The AI Investment Boom: When Will It Pay Off?

[MUSIC] Look, at some point you got to make money. You make investments in a business so that you can generate returns and make money. And we've gotten further away from that over the last couple years, instead of closer to it. That doesn't mean it's never going to happen. It just means the stakes are higher. Welcome to another episode of Goldman Sachs Exchanges. I'm Al Funathan and I'm here with George Lee, co-head of the Goldman Sachs Global Institute. Together we're co-hosting a series of episodes exploring the rise of AI and everything it could mean for companies, investors, and economies. [MUSIC] George, great to see you again. >> Great to be here. >> And this should be fun, George, because today we're talking to someone who at least in the past several years has really disagreed. If here amount, I think, or taken a different view than you on AI. Our guest is Jim Cabello, head of Global Equity Research here at Goldman Sachs. And again, you've had many debates with Jim about this topic. >> Well, first of all, it's great to have Jim here. He is both a great friend and a great thinker. And while we differ on some matters related to AI, we actually, there's much that we agree on. And it's been very fun to have this dialogue over multiple years. >> Yeah, for sure. >> Welcome, Jim. >> Yeah, no, it's great to be here. Thank you, and I agree. George is everything that makes Goldman Sachs great to me. And it's been incredible going on this journey with you, and here we are again. Here we are again, exactly. So Jim, as we mentioned a couple of years ago, you came out with what I would characterize as a pretty skeptical, somewhat out of consensus view of generative AI. And you particularly questioned the economics of the technology. You had a lot of doubts about whether the returns the technology would generate whatever really justified all of this cap ex we have seen pouring into the technology over the last couple of years. So to your zone, where do you think you've been right? >> Yeah. >> And where have you been wrong? >> Yeah, so I like to start off with where we've been wrong. And so we just published another report most recently where we started off with where we've been wrong. We called it the Mark II market two years later versus the report that you and I worked on together. So firstly, consumer adoption of AI has been magnificent. Much greater than I expected. George accurately predicted that spot on. So we've been wrong about that. One of the things that we talked a lot about in the original report, we talked about in this report is most consumers are still using a free version of AI. So really to get to the heart of the economic issue, we still really need to focus on the enterprise. But I do really think it's important to acknowledge how great consumer adoption has been and just how accurate George has been about that. The second thing we talk about in the most recent report where we've been wrong was we predicted two years ago that if the hyper scale or stocks underperform for a significant period of time, we would expect that they would scale back on the cap ex. And they have underperformed because of the significant investment in cap ex and the negative impact on their free cash flow. But instead of cutting the cap ex, they've actually raised the cap ex. So I think that that calls into question the economics even more going forward. But the reality of it is that they've massively increased the cap ex despite the stocks underperforming, which is not what we expected. And then I would add that I think the technology itself has made incredible progress, very consistent with George's predictions. And I think any conversation has to really emphasize that and acknowledge that. All of that said, I think the economics are still very much in question and of anything. I'm probably as or more skeptical on the economics today than I was before, despite how incredible the technology is. Before George we said, let me just ask you, why do you think we have continued to see all of this cap ex? I think there's a tremendous amount of FOMO at every level of the supply chain. And it doesn't mean that it's not justified. I just think that we're spending well in advance of where the economics are right now. And I think it's because everybody is afraid of what happens if the technology really takes off and finds significant positive economic use cases. And your competitors have that figured out and you don't. And I think that's everything from the enterprise level to the model layer, to the hyperscaler layer. And one of the things that we talk a lot about in our report is all of the value, all the economic value has continued to accrue to the semiconductor companies. It's been incredible economic value that's accrue to the semiconductor companies. And we do talk a lot in the report. We've really never seen anything like that, right? I covered semiconductor stocks directly for 16 years. And in every cycle, the semiconductor stocks thrive when their customers thrive. Here in this cycle, the semiconductor companies are thriving at the economic expense of everybody above them in the chain. And so at some point that has to rectify itself. Either everybody above them in the chain needs to start to generate a profit as well, or they're going to have to eventually scale back on the semiconductor spending. And that's where we make the focus of this report. So George is Jim right to be concerned about these economics? Yeah, I think there's actually one of the areas where we agree and we published a paper recently at the Goldman Sachs Global Institute that talked about the scale of this investment and just how high the bar will be, how high the hill is that we have to climb to generate sufficient payback. And the nub of our analysis is pivoting off some of the work that Jim's done is that you have to move beyond the traditional notion of disruption of existing profit pools. Jim and his team did a great piece about the advertising business and how much of that could be intervened by AI players, et cetera. And I think if you go profit pool by profit pool and sum up the opportunity, you still fall short of a significant enough payoff from what we think will be $7 to $8 trillion spent here. Now that to me is not the end game. I think the opportunity, and in fact, the imperative for this technology is to help create net new economic activity, breed new tams, create new affordances that we can't imagine. And this has been the history of major