that, that you think people should be paying attention to as it relates to the SaaS apocalypse?
- [22:53]
Ara Kharazian
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. 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 large part because of AI AEO as a category. So answer Engine optimization, I guess it's, you know, it's SEO is how you show up in Google results. AEO is the software that firms use to track their performance in 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 SEO software. I mean SEO ones are getting into it, but, 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, or it doesn't make the discourse because it's not about the jockeying between, you know, two large players, but it certainly counter SAS Apocalypse in my mind.
- [24:15]
Maxwith
Well, but it is interesting that like the, the other example we talked about was keeping track of what model your tasks should go to. Like the, 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 it, is there anywhere where it's not tied to AI that you're seeing growth where there isn't some connection?
- [24:39]
Ara Kharazian
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 it, that's a different narrative from SaaS Apocalypse. Right. Like ADEO 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, you know, so it's not something that's going to be unseated very quickly. But ADEO is like the AI native alternative. And you know, I think it would be a mistake to discount the growth of these kinds of firms just because they have AI features. And, and, and say that, oh it's, it's entirely because of, it's entirely tied up the performance of the model companies. These are very different unique products the model companies can't really compete on or haven't yet. And so, you know, AI is going to touch a lot of these developments that is, that are happening, but it's also going to grow a market outside of the explicit model company's business segment.
- [25:46]
Jack Farley
Okay, I wonder the integrations that Anthropic recently announced. Okay, you, you can access Morningstar via Anthropic, you can access, and you can access Anthropic via Morningstar. Same with Verisk for An insurance sector or you know, the London Stock Exchange Refinitive.
- [26:05]
Maxwith
Are.
- [26:05]
Jack Farley
Is that good for the software companies or bad?
- [26:10]
Ara Kharazian
Why? 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. 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 the both the free data that RAMP provides and then the ease of access that Anthropic provides to 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.
- [27:27]
Jack Farley
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, 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, the large behemoths, you know, the servicenows of the world, the Microsoft's of the world. And then also there's the private equity backed thing where it's they're 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?
- [28:06]
Ara Kharazian
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 the 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 you know, 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, 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.
- [28:55]
Maxwith
So disruption can take a lot of different forms. It can take the form of a company overtaking one of their legacy competitors. It can also take the form of sort of like defensive M and 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 and A or sort of legacy players getting left in the dust?
- [29:33]
Ara Kharazian
Well, I, 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 the 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 necessary, it's not an ad, it's just like sponsored by deloitte 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. Okay, that's an interesting thing for Deloitte to sponsor. And it goes talks about how KPMG is implementing agents for this task and PY, 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'll be something that improves our workers and augments them, but not replaces them. Da da da, da. 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 input 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 it to their clients and let firms, their customers know that, hey, that we're, we're sort of on top of this new technology, we're going to offer you something that accounts for it and you know, it's going to make us better accountants at the end of the day, something like that. Now I'm on the side of, I generally err to the side of hey, technological adoption is good. More from should adopt AI Obviously do it intelligently, don't, you know, unleash a product that doesn't make sense or that, you know, messes up your customers audits, especially in something like finance and accounting. But I was surprised about the world'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, to implement AI. At least implement AI, at least implementing AI project really quickly.
- [31:54]
Maxwith
I think if you would have asked me like a year ago, do you think Deloitte will be more cautious or, 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?
- [32:18]
Ara Kharazian
Oh, 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, 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.
- [33:10]
Maxwith
Well, on that front, I mean that's very interesting because obviously there's tons of like 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, like our attention spans are going to the 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 thing for human attention spans.
- [34:11]
Ara Kharazian
I'll say it this way. Look, 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, with results that are, I mean everyone's people have talked about this extensively. Like I know when I'm reading something that's written by an AI, it's not just this 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 note taking apps do. It's pretty good at that. It doesn't add the weird, you know, vicissitudes of how it talks. Does a good job being comprehensive. Yeah, yeah.
- [35:03]
Maxwith
For dilation.
- [35:05]
Ara Kharazian
Yeah. And a lot of reporting, you know, let's say a reporter like does their job and they talk 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 will 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, you know, 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.
- [35:58]
Maxwith
I was less talking about the writing itself and more the reading of it. Right. If you were to say like before AI, there's only so many people who are 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, is, it's the fact that AI is doing the reading is maybe going to incentivize like higher quality journalism.
- [36:45]
Ara Kharazian
I mean, I don't know. I, I don't know if AI is actually going to be 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.
- [37:07]
Maxwith
I'm talking about the everyone else. Like if, if there's everyone else and they're not being monetized and now they're at least being monetized via the, the big model companies paying something to get that. And instead of like, 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, is this story true? I mean, you know, at rock. Right. Is this true? But I do want to ask you actually about xai. Sort of a closing question here. You know, going back to your chart of the index, like xai, dangerously low on that graph.
- [37:48]
Ara Kharazian
Yeah.
- [37:49]
Maxwith
Do you think that is going to change? Why? Why is XI so low? And obviously it's such a huge part of the SpaceX IPO. Wouldn't have combined the companies if he didn't think it was important. Like what's going on there.
- [38:01]
Ara Kharazian
XI has not seen the rise in business. I mean, first of all, XI was a relatively late entrant into this market. I mean, the fact that it even grew to 2, 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 Anthropic saw over the last 612 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 xai's market share. But I think there was a good reason for that acquisition as well. I mean, when you look at what XA really had available to it, which is this incredible compute power. Also informed, it's, 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.
- [38:55]
Maxwith
All right, well, Ara, thank you so much for joining us. Everybody can find your stuff at it looks like it's on Substack. Econ Lab Substack.
- [39:04]
Ara Kharazian
I'm on Substack. It's my name on Twitter. Arakkarazian. It's on nowadays. I'm on LinkedIn and Instagram too. So find me on all platforms out
- [39:13]
Maxwith
now on all plats. Ara Karazin, thank you so much for.
- [39:16]
Ara Kharazian
Guys,
- [39:19]
Podcast Host (a16z)
thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold or sell any investment or financial product. This podcast has been produced by a third party and may include paper promotional advertisements, other company references and individuals unaffiliated with a16z. Such advertisements, companies and individuals are not endorsed by AH Capital Management, LLC, a16z, or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.