AI’s Next Winners: Signals From Private Markets

With AI startups scaling at record speed, our analysts Sabrina Hao, Adam Wood and Brian Nowak share takeaways from Morgan Stanley's Spark Conference on AI returns, competitive moats and implications for public markets. Read more insights from Morgan Stanley.

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Sabrina Hao: Welcome to Thoughts on the Market. I'm Sabrina Hao, Morgan Stanley's AI and Tech Thematic Equity Strategist. Adam Wood: And I'm Adam Wood, Head of U.S. Software Research at Morgan Stanley. Brian Nowak: And I'm Brian Nowak, Head of U.S. Internet Research. Sabrina Hao: Today, what private companies are telling us about the AI opportunity and what it means for public markets. It's Tuesday, October 20th, at 10am in New York. Last week, we were at our Spark Private Company conference in California, bringing together more than a hundred 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 pay off 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 startups 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: So yeah, absolutely, the pace was remarkable. And clearly one of the standouts from the conference when 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 a billion dollars, 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 were 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. So, I think it's natural 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, and 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, you know, in public software companies that have a big incumbent exis