C3.ai, Inc. (AI) Earnings Call Transcript & Summary

Okay. Good morning, everyone. Welcome to the conference. I'm Pinjalim Bora, Software Analyst at JPMorgan. I cover SMID Cap. Delighted to have here with me Tom Siebel, who is the Chairman and CEO of C3.ai. Tom, welcome to the conference.

Thank you.

Let's start with a brief introduction, maybe about yourself and maybe a little bit about C3 for the audience who might not be familiar with the story.

Okay. I'm a Computer Scientist. I did my graduate work in a relational database theory, went to work for a little start-up with about 20 people called Oracle Corporation. That turned out to be a pretty good idea. Ultimately, I was one of the guys who ran that business and we were involved in the commercialization of relational software, relational database software, and then built a large stack of enterprise applications. About a decade later, I spun out and thought about -- I thought about the application of information technology and communication technology, sales, marketing, customer service. That was a market that was largely unserved by information technology at that time. We founded a company called Siebel Systems in 1993, July of 1993, that also turned out to be a pretty good idea. So 6 years later, we're doing about $2 billion in revenue and had, I think 4,500 customers in 29 countries, that I believe is the fastest-growing enterprise application software company in history. In 2006, that company was acquired by my colleague, Larry Ellison, and became the basis of their CRM stack. So I did -- I invented the CRM market, as you know it today. This year, I think $120 billion business. And then we thought about what was next, and we believe that next was going to be about elastic cloud computing, big data, Internet of Things and predictive analytics, what we call enterprise AI. So we began work in 2009. In the next decade, we spent about -- we're really a couple of billion dollars, building a software stack and the purpose of that software stack was to provide an integrated platform that provided all the services necessary and sufficient to design, develop, provision and operate very large-scale enterprise AI applications. We use that stack to build today 90 enterprise AI applications that address the markets of manufacturing, agriculture, pharma, oil and gas, utilities, defense, intelligence. And then in the last couple of years, we've expanded that very significantly. That includes -- those 90 applications include, I believe, 30 applications that address some of these opportunities associated with Generative AI. So I've been talking about enterprise AI now for some time. Understand, we started this effort when AWS was this big before the Azure Cloud, before the Google Cloud, before the GPU. And now we are -- we find ourselves in 2024, and the rest of the world has discovered enterprise AI. So the world has kind of come our way. And it looks like the addressable market opportunity. So I've been talking about this for more than a decade when others were speculating whether or not there would be an enterprise AI market. I can assure you there is and there will be, and this is not a femoral. So as we address this market opportunity in 2024, '25, '26, '27, what have you, we're looking at what is going to be certainly greater than $1 trillion addressable market, maybe a $2 trillion addressable market. There is one company in the world with 90 applications that address this market opportunity, there's one company in the world. And so the game we're playing is to see if we can establish and maintain a market leadership position globally in enterprise AI. Now that sounds like a little bit ambitious, but in fairness, we said this in Oracle in 1983, and we did it, okay? And we said it at Siebel in 1993. In CRM, we did have 85% market share, again, sales, marketing and customer service when that company was transitioned to Oracle in January, I think, of 2006. And we might fail. We might end up being #2 or #3. And so that's the opportunity that's before us.

Yes. That's a great overview. You obviously have been doing it for a while. But I want to ask you the inflection that we are seeing on the infrastructure layer right now, right? I was looking at the numbers, I think NVIDIA added something like $30 billion in revenue in the last 4 quarters. Obviously, revenue up over 100% year-over-year. Huge inflection on the infrastructure layer. But we are not seeing kind of a similar inflection on the software stack at this point. We have seen the hyperscalers accelerate slightly, but still seems like there's a big gap. So I wanted to ask you, how do you see this play out, right? The massive build-out on infrastructure, how does it impact software up the stack?

