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Welcome to Thoughts on the Market. I'm Joe Moore, Morgan Stanley's head of U.S. semiconductors.
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And I'm Keith Weiss, head of U.S. software.
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Today on the show, one of the biggest market debates in the tech sector has been around AI and the return on investment, or roi. In fact, we think this will be the number one topic of conversation at Morgan Stanley's annual Technology, Media and Telecom conference in San Francisco. And that's precisely where we're bringing you this episode from. It's Monday, March 3, 7am in San Francisco. So let's get right into it. ChatGPT was released November 2022. Since then, the biggest tech players have gained more than $9 trillion in combined market capitalization. They're up more than double the amount of the S&P 500 index. And there's a lot of investor expectation for a new technology cycle centered around AI. And that's what's driving a lot of this momentum. That said, there's also a significant investor concern around this topic of roi, especially given the unprecedented level of investment that we've seen and sparse data points still on the returns. So where are we now? Is 2025 going to be a year when the ROI on Genai finally turns positive?
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If we take a step back and think about the staging of how innovation cycles tend to play out, I think it's a helpful context and it starts with research. I would say the period up until when ChatGPT was released, up until that November 2022 was a period of where the fundamental research was being done on the transformer models utilizing machine learning. And what fundament is, is trying to figure out if these fundamental capabilities are realistic, if we can do this in software, if you will. And with the release of ChatGPT, it was a very strong stamp of approval of yes, like these transformer models can work. Then you start stage two. And I think that's basically November 22nd through where we are today, where you have two tracks going on. One is development. So these large language models, they can do natural language processing well, they can contextually understand unstructured and semi structured data, they can generate content, they can create text, they could create images and videos. So there's these fundamental capabilities, but you have to develop a product to get work done. How are we going to utilize those capabilities? So we've been working on development of product over the past two years and at the same time we've been scaling out the infrastructure for that product development. And now heading into 2025, I think we're ready to go into the next stage of the innovation cycle, which will be market uptake. And that's when revenue starts to flow to the software companies that are trying to automate business processes. We definitely think that monetization starts to ramp in 2025, which should prove out a better ROI or start to prove out the ROI of all this investment that we've been making.
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Morgan Stanley research projects that Gen AI can potentially drive a $1.1 trillion revenue opportunity in 2028, up from 45 billion in 2024. Can you break this down for our listeners?
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We recently put out a report where we tried to size kind of what the revenue generation capability is from generative AI, because that's an important part of this ROI equation. You have the return on the top of where you could actually monetize this on the bottom, obviously investment. And we took a look at all the investment needed to serve this type of functionality. The 1.1 trillion, if you will. It breaks down into two big components. One side of the equation is in my backyard, and that's the enterprise software side of the equation. It's about a third of that number. What we see occurring is the automation of more and more of the work being done by information workers for people in the overall worker population being automated by this generative AI functionality. What we see is about 25% of overall labor being impacted today. And we see that growing to over 45% over the next three years. So what that's going to look like from a software perspective is an opportunity ramping up to just about $400 billion of software opportunity by 2028. At that point, generative AI will represent about 22% of overall software spending. At that point, the overall software market, we expect to be about a $1.8 trillion market. The other side of the equation, the bigger side of the equation is actually the consumer platforms. That kind of makes sense if you think about the broader economy. It's basically 1/3. B2B2 3B2C. The automation is relatively equivalent on both sides of the equation.
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Let's drill further into your outlook for software. What are the biggest catalysts you expect to see this year and then over the coming three years?
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The key catalyst for this year is proving out the efficacy of these solutions, proving out that they're going to drive productivity gains and yield real hard dollar ROI for the end customer? I think where we'll see that is from labor savings. Once that occurs, and I think it's going to be over the next 12 to 18 months, then we go into the period of mainstream adoption. You need to start utilizing these technologies to drive the efficiencies within your businesses to be able to keep up with your competitors. So that's the main thing that we're looking for in the near term over the next three years. What you're looking for is the breakthrough technologies. Where can we find opportunities not just to create efficiencies within existing processes, but to completely rewrite the business process? That's where you see new big companies emerge within the software. Opportunity is the people that really fundamentally change the equation around some of these processes. So, Joe, turning it over to you, Hardware remains a bottleneck for AI innovation. Why is that the case, and what are the biggest hurdles in the semiconductor space right now?