technological waves, whether it's agricultural revolution, industrial revolution, computer revolution, et cetera. But I certainly would stipulate to Jim's point that there's a big hill to climb for this payoff. The second thing I think we agree on though, I think we differ in terms of time scale on this is enterprise adoption is really important to this. It has been slower than we might have hoped or expected. And yet I try to anchor back to the fact that we are three and a half years into this. And this is a technology that is both novel. It's parodimitically different than old technologies because it's probabilistic versus deterministic. There's a brand new stack of technologies required to deploy it in the enterprise. And there's an entirely new set of control planes necessary to use it responsibly effectively and compiliently. And so all of that just takes time. I continue to be very optimistic about the potential for this technology to reshape the way businesses work. I'm just going to take a little bit longer than the avid consumer adoption that Jim referenced. Aren't there all these tools? That's been the big development in the last several months. There's been these agentic tools that are now in terms of seeing the vision ahead of how to incorporate some of this technology. So it doesn't that give you some optimism, some reason for hope, Jim? Yeah. The technology itself is terrific. And as George predicted a couple years ago, the piece of improvement in the technology is terrific. The economics of those same technologies is still really challenging. Now it doesn't mean they're always going to be challenging. But in a lot of ways companies are losing more money today implementing this technology than they were two years ago. So as George accurately described, the hell that has to get climbed is even steeper today than it was before. Because we spent more money and there's a lot of things that go along with just the tools. One of the things that we talk a lot about in the report is there are agents today that are terrific. There are models today that are terrific. But in some cases, the data and a lot of cases actually, the data isn't ready to be agented yet. So we're putting agents on top of data that isn't ready to be agented. And that's creating another economic challenge for companies. Again, all of these things can get addressed. But I think sometimes in the euphoria of the market talking about these technologies, we lose sight of some of the blocking and tackling things that needs to happen. There's data management issues, there's model optimization or orchestration layer issues. There's still very much the question of the SLM versus LLM dynamic that George and I have been talking about for three years now. So there's a lot of things that need to be addressed on the economic side of things. And I would just say I think the agent thing is similar to my last theme. It's even more novel than generative AI. Generally, the reasoning paradigm is a year and a half, two years old. Agents really became more prolific in the last year to year and a half. You could argue that really the takeoff and product market fit for agentic coding really only occurred at the end of last year. And so again, it's a fast takeoff, but these are complex technologies. It takes a lot to create that control plane, that orchestration that Jim is referencing. And these agents are so powerful that the need to guard rail them carefully and thoughtfully. That's its own hill to climb here that again, I think holds massive potential. One thing I looked up the other day is if you take the public pronouncements of what the independent model companies are generating in terms of their revenue and sum that up. And you compare it to how fast we got to that level of revenue in the cloud era. It's like, we've gotten a significant amount of revenue in three years from the model companies. It took something like 15, 16, 17 years for the cloud companies in aggregate to get there. So early days, but very fast takeoff. And I think the product market fit in coding is something we should take account of. We also should note that coding is probably the best application for this technology. It is a verifiable domain having the kind of product market that we have in coding expand to other domains. Maybe just a little bit more challenging, require a little bit more science and art. Look, these are the fastest growing companies in the history of corporate America, right? On the top line. Now again, the economics, the profits are all flowing to the semiconductor companies, but from a top line perspective, these are the fastest growing companies ever. Yeah. So Jim, I actually have a question on that. Your report, I think, was super interesting in terms of the outsize economics going to the foundational infrastructure providers here. Isn't it always thus, I think, back to the internet era that we both lived through, Cisco, Sun, Oracle, Intel, were big beneficiaries for quite a while. Was that different than this? And maybe it's just the semiconductor layer in particular. Yes. I think it's the semiconductor layer in particular that is unique. I mean, obviously, and when we published, Allison, we published the report a couple of years ago, we said at the time we should focus on the picks and shovels because that's where we think the economic value was going to occur in the beginning. That did play out. At some point, though, that has to shift. They can't only be those companies that benefit. And again, particularly the semiconductor companies. And when most of the losses upstream in the chain are pretty magnificent. And at some point, that has to get flipped on its head. In my mind, we can and we'll talk about this every day for the next five years. But I really think it all boils down to one thing. Do the enterprises make or save money implementing AI? If they do, this technology is going to fulfill its promise. If we're having the same debate two years from now and we're still saying, well, it's early, then we might have a challenge because at some point, when does the short term become the long term? Because we can say in the short term companies can lose money implementing the technology, but that can't happen forever. And we're also, and we talked about this, George Ewan, I before we're in one of the greatest bull markets in history and we're in a great spot of corporate America, profits in general, a great overall economic environment with some challenges, but a great economic environment. And so it's the