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.----- Transcript -----  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 di