"6 Questions Founders Should Be Asking Themselves to Drive Value from Generative AI" Base10 Partners

Hi everyone. All right, hello. Hello. Welcome everybody. Happy Friday and thank you for joining us. So I'm TJ Nahigian and I'm joined by my partner Lucy. And we're going to be chatting with you for the next 30 minutes and then I'll open it up for questions on six things that founders should be asking themselves to drive value from Generative AI. So let's get started.

Hi everyone. I'm Lucy Fonseca. I'm a partner at base 10. Joined TJ and the team about two and a half years ago to lead our growth investing efforts. And I've spent the last 18 months or so with TJ going as deep as we possibly can in generative AI and super excited to share what we have learned with you guys.

All right, and I think we need one more. There we go. Cool. So just to give a little bit of background, Base 10 is a research driven investment firm focusing on companies that are automating the largest sectors of the real economy. We're a team of about 25 folks based in San Francisco. We invest globally. Our team is made up of both investors and former founders, but really we think of ourselves as research first, which you'll hear a little bit more from from Lucy on in a second. And like most venture firms, we've been trying to figure out what is going to happen with Generative AI. And we're going to share at least what we're observing today and why. We think it's a platform shift that both incumbents and upstarts can truly take advantage of to build significant value.

So as TJ mentioned, at our core we are an extremely research driven firm investing at early in growth stage. And what that means is we actually build our own in house models to do deep research on any universe that we're interested. And usually the types of universes we're interested in are mega trends that we think are going to change how people live and work in the next 10 years. And we call that automation for the real economy. Essentially we try to research like a hedge fund, even though we're a VC. So last year we were one of the first VCs to actually share some of our research publicly on what we were seeing in Generative AI. The first piece we published was one that looked at the platform layer and the application layer and essentially set out some questions and some hypotheses that we were starting to toy with as we saw the impact of this technology. We then very quickly followed that up with a piece on essentially the infrastructure tooling or the middleware layer that was helping platforms and applications essentially use each other to be able to build. We learned a bunch from sharing some of that research. And essentially we have a Friday meeting where we go through our research every Friday, and we are always asking ourselves, okay, what have we learned? What did we get wrong last time? And what are we seeing in front of us today? Now, as VCs, our job isn't really to try to predict the future, even though sometimes it seems like that's what we're trying to do. As VCs, we are really just trying to look at what is in front of us and see what's there. And what's eminently clear is that generative AI is a platform shift and it's fundamentally changing a lot about technology today, similar to cloud, social and mobile. And I'm gonna let TJ talk about some of his experiences with some of those platform shifts.

And so I've been fortunate in my career to see a few different platform shifts. Originally cloud, then mobile and social both happening at the same time. We think generative AI is going to be a platform shift. We didn't always think that. And I can chat through why. So before starting base 10, seven years ago, I was a founder. I founded a mobile business called Jobber. It was a mobile job marketplace. And even though we were a mobile business, we really viewed ourselves as a data business. Most of our team was engineers and data scientists, and we would use data basically to make the job application process move much more smoothly on your mobile device. And we ended up using data a lot. We built out our own taxonomies, we used the latest machine learning models to train and run our recommendation engine. But I was always so frustrated. Even though we had an amazing team, we had a great data set, we got very little value out of machine learning and AI. Back in 2013, 2014, it felt really inaccessible. But that all changed. So that all changed when OpenAI came out with GPT3 about two and a half years ago. And for the first time, generative AI actually became accessible, not just to the huge companies and mega businesses out there, a la the Facebooks, the Googles of the world. It actually became usable for developers. And since then, we've seen significant advancements up and down the stack, both from upstarts and incumbents. And so our view is that not every platform shift looks alike, but they all tend to have different, similar characteristics. And so we're going to take a lot of the learnings that we've seen from cloud or from mobile and apply them to Generative AI. One thing that is true, and Lucy's going to talk about this in a second, is to, you know, when you started building in cloud, in the early 2000s, you were a cloud business. Now every business is a cloud business. Java, the business I started, was a mobile business. Now every business is a mobile business. Same thing's going to happen with generative AI.

