Behind-the-Scenes: VC Funding for AI Startups
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Welcome to another fully connected episode of the Practical AI podcast. In these fully connected episodes, it's just Chris and I, and we spend some time catching up on AI news and maybe some other topics that are just on our mind, and hopefully share some things with you that will be useful and educational as you explore the AI world and your own work with AI.

I'm Daniel Whitenack. I am CEO at PredictionGuard, and I am joined as always by my co-host, Chris Benson, who is a principal AI research engineer at Lockheed Martin. How are you doing, Chris? Doing well today, Daniel. So much is happening. Looking forward to our conversation today. Yeah, so much is happening. Summer is upon us, which is...

is great. If my allergies are any indication, then, then things are, things are changing, but yeah, there's a, there's a lot of interesting things changing and shifts happening. And in the AI world, one of the things that I'm, I'm constantly, uh,

just I see all the time in the news we we have a team you know a channel in our in our slack just a random channel where people share news stories and there's always something about you know AI startups either good or bad of of AI startups actually this this last week one of the

One of the things that was shared was this, you know, hyped AI startup that raised a bunch of money was valued at 1.5 billion builder.ai or builder.ai.

And I think basically it collapsed. There were sort of these, I don't know everything about it, but I saw sort of posts about the AI not being AI, but actually being, you know, Indian culture.

coders or developers. So it was like a builder platform, like a low code, you know, I'm going to build an application. But actually, there's some indication that there was actually just people building on the background. I don't know the full story behind that, or if that's even true. But regardless of the sort of collapse, I don't know if you're seeing a similar flurry of all sorts of discussions of AI companies, good or bad.

Yeah, and there's kind of never-ending talk of AI bubbles and fundings up and down. And I know you as a startup founder yourself have a lot of interesting experiences with the funding world and stuff like that. Maybe get you to share some of that today. Yeah, yeah, sure. A little bit of what is it actually like in the trenches today?

to raise funding for an AI startup? What perceptions are there? What questions come up? What stages are there? I'm just looking at, I searched for AI funding startup in Google News, and it's just sort of a constant feed. The ones that are popping up as we are recording this episode, legal startup Harvey AI raising funding at a $5 billion valuation.

Decagon to raise $100 million at $1.5 billion valuation. Samaya AI gets $43 million. Perplexity is raising around bringing in $500 million at a $14 billion valuation. Yeah, it may be even useful for some people out there that aren't as much in the startup world to even just parse through some of what

this actually even means, like some of these terms. So maybe that would be a good set of first questions to answer is, what does it actually mean to raise funding, regardless of whether you're an AI startup or not? And maybe even before we, you know, just as a part of that, even what some of that basic terminology means, you know, where you're raising a certain amount at a certain valuation, right?

Yeah. For those who have not been exposed to the finance side of things in the past, you know, and what is that? How does the level of valuation affect your funding and affects control? Things like that. Yeah. Yeah. Well, there's

I have to admit myself, you know, learning a lot about this in the past year as we've gone through this process. Lots of interesting, strange, you know, terms and processes and all of that. It's a whole world in and of itself. But yeah, so just if you think about building a company, you know, you're going to build an AI company. Typically,

That company might be fall into a few different categories. Right. So maybe you're an AI company that you want to provide services to others like you want to help them build AI things. This would be like a service provider type of company, a consultancy, that sort of thing.

And typically, when you're hearing about these companies raising venture capital, which we'll talk about that here in a second, they're not really focused on these sort of service provider companies. There is actually a huge market for that right now. Actually, I think I was on a call with someone and it's like,

OpenAI isn't the one really making the most money off of AI. It's like Accenture. The consultancies of the world are just making a huge amount of money off of AI because people don't know how to build these things. They don't know how to adopt things, etc. So it is a really good space to be in.

But in another category, your AI startup might be a product-based company. So you have an AI agent platform that you're building for petroleum engineering, or you have a healthcare assistant company.

Right.

