LIVE from SaaStr Annual 2024- Workshop- Wednesday

Please welcome to the stage Carl Alomar.

Jason M. Lemkin, Managing Partner at M13. I was about to say hold the applause, but then you guys actually clapped, so it made it a little bit more comfortable. But hello everyone, welcome to SASTA. I appreciate having the opportunity to come and speak to you all. This is an interesting talk, but I'm excited to share with you guys 5 key steps to building the best founders get right. So this is— everyone get out your pens and paper. This is the secret sauce. You do this exactly as I say, and you're going to have a unicorn in a couple of years. I promise you. That's definitely not true, but don't quote me on that. So yeah, I wanted to kind of try and frame down just kind of a little bit about my background. I'm a multiple-time founder. I'm now an investor. Have taken 2 M&A exits, 1 IPO exit. So I've had like a lot of opportunity to see how businesses grow and what are kind of the key components that really allow business to be successful. And now that I invest at M13, I see this every day. We meet founders all the time. We kind of understand the kind of categories of what companies need to look like, how we want to assess their potential for success. So One really important note is it's not a luck thing. I mean, luck always plays a part in business and building companies, but it's not just about a lucky draw. There is a methodical way to think about, are you building the right business? Are you building it the right way? Are you thinking about the right things, the right core things that actually build the architecture and the framework of the business that you want to put out there? So I'm gonna present this, uh, we've kind of broken it down to kind of 5 key components, and it's all very rudimentary stuff, to be honest with you, but it's like the little details that actually matter, and the thinking about the little components of where you are in your journey and how you think about the goals and the flags you put in the ground and, and how you're actually going to achieve, um, that next stage of development, that next stage of growth. So I want to start off with category number 1, and then I'll go through one by one and kind of talk about those. We're going to try and save maybe 5 minutes at the end just to allow you guys to ask a question or two if you have one. Really all starts with what are you building? You know, a lot of founders here ideating, thinking about what's the market needs, what's the opportunity, what can I build? And I tried to build a little bit of a quadrant. I had to put a quadrant in somewhere, I guess, of how to think about, you know, where are you landing, like really assess your idea. And one is around the potential of the category, one axis, and the other axis is around the amount of competition in the category. So just to get rid of the easy ones first, if there's no potential and there's no competition, then clearly it's probably a very bad idea. So just waste of time. Don't bother doing it. There is a low potential, high competition category. The question is, why would anybody ever build a business in that? But you see that a lot in, you know, pre-baked returns like commodity businesses. You know, the margins are pretty much predefined. These are not venture businesses, but lifestyle businesses, family businesses, that does happen. The interesting part is a lot of people land in the top right— exciting category but highly competitive. And the reality is, anyone that has a great idea in today's today's world, there are 10 other people trying to build that same great idea. So the next 4 items that we talk about here are really going to be the drivers of why you can stand out, how you differentiate, why you can win. But most people will land in this highly competitive but very exciting category. The greenfield, the dream that everybody finds is how do I find, you know, a category that has That's high potential, low competition. That's true innovation. How do I go figure out where I need to be, where the puck is going, that there is no one? By the time people figure out that this is a real opportunity, it's going to move up to the top right. But if you can find it, grab it, because those are rare and far between, especially in today's world where the cost of starting a business has just become so low and so many people are striving to be the entrepreneurs of the future. So really, most likely you're going to find yourself an exciting but highly competitive category. And that's a good place to be, good place to start when you think about what is my business, where does it fit in the market, what can I do with it? Next thing you really think about is how do I build the team? I've broken this up into kind of the pre-Series A and the post-Series A. I think the team changes significantly. A lot of founders in the very early days realize— they might realize that their early team needs to be much more ad hoc, much more generalists, much more broad, action-oriented people. Some early founders make the mistake of hiring true specialists before they're even funded. True specialists have a hard time navigating broad, uh, sets of requirements. When you are a 4 or 5 person founding team, you need to do everything. So you really need to surround yourself with people that have the ability to kind of stretch across different categories of expertise and, and help just get things done, executing, get things done. Um, also you want to see complementing skills. Like, if you're a great salesperson as a founder, you probably want to partner with a great technologist. If you're a great product person, you might want to partner with a great growth person. You, you need to find people that complement your skills, not people that double up on what you already know. That's just going to create conflict. The interesting thing is once you actually get funded, once you get post-Series A— so not in the early pre-seed stage, but once you get post-Series A— that whole dynamic changes. And a lot of people as early leaders really don't transition well to structured companies. And so you have to recognize, like, are you built for that early ad hoc stage, or do you have the ability to transition into a more structured structured organization. We have true specialism in key areas. Most of your early founders, as you get to Series A, Series B, are going to identify the area that they're specifically skilled at and probably take the leadership role in that particular category. And so it changes the organization. You become much more focused skill sets. You're optimizing performance. You're, you're really building a much more experienced team. And so again, from the investor's seat, As we look at businesses, whether we look at them at the seed stage or the Series A stage or the Series B stage, we're assessing the team and figuring out what's the fit for that time in that moment. And positioning yourself with the perfect team at the perfect time of your business honestly is a big key. People are the biggest key to the success of the business ultimately. So you have your category, you have your team, you're ready to go. What are you gonna build? How do you actually build a product? So we've broken the product kind of thinking into 3 stages of thinking: pre-seed, seed, and Series A stage. The pre-seed stage is really very much a lean build. I don't know how many of you have read Eric Ries's book The Lean Startup, but that— it's been around probably 2 decades at this point. Incredible book, great education on how to