Good day, and thank you for standing by. Welcome to the C3 AI Second Quarter Fiscal Year ‘24 Conference Call. At this time, all participants are in a listen-only mode. After the speakers’ presentations, there will be a question-and-answer session. [Operator Instructions] Please be advised that today’s call is being recorded. I would now like to turn the conference over to your host, Mr. Amit Berry. Please begin.

Good afternoon, and welcome to C3 AI’s earnings call for the second quarter of fiscal year 2024, which ended on October 31, 2023. My name is Amit Berry, and I lead Investor Relations at C3 AI. With me on the call today is Tom Siebel, Chairman and Chief Executive Officer; and Juho Parkkinen, Chief Financial Officer. After the market closed today, we issued a press release with details regarding our second quarter results as well as a supplemental to our results, both of which can be accessed through the Investor Relations section of our website at ir.c3.ai. This call is being webcast, and a replay will be available on our IR website following the conclusion of the call. During today’s call, we will make statements related to our business that may be considered forward-looking under federal securities laws. These statements reflect our views only as of today and should not be considered representative of our views as of any subsequent date. We disclaim any obligation to update any forward-looking statements or outlook. These statements are subject to a variety of risks and uncertainties that could cause actual results to differ materially from expectations. For a further discussion of the material risks and other important factors that could affect our actual results, please refer to our filings with the SEC. All figures will be discussed on a non-GAAP basis unless otherwise noted. Also during the course of today’s call, we will refer to certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in our press release. Finally, at times in our prepared remarks, in response to your questions, we may discuss metrics that are incremental to our usual presentation to give greater insight into the dynamics of our business or our quarterly results. Please be advised that we may or may not continue to provide this additional detail in the future. And with that, let me turn the call over to Tom.

Thank you, Amit. Good afternoon, everyone, and thank you for joining our call today. Results: Bottom line, we continue to accelerate our revenue growth and our customer engagement count and continue to gain traction with C3 Generative AI and our Enterprise AI applications in the second quarter. Total revenue for the second quarter was $73.2 million, an increase of 17% compared to $62.4 million one year ago, and accelerating from an 11% increase in the first quarter. The total number of customer engagements was 404, an increase of 81% compared to 223 last quarter. North American revenue of $61.2 million increased 28% year-over-year, while EMEA revenue of $10.6 million decreased 11% year-over-year, and federal revenue increased 100% year-over-year. Subscription revenue for the quarter was $66.4 million, constituting 91% of total revenue and increasing 12% from a year ago. GAAP gross profit for the quarter was $41.1 million, representing a 56% gross margin. Our non-GAAP gross profit for the quarter was $50.4 million, representing a 69% non-GAAP gross margin. Our GAAP net loss per share was -- the loss was $0.59, and non-GAAP net loss per share was $0.13. We ended the quarter with $762.3 million in cash, cash equivalents and investments. C3 -- AI’s partner ecosystem continues to drive significant growth. In Q2, the company -- the Company closed 40 agreements through our partner network, including AWS, Booz Allen, Baker Hughes, Google Cloud and Microsoft. The qualified opportunity pipeline with partners has increased by 75% in the past year. We’ve signed new and expanded agreements with Nucor Corporation, Roche, Con Edison, Hewlett Packard Enterprise, GSK, formerly SmithKline, the United States Navy, the Administration for Children & Families, a division of Health & Human Services, Indorama and First Bank amongst others. Over the past several months, C3 AI has helped Nucor,…

Thank you, Tom. I will now provide a recap of our Q2 financial results and some additional color on our consumption-based revenue model, which we introduced five quarters ago. Then, I’ll discuss factors that would drive our financials in the back half of the year. All figures are non-GAAP unless otherwise noted. Total revenue for the second quarter increased 17.3% year-over-year to $73.2 million. Subscription revenue increased to 11.7% year-over-year to $66.4 million and represented 90.7% of total revenue. Professional services revenue was $6.8 million and represented 9.3% of total revenue. Gross profit for the second quarter was $50.4 million, and gross margin was 68.8%. As a reminder, we continue to expect short-term pressure on our gross margins due to a higher mix of pilots, which carry a greater cost of revenue during the pilot phase of the customer life cycle. Operating loss for the quarter was negative $25 million compared to our guidance range of negative $27 million to negative $40 million. The improvement in operating loss versus guidance was driven by timing and amounts of the generative AI-related investments we made to capture market share as well as our team’s ongoing focus on disciplined expense management. At the end of Q2, our accounts receivable was $143.2 million, including unbilled receivables of $104.8 million The general health of our accounts receivable remains strong. Now turning to RPO and bookings. Reflecting our transition to consumption-based contracts, we reported second quarter GAAP RPO of $303.6 million which is down 27.3% from last year and current GAAP RPO of $170.2 million, which is up 3.5% from last year. We continue to see positive trends in the diversity of our pilot bookings with 10 industry segments represented in Q2 pilots as compared to 8 in Q1. Free cash flow for the quarter was…

Our first question comes from the line of Timothy Horan of Oppenheimer.