There's only one reason for that silicon and for that infrastructure, and that's to run applications, okay? So obviously, somebody thinks there is an application opportunity. If we look at that stack, at the bottom, we have silicon, above that, we have infrastructure, above that, we have foundation models, and above that, we have applications. Now the way that these markets work, when we came out, when the PC came out, let's think -- as we think about the IBM PC, I think the '80s, and you had that PC on your desk, that compact PC or that IBM PC, okay, the whole value stack was in the silicon and the infrastructure and you had a $200 VisiCalc application on it, okay? Today, that PC that's on your desk, to you or your company, about $200 a year in cost. The infrastructure cost you about $200 a year, okay? And by the time, you depreciate the cost, okay? And the applications of running on it, Bloomberg, whatever it may be, that's cost me $10,000 a year. You really get -- this guy that came out, you will call it, in 2007, when this came out, 100% of the value was in silicon and infrastructure. Today, where's the value? It is in the applications that run on it. Same thing will happen, okay, in enterprise AI. In the long run, the silicon gets commoditized. I'm not saying that NVIDIA isn't a great company because NVIDIA is and will be a great company, but silicon does get commoditized, hard stop. See Intel for details. And the infrastructure gets commoditized and 70% of the value will be in the apps. Now the idea, you have companies like you have enterprise application software companies in AI growing at 15%, 16%, 17% 18%, 19%, 20% compound annual growth rates. Now then I cut it in early, where I know you guys grow a lot faster than that and you're like -- but that is the top 10% of the software universe. So I'm not certain that -- of your software universe that you cover in your company. When you get north of 20%, that's the top decile of growth rates. So I think that we are seeing accelerating growth.

Yes. So it's basically a matter of time that it permeates into the software layer and you're seeing it already happen?

It's a $1 trillion, $2 trillion addressable market. More than 50% of that stack is going to be applications.

Yes.

That just happened in PC. It happened in a mini computer. It happens on the phone. I mean this is the way the world works.

Yes. Got it. Let's talk about C3.ai and the enterprise AI platform. Maybe talk about -- you have some big brands that C3.ai see real economic benefit. I think you've quoted over $1 billion of economic benefit. I think Shell, one of the companies.

Shell, $2 billion a year, yes.

$2 billion. Maybe at a core level, help us understand the main value that C3.ai platform offers to customers. Why is it so difficult to scale enterprise AI? What does C3.ai provide in that journey?

We make it simple to scale enterprise AI. It is difficult to scale enterprise AI. I guess people over decades, they've been trying to build these things out of piece parts. You have IT people that some consider themselves, they get a little bit confused, and I think there's the software engineers. Like they know how to install a single sign-on or they may be, with enough -- if they spend enough money with Accenture, they can figure out how to get SAP installed, okay? That does not make you a software engineer, okay? But every now and then they get confused and they think they are, so they take all these piece parts from used to be -- the big range was the Apache opensource stack and then all the piece parts that are offered from the infrastructure providers and other things. And they try to cobble them together into something that works. Nobody ever succeeds with that. Now -- but people don't want to buy tools to build applications. People don't want to buy tools to build CRM applications. They want to buy CRM applications, okay? I prove that. People don't want to buy tools to build relational database applications, they want to buy relational databases. We proved that at Oracle. They don't want to buy tools to build ERP applications. They wanted to buy ERP applications, see SAP for details. Same thing in an enterprise application. And when we get into enterprise AI, what are the big -- predictive maintenance, stochastic optimization of supply chain, demand forecasting, fraud detection, whatever it may be. Companies don't want to buy tools to build these things. They want to buy applications that they can install and optimize their supply chain. And so we make it easy. And so we bring our customers live and whether it's a Dow or whether it's a Cargill or whether it's a Tysons, we bring these applications live, complex enterprise applications that we being live in order of 6 months at the enterprise level. And then they have consumption pricing afterwards. And so that's the value of what we do, is that we make it easy. So you don't have to spend years and hundreds of millions of dollars trying to build it yourself.

So you're saying, you're basically stitching the different parts of enterprise AI scale, right? You have the data prep side, you have the data engineering side, you have the feature development, you have the model training and then you have the observability side if you look at the entire stack. And people can use different tools on their own, but C3 is providing one platform?