actually navigate and figure out if a product makes sense for the market before you spend money, before you build anything? What is the leanest process you can go through to navigate and figure out if you've got a real business? This is pre-seed because at pre-seed you don't have the capital to build a big SaaS platform with multiple engineers. You need to test whether there's aptitude for this product in the market. So you're identifying your ICP, who's your target customer, your early adopter, that early, early buyer. You're creating a lean, like, fake it till you make it build. A lot of it is kind of handheld, and you're like turning the wheels of the machine. You're delivering it to that customer, and you're validating that they actually want it and it's something they would actually pay for. And so it's this whole prototyping process. Once you get that in place, that begins to really prepare you to go to seed investors and say, hey, I have something that could really work here. I need to build out, you know, a real business. At the seed stage though, the expectation is that you actually build an MVP. So either you're building it right at— when you get the seed money, or you build it with pre-seed money so you hit the seed stage with an MVP of your product. So what's minimum viable product? What's the minimum product you can build that the market will accept? You need to put it out there, you need to launch it, you need to start the process of early iteration, and and you need to demonstrate some early adoption. So what's your low-hanging fruit? Who are your early adopters? What's that core target market that is going to be your, your superfans and your evangelists in time to come? And that's really the build and validate MVP stage. And then as you get to Series A, again, expectations rise. So from a Series A standpoint, now you have to have true product-market fit. You've demonstrated that you're beyond the MVP and you figured out all the nuances in your product that the market really wants, the market really engages with. We're measuring you on engagement and not just adoption, but also engagement, retention, all the components of proving that the product is truly resonating with your market. You also, at a Series A and even moving to Series B, have begun to really validate the go-to-market strategy. And we're going to talk about that in a moment. But you, you've built a product now that has an ability to get into market and you have the ability, you know how to market it, you know how to position it. And then you expand that product scope. You revalidated the product-market fit at scale. So is this going to work as I scale it? Do I still have the product-market fit? So, and you're also validating, sorry, the economics at scale. So a lot of times in the early days of the business, the economics, the margins are not as great as they could be. Obviously with SaaS you have high margins, so it's maybe a little different if you're incorporating AI into it. You might have lower margins. So how does this work at scale? Does, does infrastructure get ahead of me? Does my cost get ahead of me? Or can I demonstrate that I can build this thing at scale at a manageable economic model? So this is kind of the thinking of how we would look at SaaS companies as they build out their product from stage to stage and really validating that they're at the right stage of the product for the, for the raise that they're, that they're going for. Now that you have the product thought through, then you think about your, your growth engine. There's 5 categories. So this is on 2 slides, 5 different categories of areas of go-to-market that you can think about as you frame out your strategy, your plan, So I've just highlighted them here with some bullet points, but obviously you can dive deep into any one of these and get into, you know, real strategic discussion about how that would work for you. Content has, has played a major role. So I was— I ran DigitalOcean for a number of years before I went into investing, and one of the biggest drivers of growth for us at DigitalOcean was our content play. We started that right at the pre-seed stage and then up through the IPO. You know, we were probably pulling 30, 40% of our acquisition purely from the content that we had built over that period of time. Content is a long play. You start building content early, you build evergreen content, which is not just, you know, in the moment. It's content, educational content that people will learn and grow from for years to come. Come, and that content will build on itself and pay dividends for years to come. So a lot of people in the early days want to get to immediate satisfaction, but actually laying the groundwork and the foundations for something that provides longer-term returns is really, really valuable for any company, especially a SaaS company. So thinking about content as part of your overall strategy, I think, is a— I've had great success with it, and we see companies in our portfolio with great success in it, and it's, it's really, really valuable component of the overall model. Performance marketing— I think everybody in SaaS knows performance marketing. This is a mathematical game. Figure out your cost, your customer acquisition cost, figure out your lifetime value of a customer, figure out the ratios, make sure that you are healthy, you know, north of 3 to 1 on that, definitely sub-20 12-month CAC payback. Like you want to be in a really healthy place from a standpoint. But this is all about testing, iterating, test different performance marketing channels and identify who you're targeting, what your message says, how you convert. But that purely is a purely mathematical game. And there are real experts that kind of know how to do that day in, day out. Inside sales is self-serve. So inside sales is how a lot of SaaS companies begin. They want to go out B2B sales, they want to go basically acquire customers. Inside sales is great, it's a great way to validate the market, it's a great way to get to know your customer set and get feedback. However, inside sales— the one risk of inside sales is the scalability of it is limited. So if you're purely an inside sales organization, as an investor, the way we look at it is, okay, what's the cost structure you're gonna have in your sales organization when you have thousands of customers or tens of thousands of customers. It can get very, very heavy and it can be very, very heavy to manage and run compensation structures for salespeople can be very difficult. So often SaaS is unique in the sense that you can actually build a pretty good self-serve model in most SaaS businesses, not all SaaS businesses, but most. So going back to thinking about the product implementation and how product can drive some of your growth strategy, How do you think about inside sales as a component of, of the growth model, but self-serve as a top-of-funnel solution that brings people in, whether it's a freemium or premium kind of construct or whether it's just a land-and-expand construct? But the idea is how do you actually build that top-of-funnel more organically and then, you know, drive growth that way rather than doing everything purely through salespeople? So freemium also— the, the concept of self-serve and freemium also lends itself to this idea of land and expand. So a lot of SaaS businesses have a model around landing a customer. What's the lowest barrier to entry to get a customer up and running? And then what's your opportunity to expand that customer? How do you— can you broaden through