Now, if you then kind of subdivide those products, there's a set of products that are probably somewhat niche, like you have an AI product, but the number of people that are going to use it, if you want to think about it, the market for that AI product is fairly small, right? So maybe I have, I don't know, I'm thinking off the top of my head, but

I have an AI tool or an AI product for pug owners. I had a pug dog for a while. So I have an AI product for pug owners.

and it helps answer questions about shedding or about great things to do with your pug. Now, maybe, I don't know how many pug owners there are in the world, but maybe that's not like the biggest market that you could think of in terms of the amount of money you could make off of such a thing. I don't know if you agree or not, Chris. I don't know. There's lots of pug owners out there. There are a lot of pug owners.

Very true. Very true. So there may be those products and you could kind of think about those if you want. Like some people might call these sort of lifestyle type of businesses where you could actually, you could probably make

reasonable amount of money. Now, I don't know if the pug AI would make a lot of, you know, a reasonable amount of money, but you could make a good living for yourself, maybe. It would be. So I think kind of to the point, there's an addressable market that you would, that your startup would need to be, would need to be working towards. Correct. And it needs to be an addressable market that makes what you're doing worthwhile. Correct. Yes. And, and sometimes there's sort of two, two

There's really two things you could determine once you look at the market that is available for what you're building. On one side, there could be a market and that market could be big enough to support your business, maybe even employees pay you a paycheck. Right. But it's not.

It's never going to be a large market in the sense of billions of dollars. So really, you know, billions or tens of billions or hundreds of billions of dollars, right? Then there's another set of products that,

would have a larger market. So, you know, just by way of example, you look at certain startups from the past that have, you know, captured a very large market, whether that be, you know, Uber or Airbnb, I guess those are consumer related things. But then, you know, there's all sorts of things in the

computing and B2B side. So you look at something like Docker or whatever these kind of larger, there's a huge market for some of these types of products. So if your AI product is

doesn't just have a market of some millions of dollars that could support your lifestyle, but it's tens of billions of dollars or hundreds of billions of dollars of market out there, then that kind of categorizes you in the potential spot where you could raise venture capital and

for your startup. And so now we kind of need to define what that means, which is, if you're going to raise venture capital, basically venture capitalists are companies that they actually have their own investors. So they get together a bunch of investors, often called LPs or limited partners, that put in money into a fund. Let's say it's a $50 million fund.

And the VC firm then allocates from that fund to invest in a number of companies, in quite a few companies, actually, with the hopes that certain of these companies actually do scale up

to reach a reasonable amount of that very large market, thus becoming large, valuable companies that provide a return to that investor pool.

I'm curious. Often before the VC stage, you'll have angel investors, which are wealthy individuals that are getting in very, very early, even before the VC do and do that. Did you guys go that way or at least...

Are you seeing that in the AI space? Yeah, we didn't. And in our case, actually, we spent really a year and a half, the first year to year and a half of our business bootstrapping the business, which what that means is basically,

you know, businesses ideally make money. And if you get an, you know, if you sell enough of what you're making and you bring money in, you can pay bills and keep running your business. Right. And so that's kind of you're living essentially growing your business as fast as it can based on the money that's coming in revenue wise. And so we did that initially. Others kind of might not be in the place where they do that. They might not be able to get

or sales early enough. And so maybe there's kind of some initial money in from some angels. I've got a question for you on that. And that is, there's a point there, you know, where you're bootstrapping early on. And I remember when you were going through that process, um,

And there was a point, you know, along the way, and I don't know where that happened, but where you decide you are and you need to go the VC route to achieve your goals and stuff. What's your thinking as an AI founder about that? You know, because there are trade-offs on if you bootstrap, you know, all the way through and grow based on your own revenues, right?

You know, that gives you a certain advantage in some ways that, you know, you're not having to go through the process of VC. As a founder, how did you really kind of decide, you know, what the trade-offs were and, you know, and go the way that you ended up going?

Yeah, it's a good question. And I don't think there's any right answer to this question for different people. I think it's a question people have to answer for themselves and the business that they're building. For us, I think what we saw was that we did have a product that we were creating, which at least had some level of fit to a real need in the industry.

And there was the chance to scale that product up and gain market share. But only if we did that sort of quite rapidly, because a lot of the consolidation, I think the kind of acquisition and consolidation that will happen in the AI market, it's not going to happen, you know, 10 or 15 years from now, it's going to happen forever.

you know, three to five years or maybe sooner from now, right? So if you want to kind of participate in that sort of consolidation, potentially be acquired, then you really need to kind of scale that quite quickly, which requires capital. It's not that you couldn't grow organically, right? And still grow organically and have, again, a nice lifestyle business, but you probably wouldn't

participate in that kind of consolidation phase. And for us, that's ultimately what we, you know, decided it's, it is risky, but that's ultimately what we decided to do.

