SaaStr 815: Redpoint Ventures Playbook: How Top VCs Are Really Investing in AI

0:01Welcome to the official Sastr podcast where you can hear some of the best Sastr speakers. This is where the cloud meets. Up today on the Sastra Podcast. So it's really exciting times. Obviously, in trying to determine what the market size of this all is, is a bit of a fool's errand. And as famously, people have whiffed on, been wildly off before. I think Morgan Stanley has estimated in the next few years, 25 % of global software spend could be towards these AI use cases. And so all this is like incredibly exciting. obviously makes the best period to be a venture investor, but certainly comes with its challenges as well, right?

0:40And I think there's two main challenges that we see day to day. The first is there's a lot of AI companies being started. And what that means on our end is an interesting pattern that we have as investors is anytime we get excited or interested in a category, there's 10 plus startups in that category that pop up. And so that is a really interesting market dynamic. The second thing we're seeing is, and I'm sure folks have seen headlines and whatnot, the prices for these companies are definitely increasing. There's a lot of investor excitement. Hey, everybody. SaaS for Annual will be back. May 2026, the world's largest SaaS and AI gathering for executives.

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2:36So maybe I'll give a bit of background first on myself, and then we can dive into it. I'm Jacob Efron. I'm a managing director over at Redpoint. Redpoint is an$8 billion venture capital firm based in Silicon Valley. We've invested in companies like Snowflake, Stripe, Twilio, Ramp, HashiCorp, and I help lead a lot of our AI investing. I also run an AI podcast on supervised learning. So if you find any of this interesting, definitely check that out. But today I'll talk a little bit about what we're seeing in the AI world, how it informs our investments today. And maybe I'll start with some backdrop that I imagine is familiar to most of you, but maybe helps up the context for what we're seeing as VCs.

3:15And so to kick it off, we're seeing a massive decline in model costs today. So what does this slide show? Basically, for a given benchmark of model capabilities, the cost per token of those models is going way down, way faster than it was in the cloud era. And so what I think that means for AI application companies is a familiar pattern, where basically we get a set of frontier models that have new capabilities, we discover the wild things they can do. And then those capabilities become cheaper and cheaper until they can be wrapped into the vast majority of products. And even in the past year, we've seen some wild new capabilities from models.

3:58Obviously, they've gotten way better at coding. I think voice models have gotten a lot better. And that's then spawned a ton of different startups in these spaces. And so I think the implications of that are, as we think about things, we're super focused on what's the end use case and how powerful is that with AI and a lot less focused on what are the gross margins affected today because these models just get cheaper and cheaper over time. I think the natural byproduct of these people playing around with these models for longer periods of times is we continue to just find more and more use cases and get more and more usage of these underlying models.

4:37And so just the insane increase in usage of AI models, which is a lot faster than happened in the cloud era. And we'll talk a little bit later about some of the use cases that are driving this. But I think there's this pervasive feeling in the investment community today that even if you froze the model capabilities, there's trillions and trillions of dollars of applications just waiting to be discovered here. And obviously, for some of the most cutting edge reasoning models, we've only had six, nine months to experiment with what these models can do. And so I think over time, there's going to be more and more.

5:11And what that means is basically the power of these models, the increased usage, the fact that they're pretty cheap is that when these startups find product market fit, they're just scaling way faster than traditional SaaS counterparts. And so this is some great data from Stripe that shows how fast AI companies are scaling. And this pace of adoption breaks a lot of rules about traditional startups. As I think about some of the classic rules of SaaS investing, it was maybe don't go against some of these horizontal incumbents or don't sell into hospitals or law firms or banks. Those are customers that take a long time to buy.

5:45But we're seeing a lot of these rules be broken by just the 10x better product capabilities that a lot of these models are able to provide and like insanely fast scaling. And what does that mean is these budgets are only going to increase. I think maybe in the early days post-JAT GPT, there was a little bit of skepticism from enterprises about what some of these models would actually be able to do. But there's the combination of kind of actual real world impact these models have had, plus just everyone watching the pace at which they've gotten better, I think has led to real deep interest from enterprise.

6:24So I think these budgets are only going to get bigger. And at the same time, the models are only are going to get better. And I think we're with each generation of model improvement, we unlock a whole new set of capabilities. And obviously, a lot of focus now is around models that can do tasks for longer periods of time. And so this slide is really around how long can you let a model go and act and actually execute something or what folks often call agents. And I think as models are more and more able to do this, the set of applications that they're able to address and the set of industries they're able to address is only going to increase.