product offerings or through growth, seat growth, or consumption growth? How do you expand that offering to be a wider ticket, wider ACV on that customer? And so land and expand is a very interesting strategy. We see a lot of companies with that. If, if a SaaS company is driving a land and expand strategy, we're going to look at NRR, which is essentially your, your, your net, your revenue retention, your net revenue retention. We want to see that well above 100%. We want to see you're going to see your customers actively spending more cohort by cohort as they stay with you over a longer period of time. So again, it's a great strategy, but if you want to implement that strategy, you have to demonstrate it with statistics, with, you know, with the stats that feed back, you know, the data that feeds back validation that that's actually working. And then finally, partner channels. These again are a little bit more long-term. Partner channels are delicate in the sense that it's great to leverage a third-party distribution channel, but if you don't really know how that channel works and if you don't really motivate those salespeople in that channel, then it can actually be more work than it's worth. So it's an incredibly powerful tool, and a lot of SaaS companies have built incredible revenue streams from, from partner channels. But make sure that your partners are well aligned both the company itself as well as the salespeople, they're well aligned, incentivized, motivated to actually sell your product, incorporate your product into that process. And so those are just 5 general buckets. Obviously it's a kind of a create-your-own-adventure type, type component of the build. And so you have to really think about how you're going to put all those pieces together to ultimately build the best go-to-market solution out there. The final piece is fundraising, and we've kind of touched upon this already in the different stages of fundraising, but it's just a quick summary of kind of depending where you are on your journey, how do you think about the process of fundraising? What do investors want to see? Pre-seed is all about the idea, the team, you know, the the, you know, early kind of design partners perhaps that you have that helping you build out your product. There's limited expectation on actual data or stats. It's much more about the vision and the dream and the team. Team is very, very important. And as you'll see, I repeat many times, interest from other investors is always good. So no matter where you're investing or what point you're taking on investment rather. You want to always be talking to the market, always developing interest, always building energy around your brand that people want to invest in. When you have interest around your brand, you will consistently close your rounds of funding more effectively, more efficiently. On the Series A side, now we're looking at a real product. We want something that actually works in market, something that actually is demonstrating a fit. We have a little bit of the early signs of go-to-market motion. You understand the buckets you're working in, you understand like the methodology and the thinking about how you want to go to market, perhaps even have pretty good ratios on CAC to LTV and things like that. And, and you validated not only your ICP or target customer, you validated the size of that market. How big is that target customer? And does that actually sound like a venture, venture-sized business? Again, VC interest is always a help. And then we go to growth. And growth is where investors really want to see you as a machine. This is now a machine that just needs fuel, and, and the accelerator pedal is ready to go. So we know the product works, we know that it scales, we know that the economic scale— we know that you have a good go-to-market strategy, and we know that you know exactly where you need to funnel capital to either expand expand product opportunities to an existing customer base, or expand your go-to-market strategy in a predetermined formula of what that's going to return to the business. So that's the growth opportunity. That's what growth investors are looking for. One interesting stat is Series A companies have a 35, uh, likelihood of failure before they get Series B. Maybe in a more positive note, 65% chance of getting to Series B. Once you get to growth failure rate goes down to about 1%. Now, it doesn't mean you're gonna be a unicorn, but it does mean that there is an exit horizon much more likely in your future. So the transition between each of these 3 stages of growth is very, very meaningful to your ability to actually get an outcome in the business you're building. So I don't think any— and I'll do this super quick— but I don't think any presentation is going to be complete at SaaS without something about AI in it. So I had to put it in there. I apologize, but I don't think I have a choice. So I just want to frame out 3 ways that we see AI. Pretty much every business that we see now has some component of AI in it. We put it in 3 categories. There's AI native, which is companies that are built from the ground up on AI technology. There's AI powered, which is companies that are not core. They might be process or might be you know, typical SaaS businesses, but there's components of their business that are purely powered usually by third-party AI solutions, but they're integrated into business. And then we have interesting companies that have built SaaS companies and maybe have existing SaaS companies, but now are beginning to employ AI feature sets within an existing business. So we call that AI conversion. For us, through 3 quick examples, 3 companies we've invested in. AI-native Zenlytic, it's a business intelligence tool built from the ground up with an AI agent that really drives and runs not only the ingestion of data to create a data environment, a data warehouse, but also the ability to provide that data back to the business in, in very intelligent ways, built from the ground up on an AI platform and on a, on a language model platform. And they are— Polymorphic is a CRM-type business. It's for local governments. It provides customer support, customer success services for local governments. So it's a govtech business, but they've put in this— they built essentially a CRM, which is more of a typical SaaS offering, but they built this AI layer into it, which allows it to be more efficient, more operative, etc., etc. And then Prepared Another one of our companies is a public safety disruptor, like 911 disruptor, doing incredibly well. They built companies, had incredible traction in the market. We first invested in it, First Round is invested in it, Andreessen Horowitz is invested in it. And they've identified that as they build their offering, they're now able to actually convert some components of what they're doing into an AI component, which again is increasing the value of the offering to their target audience and also expanding the opportunity for them to grow and build and be relevant in tomorrow's marketplace. So with that being said, that is the quick summary. It's a quick, you know, run-through. I hope it helps, but we do have a couple of minutes left and I wanted to give you guys an opportunity to throw out any questions, but just please be nice. I can't take stressful questions too well, so. But no, if anyone has any questions, please, you know, throw your arm up and we can quickly jump into a couple of audience questions. Go ahead. There's a microphone here, I believe, if you want to come up and ask.