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Well, Chris, we talked a little bit about what we were trying to do in this fundraising phase. We defined a few terms like venture capitalists. I think it might be interesting for people to know, maybe that haven't been exposed to this, some of the dynamics that venture capitalists might be thinking of. Now, I feel really privileged that we've got

some folks that have partnered with, with prediction guard that are really with us and supporting us in, in a really profound way. Also, you know, aligning to a lot of how we want to build the business. But if you just kind of strip away anything, but like the capital and return pieces of how at least many VCs might think again, going back to the example that we had before, there might be,

You might have a fund of 50 million or 100 million dollars and you invest in however many, let's say 30 different companies. What you're hoping for is and you make an investment in that company. Essentially, what you're doing is you're you're the founders are selling a piece of their company and you're buying a piece of that company via investment.

equity. So you're giving away shares or equity in your company, a percentage of your company. There's various types of mechanisms of how that happened, whether it's just, you know, you value the company and you buy a certain shares or you buy the right to, you know, have a certain percentage of the company later on that converts. There's all sorts of different mechanisms around how that happens.

safes and convertible notes and venture debt and priced valuation rounds, that sort of thing. But essentially, that's what you're doing. And what the VC would hope for, let's say that we do that with 30, you know, Chris, you and I have $50 million, which I don't think we do. But let's just say we did. We don't.

And we divide that out across 30 different companies. We buy pieces of these companies. Essentially, this is a very risky thing to do. But what you're hoping is that it's sort of like a power laws sort of thing. So you're kind of hoping that one of those companies...

Just is awesome and grows like wildfire is acquired for a huge amount of money. And that your one investment in that one company returns you your whole fund. So $50 million, let's say. Then another three of those returns your fund again. So not as good of exits, right? But-

You know, not bad. So you basically have doubled your money. And then the whole remaining set of the companies returns it maybe a third time. Right. So essentially, you've you've tripled your money. But most of that has come from a very small amount of the companies that you've invested in. And you get a good you know, you get a good return for your investors who put the money into your fund.

It's a form of diversification of risk. Yeah, essentially. You're selecting a bunch of different companies and hoping that one or a small number of them will far outpace the larger collective. Yeah. Now, that's not the rule across the board, but it's kind of generally how you could think of this. There's different firms that have different strategies that make a smaller number of investments or maybe make a very, very, very large number of it.

very small investments. Right. And so there's, there's different strategies within that. And that's kind of part of that VC world and, and determining what the, you know, the way that you want to run your fund and the thesis that you have and, and all of those sorts of things, but that's kind of what's happening. So if we just go back to kind of interpreting some of these things, so I mentioned that perplexity raise, right? So, um,

They say AI startup Perplexity has a valuation that surged to $14 billion in a new funding round, and they're raising $500 million.

So what this doesn't mean is that the VCs put in $14 billion. Now, sometimes companies do raise billions of dollars like OpenAI. But in this case, the company is raising $500 million. They're saying, we're going to bring in $500 million. Those VC firms or whoever it is put in that money at a rate. So they buy a portion of the company.

with a proportionality that would make the company in theory worth 14 billion, like the percentage of the company that they're buying for 500 million. Multiply that out to a hundred percent of equity. Then yeah, then that would be the, so it's,

It's a little bit, the valuation is to some degree, and I remember this from years and years ago doing finance in graduate school, but it's a little bit manipulated in the sense of you kind of come to a determination of what percentage of the company they're going to get and what they're willing to put in for that. And then it kind of extrapolates out to what that valuation is. Yeah, yeah, exactly. And

Yeah. So this isn't a, it's sort of the, the assumed, if you want to think about it, like the, the assumed value, but it's, it's the sort of fair, maybe what you would call the fair market value. If you get an official valuation from the, you know, that would stand up to the IRS or something like that. But this is kind of, um, yeah, it's how they kind of multiply, multiply that out. Yeah. Yeah.