Yeah. And you might be sitting in the audience thinking, well, AI is not new and we certainly agree with you. And obviously the quickest way to become a skeptic is to try to build a business using AI, which is we've definitely, many of us on the team have, but what we can actually see if we just take a look at the last five years is that something pretty ground shifting is happening. The most powerful models ever made in AI were all formed in the last five years. And you can see some of them on this page here. But even if you take a look at just the pace of innovation, the number of patents filed in 2021 in AI was essentially 30 times the number that was published six years earlier. And if we just project out and keep that pace of innovation, think of what's going to happen in the next five years. We think essentially we're at the very cusp of a golden age in AI. And if we take one of the lessons that we learned from cloud is that cloud really sped up the pace of development by a lot of. If we take that and take a look at what might happen with generative AI, what we expect to see at base 10 is that the speed of both development and deployment is just going to accelerate so dramatically. It's going to make our heads spin in the next few years. And we're really, really excited about what that's going to create for innovation and for opportunities for investment.

The other way we look at this is just by looking at how much venture funding is going into the sector. For those of you founders in the audience, you know the last 18 to 24 months has not been the easiest or most fun time to fundraise. In fact, oftentimes impossible. But that's with one exception. That is if you are an AI business. And we're seeing that happen across different parts of the application and infrastructure layer. And that is already a very large number. I think the number last year is over 4 billion raised. And even, actually, even the first half of last year, this year we're expecting that to be. It's already five times more than it was last year, which is five times more than the year before. Venture funding doesn't necessarily mean things are perfectly going to work, but oftentimes that is a good indicator of where there is a lot of innovation happening.

So obviously as investors, it's really helpful for us to have some framework by which we can understand the universe and who's building where. So. So we're going to walk you guys through the framework that we use at base 10, and then we'll walk through sort of what we've observed in every one of these segments and therefore how that's impacting our own investment thesis. Maybe there's some helpful lessons in there for anyone building in these segments. So essentially there's two big categories. Incumbents and upstarts are both leveraging this technology since it's like electricity. But within those categories we see platforms, infrastructure and applications. So the platforms you can essentially think about as the model layer, that's your Googles and your metas and incumbents and your upstarts are obviously your OpenAI's hugging face coheres, those businesses. At the infrastructure layer, it's anyone essentially allowing you to use these largely generalizable models to essentially create something with a more specific use case. So those are your picks and shovels and we'll have more on this a little bit later. But Nvidia's huge winner here. On the incumbent side and on the upstart side, we've spent time with great businesses like Lambda Pinecone and LangChain might also ring a bell. And finally our last bucket is the applications. And we could probably even split this into both horizontal and vertical applications. We've chosen to keep it simple. But on the incumbent side, you have kind of your notions, gorgeous. You could probably take Google, Meta, Microsoft and run them through every single bucket here. And on the upstart side of applications, names like Jasper, Copy and Harvey should probably be familiar to the audience. So that's how we think of the world. I'm going to pass it over to tj. He's going to take you through how we are thinking about some of the incumbents to start.

Yeah, actually the upstarts. Yeah, starting with upstarts. So at the platform layer we think of this really as the model layer. OpenAI is the clear giant in the room. They've originally started as a fully open source business. We all know there was some drama led to Elon Musk actually no longer working with the business and now are a closed source. But they were really the first ones to come out with scalable large language model. Ironically, they were unable to do that. Because Google created the transformer model and open sourced that a few years prior, which they needed in order to actually create a their original LLMs. The first popular one, GPT3 came out two, two and a half years ago and since then we've seen dramatic acceleration happen there. But it's not just OpenAI who's now raised 10 billion plus dollars, most recent round led by Microsoft. There's a host of others that are innovating here from businesses like hugging face cohere AI21 just announced another very large fundraise last week, Stability AI, Anthropic and others. So we're seeing a ton of innovation happen here. This is creating new LLMs and other models that folks are using to actually get value out of generative AI and moving on. How are they actually doing this? Well, they're building their own tooling, but also they're leveraging third party tooling, as are other upstarts and incumbents. And the infrastructure layer is a really interesting one. So this is tooling that enables you to actually tweak different models, integrate them, scale them in a cost effective way, observe them and do a whole host of others. LangChain is a really popular framework in order to actually integrate and get value out of generative AI. But there's a whole host of different areas here of startups that are actually innovating at the infrastructure layer to enable folks to actually have more control and get more value and cut down costs and as they scale out generative AI for their specific applications.