Yes. We provided a platform that you can think of it like an orchestration layer. It's a couple of billion dollars for the software engineering. It's quite a piece of work. Now all of the various components are interchangeable, whether we're dealing with persistence, whether we're dealing in factor authentication, whether we're dealing with whatever, Generative AI tool, you might want to use, you might want to use Anthropic, you might want to use Llama, you might want to use ChatGPT-4 and you just plug it into that box. But out of the box, it comes with a Generative AI tool. Out of the box, it comes with relational database persistence. So under the box, it comes with a key value store. It supports various supervised learning, unsupervised learning, deep learning, but all of those components could be interchanged with open source capability or proprietary capability, but it works.

Yes. Understood. In the C3.ai, you said 30 Gen AI applications already. But from the basic level, maybe help us understand what -- how does the C3.ai part differentiates versus some of the other stuff in the market? Is that also, again, trying to ease?

Okay. For those of you who are interested in Generative AI, and I've been looking at you, and that would be everybody in the room, okay, and these large thing. I've been looking for a good book on this subject for a year. I finally found one called, What is ChatGPT, okay, and what is it doing? (sic) [ What is ChatGPT Doing... and Why Does It Work? ] By the name Stephen Wolfram, you can get it for like $19 on -- this guy, Stephen Wolfram, if you know him, this guy is the real deal, right, very highly respected member of the Academy, physicist from Caltech, WolframAlpha, Mathematica. This guy used the genuine article, okay? Okay. And in 91 pages, as you read these 91 patients, it'll take you 45 minutes, you'll know more about these language models than anybody in your organization. And I'll give you -- if you don't like it, send me an e-mail, I'll give you a money back guarantee. Okay. I'm literally putting 5,000 copies of this just to give it away, okay, so people understand. Now what -- these large language -- how big is this Generative AI market? Holy moly, guys. This is huge, okay? But there are problems associated with these things. What are the problems to the extent that you'd be using them, okay? And if I don't -- if you're not using them today. How many people use Generative AI every day? Guys, you got to use it, okay? You got to use it. Download a copy of Microsoft Edge, okay? It has ChatGPT-4 Turbo with it. You've got to use it, okay, and start using it, okay? And because if you don't, your competitor will, okay, and they're going to be doing better work than you. It is -- what this thing does is amazing. But what's the problem with all these learning models, with every one of them, okay? Number one, the answers are stochastic. Every time you ask an answer, you get a different question, okay? They don't enforce. Other problems are, we can't -- when the answer comes up, you can't tell where it came from, okay? They're not -- even with retrievable augmented generation with these RAG architecture, that will work for unstructured data, but that won't work for structured data. So it came out of ERP system or a CRM system or any sort of structured data. You can't figure out where the answer came from. None of the enterprise access controls are enforced. Hey, Bank of America, JPMorgan Chase, CIA, name the company, you have access controls. And the CEO doesn't get to -- the guy who works on the production line doesn't get to say the same thing as the CEO. Well, enterprise access controls are not enforced. We have enormous problems with the cybersecurity risk that are introduced by these large language models, which is why at companies like JPMorgan Chase, they don't like to use ChatGPT, okay, because the cybersecurity issues are daunting, and we have these data exfiltration problems that you've read about with Samsung and others, this hallucination problem is just wonderful. If it doesn't know the answer, it just makes up a story. These are just wonderful. And by the way, if you read this book, you'll understand where these hallucinations come from. There's enormous IP liability associated with these, okay, with these language models because they're trained on information over which somebody else owns the copyright, be it the Weather Company or Bloomberg. And by the way, they want their piece of the action, okay? And finally, they're LLM specific. And anybody who bets at any LLM, say in May of 2024, has lost their mind. I mean, with all of the innovation that's going on in this space with these guys leapfrogging each other, I mean there's just no telling where these could go and. You want to be able to change LLMs maybe week over week as these guys innovate each other. And by the way, there's some assumption that one of Google or OpenAI or Anthropic is going to win this. Why would you think that? I mean OpenAI can be out of business tomorrow. And was anybody read the newspaper last night? And almost on over one weekend in almost 100 businesses. Any one of these could be gone. So when you combine the large language model with the $2 billion for the platform -- oh, the other problem is they're generally unimodal. Unimodal means you can put in text or they say they're multimodal. They can get text and images. Guys, text and images doesn't cut it. You need text, you need images, you need telemetry, you need all structured data, unstructured data, so it's got to be omnimodal. Now remember the $2 billion worth of work that we did in building the enterprise platform, we've nailed identity, we've nailed access control, we've nailed cyber, okay, we've nailed omnimodal. So we basically solve all those hobbies. That dog I described, that dog doesn't hunt, okay? Whether it's coming from Anthropic, whether it's coming from Gemini, a dog does not hunt in any reasonable enterprise. But when you combine it with the C3.ai platform, we've solved all those problems, so it's secure. It doesn't exfiltrate. It gives you the same answer every time. You can figure out where the answer came from. You don't have IP liability problems. So we're installed today at CIA, NSA, NRO, National Accountants Office, the United States Air Force, United States Navy, United States Marine Corps. We're installed in some of the most secure installations on the planet Earth because we've solved those problems. And that's what distinguishes our applications from others in the space.