In theory, if you were to sell all the shares of the company and everybody was going to buy in at that rate at that point in time, it would come to that valuation. Yeah. Now there's all sorts of trickiness that can happen here because it also like...

Me as a founder, I own a certain number of shares of Prediction Guard, right? And other of our employees have shares that have been granted to them of Prediction Guard. Now, often when you raise a round of funding,

In many cases, shares might be added to the overall pool, right? So for example, the VC might say, well, we would like to have this percentage or this, you know, this many shares is what we're going to get. But we also want you to bump up the pool of shares that you have so that you can give your future employee shares. That sounds really great, but it also means that if you have more shares, you're going

Now, then right, my shares are worth less diluted. So that's what this sort of dilution means. If you've heard, if you've heard that term. Yeah, sure. Yeah. Now, that's all, I guess, the sort of setup that some of the terminology, some of what happens in these, I guess the other, maybe the other terminology to just use, which is quite relevant here, as we kind of dig down into the side of this, which is dilution.

You know, AI startups, what's it what's it like to raise? What's it like to go through this process? If you're an AI startup? The other thing maybe just to highlight before we get into that are the what people often call this sort of stages of funding. So you might have heard.

In the news, you know, this AI startup raised pre-seed, this one raised seed, this one raised series A, etc. This is, you know, kind of confusing because this gives...

the impression that these are very well-defined terms. They're somewhat not well-defined terms, but if generally you could think about things as pre-seed is like you have kind of an idea and not really any traction, maybe very few, if any, sales of your product.

Seed is maybe you do actually have a product. There's some sales, some level of traction, but certainly you haven't proved out the fit of your product to the market. And then series A is like, okay, we have repeatable sales.

We found a fit. Let's kind of pour some gas on this, you know, capital to actually get, get more sales, get market share. And if you need to do that more than you raise future series, you know, series B, C, D, E, F, whatever that, that is. Right.

So for us in raising our round that we just finished, we raised a seed round. So I would say in some ways we were maybe in some ways part of the dynamics of what I learned was maybe we were a little bit past where a lot of people called or thought of as seed because we had maybe more traction or sales than that. You had been bootstrapping a while there. Yeah, we've been selling for a while. We had, you know,

a non-negligible amount of customers and people were using the product in production and we had sales and and so a certain level of fit but maybe not where you would say you would want to see at series a where you just it's a matter of cranking the mechanism right we know exactly where we're going to get all these customers give us x amount of dollars and you get

X amount of revenue out, right? So in that sense, that's kind of where we were. And one of the dynamics that we found kind of in the current market is there's a lot of investors that invest maybe in seed or series A, like both, right? They may make investments in either. Often investors specialize in certain stages, like early, mid, late, etc.,

So for us, when we were talking to investors that would invest in seed or series A, let's say, they look out at the AI world and it's just absolute pure chaos, right? So no one knows what the killer AI app is. There's so many AI startups. Everybody's an AI startup right now. No one knows who's going to win, right? And so I think for a lot of the companies that they were looking at, right, they say, well...

Cool. Prediction guard, you're maybe what we would classify as seed. You have great traction, a really great team, which I think we do have. But there's so much chaos in the market and so much I'm having so much trouble differentiating between all of these different AI products that

I'm just going to wait until series A. And if you make it like, cool, then, then we'll let, let's talk then. Right. Because they're very unsure about what's going to kind of stand the test of time. Right. In a very kind of,

frothy, noisy AI market. So everything's AI. It's really hard to differentiate between kind of different AI offerings just from looking at a pitch deck or even having a half hour, an hour conversation. You know, how really is Langflow different than Langchain, different than, you know, Llama Index, different than, you know,

XYZ, other things? And how is that different from what you're doing? And how is that different from what the hyperscalers are doing? And that sort of thing. So yeah, that's one of the dynamics that I was going to highlight. It's just this frothiness, this noisiness in the AI market in some ways. And this is maybe not

It's a trend that has happened with other technology, but in some ways it's like if you have a really good team and you don't have, you haven't built your AI product yet, then that's a great place for people to pour in money because it's,

If it's a great team, you'll figure it out and use our money well, maybe. And if you have a product with really solid fit and a consistent kind of sales process, then that's a great place to pour money in, right? Because then like, it's just a matter of putting gas on the fire. If you're in the middle, you have a product, right? You put a flag in the ground in this very noisy market.