Now the applications at the upstart layer. So we actually started here in our research and there's a lot of fascinating innovation happening here and the way that we think about it is essentially changing mediums like text and writing, image, video and audio and even code. So we've now gotten to spend time with great businesses like Jasper and Copy and writesonic that are fundamentally changing how text and writing can happen. But we've also met businesses changing how you create music, or essentially recreating and disrupting how films are made or dubbed, or even companies like Replit that are disrupting the way that programmers code or and that's all on the upstart side. So these businesses essentially have scaled incredibly fast and the way that they've done that is they've taken these large foundational models and they've wrapped it in what sometimes feels like a really magical experience for the end user that has allowed them to scale remarkably quickly. What we have seen though in the applications in upstarts is that a lot of them are now facing challenges. And those challenges essentially boil down to one word, and that's retention. Retention has been tough for some of these and there's a couple of reasons why that might be. And well, think about it, for those that are selling to enterprises, often they're selling to experimental budgets and sometimes those budgets just run out. Right. The other reason might be that some are actually selling to more of a prosumer base, but we know that prosumers are actually kind of a very churny, inherently very churny base. And, and that can be really tough as well. And then I think that the last thing is with this wave of innovation, we've actually seen a lot of copycats. So even if you just take a look at the list of founders building in the last YC batch, there's probably like five or six doing the same e commerce customer support effort. And so then it becomes really difficult not only for the founder to be able to tell a very differentiated story, the user also might not be able to see where the differentiation is. So remember that word retention is a challenge. And if you're building here, you gotta be a little bit more strategic about this. So you've kind of heard what we're seeing at the upstart level. We're now going to tell you a little bit about what we're seeing at the incumbent level, which is also very exciting.

And so starting with incumbents, we'll start first really at the platform layer. Again, think of this as the foundational model. And I think it's been really interesting to study how each of these three large incumbents is playing it. They're all actually taking a very different strategy. So Google, I think, as I mentioned before, we'll start there. They actually came out with the transformer model and then eventually open sourced it. That enabled LLMs and other models to be built. Leveraging generative AI. I think they were caught a bit on their heels when they saw how fast OpenAI was able to commercialize their offering. And since then Google has been actually rethinking of themselves again, truly as an AI company, coming out with Bard and trying to innovate in other areas up and down the stack. Meta is another really interesting one to study. They've had incredible technology that they've been leveraging primarily for their ad model, for their recommendation engine across their ads and monetizing that in a very material way over the last seven to eight years, but also their feed model and content model as well. They came out in open source. Llama2 it's actually an unbelievably powerful model and it'll be interesting to see what happens with that. I know a lot of entrepreneurs that we work with are now actually starting to leverage that versus OpenAI's models. And lastly Microsoft, Microsoft I actually think has done a master class in strategy here for us, where they've actually opted really to partner and somewhat outsource innovation. They did that with OpenAI early on, initially gave them a billion dollars, most recently $10 billion, and actually have a lot of, I would say, preferred access as well as rights and control over OpenAI. So that was not free money by any means. And what's interesting is if you look at the market caps of these businesses, the share price of these businesses, they've all done incredibly well this year if you read any of their earnings reports. Two years ago the term AI came up, you know, maybe once, twice in an earnings call on average. Now it's coming up 100 plus times in an earnings call from these businesses. And so anyways, I think this will be fascinating. I imagine business schools are going to be studying the strategy of these three incumbents at the platform layer going forward. So we'll see how it plays out lastly, or sorry, next we'll talk about the infrastructure layer. So how do you actually enable folks to do this? And perfect example here is Nvidia. Nvidia is the, I guess, creator popularizer of GPUs. GPUs necessary to run LLMs and they've seen insane demand for GPUs. Just this last quarter they announced at their earnings, revenue was up over 100% in the corner in the quarter, which is an insane. Nvidia is now a top five company in the world by market cap. And truly propelled by this. It's probably the best pure play incumbent infrastructure company out there. But again, those other companies I mentioned, a lot of the Facebooks, Amazons, Googles of the world also benefit tremendously from this via their cloud offerings as well. So we're seeing a ton of value actually be created and captured by these incumbents at the infrastructure layer.

