Key Takeaways
- Physical capacity constraints such as power, land, and transmissions are dictating the pace of the AI buildout, which will take years.
- The next phase of AI investing could expand beyond the Magnificent Seven into healthcare, logistics, industrials, utilities, and private markets.
- AI is fragmenting into regional ecosystems.
Valerio Baselli: Hello and welcome to Morningstar. Artificial intelligence has already transformed markets, but we may still be in the opening chapters of a much bigger story.
To help us understand where investors may find the next wave of opportunities—and risks—today I’m joined by Laura Cooper, head of macro strategy at Nuveen.
So, Laura, according to your latest research, AI is not just another technology trend, but a “multiyear capital supercycle.” What distinguishes this cycle from previous tech booms, and why do you believe it has much longer-lasting implications for investors?
Laura Cooper: Well, if we think about previous tech booms, they were largely driven by speculative capital, by cheap leverage, where we had companies burn through cash without a clear line to profitability. And today we are seeing quite exceptional amounts of capital expenditure coming through. By our estimates, around USD 700 billion this year. That could rise to USD 1 trillion next year. But we’re already seeing signs of early monetization of that AI, some return on investment, and some margin expansion. And yes, clearly not all of these companies will be winners as this AI cycle unfolds. But we are really seeing strong demand that’s underpinning that capex. And I think another key feature of why we see this as a supercycle is largely because of physical capacity constraints, whether that’s power, land, transmission. This is really underpinning the AI buildout. And this will take years, not quarters, to fully unfold. And so, to your point, we still see ourselves in the early chapter of this broader AI adoption that will take many years to evolve.
Beyond the Mag 7: Where the Next AI Stock Winners Could Emerge
Baselli: And speaking of winners, public equities have clearly been the headline winners so far, especially the Magnificent Seven stocks. Do you think leadership will remain concentrated in those companies, or are we entering a new phase where AI-driven productivity gains begin spreading across the broader economy?
Cooper: Well, we have seen those Magnificent Seven companies really have this first mover advantage in this AI buildout and adoption. And we’ve seen their performance, about 300% returns over the past couple of decades alone. And that compares to the broader S&P 500 of only about 80%. Now, in the near term, we still think that equity leadership can extend in that tech sector, because if we look at earnings alone over the past quarter, they generated more than 40% profit growth, far exceeding analysts’ expectations. But going forward, we do expect we’ll see more of this diffusion of AI as other sectors begin to increasingly adopt it. And we do think productivity gains will materialize in terms of logistics, healthcare, cybersecurity, industrial automation. And we think that’s really the next wave of AI, where this is not yet priced. And then importantly, this won’t just be a US story. If we look at European, Japanese industrials, they’re more attractively valued than some of their US peers. And again, as this AI adoption unfolds, we think there are attractive opportunities for investors across the globe.
AI Opportunities Across Credit, Infrastructure, and Private Markets
Baselli: That’s very interesting and this brings us to another very important point. You see investment opportunities now extending far beyond US mega-cap tech stocks. So, where do you find the most compelling opportunities across private markets, credit, and real assets?
Cooper: It’s a great question. I think one could say, well, all of the above, essentially, because what we’re seeing is increasing opportunities across the capital stack. So, as I mentioned, still, that equity kind of buildout in terms of the tech leadership and some other sectors there. But increasingly we’re focused more on financing the AI buildout. And so that in terms of credit, whether we look at US investment-grade utilities that are entering one of the most significant grid upgrade cycles, if we look at high yield, in particular, select industrial and technology issuers are increasingly going to be quite attractively valued. And then, importantly, looking at the private credit space, that is going to increasingly be a key component of this AI buildout. If we think about direct lending for data center operators, for cooling power management specialists. If we think about infrastructure, both on the debt and equity side, this will be critical as we look to build out more grid transmission, battery storage in particular.
And I think crucially, if we think about previous productivity booms, yes, we saw early leaders like tech companies, but the ultimate beneficiaries were in real assets over time. So real assets, we think, play a critical component in portfolios in this AI buildout, whether that’s digital infrastructure, power generation as well. These offer inflation protection, contracted cash flows, revenue streams over a long period of time. So, I think that will really provide a nice ballast in portfolios. Certainly offsetting some of the volatility that could arise in some of the equity sectors.
The Global AI Race Is Creating Regional Winners
Baselli: Also, geopolitics and industrial policy appear to play a central role in this supercycle. Overall, when we think of AI we almost immediately think of the US or maybe China, not really Europe, sadly. How should investors think about the regional fragmentation of AI, and where do you see the most attractive opportunities globally?
Cooper: So, AI is fragmenting into regional ecosystems. That is largely being shaped by a number of factors, whether that’s industrial strategy, capital availability and regulation. And so, the US has really proven to be that innovation leader. Their ability to deploy this AI at scale and speed has kind of provided them with the most attractive opportunity set for investors, at least in the near term. But if we think about Europe, I think a competitive advantage that Europe will have is their focus on regulation. Now, that’s been a bit of a barrier now, but over time, as there is an increased emphasis on regulation in this AI space, then perhaps Europe will be well positioned to capture really more of those opportunities, but really from more of an adopter perspective rather than an innovator at this stage. I think for Japan as well, AI deployment has really been focused on solving structural challenges around labor constraints, productivity gaps.
And so that’s playing out in the equity space. It’s one of the reasons why we still have that kind of overweight to broader Japanese equities. And then Asia is really a critical player at this juncture. So, China is really a strong leader in terms of robotics, logistics, smart cities. They’re highly vertically integrated. So, they will be a key player going forward. And more broadly, thinking about the APAC space, so crucial in terms of the AI supply chains from semiconductor manufacturing and elsewhere, and they’re going to increasingly play an important role. So, I think this is a broader ecosystem of regional components, and it really offers investors the opportunity to not be kind of restricted to one particular regulation regime, but really having more differentiation, I would say, and diversification.
The Biggest Risks Facing the AI Boom
Baselli: Finally, if you had to identify the biggest risks investors may be underestimating in this AI supercycle today, what would they be, and why?
Cooper: So, I think the risks come in three different forms. So, concentration risk execution risk and then policy implementation risk. So, on the concentration risk we’ve clearly seen a lot of the capital expenditure really and the earnings really concentrated in those US tech companies. And so, any signs of that capex being pulled back or any concerns around whether they will be able to generate significant returns on this investment, could start to see more pressure materialize in that space, in particular. Execution risk: AI is very capital-intensive. It’s supply chain dependent. So, if we start to see any signs of barriers in terms of this AI buildout, that could start to see return realization slow, even against a backdrop where demand is still quite strong.
And then the policy, the risk around kind of what we’re seeing from physical capacity as well, regulation could play an increasing role if we start to see more differentiation on the policy front, that could curtail some capacity to deploy this AI. We’re already seeing that in terms of some data center projects emerge in some countries. And as well, power. I think power is a physical capacity constraint. We’re already expecting power needs in the US to increase by about 40% by 2040. Data centers alone could consume about 8% of total electricity in the US by 2030. They just don’t have the grid capabilities to fully realize that. And I think that’s a crucial component that could prevent some risks. But those risks are certainly not reason to not embark on AI particularly, really capturing a lot of the revenue generation that we think will materialize in this AI buildout. Because really, this AI supercycle will be a dominant feature for the investment landscape over the next decade or so.
Baselli: Laura, thank you so much for your time. For Morningstar, I’m Valerio Baselli, thanks for watching.