[FOREIGN LANGUAGE] Hi, I'm Abhesh. Just on your last slide, it yourself.

So these are the companies that you have invested in. Are there any specific companies, sectors, or types, whether native or powered, that you are watching closely now?

Of course, not without giving away any secret sauce of who you're going to invest in, but what kind of stuff in AI is exciting to you now? Yeah, well, we invest in 4 core categories. We invest in the future of money, the future of health, the future of work, and the future of commerce. SaaS plays part in all of those. And so, you know, for example, with commerce, like, we think about commerce enablement tools, um, or with workplace improvement, we think about productivity tools like Zenlytic. And so we, we think a lot about how AI plays a role to increase efficiencies and productivity in each one of those categories. We also look at a little bit of the infrastructure layer of what's underpinning AI. So whether it's you know, infrastructure like hardware infrastructure systems and distributed infrastructure, or whether it's actual tooling and platforms that actually make AI builds more effective. So those are basically a lot of the areas that we think about with AI.

Thanks.

Hi, you were mentioning a book on lean build.

I couldn't catch the name.

Could you just— The Lean Startup by Eric Ries. And I think we probably have time for one more question. Good. So if you're looking at pre-seed companies, how would you look at a company that's using fractional employees from other companies to help build their— I think it— I think at pre-seed, anything goes. I think you need your core founding team. That's what you're going to be judged on. But the reality is pre- pre-seed companies can't afford to build a big engineering team, so you beg, borrow, steal. Honestly, for me, if you're the type of person that can innovate and figure out how to get something done without the resources to do it, then that's the type of founder I love to, I love to work with. So in that regard, yeah, anything goes. Make it happen. That's the secret. So I think that's it. I probably have time for one more question if anybody has one. Otherwise, Okay, looks like he beat you to the punch. Generally speaking, in the different phases of investment, what kind of revenue numbers you're looking for in those sectors? Because people keep suggesting to keep going up. Yeah, you know, it changes with time because the markets change. In an AI world, we're seeing people establish pretty revenue relatively quickly, but then because of the opportunity. Actually, people are investing Series A at a lower revenue target number. I'd say pre-seed, you don't really need meaningful revenue. Um, at a seed stage, a lot of people want to see early signs of revenue. It's really Series A where revenue becomes much more meaningful. Uh, it depends on the type of business. If it's an AI-powered business, I think if you're in the range of $1 million of ARR, you can get a Series A. If you're between $5 and 10, you can probably get to a Series B, but it's the trajectory. What's often, I think, a misnomer is people think, hey, tell me the stats I need and then I can go raise money. It's not about the stats at the moment. It's about the trajectory of the business.

Sure.

And so you've got to just demonstrate this ability to repeat, repeat growth and you've got to show a trajectory and a pipeline of where it's going. And that's what people will invest in. But I think from a Series A standpoint where you really want to validate that you really have a market.

Yeah.

So you probably got it. You want to be in and around or north of a million ARR and you should be in a good place to kind of have those conversations. Awesome. Thank you. All right. Well, thank you so much, everybody. I appreciate your time and enjoy your conference. To our speakers and fellow attendees, we ask that you please silence all devices.

Please welcome to the stage Bella Liu, CEO and co-founder at Orbee AI, May Habib, CEO and co-founder at Writer, Dan Saroker, CEO and co-founder at Limitless, and Vanessa Larko, partner at NEA.

It's a very high—

wow, clap!

People are clapping, this is crazy.

You can switch me around, Dan.

Hi everyone, thank you so much for coming to our chat today. I'm really excited to have our panelists that I've known for years, and it's a very exciting topic. So I'll dive right in, quick intros. I'm Vanessa Larko, I'm a partner at Enyea, I'm an investor in early-stage startups. I have May Habib here, she is co-founder and CEO of Ryder. Dan, co-founder and CEO of founder and CEO of Limitless AI, and Bella Liu, CEO and co-founder at Orbi AI. So I'm going to quickly run through the state of generative AI. These are Q2 numbers. I grabbed them from CB Insights, which I scour all the time. As you can imagine, deal count has gone up. So investment in AI and generative AI continues to increase quarter over quarter with no signs of slowing down. The average deal size and median deal size are up as well. So companies are raising quite a bit of capital. We call them their war chests are getting replenished frequently. And the interest in generative AI is holding strong. So this is from Google Trends search terms, right? There was a huge increase in generative AI and it's holding. It's not like it's a fad that has gone past us. So What this is telling us is that the space is continuing to grow more and more crowded every day, and it has no signs of slowing down. And venture capital has no signs of slowing down investments in this category either. And as some customers are trying to figure out which of these solutions are right for them, what should they buy, what should they try, and there's more startups than ever, so it's gotten rather noisy, rather crowded. I think we're all seeing that, we're all feeling that. So that's why we think it's more important than ever to figure out how to cut through the noise and get the solution to your customer that they need. So that's why I have this incredible panel, um, and they'll go through their products and what they do. But the, the most interesting thing about the people on the stage is that they've taken very different approaches to reaching their customer and how they go to market, and they have very strong opinions on this topic, so I'm excited to jump right in. So Mei, will you tell us a little bit about Writer?

Yeah, absolutely. So Writer is a full-stack generative AI company, and we combine LLMs with the microservices that enterprises need to build really bespoke custom AI apps, agents, workflows. We're based here in San Francisco. We're almost 300 people. People across SF, New York, and London.

Yeah. And Dan, tell us a little bit about Limitless AI. You're wearing it too.

I am, yeah. So Limitless is a personalized AI powered by everything you've seen, said, or heard. It's a web app, a Mac app, a Windows app, and a wearable. So this is the first time out in public I'm wearing the Limitless pendant. And it captures what you say and what you hear and makes it useful. So for example, if I just say naturally through my conversation, I'm so grateful to be be here on this panel later today, and my to-do on my app, it'll say, to-do, send Vanessa a thank you for being so grateful for being on this panel. So it takes things that you were otherwise going to do naturally. Anytime you imagine taking out a piece of paper and a pen, instead of doing that, you can now just speak. And that ability to sort of augment human intelligence with artificial intelligence is really what we're trying to focus on. We don't want to replace humans. We want to augment humans. And we think wearing a pendant and using, uh, the apps will, uh, give you that feeling of having a superpower.

Yeah.

And then, uh, tell us a little bit about Orbi.

