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AI Spending Could Reach $2 Trillion by 2030

Market Movers: The Opening Bell

September 29, 2026

Video Transcript

Today's capital market segment has been brought to you by Alpaca. While hyperscalers are on course to spend a staggering $800 billion on AI CapEx this year, with Wall Street projecting that figure to top $1.1 trillion by 2027. Yet, even as combined cloud revenue growth accelerated to 48% with a $1.7 trillion backlog.
Market skepticism has driven AI in multiples, down from 32 times forward earnings to 22 times. And with Micron reporting earnings this week and testing the memory bottleneck, investors are demanding to know where real capital efficiency lies. Well, joining us live this morning here at the New York Stock Exchange to break down his tech allocation strategy is Jonathan Cofsky, co portfolio manager of the Janus Henderson Global Tech and Innovation Fund and co sector head for technology at Janus Henderson.
Well John, great to have you here. Thank you so much for joining me.
Yeah, thanks for having me.
Well, here we are about to head into the final quarter of 2026. And the focus has been on AI, not just the technology, but also investing. So where do you stand when it comes to the build out?
Yeah, I think we're still pretty early in the build out. If you look at AI CapEx, it's going to be, you know, $700 or $800 billion this year. And expectations are as we get to 2030, it's going to be close to $2 trillion. So there's still a lot to come. But the question is what's the return on that spend.
Yeah. And that is the big question mark, especially as we look at the hyperscalers. So what do you make of what you're seeing from those hyperscalers?
So the hyperscalers have all seen a pretty good acceleration of revenue. And the returns are lower than their historical returns, but they're still fairly good, especially on the amount of capital they're deploying. I think the bigger question is how much revenue are the AI labs going to have over the next several years because they are, in aggregate, you know, 40, 50% of the backlog at these hyperscalers?
Yeah. And speaking of which, there have been many comparisons with AI and other innovations and technology revolutions that we have seen in the past. So how are you viewing artificial intelligence as an investment theme, as well as from an innovation perspective over at Janus?
I mean, we view this as potentially the biggest investment theme of our lifetimes. It's generational, and we've been looking at historical build outs, whether it's railroads or the dot com boom, etc. and if you look at $1 trillion or $2 trillion of CapEx, it's actually in line with historical spends for build outs like this.
Um, so that is in line. The question is, you know, what will be the economic impact to GDP, productivity, job growth, etc.?
And those are those unanswered questions. And without a crystal ball, we don't know what that will turn out in terms of data. But of course, when it comes to portfolio management, what are you doing right now when it comes to AI?
So some of our favorite ideas are the real choke points of AI where there's really no alternative and they win whoever wins on the AI side. So it's companies like TSMC. And then on the semiconductor equipment side you have ASML, Lam, KLA who are market leaders in really nicely growing TAMs and then some of the mega caps like Nvidia, Amazon, Microsoft that are also benefiting from the builds.
And of course, when it comes to the space, you're talking about opportunity as well as risks. So earlier this year, there was a new term that was added to our lexicon and that was SaaS apocalypse. So tell us about your concerns when it comes to software AI.
Yeah. So I mean, software itself is a replacement for labor. And AI is also a replacement for labor. So there are parts of software where there's probably more risk from AI, but there are also parts of software that are extremely sticky and will probably work in conjunction with AI. But there's just a lot of doubt and question marks.
And when you have that, it pressures the terminal value and multiples of software stocks. So it's right to impact the multiples. But we do think there are going to be some names that make it to the other side. But it it's going to be harder. It's going to shake up the leaderboard.
So what comes into the calculus when you're looking at those names that are going to actually make it?
So it's how sticky are they? What's their retention with customers? And then the big thing is how fast can they incorporate AI into their products. And the key is not just do you have product announcements, it's are your customers actually using it and is it accelerating your revenue growth?
And of course, when we look ahead, not just to the rest of this year, but also beyond, a lot goes into the calculation, even when we're talking about something like TAM. So over at Janus, how are you viewing the outlook as we move forward?
Yeah. So I mean, given the level of spend, we look at the TAM both from a basis of how much revenue and output do you need to support the CapEx. And then the bottom up TAM of, you know, what you need to believe in terms of AI labs, software companies, enterprise adoption, etc. but ultimately you have to make some assumptions about how much consumers will adopt it.
And then, you know, what are either the revenue productivity gains or labor cost savings.
And finally, John, before I let you go. Of course, there are so many headlines when it comes to artificial intelligence, and when we're talking about the regulatory landscape and even the IPO outlook, there are a lot of question marks, and people are still trying to figure out what's happening. But when you're separating the signal from the noise, what do you think is important?
So I think the important thing is one, to see OpenAI and Anthropic have successful IPOs, because that's a real bellwether for investors right now. Um, you know, and then the next thing is the 2028 elections, because there's a lot of rhetoric about, you know, do people want data centers. And there's a lot of pushback for that.
And, you know, I think it'll be interesting to see what happens as we look forward a couple of years. But, you know, the large tech companies are full steam ahead.
Well, a lot to keep our eyes on. I appreciate your time. Thank you so much for joining us here at the New York Stock Exchange, Jonathan.
Thank you.
Thank you.
Yeah.

AI infrastructure spending continues to accelerate, with hyperscalers expected to spend hundreds of billions of dollars on AI infrastructure this year and potentially close to $2 trillion annually by 2030. Jonathan Cofsky, Co Portfolio Manager of the Janus Henderson Global Technology and Innovation Fund and Co Sector Head for Technology at Janus Henderson, joins Remy Blaire from the New York Stock Exchange to discuss where investors may find opportunities as the AI build out continues.

Cofsky says the AI investment cycle is still in its early stages, but the key question is whether the massive capital spending will generate sufficient returns. He discusses the performance of hyperscalers, the growing role of AI labs and the importance of understanding how much revenue and productivity gains will ultimately come from these investments.

The conversation also explores the companies Cofsky sees as critical points in the AI ecosystem, including TSMC, ASML, Lam Research, KLA, Nvidia, Amazon and Microsoft. He also discusses the pressure AI is creating for software companies, with investors increasingly focused on customer retention, product adoption and whether companies can successfully integrate AI into their existing products.

Cofsky says investors should look beyond AI product announcements and focus on whether customers are actually using the technology and whether that adoption is translating into stronger revenue growth. He also discusses the broader economic questions surrounding AI, including productivity, employment, consumer adoption and the potential impact on GDP.

Looking ahead, Cofsky highlights the potential importance of major AI company IPOs, regulatory developments and the debate around data center expansion. He says large technology companies continue to invest heavily in AI, while investors assess how the next phase of the build out will translate into economic and financial returns.

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