Yeah, over under Nvidia gets to 700. Finally, we're going to talk a little bit about the incumbents at the application level, which have by no means been sleeping at the wheel. And the reason why Nvidia is benefiting so much isn't just because you have, you know, applications at the upstart level. It's because every single company is essentially developing an AI strategy and no executive essentially wants to be caught without an answer as to what they're doing with generative AI. And you can see that just in the sheer number of instances in which these companies are talking about generative AI to their investors. That's increased over 80% year over year if we look at earnings calls. But something that's actually an even more dramatic shift that we're very excited about is poll from the audience. Anyone here still code? Okay, a few hands. Maybe you're finding yourself a little bit more productive these days. Maybe a lot more productive these days. Yeah. All the engineers we're talking to essentially are infinitely more productive. And that's because GitHub CodePilot is now writing 50% of code. And that's every other line of code to put that in perspective. And that is not only incredible when you think about kind of the pace at which these applications can innovate. If you just think about the access and the democratization of who can actually create, that can get you really excited about what's happening here. I think Even us lonely VCs will probably be able to create these slides with maybe Microsoft Copilot next year, which would be great. But ServiceNow, for example, has also added generative AI capabilities into their products. So you can actually do summarization and text to code in there. And TJ and I spent a bunch of time with our sort of growth stage portfolio companies understanding how they're thinking about generative AI companies like Secure Frame, which you guys have on your lanyard companies like notion. And what we are seeing is that these businesses are actually being absolute ninjas about experimenting really, really quickly with generative AI. And some of the lessons that we've seen is that one, they're often doing it in house. They're just experimenting with open source tools and kind of just building as they go. The second thing we've seen is, you know, in addition to just using their own engineers, is they're not often going out and trying to buy or partner the way you might see in some other waves. Sometimes they're acqui hiring. But that I think is a fundamental lesson if you're trying to sell to some of these businesses, because a lot of them are actually building in house. And it's important to keep that in mind. So now you guys have kind of a view of how we see the landscape. Now we're going to take you into a little bit of our investment thesis and kind of where we're focusing a lot of our time and what we think are some lessons for the folks in the room.

And as we're doing that. One of the things that we're spending time on is understanding where generative AI actually is going to have true product market fit. And one lesson that we've learned in the past when we've observed platform shifts is companies that weren't necessarily built for xyz, platform shift, for cloud, for mobile, et cetera, happen to be perfectly positioned for that. So there's a few early categories that we've been drilling into. These are areas like customer support. At base 10, we work with six or seven businesses in the customer support space. And I can tell you that the expectations from purchasers of that software is incredibly high about what generative AI can do for their end user experience. The reality is a bit far off still, although it's starting to converge. And we're seeing this happen not just in customer support, with businesses like Intercom, which has fin. Some of you guys may use it. They're saying it automates about 50% of chats or Gorgias as well. We're seeing that happen across a whole host of other areas. So in DevOps, when we were getting started at Jabra, the business I started, I used to do qa. It was brutal. You had to do it manually. Now actually you can automate a lot of that using generative AI. We're seeing that happen in legal documents, we're seeing it happen across low and no code applications and getting more value out of that. We're seeing that happen in medical billing, actually reducing again human workers. And we're seeing it happen in data providers. Clearbit's a good example of that. Which essentially just relaunched their entire platform three or four days ago, leveraging generative AI. So Lucy, maybe you can chat through what our original hypothesis was and what it's changed to.