right thing for companies to do to be making investments for the long term in this kind of environment. If we, and we all hope this doesn't happen, but if we did hit a more of a rough patch in the markets of the economy, some of this spending that the market is okay doing in a good environment might get scaled back a little bit. No, I don't know, that would be a bad thing, right? Like I'm in this report, I talk a lot about how I would favor the hyperscaler stocks today over the semiconductor stocks, which is a change from two years ago. And the reason for that is if there's three scenarios that you could see playing out over the next couple years, I think the hyperscaler stocks will perform the semiconductor stocks in two of those three scenarios. One is corporate start making a profit, enterprises start generating a return on this investment. Then I think that there's economic value to flow throughout the whole chain, not just the semiconductor companies. And I think the hyperscaler stocks would get rewarded because there's doubt on that those stocks have under before in the market by so much. The second is the hyperscaler companies say, look, we might need to moderate the spending a little bit. And we're not talking about companies going from 200 billion a year in catbex to zero. We're talking about slight moderation of the catbex paste so that they can start to claw back some free cash flow. And I think the hyperscaler stocks would outperform semi stocks in that scenario as well. The one scenario where the semi stocks continued outperform is the status quo persists where the only company's making money in the chain and the semiconductor companies. Again, this has gone on longer than I thought to begin with, but that can't go on forever. We can have all these incredible companies upstream in the chain losing money because again, even in your internet analogy, eventually the best companies upstream started to make money. So totally. I know that's not how the market is positioned right now. The positioning of the market is incredibly bullish on semis and bearish on everything below that. At some point that has to turn around. I would say that at two years ago, you said 18 months, two years. Yeah. And now we're two years forward. You're talking about another two years. So the market seems to have some faith in this. Oh, the market. Look, we're again, this is the best bull market of our lifetime. I don't think there's any question about that. And this is a reason it's a little bit of a circular. This technology is the reason for it. And then that bull market is powering a lot of the investment that's happening. I think at some point, again, the short term has to become the long term, but the market's giving a long leash as well as should because again, I think the market's looking at the progress on the technology and saying, well, eventually the profits will flow through. That may well happen. I would just like to see that happen at some point. One of the returns issues that Jim and I have talked a lot about that I think is fascinating to consider here is to what extent, even if you are an extreme believer in the power of this technology like I am, there's a potential in the enterprise that the advantage you gain from deploying it is somewhat fleeting. In other words, there's a chance that you can create some temporal advantage by being the first person on the block to effectively deploy the technology to lower your engineering costs or increase your straight through processing times whatever, but ultimately everyone in your sector is going to catch up and will the margin of edges get competed away. And then second, like many technology evolutions we've seen in history, will the surplus we generate vanish into consumers pockets in a way. And so this value capture and then measurement of ROI, which is a super complex topic around technology. I think is one of the most fascinating dimensions of this where in a way, companies are and will deploy this technology because it seems so intuitive, logical, and it feels like it's creating savings, advantage, all of that. And yet the measurement of that intangible hard terms is sometimes very elusive. And so this adds to the uncertainty that Jim's talking about. That's so incredibly well said. And one of the things I've always loved about having these discussions with George is he's so balanced on this, right? You look at some of the questions around this is are these expenditures just the cost of doing business now and it doesn't really give you a big advantage. It's just the cost of being in business. And then it becomes an issue of scale, a scale really what matters in business now more than ever. I mean, scale's always been a big advantage. But when you have some of these models come out and all of a sudden you wake up one day and you've to spend $50 million as an enterprise on a new model, you didn't even know that wasn't part of the original plans. And that's not really revenue generating. That's almost more defense than offense. You know, there are fewer and fewer companies that can afford to do that. So scale becomes an absolutely massive advantage in this environment. Absolutely right. I mean, the other interesting side of the coin of gosh will, we make these investments and while margin advantage get competed away, the other side of that coin is if I don't make these investments, I'm at a permanent margin disadvantage to my and this is one of the game theoretical things that I think continues to drive spending. The other big topic of course, Jim is the implication on employment and you've made some pretty interesting comments about Wall Street or management perception, C-suite's perception of the impact of this technology on worker productivity versus what workers are actually experiencing and what the tech companies are saying. Talk to us a little about that misperception or gap in perception that you're serving. Pretty much every third party survey that has been done finds this big gap between the C-suite expectations around the impact of the technology versus the line workers view on the actual impact that the technology is having. Now there's a lot of different parts to that right. There's so many different components. Now every survey pretty much says the same thing which is the line workers aren't getting as much benefit from it as the C-suite expected. Now every survey, every analysis has to be taken in context and obviously the C-suite is bullish on their investments or they wouldn't be making the investments