Change agents: What agentic AI means for hardware and software  

“The Angle” Music

Cold Open
Dom Rizzo
All of a sudden, you're not just attacking the enterprise IT software market, you're attacking the trillions of dollar knowledge workflow."

Jennifer Martin
Welcome to The Angle from T. Rowe Price, a podcast for curious investors. Just a reminder that outside of the U.S. and Australia, this podcast is for investment professionals only. I'm your host, Jennifer Martin, a global equity portfolio specialist at T. Rowe Price Associates here in Baltimore, Maryland.

The next phase of the AI revolution is here.

Chatbots answered questions. AI agents are going a step further and executing digital tasks for us.

Here to help us understand the rise of agentic AI and its implications for investors are Dom Rizzo and Frank Shi.

Jennifer Martin
Dom has been with T. Rowe Price for more than a decade and is a sector portfolio manager who also co-leads our technology analysts. I like to think of Dom as half-man, half-GPU. He has a lot of processing power. Frank Shi is an analyst covering global semiconductors and has stayed up late to join us from Hong Kong, and, in many ways, Frank is like AI—he’s on 24/7.

Dom and Frank, welcome to The Angle.

Dom Rizzo
Thanks for having us, Jennifer.

Frank Shi
It's great to be here. Thanks, Jennifer.

Jennifer Martin
Dom, the last time you were on The Angle, you asserted that AI could become the greatest productivity enhancer since electricity. As we are transitioning from chatbots to agents, what does that mean for this story and the pace and scale of AI adoption?

Dom Rizzo
Well, let's first take a step back, Jennifer. It's amazing to think about all that's changed over the past few years. But what did I mean when we said AI has the potential to be the biggest productivity enhancer since electricity?

I think AI is going to result in an unlock of productivity that's going to result in a strong economic boom.

And there's only three ways to grow an economy. There’s:

• Capital: Throw more money at the problem;
• Labor: Throw more people at the problem; or
• Productivity: Do more with the money and people that you throw at a problem.

And I think we're at the early stages of an economic acceleration driven by a productivity boom. And the big change since I was on the podcast last is the change from simple chatbots to agentic workflows.

A chatbot gives you a response. You're just talking with ChatGPT. But an agent can complete tasks on your behalf. It can break a goal into steps, use tools, check the results, and keep going for hours. That is the economic leap. We're moving from just talking about information to doing work.

Now where have we seen that pop up?

The first and best use case for agentic workflows is coding, right?

And it's the clearest early proof because the feedback loop is just brutally objective. The code runs or it doesn't.

Let's look at some of these numbers. I mean, these numbers have been phenomenal. Last summer, Anthropic was just on a USD 5 billion run rate in revenue. Now, because of the rise of Claude Code, they're close to USD 50 billion.

Jennifer Martin
That is huge. Put that into context: USD 5 [billion] to USD 50 billion—how many enterprise software companies is that?

Dom Rizzo
It's so funny to think about it that way. It's almost three ServiceNows, Jennifer. So, I mean, I've never seen growth of an enterprise application like this. This is true virality within an enterprise on the back of coding agents. And it's tools like OpenAI Codex, tools like Claude Code, tools like Cursor that are really starting to take off within the enterprise. And I think it's going to result in this economic boon.

And if you think about what is coding, coding is the unlock that allows you to get into different workflows—finance, legal, HR. And so, all of a sudden, you're not just attacking the enterprise IT software market, you're attacking the trillions of dollar knowledge workflow.

And how do we make all of these people—whether in finance, legal, coding—more efficient, make them more productive? And will enterprises pay for that productivity? So far, the evidence is absolutely.

Jennifer Martin
Yes, definitely. And you're seeing the adoption, even at our own organization, which I know we're going to talk about, we've had some really good milestones internally. And so maybe I would like to ask Frank—it's easy to see why the proliferation of agents, which increases the demand for tokens, would be a broad tailwind for infrastructure. However, nuance always matters in these discussions. Could you explain what parts of the hardware supply chain appear well positioned as AI goes from answering questions to executing complex tasks? And maybe we start with what is a token?