But it hasn't really proved out yet in terms of the fit. That's where, you know, there's a lot of uncertainty and the questions are really about competition and differentiation. So.

As you've kind of personally gone through this process and, you know, I know earlier on we would be talking between shows or, and, and stuff and you'd be, uh, going around, um, and, and kind of bootstrapping, you know, you were getting business and you were getting everything moving and running a business, uh, in those early days. And then, then a little bit later, there was the period where I would start talking and you were,

you were constantly dealing with funding responsibilities as a, as a founder CEO. What surprised you as you're kind of going through this learning process and, and you're, you're trying to accommodate that. And there was clearly some sort of shift of activities. And I'm not,

privy, you know, just, but I noticed that as your friend, you know, that, that it seemed like you were always doing these and then it seemed for a while you were doing those, but obviously you still have to keep running the business along the way. How did that change the activities and your expectations, uh, and your perception of your own, of your own business while you're running it? Yeah. You know, it's, it's, it is really interesting. Um,

to go through this. So, you know, just by way of context, you know, at least up until very, very recently, and I'm talking, you know, in the past month or so, everyone in our company has been technical. Myself, you know, our CTO, all of our engineers, AI engineers, backend engineers, frontend engineers, we're just like, we know how to

build AI systems well. That's our specialty and we're all technical and that's what we do. And we're proud of that. We're proud of the kind of strong technical base, right? And so as a result of that, I'm the one, I mean,

partially because we engage on this podcast, you and I both engage in the AI community directly. It's natural for me to sort of shift into that. Well, hey, if there's a potential customer, I'm jumping on a call with them to talk through whether this is a good fit.

do that discovery, eventually get through the contracting, start a partnership, that sort of thing. So everyone in the company, we have no salesperson up until recently. And so I was doing that piece.

Well, if that kind of shifts then to taking, you know, five to seven VC pitches a day calls, the follow-up, you know, paperwork and like, hey, could you get me projections for this and that? Obviously that sort of reduces capacity, right? For you to do those other things. Now, thankfully, like,

product-wise, engineering-wise, they're rolling along, right? Because our CTO has it. He can build out that team in his sleep and product-wise is good. But yeah, I definitely... You see that sort of distraction on the sales side. And part of what you're saying is, well...

part of why we're raising this money is to kind of build out more, more on that, uh, go to market side. So I kind of just need to get, I need to get this done and get back to focus and get back to this. Otherwise, you know, I appreciate you all, but, but we're, uh, like time is ticking and we've got a product that can sell and, you know, people are eager and they see the need for the product, but I just, like, I can't, I can only have so many conversations in a day and, and,

If those are five VC conversations, it's time that isn't with, you know, five customers. Right. So, yeah, that's that's really, you know, a thing and something to definitely balance if you're in those sort of positions out there. Yeah. Something to consider with that.

I'm curious, you know, you touched on, you know, kind of the notion of going from being a technologist and doing the technology, you know, to kind of having to administer, you know, the company as the CEO in that way. And, you know, how hard is it? I think my observation was you've made this transition very, very well, right?

But I am curious your own perception because, you know, for your in those early stages when you're when you're just running the business and that kind of bootstrap phase that we talked about, you're kind of doing you're doing sales and stuff, but you're still building the company. And part of that is it's a technology company. And and I know darn well that you were coding and you were doing all of the things, you know, in those early days as it was starting to grow and you're going in, you're transitioning to this VC stage.

How hard personally is it for you to let go of those responsibilities and distribute those out to people on the team when you know, maybe in some cases, you know, I can just knock this out. I'm really good at this particular skill. I know how to do this thing. I've done it many times, but yet, you know, you can't now. What's that like?

Well, and also a lot of those things I enjoy, right? It's like... I know you do. You enjoy... We've talked about it on the show. I enjoy data munging. I enjoy building these applications. It's a sick, sick thing that you admit periodically. Exactly. So...

Yeah, it definitely is a shift. I think, you know, now in our platform, you know, which we're, so this is a self-hosted AI system that we're deploying with customers.