Yeah, when we first started, you know, we were throwing darts at the wall in terms of what would actually drive defensibility, where were, where would the real moats be found? And we kind of had five areas that we thought might be those. So we saw businesses like Jasper really leveraging the community and brand they'd built over many, many years. We saw others like Copy really leaning into the user education. Everyone essentially said that they would have access to proprietary data or that they got the workflows of their customers the best, or maybe they would be able to hyper customize in a way where they would win. But now TJ and I are pretty sure that only three things matter and it's whether you can distribute. So it's distribution, it's your data and it's the workflows. Only these three things matter. And because only these three things matter, what that has meant for us as investors is we actually are developing a thesis that a lot of the value, not all, but a lot of the value in this early platform shift is going to be captured by the incumbents. And naturally it's because they already have huge advantages on distribution, data and workflows over their upstart competitors. So we're going to walk through some case studies where we've seen that and then open to any challenges from the audience.

So one great case study is a business called Casetext. For those of you unfamiliar, it's a legal software platform. They actually sell into mid market and enterprise law firms. If any of you have ever tried to sell to a law firm or interacted with a law firm, you know it can be quite challenging. Getting through legal review at a place like Oreck is no easy task. Casetex has been doing that for the last 11 years and they built actually a pretty strong platform doing so. But the business got up to about 10 million or so of recurring run rate revenue and kind of stalled out over the last few years. So that's about 10 years in. If you're sitting there as the founder of that business, you're thinking, oh man, this is tough. Like what am I going to do? Kind of been flat for a while here. Then Generative AI comes out. They were able to easily plug into initially GPT3, then three and a half, now four, and launch their version of copilot called Co Counsel, which actually will go out and do a lot of the tasks that a lawyer needs to do routinely in their work in terms of surfacing information, summarizing that and then actually creating responses to that. They launched this at the end of last year and that product took off. So they launched it as an add on, went from 0 to 9 million of ARR in seven months, created a ton of interest, both from VCs but also from Strategics. And a few weeks ago they announced they were getting acquired by Thomson Reuters for about $650 million of cash. Thomson Reuters, historically incredibly price sensitive buyer. Right. But they were able to do this because they already had distribution into these law firms and those law firms really wanted to use AI. Turns out if you were an upstart, you actually had a hard time getting through Legal review with these law firms. Right. They also had data as well. And so they were able to leverage that into huge groundswell that reignited their business and drove a bunch of Growth and great outcome for them. Maybe you can talk about our portfolio.

Yeah, I love the case text example, but I love the Notion example even more. In our portfolio, we've gotten now to spend a lot of time with this team. I also use it every day. So I've been able to kind of leverage the benefits of Notion AI in my own workflow, which has been great. But so they launched the first of many upcoming AI features and they've been able to price that in such a way that our guess is it'll probably be 100 million plus ARR business in the near term for them. They're charging essentially an upsell of seven to ten dollars to be able to turn on Notion AI. And there's, I think, some fundamentally, really important lessons here in terms of how Notion has executed. So the first thing is, you know, they turned this feature on to a user base of 30 million people, including their very much growing enterprise practice. So think about just the expansion, expansion opportunity there. And the embedded distribution is just a fundamentally huge advantage. Right. Secondly, when I'm using Notion AI, I'm using it in my workflow. I'm using it in a tool that I use day to day, and it has now access to my data, to my workflows. And as you guys know, these models are incredibly generalizable, but what makes them really, really useful is when you feed them very unique and often private data. Right? Right. So that's a huge advantage that they have access to that. And then finally, now that we've spent a bunch of time talking to Akshay and Ivan and the team and really understanding kind of their philosophy around how they're using generative AI, it's very clear that this one notion AI that you guys maybe have access to now is a single feature. Right? But they think about generative AI as core technology, as electricity, which means that they have now gone back to their original strategy that they, you know, had eight, nine years ago, which was making toolmaking ubiquitous. That's the core mission. And they're thinking about all the ways in which generative AI can now power that, which is how everyone we think should be thinking about generative AI. Let's share one more example and then go from there.

