AI has been a hot topic on Wall Street lately, with top technology companies investing heavily in AI leading the recent market rally. With more and more companies getting involved in AI, it can be difficult for investors to research the names and find the best entry point for their portfolio.
Morgan Stanley equity analyst Stephen Byrd joins Yahoo Finance to share some of the best AI picks that are under the radar for many on Wall Street, and what investors should keep in mind when it comes to the AI sector. Explain.
Bird points out one of the biggest drivers behind his choice and why there is so much room for growth: “In these data centers, we're basically seeing the cost of computing, the cost of computing, go down by about 50%. That's really it. Nvidia (NVDA): These new chips are more efficient, and their cost We believe that the demand for different generations of AI applications will increase as the technology declines. This is our view on next-generation AI adoption across many industries.”
For more expert insights and the latest market trends, click here to watch the entire episode.
This post was written by Nicholas Jacobino
video transcript
Sheena Smith: Well, stocks are off to a strong start this year. Overall, the excitement is accelerating its momentum upwards. It pushed major average stock prices to all-time highs. Now, for investors looking to buy into the bandwagon, there are more ways to leverage AI than just the names NVIDIA and Microsoft that come to mind.
Morgan Stanley released a recent note examining companies that are actually leveraging AI, and they named some hidden AI gems. I'd like to turn to Morgan Stanley equity analyst Stephen Byrd. Stephen, thank you so much for joining us on Yahoo Finance.
So please just talk about your paper here. Because when many investors are thinking about getting into AI, they want to get a piece of this massive rally. They think of those obvious names. But you're looking at some of the companies that are driving AI. What is the real opportunity here for investors and why?
Stephen Bird: Yes, thank you for joining me. The opportunities here are around infrastructure. It's quite understandable that the first port of call is his Nvidia. We understand that and we support it. But the growth rate of the required infrastructure, from power generation to power electronics to the data centers themselves, will continue to accelerate. Many of these stocks aren't participating in the bull market that AI's first derivatives play actually causes, but that's essentially what they're looking for.
Madison Mills: So let's talk about the economic model for data centers. In particular, we're talking about how computing costs are falling. What is the cause? And what does it tell you about certain trends that investors should stay up to date on?
Stephen Bird: Well, I have to say that I was very surprised by the results of the data center economics evaluation. These data centers essentially see about a 50% reduction in compute costs. It's really all about NVIDIA. These new chips are much more efficient. And it flows throughout the ecosystem.
As these costs fall, we believe the demand for a variety of Gen AI applications will increase, and this is essentially the thesis of our view of Gen AI adoption across many industries. This will only increase the demand even further, and the demand on all the power-hungry infrastructure that all these data centers will require.
Sheena Smith: Stephen, how undervalued do you think this opportunity is by investors at this point?
Stephen Bird: That's interesting. There are some obvious plays that are part of the infrastructure dynamics. Yet, for too many companies, it is a key enabler, yet poorly understood. For example, fuel cell company Bloom Energy's stock price is not looking good. Still, this is the type of technology that allows data center companies to quickly bring new data centers up and running.
Constellation Energy, on the other hand, is a stock we really like. The ticker is CEG. Stock prices were very strong. we support it. Explain economics in detail. In the case of a large nuclear company like Constellation, it is very likely that a very large data center will be installed at an existing nuclear power plant, effectively serving as a supercomputer. So while that idea has caught the attention of a lot of investors, other names like Bloom haven't really gotten any attention at all.
Madison Mills: And when you look at Bloom and Constellation Energy, you said they don't get a lot of attention, which is nice for our audience. I think it's probably a good time to get in. But for us to reflect your understanding, what was the most important thing from a fundamental perspective when considering these picks?
Stephen Bird: absolutely. What struck me about our analysis was time. This means power-on time is critical for data center developers and hyperscalers. Economically, it is incredibly valuable. So we looked at various companies that could substantially reduce the time to power.
Constellations can do this by using existing cores. You can immediately list data centers outside of nuclear weapons. Bloom Energy's fuel cells can be installed in 50 days. This is a very quick solution, but I think it's missing in the market. What we're used to seeing in data centers is that you just connect your data center to the power grid, you get renewable energy, and you're ready to go.
We are currently experiencing problems because the grid is very congested. Bloom offers a low-cost solution. This is simply missed because traditional thinking has not completely changed to this concept that the grid is constrained and time is absolutely critical. So that's exactly the lens we were looking for.
Sheena Smith: And Stephen, take this opportunity to put it into even greater perspective for our viewers. How early in this adoption cycle are we in terms of AI? And it's a question of future benefits, future benefits that we're seeing from some of the companies that we're seeing on our screens, like Bloom Energy. What does it tell us about growth?
Stephen Bird: That's a very important question. We believe we are still in the early stages. I say that because I've been watching the pace of evolution of NVIDIA chips, and also the evolution of other companies' chips. We take a broader look at the future of computing required for Gen AI. We are seeing very rapid growth. Gen AI's growth in terms of power generation increases will be around 100% annually for years to come.
And what's interesting to me is that as the cost of computing decreases, new use cases emerge. Therefore, the demand for computing will only increase. We are in the very early stages of this movement. And now investors are just starting to look at what I think is the value chain for powering these data centers, so we're still in the early stages in that respect.