Yes. So at Orbi, we want to use AI to basically automate all the processes that you don't want to do, and we can enable enterprise efficiencies at scale. And so behind the scenes, we use— we build our own foundation model. That can essentially understand all the actions you do on the screen, like your clicks, your type. You go to a website, you make a decision, and then we generate the action that can automate the tasks. So, um, um, basically like we sell to large enterprise type of customers, and the initial use cases are mostly in finance and accounting. As an example, uh, we help one of the largest Fortune 500 customers to automate their finance payment reconciliation processes, saving them millions of dollars and reducing critical errors. So essentially, at Orbi, we want to automate all the mundane and repetitive work that you don't want to do so you can focus on enjoying your work and creating more value.

It's a common trend across the three of you, but in very different ways. So I want to talk through the ideal customer profile, because with technology that's so powerful and so flexible that can address so many use cases, you know, where did you start? Who was your first ideal customer, and how did you determine who that should be? May, who's ready?

Sure. It's definitely changed for us over time. Our ideal customer today is a Global 2000 company, and our buyer is the CIO. And that was not the case even 18 months ago. We transitioned from product to platform, and we transitioned then from a line of business VP or even senior director being our buyer to a C-suite leader buying a platform for use across multiple departments. So it's definitely evolved, and it's evolved as our platform and capabilities have widened and deepened. And I would say in generative AI, it's also matched the moment. The generative AI budgets in the Global 2000 are now squarely in the CIO's org, even when they serve other departments.

Yep.

Dan, where did— I mean, I think that's been also an interesting exercise.

Yeah, for us. You know, I've had a lot of failed startups and they all have one theme, which is trying to build for somebody else. I'm just not good at building products that I wouldn't want to use every day. So the original thesis for what we're building at Limitless started with, what's the product I wish we had? And it goes all the way back to in my 20s, I started to lose my hearing. And when I turned 30, I tried a hearing aid. It was magical. It felt like gaining a superpower. To lose a sense and gain it back again is this magical feeling. And ever since then, we've been on this hunt for ways that technology can augment human capabilities and give us that same feeling. So it starts with that, which is like, that is the core problem. And the people for whom that is the most, that resonates the most, we describe as context switchers. They are more pirates than navy. They're usually in back-to-back meetings. They seek forgiveness, not permission. And so our product, while it's adopted by a lot of people who are busy and they're probably expensing it through their company, it's actually much more bottoms up, much more of an individual user at a company, 'cause what we do is weird. It's different. It's new. And you kind of need to be the kind of person who says, "Boy, it's more important to me to be productive and at home for dinner with my kids at 5 o'clock every night, and if a tool like Appendant can help me do that, then I'm willing to sort of overcome the newness and maybe the social awkwardness that might come from that kind of a product." So that's our ideal customer profile. It's more pirates than Navy and people who are really busy.

Yep. And Bella, yours was also like you decided to start and focus on the finance use case, but it could have been any use case.

Yeah, so exactly. And it's like very interesting. This is applicable really to everyone, but then we quickly just like find this very natural pull from very large enterprise customers because they have the most pain with is efficiency, essentially. So we designed to focus on large enterprise customers, and then quickly we discovered that, okay, finance and accounting is really the sweet spot. They have like 60% of the processes that are repetitive and transactional that should be automated, and also they're very complex and they cannot be easily automated with existing rules-based type of technologies. Have to use AI.

AI.

And also one interesting discovery is that finance and accounting teams, they seem to, you know, be a little bit more, you know, relaxed in terms of like budget constraints in a way. So we decided to double down on that segment.

Turns out when you own the budget, you can be a little more flexible with the budget. Surprising. So when it comes to selling the product, so we talked through your buyer, how do you sell to them, right? How do you get to them? How do you pitch to them? What does that look like? And is it different, you think, with generative AI, or is it the same playbook for that buyer no matter what you are?

It's a whole, like, other level that you got to clear. It is so noisy.

Harder.

Way harder because it's everything everywhere all at once. If you are selling AI to the CIO right now, get in line, right? You're the 30th pitch that week. And so the only way to stand out is to nail their exact problem that they are struggling with today. So take, um, a part of our product called Knowledge Graph. This is graph-based drag that connects into an assistant that, um, the CIO might want HR to be able to build for employees. The ability for us to go in and actually literally by like minute 3, show them the types of questions that had been failing inside of their DIY application and the ones we're getting right. Like you need that light bulb to go off in literally 3 minutes or they're gonna be checking their phone or doing something else. And so for us, we call it demo domination. We literally have a full internal enablement play on this. And if you you are pitching a, you know, Fortune 150 insurance company that does property and casualty, like you are literally showing a demo for how a, like a claim adjudicator's day would change with Ryder. It is so specific. Our reps will call people who used to work in ops at a call center to pitch to an SVP of ops at a call center. So it's just so specific. The solution that you are selling, they need to leave that call and say, I want this right now. And with generative AI, it's software that builds software essentially. And so you just can't go in there with like, I've got, you know, a cool widget or I've got like an enterprise generative AI platform. Like there are 30 companies saying that.

That's really helpful.

Did you say Demo Domination? Yeah, I love that.

Like that.

Yeah.

We give everybody wristbands, headbands, like it's a whole thing.

Yeah, I totally agree with that. And I think with generative AI, it is really important to show, not tell. A good example of one thing that we did just yesterday, actually, we posted a video of me wearing the pendant. I'm speaking and I happen to have an identical twin brother. And so he stood next to me and he started speaking and it was able to tell the difference between our voices.

Awesome.