So, lastly, I'll end on another great example, Gorgeous. We thought we'd bring this up because Jason, the founder of Saster, is actually an investor in this company, and we've gotten close to Rahman and the team over time. For those of you unfamiliar with Gorgeous, it is a ticketing platform for customer support focused Just on E Commerce and really started primarily with Shopify. So I have this really constrained use case of how do E commerce merchants actually manage and interact with their, with their end customers. They started the business in 2015. The original thesis was actually to automate that. Turns out, just like me at Jobber, it was pretty hard. They actually couldn't do it. And so they ended up building kind of the workflows and software support system to enable individuals to go in and do that and respond to customers. At the end of last year they ended up launching Automations. Turns out their customers actually don't know what AI is nor do they care about it. But what they do care about is not having to respond to their customers to give a better, a better customer experience to their end customers. So they launched Automations. They launched it as an add on product price at about 50% of the core offering. And initially it started to automate 7% of tickets. It's gotten up to 18% today. They think within the next six to 12 months they'll be able to automate 50% of the back and forth interactions between E commerce merchants and their end customers. Again have significant amounts of data that they can train this on. They already have distribution. They are I think the largest ticketing platform for Shopify and so already have distribution across 10,000 plus merchants that could leverage this. And they were able to actually innovate in a way that makes their business model much more attractive and a higher retention product for their end customers. And so moving on, we'll talk through a little bit of what we've been seeing from the business model perspective. So we're seeing a few things happen. Number one, we're seeing ARPU increase, ACVS increase and LTV go up. Some of that is by launching essentially add on products, those can be priced as subscription fee or it can be priced transactionally, that is per conversation in the example of Gorgias, which they're actually switching to after starting with a subscription model. And we're seeing that price typically at 50 to 100% of the core offering, on average, about half their customers are adopting it. So you get 30% increase in ARPU, an increase in LTV. Secondly, we're seeing drop in costs. So in the case of medical billing or in the case of QA testing for software, you used to have to do those things with humans. You're still going to have to do them with humans, but you need a lot less of them when you can leverage generative AI to do that. And so those businesses are not dropping their prices by the way, they're keeping them the same. And so you actually get a significant margin increase that increases your ltv. Lastly, we're seeing retention increase pretty dramatically across businesses with data distribution and the right use case. And so that's increasing LTV as well. The other thing we've been noticing is some forward thinking companies are pulling it into their onboarding and conversion funnels. Better product offering can actually increase conversion anywhere from 20 to 50%. And so it's another area that we're paying attention to. Overall, we think for the right types of businesses, this could have a really dramatic impact on driving increased revenue growth, cutting costs, increasing profit margins, ultimately creating a lot more enterprise value. And then lastly, returning to our hypothesis, you know, as we think back through cloud, through mobile, what can we learn from them? Well, one thing that's interesting is Amazon actually wasn't built as a cloud business. It wasn't built as a storage company, it was an incumbent E commerce business. They just happened to launch AWS and they ended up capturing way more value, just them in and of themselves than the entire on prem storage market before them. We're also seeing Google capture a ton of value through cloud, even entering much later. Right. We're seeing Microsoft do the same with Azure. Meta was not a mobile company. It wasn't built as a mobile company. They captured more value from mobile than AT&T, Verizon, et cetera combined. Just meta. We think the same thing could happen in generative AI. That doesn't mean there aren't going to be upstarts that win here. There definitely will be, but. But where is most value going to be captured? Our view again is incumbents are going to capture a lot. We're already starting to see that. Right. Like Meta, Google, et cetera have traded up dramatically this year. Nvidia has traded up dramatically. They're capturing a ton of value already. But we're also going to see a lot of upstarts as well. Just have to be really careful where you play. And so Lucy, maybe with that I can pass it over to you in terms of some of our key takeaways we'd like to leave you guys with and then we'll open it up for questions.