Thanks a lot, guys. Really appreciate the time. Can you give us a sense of what you’re seeing with AI in terms of productivity improvements? And what is the major bottleneck that you think customers need to overcome to really start implementing services? Thanks.

I’m sorry. The question related to gen AI?

Yes, specifically on gen AI. What type of productivity improvements do you think customers can see on specific applications? And what is the major bottleneck for them adopting G AI?

The major bottleneck as it relates to generative AI relates to the problems that are inherent in these large language models, and they’re very real. I mean, as you know, if you ChatGPT or Google Bard, both of which are like excellent products, but the answers tend to be sarcastic. So every time you ask a question, get an answer, the -- if it doesn’t know the answer, it hallucinates. The data access controls are not in force. So the CEO and the person on the factory floor get access to the same information. We’re -- Carnegie Mellon and others are now identifying huge cybersecurity risks that are associated with these large language models to corporations and government entities. We have IP liability problems that people are concerned about because these large language models are trained on and have access to all the data into the internet. This is weather, stock prices, what have you. Those -- somebody has the copyright to all those data, be it the weather company or Bloomberg, and they want to get money. So, the quintamenials [ph] of the world are going to build big businesses litigating these issues in the next 10 years. We have -- so there’s very real issues. The other issue relates to almost all the solutions that are being offered are LLM specific and in, say, December of 2023, to hook your wagon on any specific LLM is kind of crazy because next week, some is going to leave rocket by a factor of 10. You need to be able to switch, you need to be LLM agnostic. So I think those are really the hobgoblins, cybersecurity, hallucination, information security that are basically making -- so many organizations will not allow any generative AI application to be installed. What’s unique about the C3 AI solution is -- we can talk about this some other time or you can look it up on the internet. But by combining with the 14 years of work that we did with the C3 AI platform, we’ve addressed all those problems, cybersecurity, data security, hallucination, what have you. So I think that’s the hobgoblin. That’s what slows things down. And people need to be need to be satisfied to those issues resolved and if they’re not resolved, you’re not being installed at any reasonable organization like General Motors or JPMorgan Chase or you name it. Now as it relates to productivity increases, holy moly, they’re going to be staggering, whether you’re a lawyer, whether you’re a realtor, whether you’re a physician or whether you’re running a paper machine or whether you’re operating the infantry or the space command. I mean you -- if you do not have or not being supercharged by generative AI, your competition will be, okay? And if they are, and you’re not, they win, you lose hard stock.

So specific to your customers, what do you think the bottleneck is for adoption? If you have -- it sounds like you have all these problems pretty much resolved for them. What do you think they require at this point to really start adopting?

Well, we just -- our sales cycles are pretty fast. Our sales cycles for generative AI has been close as 24 hours. And basically, our offering is, okay, we’ll bring the application live in 1 or 2 months, if you like it for, I don’t know, $0.25 million or something. And if you like it, keep it. So this has to do with people evaluating bond portfolios, people running paper machines, people running steel mills, the intelligence community, missile defense agency, others. So, we just -- many of them are existing C3 customers, although increasingly, we will be serving -- 9 out of 10 will not be existing C3 customers. But we have to address their concerns that identified. We seem to be able to address those. After that, we just bring the application live, we get it live in 4 to 8 weeks and if they like it, keep it. And so it’s a pretty short sales cycle for us, and you’re seeing a very substantial increase in the pilots that we’re deploying. You can expect -- we’re expecting a pilot to production conversion rate of -- it looks like about 70%. And so it does look like a big opportunity.

[Operator Instructions] Our next question comes from the line of Mike Cikos of Needham.

I wanted to ask first about the subscription gross margins, and this probably goes back to Juho’s prepared remarks. But it was good to see gross margins actually increased sequentially despite the increased pilot count. And I know that you guys are calling out the short-term pressure just based on the growing mix of pilots. And so, can you help us think about like what was it that actually went better for you guys? Because I think we were expecting a little bit more degradation in the subscription gross margins versus how you guys -- how the quarter actually came through?

