Heath Terry, Citi Research head of tech and communications, joins CNBC's 'Money Movers' to discuss Citi's new coverage of private AI companies, how he's thinking about bottlenecks, and much more.

Citi has begun formal coverage of the artificial intelligence sector, projecting that AI revenues could reach $780 billion by 2030, representing nearly 80% annual growth over the next five years. The analysis, led by Citi's Head of Tech and Communications Research, highlights the early stage of the industry and the substantial investments being made, including $400 billion in infrastructure this year and trillions more expected from major technology firms and governments.

The report identifies customer support, code development, and knowledge retrieval as the most profitable current use cases, with efficiency improvements potentially leading to $275 billion in cost savings. While regulatory challenges and infrastructure bottlenecks are noted, Citi expects these issues to be managed as the sector matures. The analysis also emphasizes that scale and access to resources are key differentiators for success in the AI industry.

Citi launches AI sector coverage: Here's what to know

Welcome back, Citi, launching coverage of the entire AI sector in a new note, keeping tabs on all the private companies to watch. Forecasting AI revenues will reach $780 billion by 2030. That's nearly 80% annual growth over the next five years. Our next guest authored that report, joins us this morning with his Outlook old friend of the show, Heath Terry, Citi Research Head of Tech and Communications here at Post9. Welcome back. Yeah. Heath, it's great to see you again. Thanks for having me. So we're at the beginning of this. Is the general view? Yeah, we are still really early stage. Talk about what went into the model and how you're coming up with these numbers. Yeah. So this was really an attempt. Right now, because we don't have public AI companies, there are a lot of analysts who do cover the public companies around the space, Alphabet, Microsoft, NVIDIA, that are coming at it from their perspective. And so they see it as a software analyst or a semiconductor analyst. We try to approach it as an industry and treat AI as an industry. We've got $300 billion companies in this space. They're just not public. And so what we saw and what went into it, particularly the model that we built around this, was the $400 billion worth of infrastructure investment that's going in this year, $2.3 trillion just from the hyperscalers and another trillion on top of that from sovereigns going in over the next five years. And then you get the output on that, which we looked at as token consumption and then the price that's getting paid for those tokens. Are the bottlenecks, whether that's you know, grid or equipment or I guess the cost being more than the value, are any of those bothering you at this point? Look, they do. And we're paying a lot of attention to them. We are kind of taking the attitude that these bottlenecks are going to get solved. And really, when I say solved, I mean move somewhere else. So if there's electrical equipment that's a bottleneck, you need transformers. Those things end up going to the highest value application, which right now, with all the money going into it, is AI. So that data center that needs a transformer is going to get it. That farm in another country that was hoping to get that transformer probably doesn't. And so we're really just trading bottlenecks from AI to another part of the economy. In every industry that I cover, whether it's casinos, travel and leisure, whether it's insurance, the question from analysts is always about, well, how are you using AI? When you're evaluating AI, where are the best use cases? Where are the most profitable, do you think, use cases? So right now, it's what we call the first three, which is customer support, code development and knowledge retrieval. And if you look at those three combined, you're seeing this sort of average of around 30% improvements in the efficiencies that you get out of that. And that math implies that we're going to see about $275 billion in cost savings across those three areas. When you take and extrapolate that out, which is fair in some places, not fair in others, to healthcare, to industrials, to financials, you get up into the trillions in terms of the impact that you're going to see. And it's just a question of how quickly that starts to roll out. And given how many companies are new entrants into this space, what's the differentiator? Yeah, so right now, it really is scale. You know, for all the venture money that's going into this space, AI is a big company technology. The more data you have, the more infrastructure you have, the more money and talent that you have to be able to go after this, the more likely you are to succeed. And so that's really kind of what we're looking for here. There are going to be, and we really do believe that there will be some sustainable, durable companies that break out of this. You're seeing some of those already getting to billion-dollar revenue lines faster than anyone else has. That's what we're going to watch for, but we're still really early in that process. Where are you on the hardware, software discussion, and whether or not software is vulnerable? Well, software is certainly vulnerable, right? And it's vulnerable in a couple of different ways. It's vulnerable in the aspect of there is going to be a change in the way that software is priced. And we've gone through this before, going from licensing models to seat-based models to consumption models. And where we see it heading in this direction is, if you've got your developers that are 30 percent more productive, and in theory you need 30 percent fewer developers, and now that's very debatable, that's going to impact your pricing. It's going to impact all of these seat-based models that are out there, which is why you're seeing them try to transition in some places to consumption-based models. And that's going to be a difficult transition. The other part of software that people look at is the, I'm going to sit down in front of an LLM, tell it what I want to do, and it's going to create for me the software that I need. And yeah, there are some applications where that might make sense, but you and I aren't going to sit down at the beginning of the morning and ask an LLM to recreate Excel for us simply because we don't want to pay Microsoft X number of dollars a month. Right. I'm curious, we're hearing from OpenAI today that it's going to make some changes to ChatGPT. It's being sued by parents of a teenager who committed suicide. When you're evaluating AI opportunities and potential profit, how much are you factoring in societal impact that may in turn prompt the kind of regulatory crackdown that could interfere with profits? Yeah, regulatory is one of the biggest things that we have to watch in this. And we've seen a lot of this already in certain countries, in certain states, is they've tried to set up guardrails around this. We do think that that will probably slow some of the growth that you're going to see here because it's always very difficult for regulators to get this stuff right in the timeframe that they need to and in the right way the first time out. But we don't think it's going to be enough to really slow this down in a meaningful way. And to be honest with you, we're really focused on sort of the enterprise adoption and the consumer applications that you're seeing where those guardrails are being put in place by the companies themselves. The restrictions that a Citi, a Pfizer, a John Deere are going to have around the way that they're implementing AI are going to be much more restrictive than anything that you see from regulators.

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