Frank Shi
Token, in technical terms, is a measure the basic measurement in AI. But I would like to think of it as a sliver of intelligence. You can use that to address enterprise problems, to provide consumer services, to find cancer-curing drugs. And all of that, together, contributes to the massive demand we are seeing for token[s].

On the hardware side, it's very obvious to think of the beneficiary as the GPUs. However, what agents has changed is that it's moving the bottleneck from the accelerator to the entire system. Within that, CPUs, memory and storage, and networking are the three areas we're seeing massive changes.

Starting with CPU, I think of CPU as the operation team around the all-star GPU, and the funny detail here is that the CPU is significantly cheaper than the GPU, so you are very well incentivized to make sure that you have the best CPU to keep your all-star player always fed and ready to play. And here the CPU's job in the system can be scheduling the work, launching sub-agents, running Python, querying database—all the nitty-gritty details to actually make the agent useful.

On memory and storage, this is where the incremental system performance as well as the model performance improves substantially when you add more memory. Think of it this way: When you add one additional unit of memory, it significantly improves the system’s throughput, meaning that the model companies can generate more revenue per dollar of AI spend they have done. To the models, memory also increases the context it can have and allows the system to better recall what you have discussed with it—[for example,] what are the right documents to retrieve when you're trying to address a certain enterprise workflow? And that dramatically increases the value of the model to the end users.

Lastly, on networking, the networking is now the computer. There's one way of trying to squeeze everything—every model weights, every context—into one GPU, one piece of memory. There is another way of spreading them out across the system and making sure that every part of the system is properly configured, and that requires networking to manage the traffic, schedule the data movement, and make the system the most efficient.

All of them combined makes the system a lot more efficient and makes the model a lot more useful for the customers.

Dom Rizzo
You know, Jennifer, Frank is really getting to this point that Dave raised a few weeks ago on his podcast. AI is not software; it's token manufacturing. And token manufacturing is inherently hardware-intensive, and Frank just laid out all those great points. It's the CPU; it's the GPU; it's the memory; it's the networking—all working together in concert to manufacture more tokens.

Jennifer Martin
Both of you have done such a phenomenal job in navigating how hardware needs shift as AI workflows have evolved. The other thing that we've talked a lot about on this podcast is really about bottlenecks and shortages. This robust demand means that many parts of the AI supply chain are in shortage. And so how do you assess these relative opportunities?

Dom Rizzo
Look, you know my framework. We always stay close to our framework in these hard moments: linchpin technologies, innovating in secular growth markets, with improving fundamentals, at reasonable valuations. And right now, probably the thing I think about most is that linchpin part of the framework. Not every shortage is a durable economic moat. There's a difference between linchpins and golden spikes.

Linchpins are mission critical, hard to substitute, slow to add, and still very valuable after supply chains catch up. Golden spikes are scarcity and in a moment in time.

Frank has done a lot of great work here. Maybe you want to expand, Frank, a little bit on what's happening in memory and why we’ve seen maybe some of the linchpin change that's happened in CPUs.

Frank Shi
Absolutely, Dom. If we think of what creates or makes for durable economic moat, it's the fact that you have technologies that others cannot own or you have a significant cost advantage that others cannot surpass.

Traditionally, people think of memory as a commodity. And they're having their moment in time because of the historic undersupply and the pricing power that has granted them. However, I think a structural change happening to the memory sector is high-bandwidth memory.

And what that has changed is that, one, it makes memory more customized than standardized, and that confers better pricing power and margin to the certain segment of the memory products. And secondly, that also gives memory companies a better visibility to the demand and technology roadmap of their customers, turning them from just a commodity vendor into a real technology partner to their customers.

Compare that to, for instance, a CPU or GPU, or even networking gear makers. There you have players, especially leaders, who have very differentiated technologies or very differentiated cost structures, and also significant tie-in and customization with their customers, allowing for what we consider a more durable economic moat.

Dom Rizzo
And it doesn't mean that these golden spikes can't be great investment opportunities, Jennifer.

What Frank is laying out is knowing the difference between short-term shortages and long-term structural changes in businesses. And probably the most important question that we're asking ourselves regarding the memory debate today [is], with the rise of high-bandwidth memory, are you actually moving from just a shortage-based business model to more of a linchpin status?