7:05So it's really exciting times, obviously, in trying to determine what the market size of this all is a bit of a fool's errand. and is famously people have whiffed on, been wildly off before, but I think there's all sorts of estimates of just, hey, this is a really large opportunity. I think Morgan Stanley has estimated in the next few years, 25 % of global software spend could be towards these AI use cases. And so all this is like incredibly exciting, obviously makes the best period to be a venture investor, but certainly comes with its challenges as well, right? And I think there's two main challenges that we see day to day.

7:46The first is there's a lot of AI companies being started, which makes us, if you're an ambitious founder today, what more exciting space to go build in than applying these models to a bunch of end industries. And I think YC is a great proxy for what types of companies are being founded. And so you're seeing the proportion of AI companies go way up. And what that means on our end is an interesting pattern that we have as investors is anytime we get excited or interested in a category, there's 10 plus startups in that category that pop up. And so that is a really interesting market dynamic. The second thing we're seeing is, and I'm sure folks have seen headlines and whatnot, the prices for these companies are definitely increasing.

8:29There's a lot of investor excitement. And so we looked at data of all the kind of series B and C companies we'd looked at that were non-AI and then AI. And as you can see, of the AI companies, they're raising larger rounds. They're raising those rounds at higher prices. Now, admittedly, a lot of that is justified by the higher growth rates that these companies have, the kind of step change in adoption. But these prices and the sheer number of companies definitely make for a challenging investing landscape to navigate today. And so maybe I'll share a few observations on the broader market before I talk about how we think about navigating all this.

9:08The first thing I'd say is these products are hard to build. Yes, there's a lot of companies that we see in each space, the 10 plus in each category. But I would say a common trend is that in any category we look at, there's really two to three of them that ride to the top. And they're providing a better user experience, they're scaling faster. And I think the reason is there's not a ton of tooling that exists for a lot of these problems below the line here. And so companies are building their own scaffolding around the models. And with the pace of model progress, that scaffolding has to change really quickly.

9:44And it's actually quite challenging to build effective AI applications. And we see a real difference in what some of the best companies are able to do. So the second point I'd make about investing in the space is our thinking on what makes an interesting application company and what application companies need to do evolves over time. I feel like there's just a fire hose of new progress in model advances. And it's important for us to have strong opinions on what makes for interesting applications, but we have to loosely hold them in this just period of rapid change. And I think maybe one example of this that could be interesting is the idea of kind of building and fine-tuning your own models for a specific domain.

10:30So immediately post-JATGPT, there was all this buzz around, hey, enterprises sit on tons of data. What if they pre-trained a really large model that was specific to finance or law or healthcare? and so famously I think Bloomberg trained a model Bloomberg GPT that outperformed the the latest OpenAI model of the time and then three months later OpenAI shipped a new model and it was way better and I think that consensus has been hey massive pre-training for an individual domain doesn't make a ton of sense and for a while even fine-tuning was somewhat effective not super effective and it felt like the game was all about just building really good workflows on top of the model APIs.

11:14But the market does evolve. And in the last few months, OpenAI shipped reinforcement fine tuning, which is kind of a new way of fine tuning that they offer. And it's actually quite good. And so it actually is starting to seem again, being able at least to not pre-train, but fine tune data on a specific domain could be really helpful for a lot of these application companies. And so it's just a constantly changing and evolving landscape and one we're always trying to stay on top of what's happening. I think one of the most important things as we think about what's happening is what's actually working.

11:48And so maybe to hit on what we've seen really work today, I'd say broadly, there's four types of applications that have real fit, and then we see them applied in a ton of different ways. The first is chat, but it's a great use case for these LLMs, works really well in the customer or support space. The second is the ability to basically search over documents and summarize those documents. And so you've seen that in the horizontal space with companies like Lean, but also in verticals, legal AI, companies like Lagora or construction, companies like Trunk Tools. And then these models are really good at speech to text and text to speech.

12:25And so you've seen that in the healthcare space, companies like Abridge that are transcribing medical visits, as well as companies like Liberate and Assort that are doing the front door of insurance brokers or medical clinics and being able to field more calls than folks could before. And then the last bucket is coding, right? These models are unbelievable at coding. They're only getting better. You've seen the explosion of companies like Cursor and Cognition, Poolside, Sourcegraph, a whole host of companies in that world. And so I'd say to date, the categories that feel like they've worked best are coding, customer support, legal, and healthcare.