Yeah, absolutely. So any upstarts starts building with generative AI in the audience. Okay, so maybe you're thinking we're telling you to stand down. That is not what we're trying to say. What we're saying is actually in order to beat the incumbents at anything, you're going to have to be even more strategic. And so here are. If TJ and I were building an upstart business in using generative AI, here's what we would be asking ourselves. The first question would be what niche, market or vertical can you absolutely dominate? Not rejigger, you know, a slight solution to, but actually dominate in a way that's not completely not adjacent to particular incumbents. The second thing, and I hope that the presentation really nailed kind of this particular point, but retention is absolutely key. And generative AI has actually fundamentally shifted the way that consumers and users actually expect to interact with your product, for example, in a more conversational like manner. And so what can you change about your product to really drive product market fit in a way that tackles all of the retention issues that some of these upstarts are facing? And then finally, and this is core to everyone in the room building around this. But how do you make generative AI core and foundational to your product and not just a feature? And I will say that some of the most interesting generative AI companies that I've met don't even talk about generative AI. They don't even mention it. But it's powering everything in the background. And what they're selling is just a value prop. Obviously, maybe you wouldn't do that because the multiples are so high with generative AI now, but you're building a business for the long term and it's really fundamental to keep this point in mind. So those building in the incumbents. Any incumbents in the room building. Okay, we've got a few lonely folks. So here are the questions we would be asking ourselves if we were incumbents building here. The first is, let's go back to our original mission and the core problem you were solving for your customer. And what are the one, two or three problems you can now solve that you just couldn't before when you were starting because this technology did not exist? And how do you tackle those and throw resources against those? The second thing we want you to do is essentially take out your P and L, take out whatever metrics dashboard you use, and go through every metric and ask yourself, how can generative AI actually help me optimize my cogs, optimize my ltv, optimize my conversion, whatever it might be. And the last question is, we talked a lot about the pace of AI innovation and you guys can see this, it's dramatically fast. So how do you actually experiment and test in a way that meets the pace required for this platform shift? And there may be cultural organizational changes you might need to make in order to be able to do that. All right, so we've asked you guys some questions. Thank you for your time. TJ and I are happy to take questions from you guys.

All right, any questions? Yes.

Hey, that was fantastic. Thank you. I'm asking you about a healthcare vertical. You talked about medical billing and things like.

Like that.

How much sort of deep dive have you done into that and how do you think it changed? Not so much the core healthcare, but ancillary sort of thing.

So that's a great question. And I think, just to repeat the question, it's how much time have we actually spent in and around medical billing? For those of you less familiar? Billing and revenue cycle management is a huge pain point within the US's healthcare system, within every company. And oftentimes understanding what billing codes to bill billing those, properly figuring out if you're actually going to get paid for those is truly a black box. Oftentimes you actually need people to go and do that work. You need people to call into insurance companies. And so it is a very manual, arduous process that a ton of folks work into. We are quite bullish over the long term that a lot of that will end up improving and be automated. Some of that leveraging generative AI. We're still really early in it and so so far we've actually seen upstarts innovating quite a bit there. We've seen this in other areas, though. Oftentimes, though, how that actually gets popularized is through those incumbents. So we are going to start spending more time there as we see things crack. I was actually on with one of our founders last night of a business called vocode. They essentially stitched together generative AI for voice calls and are now automating calls to those insurance companies to check on statuses, understanding what will be actually received in terms of payment. So we're seeing a lot of stuff happen there. I think we still have a long way to go before it actually more broadly hits the the industry, though. All right, next question.

Hey, thank you. Wonderful session. So I had a couple of questions. One on the incumbent list, I didn't see a big call out about aws. I know they've also been quite active in the space, so I was kind of surprised that you had not called it out.

Yeah, no, absolutely. And I think that we mentioned that all of kind of a lot of the incumbents that have done the cloud players, essentially you could run them through the entire incumbent list from first category to last category, they've been massive and obviously have benefited and continue to benefit tremendously from the switch. There's only just so many times we can say aws.

Gotcha, gotcha. The second question I had is, so I know like, basedin focuses on investing in companies that are addressing automation for.

The real world, right?

Like broadly speaking. And I was interested to see if you folks would ever venture into kind of the edtech space.

So I don't have a clear answer to that, but I will say that in our first research piece that we wrote about kind of the application layer, one of the obviously big questions is how do you actually sort of govern these models over time? You know, and one of the hypotheses we had is, you know, in terms of which models win is does the most ethical model win? Right. At some point, just given the huge ethical concerns around where AI is going, is there a kind of differential aspect and a winning aspect to just building the most ethical model? We still don't have an answer to that. And TJ and I are kind of excited to maybe dig into the space, but it's not a place that we've invested in yet.

Okay, and your main concern around edtech is the concerns around ethics. Is that the summary of your response?

Oh, sorry, did you ask about EdTech?