and the line workers in some ways are scared that this technology could replace what they're doing. So we have to take these surveys with that as context. However, all the surveys do say the same thing which is there isn't as much productivity today is where the expectations were when the investments are being made. There's things that can change that. Again, I think the data layer is a huge part of that right because so many times people are doing these queries today and they're coming up with the incorrect results or incomplete results and a lot of that has to do with the data across an organization not speaking with one another. So I do think there's a pretty significant disconnect today on that. Yeah, two two issues that Jim touches on there. The anchor fascinating is this issue of C-suite versus line worker illustrates a really interesting divergence in the Sikko system which is as a big company with incumbent work habits enshrined workflows ways of doing things legacy systems, etc. retrofitting this brand new technology has its own challenges and there's a certain drag coefficient to it that may be responsible for what Jim's talking about. Like line workers going like I have to change the way I do things or I'm not used to this, etc. Where you can see the real takeoff and productivity of these technologies is in very young companies called them new native AI native companies which are built from the jump for this technology. They're organizationally designed, their technology stack is designed and living close to Silicon Valley, I get to see when you're starting a company in this era, the amount of productivity gain you can get from this technology is extraordinary. Now in my optimistic sense, I hope that that means that same kind of productivity leap will be ultimately available to bigger companies as we complete the retrofit but that is an uncertainty and certainly the time gap there is uncertain. The other thing we haven't talked much about is this somewhat sudden turn towards populist resentment of AI and whether it's being seen people booted commencement addresses or being shot at in their homes because they're responsible for deployment of a data center in a legislative region, etc. We're getting to a point where this is a deeply unpopular technology, interestingly almost uniquely in the US versus other parts of the world and that may be one of the things that really contributes to slower progress here that I'm trying to keep my eye on closely. Yeah, it's a really challenging issue. I try to stay away from a lot of these political kind of issues because I don't think there's any winning these debates. I have had some real conversations with some of the people that have been recently in the middle of some of those things that you just talked about and it's a shame. I think a lot of it's going to potentially come to a head around the midterm elections where we're going to see if people are going to vote along these lines and I would put all of it in the broader economics bucket, right? Like I've tend to focus on enterprise economics. I think the overall expenditures versus the returns and where those returns are accruing in the supply chain, how much of it is going to flow back to the individual. Can we make a case that individuals are benefiting economically from using the technology and that's going to offset some of the increased electricity costs and things of that nature? I mean, I think those are all questions that need to be answered. Yes, I think just to wrap that as I'm hearing both of you, I mean, I don't think there is too much daylight between your views. I think the consensus between you is that there's just a lot more that we need to observe and learn over the coming years. And I think Jim, you're really just questioning at what point will we hit a wall if we aren't seeing some of these advances and productivity gains and enterprise revenues? Yeah, look at some point you got to make money. I mean, you make investments in a business so that you can generate returns and make money. And we've gotten further away from that over the last couple years instead of closer to it. It just means the stakes are higher and like George started off by saying the wall we need to climb is a little higher. It can absolutely happen and in the last report I tried to lay out from a technical perspective or quasi technical perspective. What the kinds of things I think people need to be focused on. I don't think investors should just be blindly assuming it's going to happen. I think you need to question along the way, but question with a reasonable balance of instead of saying it's never going to happen or it's definitely going to happen. Kind of laying out the markers of here's what needs to happen, you know, moment in time today to gauge the most likely outcomes. Yeah, this is illustrative of the fun that we have with this dialogue and as Jim says, we're so early that this kind of debate will continue for years to come. I think one of the great things about our partnership on this, like Jim is extremely good at trenchantly and accurately pricing the spot of where we are in this technology. My inclination and suppose in some ways, my role is to look a little bit farther down field. That implies a leap of technological and in some ways economic faith to do so, but that balance I think gets us to a really nice synthesis of where we are today and where we might go. Thanks again, Jim for joining us. This has been great. Thank you so much. It's been a pleasure as always. So George, again, fascinating conversation. I say that every single time, but I genuinely mean it every single time. And I think that you did a good job summing up the views and where you guys stand. Any takeaways from you? It's like fascinating and by Allison, it's always such a pleasure to do this with you and I agree these are always fascinating discussions. This one's particularly fun because it's part of this linear legacy of us for Jim and I for the past three, three and a half years talking about this issue. And it's really fun just to see how both of us have evolved our thinking. And one thing we probably most agree on is the number of unanswered questions ahead of us, which makes this really exciting and fun to talk about. And it means I think we're going to have to bring him back on. Absolutely. Absolutely. Thanks. Thanks. Thanks for watching. You too. This episode of Goldman Sachs and Stages was acquitted on Tuesday, May 26, 2026. 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