What we've seen with CPUs, as we've gone from training to inference, they've become more important to their customers. Their linchpin status has gone up with their customers. And I'll give you an example. In training, the trade ratio between GPUs and CPUs is roughly 8-to-1. On their last earnings call, Intel talked about the trade ratio between GPUs and CPUs in inference being 1-to-1.

That is the definition of linchpin status going up.

Jennifer Martin

That's perfect. I mean, we could spend the entire podcast talking about bottlenecks and infrastructure. But I think we have hit on infrastructure. So maybe we should pivot a little bit to the technology sector that struggled the most this year, which is software, and a lot of it is because of the rise of agentic AI changing the economics and the competitive landscape. So maybe, Dom, why don't you start with some thoughts on that phenomenon?

Dom Rizzo
What's happening in software?

Basically, I think you can think about it as software being repriced around its new user: the agent. It's no longer the human. The user of the software is the agent. And what happens in that world? Three things change.

First, the user interface can basically disappear. The agent just talks directly to the software through APIs or direct data integration. They go headless, as Salesforce likes to say.

Second, pricing changes radically. In many ways. The software companies were fat and happy, as they used to price based on seats. And guess what? They probably overcharged based on those seats, and they sold a little too many seats in an organization. Now we're moving to usage- and outcome-based pricing where only the strong survive.

And third, the incumbents have to now spend aggressively just to defend their workflow. The cost of manufacturing software is down because of agents. What does that mean? Your traditional workflow is now subject to commodity pricing, and commodities quickly coming in and taking you out from underneath.

So three things have changed:

• User interfaces change;
• Pricing has changed; and
• The incumbents have to spend aggressively just to keep up.

Jennifer Martin
So the panic around software does remind us of other selloffs that we've seen—for example, almost, gosh, 20, 30 years ago in brick-and-mortar retailers—what were the worries about disruption by Amazon. And a lot of this fear—at the time, very well-founded—but there are a lot of big box businesses, auto part retailers and so forth, that have proved resilient, surviving this disruption risk and are ultimately thriving.

So what might be some of the qualities that position software companies to succeed in the AI era? Because it seems like a really interesting area to look at.

Dom Rizzo
Well, AI is not going to kill all software. I think it's simply going to expose what the software was actually worth to begin with.

If you own proprietary contexts or data, control a mission-critical workflow, have trusted permissions and auditability, or own distribution, there is a real path to you driving an economic moat over time.

However, that is countered against this reality that the frontier labs need to own their customer in order to justify their economic existence, and they are delivering products that clearly delight the user.

And with that, we are seeing the magic that a model plus a harness together can create in software. And it's just a dramatically better user experience than what traditional enterprise software has looked like in the past.

In many ways, it's like when people brought their iPhone to work. It was such a better user experience that the internal enterprise user demanded we move to an iPhone-based world, and I think the AI is very similar. It is such a better user experience that the user is going to demand that we move to a world of AI-native software applications.

Jennifer Martin
And do you want to spend 60 seconds explaining why the frontier models are advantaged with their model and harness?

Dom Rizzo
Yeah, this is probably the thing that most people underestimate when they're looking at token manufacturing. They say, oh, all models are commodities. And Frank can talk about this a bit more. I don't believe models are commodities; I actually think all of the economic value, or maybe close to all the economic value, will be derived at the frontier over time.

And one of many reasons I believe this is because the marriage between a model and a harness. A model and a harness, in many ways, is like a great spouse. They make up for your deficiencies. The harness makes the model better, the model makes the harness better, and they work in concert to deliver that magical experience.

Just look at what SpaceX had to do recently. SpaceX had a great model in the form of Grok, but they didn't have a great harness. What did they do? They had to purchase a harness with Cursor. OpenAI has a very strong model and harness. Anthropic has a very strong model and harness. Those two things together can create products that delight the user and then you can use that delight to capture user time and aggregate the rest of the enterprise, making everything else a dumb data pipe.

Frank, maybe you want to expand on why we don't think the models are commodities over time because that is probably the most important thing underlying the economics of the buildout.

Frank Shi
Absolutely. A token and the model behind the token comes and differentiates. In today's world, frontier models provides the best intelligence at the highest cost. And they come at the highest cost for good reason: because they solve the most economically sensitive tasks at the best ROI for the customers. Obviously, that is going to change as some of the open weights and less-than-frontier models catch up on capabilities.

