AI’s Next Winners: Signals From Private Markets

Sabrina Hao (0:00)

Welcome to Thoughts on the Market. I'm Sabrina Hao, Morgan Stanley's AI and tech thematic equity strategist.

Adam Wood (0:05)

And I'm Adam Wood, Head of US. Software Research at Morgan Stanley.

Brian Nowak (0:09)

And I'm Brian Nowak, Head of US. Internet Research.

Sabrina Hao (0:11)

Today, what private companies are telling us about the AI opportunity and what it means for public markets. It's Tuesday, October 20th at 10 a.m. in New York.

Last week, we were at our Spark Private Company Conference in California, bringing together more than 100 startups across software, robotics, AI infrastructure and beyond. There was even a company helping to manufacture pharmaceuticals in space. It was a really interesting week to be at the conference, given everything that was happening in the market. But really, we came away feeling even more positive about the AI cycle. While public market investors are waiting for returns to justify trillions of dollars in AI spending, we're already seeing that payoff in private markets. AI demand is real, companies are delivering tangible returns for customers and startups are scaling faster than ever.

Adam, let's start with growth. We heard from startup scaling at extraordinary speeds. One was approaching 4 billion in annual recurring revenue in two years, another reaching a billion in under a year. What really stood out to you about the pace of AI adoption? And why aren't we necessarily seeing that same growth at established software companies?

Adam Wood (1:16)

So yeah, absolutely. The pace was remarkable and clearly one of the standouts from the conference. And we used to discuss how quickly a company could get to a $1 billion so-called unicorn valuation and now we're discussing how quickly they can get to a unicorn revenue run rate of $1 billion, which for me is quite incredible. But I think I wouldn't make the debate simply about private versus public companies. I'd make the distinction between two different types of software. I think we have to make a distinction between largely coding and inference tools. There are a number of coding and inference software companies at the conference and application software. And I think there's a big difference in terms of time and complexity to bring these products to market. I think the coding and inference, which tends to be the building blocks, as I would call them underneath the application layer, they can be constructed much more quickly. And I think because we can adopt when we're still building bespoke, which I think is a lot of the phase we're in today, we're still building bespoke software rather than buying off the shelf. They can be adopted earlier as well. So we've got faster time to build and faster time to adopt by companies. I think it's natural that you can see very fast growth in those types of businesses very quickly. I think in the application landscape, it's a little bit different because those applications have to deliver complete business solutions for customers. In finance and customer service, that means integrating data, understanding workflows, and putting controls in place. There's much more to build and much more to implement. Generally, the adoption of those tools takes place a little bit later. After we've gone from the bespoke phase where companies are building themselves, we go to a phase where companies see that there are off-the-shelf packages that are available that companies have built out. So I think it's both a complexity of build debate and also a time in terms of where we are in the software adoption cycle. The second thing I'd say is in public software companies that have a big incumbent existing business, sometimes there can be that big incumbent business that's not growing very quickly, and a very fast growing AI business in there as well, and that very fast AI growth business can be hidden a little bit by that large public software company that exists. So I don't think it's as straightforward as just saying, private's growing very quickly, public not. I think there's a couple of layers of complexity in there in terms of what type of software you're dealing with, and then how big the existing kind of business is in a software company, and what is kind of existing software growing a little bit less quickly, and then the AI bit that could sense you'd be growing very fast.

Sabrina Hao (3:30)

Absolutely, that makes a ton of sense. Maybe on this theme of full stack solutions, you know, Brian, we heard about the shift from AI assistance to agents that can actually execute work. You've done a lot of work on this both across consumer and enterprise. You know, what did you learn about how quickly agents are advancing, how does it shape the outlook for the Internet names that you cover?