I don't know that any of my original code exists anymore in that system. I built out that first MVP along with our kind of founding engineers. Shout out to Jake and Ed. But that's been completely refactored at this point. And I don't know if any of that survives. What I think I've realized is

is a couple things. One is I personally love digging in with customers and at the application level and really more on the developer relations side. That's why I do this podcast, right? We're always talking more to those practitioners, people that are applying the technology, right? That's a huge passion of mine.

And so that's actually in our context, because we're building this AI system, really the main work on the product is really infrastructure and platform engineering and that sort of thing. There's obviously AI involved in it because it's an AI system. But really the AI stuff is really in how people are using the platform, right? Which is, I think,

seeing that as the exciting piece to dig in was part of what happened. I think the other thing that was actually something I think my wife helped me see over time was that

You giving up, if you want to think about it as giving up you, you releasing the reins on something that you're holding tight to work wise, whether that be a certain technology or a piece of the product that you're digging into or that sort of the thing, that sort of thing. It's not, you don't have to view it as a loss because basically what you're doing is you're

is you're creating an opportunity that someone else can step into that they didn't have before. So one of the other engineers, one of the other team members, as I delegate or release those things or step out of certain responsibilities, it gives someone else a challenge to step up and an opportunity for them to learn and grow in their career and actually be blessed immensely to have that opportunity and to grow in that way. And so I think it,

As soon as I started viewing that as a

as a net good, not a, you know, a positive, not a loss that this is actually creating some, an opportunity or an environment where people can level up so quickly as part of the joy of startups, right? You get to do all these things that you didn't think you'd get to do. That, that I think is a, it helps my mind and really understanding that. And so, yeah, it's awesome to see the team humming along and the product coming, coming along. And it's also fun just to go into, you

our ticketing system and say, Hey, what if we had this feature and that, and I can just create a bunch of tickets now, and then I don't have to actually implement them. So it's a lot easier to create tickets now. With, uh, you know, we always wind up with what we kind of internally call our, our idea about the future asking, you know, the future when people are talking, um, I want to, I want to lead into that for a moment and just say, you know, you and I have been doing the show now for a

I guess it's been since the beginning of July 2018 is when we first released. So you're talking seven years nonstop of doing the show. And as my friend, I have seen you evolve tremendously over that time period. You were very technically focused early on and still are very technical, extremely technical. But you've taken on these new roles and evolved over the years. And so the

the Dan who is my friend now is, has evolved tremendously since the, the, the Dan that I first met way back. Thanks man. Um, I'm, I'm curious, how do you see yourself in, in like, where do you see yourself going in that, you know, kind of give yourself your own future question that you would be asking the guest to kind of, how do you see things going forward as we wind up? Yeah. Well, I mean, part of, this is very much a more philosophical answer, but, um,

I do really view now, and I didn't always see this, but folks like the Praxis Guild that I'm a part of down in Indianapolis and the work that they do have helped me see that really venture building and those that build ventures are...

Venture building is the thing that shifts culture, right? And so when I look out and I see all of these AI companies that are for the most part

very exploitative, you know, not calling out names here, but like even what we started out the conversation with, you know, the fact that you would represent an AI company and just have, you know, developers in India behind the scenes, like pretending to be bots, like things like that. You hear about something like that. It seems like every day. And I think

what I view as aspirational for us and what I desire is to see our company set a different standard and actually shift the culture around, you know, actually enhancing or advancing trust in human institutions in our case via, you know, private secure gen AI systems. So that's really my vision for what that

what I would like to see there. And I think, uh, it's viewing it from that redemptive or that restorative perspective is something that, uh, that yeah, is really exciting for me. So yeah, I appreciate you going on the journey with me, Chris. It's been fun. Yeah. It's been, it's been interesting to see you doing all that over time. So, uh, as always wishing you well, I think I'm prediction guards biggest fan outside the company. Um,

So thanks for sharing today. This has been really fascinating for me and kind of just hearing the journey. I've heard snippets of it along the way as you've talked, but actually we've never done this deep dive into the conversation. So thanks for sharing with me and the audience. Yeah, of course, Chris. We'll talk to you soon. Take care, man.

All right, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner, PredictionGuard, for providing operational support for the show. Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the beats and to you for listening. That's all for now.

But you'll hear from us again next week.