But what we are seeing at the frontier labs is that, through various pretraining reinforcement-learning techniques, they continue unlocking new capabilities, pushing the frontier of what a model can do.

Imagine having one model that can actually develop a cancer-curing drug. How much would you pay for that type of model and that type of token versus the next model that can do your email summary?

The massive performance difference is what's keeping a lot of the economic value at the frontier labs. And as long as the frontier of the intelligence and capability continues getting unlocked, we think a lot of economic value will remain with the frontier labs.

Dom Rizzo
Jennifer, one question I ask myself all the time is what percentage of economic value in day-to-day life is driven at the frontier, right?

I think what we've seen—and it's and it's probably resulted in more inequality over time—is that returns to extraordinary people, returns to extraordinary ideas, keep getting higher and higher. And that's because so much of our economic value and so much of our GDP growth is happening at the frontier.

Like Frank said, looking for cures of drugs, you know, your best performing…what is the difference between your average lawyer and the best-performing lawyer in the world? What is the difference between your average portfolio manager and the best-performing portfolio manager in the world? I think there is a dramatic difference between those two. And now with AI agents, we can arm those people with frontier intelligence and then we can supercharge them.

I think it begs some real strong society questions that we have to answer. What is this going to mean for growing inequality? But I also think it means that the returns to the frontier can be exponentially higher than the rest of the models in the world.

Jennifer Martin
Super, super interesting. And I think most of our discussion so far has centered around the implications of enterprise AI adoption, which makes sense because businesses, as you just highlighted, are willing to pay for productivity. Let's shift a little bit and talk about your views about how agentic AI might evolve for consumers.

Dom Rizzo
Well, Frank, maybe you want to start with how you think that AI can change the smartphone landscape over time, because I know that's something that you've been spending a lot of time on. And then maybe I could talk about our views on the potential for an AI chief of staff in day-to-day life.

Frank Shi
For consumer agents, I think smartphone is the natural interface because a phone already contains our identity, location, preferences, even bank information. That makes for a natural place for an agent to understand the context and take action on behalf of a consumer.

Now, a smartphone as an AI-agent interface doesn't just mean the hardware. You have to own both the hardware and the operating system, such that the agent can have nuanced and privileged access to your information.

Here, we think that the operating system and the chip owners and the hardware ecosystem owners—companies like Apple and Google—obviously have an advantage. But at the same time, the super apps—the Meta, etc.—who owns significant critical mass of consumer relationships and data and economic incentive will be looking at owning that interface as well.

And that's why I think the landscape is going to look a lot more interesting and lively in the next few years, with both incumbents trying to develop AI agents, leveraging their existing relationships with the clients, and also the new entrants who wants to take their super-app and turn that into a consumer-facing agent interface.

Dom Rizzo
And probably the biggest question, Frank, when we debate this concept of the AI chief of staff in your life who can see your calendar, inbox, bills, bank accounts, preferences, take actions on your behalf, is what is ChatGPT’s role in that world? The long-rumored hardware project—what is that going to be? Is the user interface for AI different than the traditional smartphone user interface, or is the smartphone user interface actually the perfect one, and the operating systems have an advantage?

So this is all something that we're debating.

What's really fascinating here is how the economics are going to play out. What have we learned so far? Enterprises are willing to pay for productivity. Consumers? Some are, but not all. So there has to be a different way to monetize this user time. And I think the obvious answer is ads.

Ads feel like such a dirty word to so many of us. But they have resulted in more consumer surplus than anything I can think of. The consumer internet is free because of the advertising business models of Google and Meta. And whether or not you think Instagram is a good or a bad for society, I can bet you that you sat there and scrolled through Reels on a Tuesday night after a long day at work. And that user attention, that time, is very valuable. Those eyeballs are very valuable. Now let's take that to the chatbots. What does that mean for the advertising model of chatbots?

Similar to how you scroll through Reels, many people spend a lot of time on chatbots. And I think the reality is that that's actually a more intent-driven search profile than just traditional Reels. The difference between searching for video content on the Amalfi Coast and your upcoming trip to Italy, versus planning it within the chatbot. And what does that mean for an advertiser? I think that the whole plan of your trip to Italy could be very, very, very valuable.