13:01But I think we're still just in the very early innings of applying this amazing set of capabilities to a ton of different end industries. And so there's inevitably going to be a bunch more applications or end industries that emerge over time that have great use cases with these model capabilities. And then the thing that we're always thinking about is every few months, it feels like the models get better. So what's the set of use cases that's going to get unlocked as these models get more reliable, they have longer term reasoning and memory, and we're constantly trying to figure out what that set of applications is.

13:36and i guess the theme of this was our investment framework and how we think about investing in the ai world and i thought i'd put together a simple three-part framework as a former consultant it always has to be three but three questions we're always asking ourselves as we're evaluating these applications. So the first is, is there a really effective wedge for AI? And I would say that generally, the bar for what product market fit and what an effective wedge looks like has gone up in AI because you've just seen explosive growth for companies when they do have them. And I think there's this interesting thing we see in AI today where there's clearly a huge top-down mandate for companies to invest in AI.

14:23Boards are telling their CEOs, what are we doing in AI. So there's a lot of willingness to experiment with AI products today. And I think one of the key things we have to do is when we see a company that's growing really well, it's, is this just, hey, a company's messing around with what AI can do. And maybe this is something that they adopt. Maybe it's something they don't adopt, but they're willing to pay some money just to see what the product does. Or is it something that the end users really love? And so we're always trying to talk to the folks who are actually using the product and figuring out like, is this a game changer or not.

14:53And you can really tell there's just some products that people absolutely love. And as you think about this kind of race to be, I think there's a race right now to be the strategic partner to hospitals, to law firms, to banks, to insert any kind of company. And I think a key part of that race is finding the right wedge where AI can just deliver a 10x better experience, It's a killer use case that everyone just loves using. So we really look for that, and that's a prerequisite to us looking at any kind of company. The next thing we do is we think about how much more a company can do from this wedge.

15:32And so there's a few things behind that. One of them is, I think, in larger industries, there's generally more that AI can do over time. And so we like healthcare, legal, finance, large industries versus some of the more niche ones. And then I think also there's a question of, are there 10 other use cases within this industry that this set of models is really well poised to go automate? And so I think that one way you could think about this is there's lots of great wedges that exist, but maybe in smaller markets. So you could imagine, one thing I would love is I'm sure you could make a great AI product to write sourcing emails to reach out to a bunch of companies for venture capital firms.

16:12and this would probably be a great wedge that could get you some initial scale pretty quickly the issue with that obviously is there's only so many venture capital firms that industry is only so many is so big and at least today it's not totally clear what else models could do within a venture capital firm maybe they could do a bunch i'm sure over time they'll replace plenty of what we do but at least today i think that's a great example of a potentially very compelling wedge that doesn't actually translate into a really interesting company to invest in. And then the last bucket I'd say we think a lot about is the extent to which quality will matter.

16:51And the reason this is important is, like I said before, in any category we look in, there's always going to be a bunch of players building there. And there'll always be somebody who's going to say, hey, for 10 % of the price, we'll sell something that's 50 % as good. And the markets that are most interesting are ones where you would rather have the higher quality product. And so maybe an interesting observation has been that in some ways, some of the easiest things to sell AI products to today or automate are things that maybe have already been outsourced that business process organizations are doing already.

17:26And the interesting thing about that is they might actually have a real race to the bottom in pricing. And so we're really focused on things where quality of the end application really matters and people will pay for kind of best in breed. And so I figured it'd be fun to take these three questions and take a look at how they apply to a few investments that we've made in the AI application space. So there's two that I'll talk about that I'm on the board of. One is a healthcare AI company called Abridge, and the other one is a legal AI company called Labora. So let's start with Abridge. Abridge, we co-led their Series C.

18:04They recently raised a Series E at$5.3 billion. It's an incredible company. What they basically do is they offer a product that records a visit between the doctor and patient and then transcribes that visit into the electronic health record in a way that the doctor doesn't need to spend a ton of time writing the note themselves. The insurance company gets the information on what happened in a way that is effective for billing. And the patient gets the doctor's attention as well as can get a summary of what happened in the visit. And this is, if you think about that three-question framework on the wedge, you're not going to find a better wedge than this.