Just to be clear, the CIA, NSA, NRO, all of these are using Gen AI?

I did not say NSA. I said CIA.

Okay. These are using Gen AI applications?

As I announced on the last conference call. No news there.

Okay. Got it. So what has been the feedback so far from these customers on the Gen AI use cases, the Gen AI applications that you have?

I mean it's just the productivity increases that people get from these are just amazing. Let's take a law firm, for example, there's one law firm that is -- that all of you know, that takes a lot of companies public that you guys are following. And what we've done there is we've taken the -- we think about -- it's a small language model. Think about an enterprise language model, okay? And with LLM, okay? And what we've done is we've loaded for this law firm, the corporates of SEC.gov EDGAR, okay, and through a language model. So this is every S-1, every 10-K, every 10-Q, and that's what we use to train the language models. So when they want to take the next company public, when you guys are -- all these LLM companies that are being financed at $1 billion, $2 billion, $3 billion, $4 billion or $5 billion, $6 billion, $7 billion valuations today that you guys will be taking public next year, okay, when you need to write the S-1, you put in the name, you put in the financials, you put in the first few risk factors, keep the carriage return, and 45 minutes later, you have 200 pages of the first draft of the S-1 written. That's 2 weeks' worth of work done like right now. I mean, what's the productivity increase? It's huge. So those are examples.

Yes. Wow, it seems like not that great for lawyers.

No. No, I don't think it replaces lawyers. It makes it more productive. I mean there is -- not in this drudgery. I mean why, how hard is it to develop the proper list of risk practice for that company when you can look at the universe of risk factors that have ever been registered to the SEC and select those that apply. I mean -- so I think it doesn't replace any jobs, but it makes -- makes these people certainly more productive.

Yes. Let's switch gears and talk about macro. What is your sense? I mean, I'm still surprised that we are talking about macro by the way, started in like June of 2022, but here we are. What is your sense of the spending environment at this point when you're talking to people? Can you characterize kind of the demand environment or the business confidence?

In enterprise AI?

Enterprise AI overall from your customers?

So I can't talk about silicon. I can't talk about anything other than enterprise AI because that's all what I do today. But as it relates to enterprise AI, I mean at JPMorgan Chase, there is no budget for AI. It's whatever Jamie wants to spend, okay? You think he's constrained by a budget? You think your people here are constrained by budgets in AI? You think they're constrained at Bank of America by budget? You think they're constrained? I mean nobody is constrained by budget as it relates to AI. These are CEO-level decisions, and he or she makes the budget then it happens. So you're looking at a -- these people at TSMC and NVIDIA are not building all this infrastructure for the sake of having big warehouses full of silicon, okay? These things are running learning models. These learning models are supporting enterprise AI applications. So I think that this market is developing at a very rapid pace.