The AI Moat No One’s Paying Attention To | Will Mandis

I do think the constriction in experimental AI spend will rip people's faces off. As you think about what the dominant AI companies of tomorrow will be, they will be the ones that touch incredibly deep parts of the enterprise workflow. I'm not necessarily sure that companies that are doing 500 million in revenue that were doing zero last year, where 98% of that is being transacted on corporate cards, I don't know how sticky those are.

Will Menaitis, co founder of Science IO, one of the earliest, the most ambitious AI startups in healthcare. Science IO was recently acquired by Veridigm for 160 million and will serves as their senior VP of AI. You were really early on that there was no ChatGPT at that time, so you must have been innovating quite a bit in solving technical and probably regulatory problems for the first time. Talk to us what some of those look like.

Yeah, I think it's funny to imagine this now looking back on it, but like so much of the substrate that made generative AI possible just doesn't exist today. When we started science, usage based billing was very much an unsolved problem. The idea that you would pay for tokens was a thing stripe did not. Industry today, certainly we didn't have the infrastructure to do scaled model posting. You have via every single cloud provider today. And at the time we were still buying GPUs that we'd install on gaming PCs and keep them in our office and train on premises. It was just an entirely different world. And the techniques certainly back then were a little more open than they are now. I think if you're in the industry today, the state of knowledge about how to train a very, very large foundation model is somewhat known. But if you're outside of industry attempting a de novo training run, you're kind of up the creek without a paddle. But at this point it was literature, so it was interesting to see kind of commodification happen at the model layer, despite that information becoming more and more propri. And ultimately the thesis we had, which I think ended up being correct, was that all these vertical domains would require custom models. That doing anything in a complicated, highly regulated space was really a result of the data used to train the model on and the efficiency of that training process. And that's really kind of how we ended up in Varadigm is it was the best data in the world, but it took a while to to figure out where the industry was going. When everybody has access to the same cutting edge models, how do you Guys, think about differentiation and defensibility and building a moat. And how does a company like even cursor actually sustain that moat? Yeah, it's a good question. I'm not sure there are obvious answers and the answers I would give you seem to change every 14 days. So I'll caveat that I'll give you kind of today's po. I think there is very limited differentiation if the business you're in today is building big foundation models. Like if you come to me today and you're like I'm going to build OpenAI but marginally better. Even if you have some technique that makes you 80 times cheaper, 80 times better, I just don't. It's really hard to imagine a business there that compounds over time. It's too volatile, there's too much diffusion. The secrets you have diffused too quickly across organizations. That said, I think there's a good lesson there that scale is still fundamentally emote. Part of the reason OpenAI APIs are still so well used and even something like Google Geminis are not is just scale. It's just the default developer option. It's easy to use, it's easy to onboard. That said, I don't know where those moats go over time. I do think fundamentally go to market is still the most important moat. Big customer bases that like your product have signed multi year contracts with you and are not charging you on a credit card month to month is still the most defensible thing in the world. And I think it's a thing that basically no one is spending time on today. So much of AI is chasing really fast, really volatile consumer revenue. That's amazing. It's just fast on, fast off. So I do think fundamentally go to market matters, technology matters to a degree, data matters, workflow matters, talent matters, funding matters. But it's a much more amorphous thing than software used to be.

I think at the application layer. Right. Some ways to think about it are first of all, the things that Google and OpenAI are absolutely going to go after and get are going to be anything that has the horizontal kind of broad tam, especially text base or more simple image, text, video, they could ship that at scale. So anything that's focused on that or anything where they can leverage their distribution leverage. Right. So it plugs into these multi billion user surfaces. So if it plugs into Microsoft products, Google products easily, that's fair game. And you should expect that they're going to add that the old like why won't Facebook add this or Instagram add this As a feature, I think that's what they're going to go after, right. That what they're really not going to go after in the near future is the hair on fire. Deep domain specific workflows, right? Going deep into workflows, owning that workflow process end to end, right. Regulated or high liability heavy verticals, right. Where there's just financial compliance, insurance compliance, nuclear defense. Like it's just that's not their market. They want to sell the model, they want to, they want to own the market, they want to sell their APIs. You know, where there's deep integration required with legacy systems. So it's not just automating a workflow, but automating a workflow with integrations. Right. As part of your solution because you understand that. And then the compounding data loops, right? So you have like continuous data loops that, that are helping improve it. So if I summarize that there's this four layer data modes, right? So you have the data, you have the workflow depth, you have the distribution wedge and then you have the model agility. So any contrarian takes for founders today.

Do you think the constriction in kind of experimental AI spend will rip people's faces off? I really think today we're spending a lot of time looking at things that are like 0 to 100 million in 48 hours kind of stories. And there's this really big focus on how much you can scale revenue overnight. Candidly, I think extremely fast growing revenue is often extremely fragile revenue. And as you think about what the dominant AI companies of tomorrow will be, I'm not sure those will be the ones that grew the fastest today. I think they will be the ones that touch incredibly deep parts of the enterprise workflow and are able to be sticky and able to go through these big procurement motions. I'm not necessarily sure that companies that are doing 3, 4, 500 million in revenue that were doing zero last year, where 98% of that is being transacted on corporate cards, I don't know how sticky those are. So I love enterprise sales. I think it's kind of a lost art, but I do think tacking towards the enterprise will be a really people that will remember that as a good tactic.

Right now, if I'm just starting out, I don't have an idea yet. I don't have. I'm not biased to a solution. How would you think about what the white space is or what's most important to think about?

Yeah, so it just got a hundred times easier to build software in the last year. Right. Like cursor, like cursor makes me a good software developer. That's like crazy, right? That's like superhuman stuff. And that means that counterintuitively it just got way more expensive to build truly great software because a lot of the low hanging fruit are going to be much more easily picked by like 20 year old college kids with cursor, which means that the stuff that's really interesting, the stuff that's really durable is either intensely technologically difficult. There is like a big build moat or a big integration moat, which is interesting and that's a path you can go down. Or things that are intensely difficult to sell. And I think tech has been engaged in something of a shell game of kind of high end tech enterprises selling to high end tech enterprises. That TAM is large, but it's not infinite. And if you're going to build a big AI business in a traditional industry, a lot of it's going to require going through the procurement departments of companies that are not native AI procurers are certainly not going to put $30,000 a cursor on their corporate credit card. They're not using ramp to save 5% on their expenses. This is a fundamentally different motion. And learning the motion as soon as possible and getting excellent at it as a company I think will be a strategic advantage over time.

So in the end, you had a great arc over a relatively short period of time to bring it to a nice exit. What would you say are maybe your top one or two biggest lessons you'd pass on to today's founders that are building in an entirely new environment?

I think you need to be, yeah, you need to be really sober about where differentiation comes in this space. And I think kind of fundamentally, at the end of the day, if you're building a model company, which tpd, if that's an advisable thing to do today. But if you're building a model company, the things that matter are access to the workflow to get really high quality reinforcement learning feedback from experts, and access to highly proprietary data sets. And basically everything else is around error. And as we enter a very weird stage of the market where I think there is more demand today than will exist in two years. Right. There's a ton of experimentation happening right now and you're seeing revenue grow really quickly. Being able to make that revenue durable over time, to be really sticky to be a part of the workflow is going to take a lot of work, a lot of proprietary data and a lot of access to that reinforcement learning. So I think the importance of things that look like BD are going to come back. Like, it's not just, you know, low level enterprise sales anymore. It's like big strategic deals. And I also just think the relentless focus on talent will continuously be a differentiator.