And that might seem like, oh, cool. Tech, why does that matter? Well, understanding who says what is really important. It's the context of everything you've said and heard that really makes a big difference. You know, when I say I'm gonna do something, that's a to-do list. When somebody else says that they're gonna do something, that's just interesting. And so I think being able to do things like that, like speaker attribution, be able to beat the twin test— those are the kinds of demos and magical moments that I think really open people's imagination. I've learned that, you know, when you're close to the technology, it's really hard to remember that most of the world doesn't know what's possible. Like, they don't know what to type into an empty chat box when you can chat with your, your large language model. And so you really need to walk them to, to the oasis in the desert because there's a lot of hype out there. And unless you can actually show as opposed to tell, I think it's hard to cut through.

Yeah, I think I agree with like for sure, like demo is magic. One of the hurdles for us in the early days is also like every customer wants to do a POC. That's like 100% basically. But then what we found is that as you like start to have more success cases, you have more references, and then that starts to get much better. So I think my recommendation is also try to get references early on, and then that will significantly help you later.

Yeah, it sounds like the specificity is super important. That's like the trend I'm seeing where you're like, I need the ops person to sell to the ops person because they speak the language. And in Bella's case, it's like, I need my reference that is VP of finance to talk to this VP of finance and speak their language about how this actually helps. But it's the specificity I think that's key. And the other really interesting thing here is Ryder and Orbi follow a tops-down sales model where Limitless follows a bottoms-up sales model. And I hear from different people all the time, like, top-down is way easier for generative AI. And then I hear from others like, bottoms-up is way easier with generative AI. And so I guess I pose it to the panel, like, is the grass greener on the other side, or are you really glad you're top-down, or you're really glad you're bottom-up? It's a sandwich.

It's a sandwich. Right now, the terrain changes so quickly, most people don't feel empowered empowered to make decisions bottoms up in the enterprise on what to use or who to bring in. So it is a huge relief for everybody, for the leader, the head of AI, the head of the AI Center of Excellence, etc., whoever's in charge, to be like, try this. Then when you're in there, the love has to come bottoms up. So it's a sandwich. You get in top down, but then you inspire and, and win bottoms up.

Dan, do you miss the top-down days?

I mean, well, there are days I miss the top-downs. So my last company was Optimizely, which started as a bottoms-up sort of— we described them as mere marketing mortals, people in the organization who just wanted to prove the value of marketing, and they used A/B testing to do that. And eventually we grew into more of a tops-down traditional enterprise model. So I saw that emerge, and, um, you know, there are a lot of benefits to that. I think for product we're building, we were kind of forced into the model we're in, which, which I like, because ultimately people don't want to wear— if they're gonna wear basically a Bluetooth microphone everywhere they go, they don't want to be— there's— we don't want any perception that this is some kind of surveillance tool by your employer, by the government. And so just by virtue of the product that we're building, it really needs to be a decision that the person wearing it needs to make. And so that's why I think, you know, that's— and I'm very happy. I do think it's kind of hard mode because because you can't just pick up a phone and call people. Like, you know, there's just too many people to call. But when it works and it resonates, you do get this massive, you know, audience. There's 7 billion people on this planet who can independently make the decision to purchase what we build. So I do think it's harder maybe initially, but, you know, as we're seeing, hopefully over time it gets easier if we're able to do the pure things and make the product on its own sell without having to pick up the phone.

Yeah.

It's very interesting for us. Is actually not a choice that we cannot go bottoms up. It has to be a top-down motion because we're basically used to automate some of the most mission-critical type of enterprise processes. So that means you have to go through some of the security hurdles. It has to be installed by large teams to automate like your finance accounting processes. So we basically top down from the beginning. But I think one thing that is really important for us, really, to keep in mind is that the product needs to be really easy to use. So it should be as easy as, like, bottoms up. If I just give it to a day-to-day user in Fortuna content, can they just install it from day one? So that's a principle we really keep in mind. Even the whole sales motion is top-down.

Yeah.

Makes sense. So you guys have all alluded to this, like it's crowded, it's moving quickly. People don't feel empowered to bring in generative AI apps because they're changing so frequently. How closely do you monitor the landscape and the competition, especially Dan? I feel like you've had a lot of thoughts on this on Twitter.

Yeah, in the competition, it's really— it would be a mistake to over-index on competition. I've learned you know, I think my first startup, I just sort of wanted something to work. And I didn't really think much about competition. I didn't think about kind of the 4D chess of what happens if we succeed. And now I've kind of come to the acceptance that either what you're doing is a good idea or a bad idea. If it's a bad idea, it'll fail. If it's a good idea, it's a question of when, not if, some big tech company's gonna come in and compete with you. You know, another product we built, we had Microsoft come in and do sort of a shameless imitation of it. And that, you know, that in itself I kind of anticipated it. But, you know, you have to kind of build a business with a moat that knows that that's a question of when, not if. And so I think I was more naive probably my first time around. Now I just recognize like, hey, what are the things that, you know, fundamentally are— if everyone knew what I knew, if they had our full strategy doc, if they had access to everything we know, what could still endure in a world where if it's a good idea and there's lots of hype and lots of money, these big tech companies are going to try to eat your lunch. So that's the approach I've taken.

Did you get a lot of calls when the Microsoft announcement came out?

Uh, you know, I got a couple. I think a lot of the people who had been following me along for a while realized, okay, this is kind of a question— is inevitable. They, you know, the beautiful thing about that though is it really did validate what we were doing because, you know, we were kind of crazy. Like, we're out there trying to do a brand new thing in a brand new market, and to have a company like Microsoft so shamelessly just take that thing, you know, in a year and a half later build what we had had was, you know, you know, in the moment, I'll be honest, it doesn't feel great because you're like, oh my gosh, is this the end? But after a while, you sort of sit back and realize, okay, in a world where the vast majority of the world does not know what you do or why they should care, this is only gonna build a market. And ultimately, you know, the ideal thing you want, which is I hope the dynamic we have, is you have a lot of competitors who talk the talk but very few who walk the walk, because they're building the market, and then all you have to do is deliver deliver. You don't have to spend a ton of money sponsoring SaaStr. You know, they're going out there like, you know, tell— sorry, I shouldn't have said that. You're going out there evangelizing for the idea, for the problem, getting people excited. And though people try the product, like, oh boy, this is a bummer, I really wish the database was encrypted, or whatever. They go out looking and you just happen to be the product in the right place. So I'd rather be in a competitive market than in a market nobody cares about.