Jennifer Martin
You're making some of those consumer internet companies very happy with that insight, Dom.

Dom Rizzo
Well, let's see what happens. Whether or not this ends up being a net benefit to their business or a net negative to their business, we actually don't know. And so much of it depends on how much is the agent going to take over the workflow versus how much is the human actually still in the loop?

Jennifer Martin
There's a lot to unpack with the consumer, and I think it's still early days.

But what is very obvious to me when I spend time with both of you is the agentic AI is bringing a lot of change, and both of you are using these innovations at work. And I would put you in the leading frontier. I guess, I think we have a group at the firm called “AI Wizards,” so do you guys have your wizard hat? (They actually handed them out, for our audience.)

And maybe, maybe, Frank, we start with you. You did a lot of work over the holiday, Christmas holiday on your, gosh, tech stack, for lack of a better word. So maybe explain all the work that you've been doing to help us.

Frank Shi
Absolutely. Now, if we think about what an AI can do for us as investors, it's truly about letting us do a lot more than what we previously have time for such that we can focus on the most important task of our day, which is finding return-generating insights and act[ing] on those insights.

So in my case, I have been able to automate an important part of my day-to-day workflow, such as gathering through information, looking at various filings and expert transcripts, and actually picking up new signals in terms of what is changing day to day in the tech landscape.

The second thing that we typically do after earnings season is actually going through filings and make sure we understand all the important accounting changes and some of the incentive changes for the management.

Those used to be very labor-intensive tasks and takes hours of work after each earning season. Now I can do that in a few minutes and see a time series of how these important factors have changed.

Those are just a small part of the things I have tried to automate with my sub-agents, or AI agents, and with that, I'm freed to actually think a lot more on what matters to my stock, what's driving the stock price change day to day, and how to do my job better as an investor.

Jennifer Martin
That's super energizing.

Dom, you got to tell us about your agents. Maybe your harness. Did you name your harness?

Dom Rizzo
I haven't named my harness. We've debated a few different names.

If we take a second, though, and we think about this moment in time—how many people are actually using true agentic workflows in the investment process? I actually think it's very small. Eventually, it will be everybody.

Why have people not used agentic workflows so far? It's because it's hard work getting them going. Right now, my agents are the equivalent of a junior analyst.

Jennifer Martin
That's an impressive—that's a move—because a year ago we didn't have that ability.

Dom Rizzo
That's an incredible move. It's an incredible move. They can build charts. They can build models, do research for me into each of my different points of my framework. They can make trade recommendations—all sorts of things.

However, to get to that point, it has required two people working full time with me. One we call the plumber, John, who makes sure that the data is clean, easy to integrate. And Albert is another AI wizard who helps me cast spells by helping me build skills.

So if you think about what we need today to create that junior analyst, it's two full-time people. And this is why I'm not a jobs doomer. I think there's going to be so many jobs over time created by AI. And, so far, all evidence is that AI creates jobs, not loses jobs. And I think we're seeing it in our day-to-day workflow.

Going forward, this is just going to get better and better and faster and faster, Jennifer. And frankly, if you don't use it, you're going to be left behind. So I'm so excited about what we're doing internally.

Jennifer Martin
What I think, what is probably underappreciated by our audience is the importance of both of your investment framework[s]. There's a lot of rigor in it. There's systematic ways of looking at it, which benefits these tools in a really, you know, complementary way. And it makes both of you very super-powered, so I'm glad you're our wizards today.

I just want to say thank you. This has been a really wonderful conversation. Lots of great insights shared by both of you. And you know, on behalf of our audience, thank you.

Dom Rizzo

Thanks for having us, Jennifer.

Frank Shi
Thanks for having us.

Jennifer Martin
We covered a lot of ground today. What resonated with me was just how large the market for intelligence could be, and how increasing demand could benefit different parts of the supply chain, including CPUs and memory.

Again, I'm Jennifer Martin. Thank you for listening to The Angle. We look forward to your company for on future episodes. You can find more information about this and other topics on our website. Please rate and subscribe wherever you get your podcasts. The Angle: better questions, better insights—only from T. Rowe price.

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