18:40It is like truly an incredible AI use case because doctors used to have this thing called pajama time, where they would spend three hours at the end of a really busy day just typing up a bunch of notes late at night, in their house, in their pajamas. And it was one of the things that was just leading to massive doctor burnout. And so for the doctors, this is really a total lifesaver of not having to do this extra work and being able to focus on the patients in front of them. For the patients, if you've ever been to a doctor's office and they're typing away at their computer the whole time, not looking at you, it's a way better experience when the doctor is able to focus on you.

19:15And then for the hospitals themselves, the ability to correctly document what's going on and then get paid for the services they provide is also really interesting. And so this is a use case that is just spread like wildfire. And if you talk to any doctors that use Abridge, I've never heard people speak so positively about a product. And so incredible wedge, what they, what also makes Abridge a really interesting company is there's tons to build on top of that down the line. And Abridge's CEO Shiv Rao always says that everything in healthcare flows from the doctor-patient conversation. And it's totally true.

19:52And Abridge is basically creating this net new data. And they have an amazing opportunity to just build a ton of interesting products on top of that data. And I think what Abridge does so effectively is they sell this scribe product that's here today that people love that works, but also paint this really compelling future vision of all the different things that can do down the line. And then that third bucket on quality, I think healthcare is totally a space where quality really matters. Who would want to go to a health system where they said, hey, we're using the cheap AI that's 50 % is good, but 10 % the price, right?

20:28Like healthcare is really a space where quality matters. And I think Abridge has done a really great job of kind of leading the science of what does it mean to have a high quality medical AI product? And how does that basically, how do you evaluate whether these products are hallucinating, whether they're recalling everything that's happened in the visit correctly. These things have major consequences if they're done wrong. And so I think they've done a really great job doing that. And basically, in being early to build these products, getting cutting-edge science, they've really gotten an incredible distribution flywheel going, which has been fascinating to watch.

21:05And I think it's a great example of a really successful AI company. The second company I want to talk about is a legal AI company called Legora. We led the series A here. They recently raised a great series B. What Legora basically does is it's an end-to-end platform, collaboration platform for lawyers, where they can do, they can review all of their kind of documents and work they're doing. They can draft and compose net new legal documents, as well as do research on whatever it is they're digging into. And so I think, again, if you go back to the three-part framework, what makes Logora so interesting is this wedge is really compelling.

21:46There's some things that models do really well. We talked earlier about search and summarization. Bulk document review is a really good fit for models today. Research over a bunch of different cases or documents, as well as drafting and writing. So a great wedge that lawyers find a lot of value in today. And then I think over time, there's clearly a feeling that, hey, legal is this text in, text out space. There's going to be more and more that models can do over time. And then it feels like this is an industry that's fundamentally going to change with AI. And there's a lot more that a company like Lagora can do.

22:18And then similar to a bridge, that third question, quality really does matter. The top companies in the world, the leading private equity firms or tech companies that They use law firms and they want their law firms to be using the highest quality tools, right? And again, whoever builds the best product in this space, I think really has a right to win that category. And it may be an interesting data point for our listeners. One thing I think is fascinating about Legora is they've been a really successful second mover in the space. So Harvey was really the first company in the legal AI space. And Legora started afterwards, but has really caught up and built an incredible product.

22:56And I think what's enabled that is a few different things. One of them is we're still in such early days of what these legal AI products can do. I think today, whatever these products can do is probably 5 % of what they'll be able to do down the line, that there really is an opportunity for a leapfrog as these capabilities continue to improve. And the second thing that was interesting is one of the challenges of coming to market later is sometimes people want to pigeonhole you into, hey, we already have an end-to-end solution. You just do this one thing or that one thing. And it's hard to build an end-to-end product when you do that.

23:28Lagora is a team out of Sweden. And because it was started in the Nordics, actually had a real opportunity to early on in the company's history, work with these leading Nordic law firms and build out an end-to-end product there. And so they were able to build this really effective end-to-end product and then bring it to the rest of the world. And I think as we think about what makes for really effective teams in the AI space, the Lagora team is, you know, about the fastest team you can find in terms of just shipping new things, getting the capability of these models into the hands of their customers, and building just really compelling workflows on top of the models.

24:05And so these are two amazing AI companies we've had the privilege to partner with that really, I think, fit into this overall framework. And so before opening it up to questions, maybe I'll just end with some basic thoughts on some of the things we've learned so far and seen in investing in AI applications. The first is, it's amazing how fast people can build brands and become almost incumbents in the AI application space and just the compounding advantages that come to that. And if you ask someone on the street, name an AI coding company, they'll probably say Cursor or Support Sierra or healthcare or bridge.