Well, it's good that Microsoft didn't mess up that launch.

It did help.

It is out there after they like opened the market. They were like, just kidding, we messed this up. We'll be back in a year or two with a real product. May, I mean, you're a platform that's like bullseye target for these big incumbents. You worry more about the incumbents or the upstarts that are copying you that are venture funded?

Yeah. And we actually just got our very first like carbon copy copycat. I'm like, milestone, everybody. This is, this is awesome. Wonderful. But seriously, competition's amazing, right? It puts a startup in the hot seats exactly where you want to be. And the big guys are creating the market for you. So it is absolutely what you want to be doing. For us, the way that we have thought about our strategy and how we stay competitive over time, the story of Ryder is the story of the transformer. And as the capabilities of transformers customers have increased and we're on the precipice of this next big step function increase, I think on par with stripping out the decoder and kind of your GPT-2 to GPT-3 transition. Our ability to deliver value on top of those capabilities increasing is incredibly differentiated to our customers and our customers who, you know, have been with us for— we just had our third renewal into it was our very first enterprise customer. And that is a source of enduring competitive advantage. I don't talk about moats anymore because moats can be overcome, especially if you're Microsoft, and one day you will be selling salmon in the North Atlantic Ocean, right? This is an incredibly ambitious company. Focus is your friend, and being able to really take the customer, right, the vendor relationship for them is their ticket to innovation. Innovation and really being able to show everybody just what your history has been, what your trajectory has been delivering on that. Like every week of the sales cycle, there's new shit in the product, right? And that, that is what we can do better than the hyperscalers is just move with extreme focus and speed and stay really close to the customer. They're building for the lowest common denominator. And so it's crowded. I wouldn't actually say generative AI is that competitive. Honestly, there's so much space, there's so much to do. There's a lot of choice and it's about prioritization, right? So how can you help your customer prioritize their generative AI strategy? That's what allows them to then figure out what products to buy. But, you know, as you think about all the different things you can sell in generative AI, you know, it's you and Microsoft, which means it's you and nobody.

Yeah, yeah, yeah. I vividly remember our chat, our call. I think it might be 2020 or 2021, like when the transition from GPT-2 to GPT-3. And you're like, this is our moment. I'm starting this company. I know this space. I know the buyer backwards and forwards. And it's been amazing to see you capitalize on those unique insights you had about the buyer, about the companies and how they work with strings. Like your first thesis was around string management. So it's, it's been incredible to watch.

Great.

And Bella, tell us a little bit about how you think about the— because you're an earlier-stage startup, you've just raised. And so how do you think about the competition? Do you even think about the competition?

Yeah, of course. I think for us, I actually think about competition both for right now, like the short term, when we think about just going to customer, what is the competitive landscape? How do we pitch? What's the competitive value proposition, etc.? But we also think about it as a company. What's the long-term mode? I actually think about the long-term mode as well. That's why we are building the foundation model in-house, because we believe that this will really differentiate us in the long term. And because of this full stack, we'll continue to iterate to build a flywheel, etc. So, but I don't really think about it every day because because a lot of times it's really noise. So it's really about this framework, and you have the hypothesis, and you just constantly just validate. Every few months, you look at the landscape. Is this still the same? Is there anything really completely different? And interesting is not like completely different in a way, and it's like adding new players, but the trend generally is similar in a way. And, but I, I, one thing that I do want I saw, think about is like, I realize startup is really competitive in terms of speed. So one thing I told the team is like, we have to execute really, really fast because everyone is moving extremely, extremely fast.

Yep.

So moving on to market positioning. In a lot of the times, people are going to Google you. They're going to look you up and see what is this about. You know, how, how much do time, I guess, have you spent figuring out where you position yourself in the market? Do you compare against an incumbent? Do you create a net new category? Or do you just iterate on the message that you've heard from customers? Like, how do you think about product positioning?

Yeah, all the time. We have a customer advisory board and a growth advisory board. They each meet quarterly, and we like to kick off actually with product marketing, taking our customer and advisors through and just getting like those quick reactions. I'm constantly testing new slides, new messaging, product demos. Our PMM team like half hates me half the time because like I see something from them and product and I'm literally pitching it 5 minutes later, 5 minutes later. I just did that on the way here because you want to get market feedback as absolutely quickly as possible. And for us, what we've landed on is— and this, this, this is iterative, but it's time to value. And time to value has been, you know, that's been durable for us. That hasn't changed that much in the last 6 months, I would say. You know, the billboards now in SF say ROI for AI, right? And the ability to really help your team operate and cascade down from what you want the brand to stand for. And for us, our, our vision is to empower people and transform work. And by transform, we mean it's utterly unrecognizable, right? What your teams are doing 2 years, 3 years after Ryder. And Accenture tells us that, L'Oréal tells us that, UnitedHealthcare tells us that. And it's so validating. And so, you know, we've got this brand that's based on the human experience at work, which means like the emoji robots are fucking banned, right? Right. Like you put that in your LinkedIn title, you work at Ryder, emoji of a robot. Like our comms team and our marketing team will be after you. Right. Like that's not who we are. The human beings in our marketing are not stock. Right. We made that decision about 6 months ago. And so it is about putting that human at the center of the AI conversation and empowering them to help them create a a different kind of day. And you guys, I think your companies are doing really similar types of things to elevate human potential with generative AI. But, you know, those are the kinds of things that help our brand differentiate from the Microsoft, right? And so you're in there with a really compelling product and a vision that they see reinforced every time they interact with your company, which means you gotta be really crisp. And I do think it comes from the founders, you know, how you're differentiated and what that position is going to be. Um, because you gotta, that's gotta be something that is really consistent in the way you show up and the way that your prospects see you show up.