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Well, this has proven to be an extremely computationally intensive application. And I think it started with training, where you started seeing tens of thousands of GPUs, or XPUs, clustered together to train these big models, these large language models. And you started hearing comments two years ago around the development of ChatGPT, that the scaling laws are tricky. You might need five times as much hardware to make a model that's 10% smarter. But the challenge of making a model that's 10% smarter, the table stakes of that are very significant. And so you see those investments continuing to scale up. And that's been a big debate for the market. But we've heard from most of the big spenders in the market that we are continuing to scale up training. And then after that happened, we started seeing inference suddenly, as a big user of advanced processors, GPUs, in a way that they hadn't previously before. And that was sort of simple conversational types of AI. Now, as you start migrating into more of a reasoning AI, a multipass approach, you're looking at a really dramatic scaling in the amount of hardware that's required from both GPUs and XPUs. And at the same time, the hardware companies are focused a lot on how do we deliver that so that it doesn't become prohibitively expensive, which it is very expensive, but there's a lot of improvement. And that's where you're sort of seeing this tug of war in the stocks that when you see something that's deflationary, it becomes a big negative. But the reality is the hardware is designed to be deflationary because the workloads themselves are inflationary. And so I think there's a lot of growth still ahead of us, a lot of investment, and a lot of rich debate in the market about this.
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Let's pull on that thread a little bit. You talked initially about the scaling of the GPU clusters to support training. Over the past year, we've gotten a little bit more pushback on the ideas or the efficacy of those scaling laws. They've come more under question and at the same time we've seen the availability of some lower cost but still very high performance models. Is this going to reshape the investments from the large semiconductor players in terms of how they're looking to address the market?
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I think we have to assess that over time. Right now there are very clear comments from everybody who's in charge of scaling large models that they intend to continue to scale. I think there is a benefit to doing so from the standpoint of creating a richer model. But is the ROI there? That's where I think your numbers do a very good job of justifying our model for our core companies, where we can say, okay, this is not a bubble, this is investment that's driven by these areas of economic benefit that our software and Internet teams are seeing. I think there is a bit of an arms race at the high end of the market where people just want to have the biggest cluster. We think that's about 30% of the revenue right now in hardware is supporting those really big models. But we're also seeing to your point, a very rich hardware configuration. On the inference side, post training model customization, Nvidia said on their earnings call recently that they see several orders of magnitude more compute required for those applications than for that pre training. So I think over time that's where the growth is going to come from. But right now we're seeing growth really from all aspects of the market.
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Got it. So a lot of really big opportunities out there utilizing these GPUs and ASICs, but also a lot of unknowns and potential risks. So what are the key catalysts that you're looking looking for in the semiconductor space over the course of this year and maybe over the next three years?
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Well, 2025 is a year that is really mostly about supply. We're ramping up new hardware, but also several companies doing custom silicon. We have to ramp all that hardware up. And it's very complicated. It uses every kind of trick and technique that semiconductors use to do advanced packaging and things like that. And so it's a very challenging supply chain and it has been for two years. And fortunately it's happened at a time when there's plenty of semiconductor capacity out there. But I think we're ramping very quickly and I think what you're seeing is the things that matter this year are going to be more about how quickly we can get that supply. What are the gross margins on hardware, things like that. I think beyond that we have to really get a sense of these ROI questions are really important beyond 2025, because again, this is not a bubble. But hardware is cyclical and it doesn't slow gracefully. So there will be periods where investment may fall off and it'll be a difficult time to own the stocks. And that's, you know, we do think that over time the value sort of transitions from hardware to software. But we model for 2026 to be a year where it starts to slow down a little bit. We start to see some consolidation in these investments. Now, 12 months ago, I thought that about 2025, so the timeframe keeps getting pushed out. It remains very robust, but I think at some point it'll plateau a little bit and we'll start to see some fragmentation and we'll start to see markets like the reasoning models, inference models becoming more and more critical. But that's where when I hear you and Brian Nowak talking about sort of the early stage that we are of actually implementing this stuff, that inference has a long way to go in terms of growth. So we're optimistic around the whole AI space for semiconductors. Obviously the market is as well. So there's expectations, challenges there, but there's still a lot of growth ahead of us. So, Keith, looking towards the future, as AI expands the functionality of software, how will that transform the business models of your companies?
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We're also fundamentally optimistic about software and what generative AI means for the overall software industry. If we look at software companies today, particularly application companies, a lot of what you're trying to do is make information workers more productive. So it made a lot of sense to price based upon the number of people who are using your software. Or you've got a lot of seat based models. Now we're talking about completely automating some of those processes, taking people out of the loop altogether. You have to price differently. You have to price based upon the number of transactions you're running or some type of consumptive element of the amount of work that you're getting done. I think the other thing that we're going to see is the market opportunity expanding well beyond information workers. So the way that we count the value, the way that we accrue the value might change a little bit, but the underlying value proposition remains the same. It's about automating, creating productivity in those business processes and then the software companies pricing for their fair share of that productivity.
- [11:53]
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Great. Well, let me just say this has been a really useful process for me. The collaboration between our teams is really helpful because as a semiconductor analyst, you can see the data points, you can see the hardware being built, and I know the enthusiasm that people have on a tactical level. But understanding where the returns are going to come from and what milestones we need to watch to see any potential course correction is very valuable. So on that note, it's time for us to get to the exciting panels at the Morgan Stanley TMT conference, and we'll have more from the conference on the show later this week. Keith, thanks for taking the time to talk.
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Great speaking with you, Joe, and thanks for listening.
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