EdTech, yeah.

Oh, EdTech, yeah.

So we actually, we have started to look into it and around some edtech spaces. One really cool application we've seen is a business called Speak. A friend of mine started this business and they've been working on it for six years. They have a mobile application that enables you to learn a new language mainly by talking their hypothesis. You actually learn a lot more by talking than by writing it down or doing. And they're seeing really strong early results. But the amount of progress they were able to make in the first five years is very different to the amount of progress they've been able to make in the last year and a half because of how quickly these new models are accelerating. Both large language models, but understanding speech to text synthesis and etc. And so it's actually a business that OpenAI's venture fund has actually invested pretty heavily into. Anyways, we're quite excited to see how that, how that goes. I think there's going to be a bunch of other applications in EdTech as well, so we'll see.

Okay. Very quickly, we've spent time with another kind of early edtech business that's fundamentally reimagining how content is created for kids in a very. So imagine if you could hyper customize content for every child using generative AI. That's that. Those kinds of questions we get really excited about. And EdTech is EdTech and ethics are places we spend time in.

Yeah, yeah, sorry and sorry to take more of your time but the last question is what would be the best way to reach out if you. If I had a concept or an idea around edtech which is why I was asking that question.

Yeah. Send A note to partnersase10vc and we look at every email that comes in and respond.

Okay, do you focus only on seed or.

We're investors from the earliest stages of a private company all the way to pre ipo.

So maybe let's take a question from this side.

Thank you. So insightful presentation. My question was about some of those examples you showed where people were getting a price per premium by building gen AI into their product. And I'm wondering how sustainable do you think that is over the long term and maybe what separates companies that will be able to sustain that price premium versus others where it might get traded away?

Really great question. We think a lot about this. I think we're still really early and what we saw was that for upstarts at the application layer, they came out with a pricing model and a go to market model that worked really well for a while. I would say like a great example of this was in the copywriting space. So using text to either summarize or create things like poems, essays, documents, et cetera. And so there were a handful of companies that nailed both the product market fit and repeatable go to market model. Doing this you can think of like the Jaspers, the copy AIs, the writesonics got very large very quickly. Some of them attracted large amounts of activity venture funding. And then in November last year OpenAI came out with ChatGPT. You're seeing businesses like Notion just embed AI right into their product Office. Microsoft Office is coming out with Copilot where this is just in word, right? And what we've seen is those businesses have struggled and the reason why is it actually the incumbents have distribution data and so they're able to capture more and more of that share. I think the number one question there though is retention. And then we'll see over time if there are pricing kind of discerns from end consumers or end businesses based on that. But I actually think consumers tend to stick to workflows that they know where they get the most value out of it. So we'll see. But the number one thing we're Looking at today is retention as Lucy mentioned, to understand how sustainable this is going to be.

Okay, we have time for one more question.

For your overview. Really deep pragmatical one, we as a B2B SaaS need to do a lot of the technical support and we are looking for the real optimization of the let's say and replacement tier one support with say AI. And we know we need to do it in like multi language environment. Like at least 7, 10 languages should be covered. How that's really realistic right now. Like it's just ideas need to wait one more year or like real solutions exist.

Yeah, so great question. Huge demand for folks in and around customer support. Yes, we are huge believers that everything or all a lot of customer support and engagement solutions will be touched and solved via generative AI. It's still very early in what the reality is of that, but the pace of innovation is truly mind boggling. So there are I know six different companies, early stage companies that are doing just technical support, automating that based on your docs. I don't know any of them that are doing it in multi language yet as an example but, but I think actually going from one language, call it English to multiple others is a very solvable problem that they will eventually get to. It's just not top priority at the moment. So anyways, there are a handful of companies doing that and I think there's going to be a ton of innovation there over the next 12 to 24 months.

I think it's just a convergence because we've seen for example since we do real economy, we've seen businesses do multi language, for example on factory floors to automate certain processes. And so for us I think taking that technology and applying it to customer support is probably around the corner and we just need, you know, some hungry young upstarts to go do that. So thank you guys so much, really appreciate you all coming and TJ and I will be out here if you guys have any more questions.

All right, thanks. All right.