Yep. Yep. Dan, I know that's, you've iterated on this a bunch. Yeah.

I, I think it starts with, I agree with everything you said, but I think it really does start with who you're trying to go after and empathizing with them, thinking about the world through their eyes.

Yeah.

And what is the kind of a position or messaging that will really cut through and resonate with them. If you have this sort of bland sense of who you're going after, you kind of make an average best message that doesn't really resonate. And, you know, I'm a fan of this book, Play Bigger, that talks about how buyers today make decisions around buying categories, not buying products. They wanna buy a category. They wanna buy a thing in a category. And if you can be thought of as the category king, the leader in that category, That's the little part of somebody's brain you want to own. And that's how I think of market positioning is like, what's the tiny little part of their brain when they think of personalized AI? What's the thing they think about? And so that's how I think broadly around market positioning. And, you know, the worst thing you can do is sort of be the average best and sort of take somebody else and parrot it because people kind of pay attention. You know, I've been very, you know, I get a lot of critical feedback on Twitter, on X, and like if you're too overhyped and you're sort of saying too many buzzwords, it has such a big counter-effect in this market where people want to, you know, cut through the noise. They want to, you know, they want to see something that's real, and goes back to my point around a demo. So I think that's important to recognize is there are some kind of third rails around market positioning where it may be, you know, it may be precisely right but perfectly wrong. Like, you said all the buzzwords, but like overall it kind of emotionally doesn't land. So that would be my advice is to empathize with the user and find something that emotionally resonates and try to own the category around that.

Yeah.

I think that for us, we did have quite a bit of challenges with marketing positioning. Vanessa knows really well. So Vanessa is our investor from the beginning. And because we're in this category, we tackle the problem of automation. So there is this traditional category of RPA that people are familiar with, but it's essentially basically based on rules. And we don't want to be in that category. We're not like a better RPA. And but we're not exactly like generative AI in a way that— because in a lot of people's mind, generative AI is more about generative. And while we're really in this actions place, we're generating actions instead of generating chat or emails, etc. So it was really very hard to position us at that time, so we basically have to completely create a new category, but we decided that, hey, this is different, we have to create a new category, and so we start, we landed on this position, you know, generative process automation, and it was very challenging because when you go to Gartner and Forrester, there's no such category. You have to educate everyone from scratch, including customers as well. But I think it's some— in a way, that customer actually resonates with this concept. It's something that they somehow familiar, but they also understand this concept of generative. So it's a constant iterative process, but it's a lot of effort to completely create a new category. And it's also a risk in some way that we discuss with our investors as well, because it requires a lot of effort for startup to create a new category.

It's expensive to educate a market.

Expensive.

I think that what I'm seeing is the most— the fastest growing startups are the ones that are resonating and succeeding. They want to take budget from an existing solution, but they don't want to be compared to that because it's not that category. It's a new category. And it's a fine line of we want the budget from our PA or we want your budget from XYZ. But we're not that. We're actually something different and better. And it's a better way of working than what you've been doing traditionally. And even as an investor, when I look at startups, I don't want a— we're like Salesforce but generative AI native, or we're like Workday but generative AI native. Like, they're doing a great job shoving generative AI into those apps. I want a net entirely new category. And that's what gets me excited about this wave. That's why you see a ton of venture dollars pouring pouring into this wave because we do believe the way people work will change. And there's all these markets and categories are up for disruption, the categories themselves. And so it's great to hear from you all because you're creating new categories, entirely new categories. It's just hard to, to educate and position and explain the categories. But that focus on the customer, I think, is critical. Okay. So I'm going to shoot into a quick lightning round. So one-word answers if possible. SaaS pricing, is it going to be seat-based, consumption-based, or outcome-based?

All three.

What? Cheating.

Value.

Oh, I don't know if everyone's making up words. All right. I'll come outcome-based. Okay. Sorry, I have them in my notes. What are the biggest, what are the biggest headwinds to mass AI adoption?

Literacy.

Mm.

Fear.

Mm.

User experience.

Okay. Should AI be regulated?

A yes or no to that? I guess no.

No. No.

Yes. For trust.

For trust. To build trust. Okay. To overcome the fear. I think, like, Somewhere in the realm of potential regulation, but okay, fine. Favorite AI app that's not your product.

I love Descript.

Oh, okay.

ChatGPT.

Oh, okay.

It's true.

It's true. I get it.

I actually really like your product because every founder needs superpower.

I do see a lot of founders excited, like everyone I talk to. Including my mom, who was like, "I want a limitless expenditure!" Like founder CEO. Great. Well, I want to briefly touch on some of the takeaways I've learned from this session. I love the demo domination. I think like showing is better than telling people. I think they need to see how this applies to them, to their job, to the task at hand to really believe it. Being very specific with your buyer. If you want You want to cut through the noise. You can't use froofy language or all the buzz terms. You need to talk to them in the language using the terminology that they expect and empathize, deeply empathize with the problems that they're facing. And then the other thing I found compelling was all of you are really focused on the human aspect of it. I haven't heard anyone here say, let's replace the humans or we don't need people. This is all going to run. It's like Terminator style. Like everybody here like actually focus on the human, talk about the human, and superpower the human. And that seems to be what's resonating with buyers and users. Nobody wants to feel dispensable. And focusing your product around that. So hopefully you took away more than that, but it's been an absolute pleasure to be on stage with the three of you.

Thanks, Vanessa.

Thank you.

Thank you. Thanks, everyone.