Thanks, Mike. This is Juho. So yes, in the big picture, as we announced five quarters ago, as we’re seeing, we are expecting the gross margin degradation for the subscription to continue. Now, in the -- in this particular quarter, we were very pleased to see some improvement on a sequential basis, but I think we would expect flattening to down again on the next quarter as the pilot count increases and is going to put pressure before the consumption amount start picking up and offsetting that.

Got it. Thank you. And if I just shift down to OpEx for a second as well, I guess two quarters here. So first, I know that you guys are increasing the anticipated operating losses here. Last quarter, we had cited increased investment in like branding and lead gen and awareness, right? So can you help us think through where you guys are doubling down? And then, the second piece there, there was obviously that article that came out in Bloomberg, I think it was in mid- to late-November citing headcount costs -- headcount cutting, I’m sorry. So can you just comment on the validity of the Bloomberg article? Just because I think people are trying to see if you did make those headcount cuts, how much are we doubling down on these investments, or if that article proved to be false?

Hi Mike, it’s Tom. Doubling down, we’re doubling down on data scientists, we’re doubling down on large language model engineers, we’re doubling down -- a lot of it is going into engineering, but also candidly in lead generation. I mean, there’s an opportunity now as we move to these marketplaces to be dealing transactions in hundreds to thousands to tens of thousands of units rather than scores. And that I can assure you is the plan that we have. As it relates to -- I’m not familiar with Bloomberg article that you talked about. It sounds like somebody mentioned something that we did some layoffs in the quarter. Mike, we do performance-related layoffs every quarter, okay? And the -- so we -- I think last quarter, we had 42,000 job applicants. We -- how many people did we hire, Juho? Order of 100. And these people, yes, they went to MAT. Yes, they worked at Bank of America. Yes, they went to Chicago TSB and they command an F1-8 squadron. And so, we’re constantly upgrading our human capital, and we move underperformers out regularly. So if somebody said that in a Bloomberg article, I don’t know what they said. What I told you is the truth.

Our next question comes from the line of Kingsley Crane of Canaccord Genuity.

I wanted to touch on the pilot program. You mentioned that you’d move to a lower entry price point for pilots. Could you give us a sense of the magnitude of that change? And then has the minimum fee post pilot also changed? I’m curious what kind of upsell you’re seeing upon conversion, if any?

I think the standard pilot that we have at generative AI and the enterprise is like $250,000. But that being said, you can get the AWS -- generative AI for AWS, which basically handles documents like every other LLM, handles text, it’s not really multimodal, but that’s free for 14 days. So, that would be pretty available. Is there a question that you asked that I didn’t answer?

Okay. Yes. Thank you. That’s helpful. And I just want to touch on OpEx as well. So, I think it makes sense that you want to invest more in both, LLM engineers and lead gen. And it looks like that’s particularly hitting harder in Q4 of this year. But as we think about fiscal ‘25, it seems like some of the nature of those investments would naturally continue as you scale in the some large opportunities. So, is it about timing in this year, or are you expecting those to continue next year?

Kingsley, I expect them to continue next year. But if you look at the guidance that we gave you in terms -- about six quarters ago, what we see is the consumption over the first 12 quarters in terms of CPU seconds per new customer. We just did an analysis of, Juho, I don’t know, about 30 customers -- or 12 customers. And those data that we predicted, I think 6 or 7 quarters ago and provided you, it’s uncanny in how accurate it is. It’s basically plus or minus 10%. And so if you look, as these things kick in, in quarter 5, 6, 7 and 8, the consumption numbers get pretty big. So you can expect that -- we don’t really need to cut back on the investments to get to the point of cash positive and non-GAAP profitable. So, the top line kind of takes care of that.

Our next question comes from the line of Sanjit Singh of Morgan Stanley.

Great. This is Steve on for Sanjit. Tom, maybe starting with you. I mean, with a couple of quarters of the consumption model now under your belt, clearly, you’re seeing a lot of sort of quantity of deals and pilots. Is there any way that you can frame or give us a sense of the quality of those customers that went with the consumption model early on? I guess, any sort of scale in terms of spending or growth profile that they’re hitting now that you can kind of shed up some light and give us the quality piece where you’ve given us, I think, a lot on kind of the quantity piece of those yields? And then for Juho maybe, could you just give us some color on the subscription revenue versus the services revenue this quarter, and then also maybe the partner impact and sort of what that looks like on a go-forward basis? Thank you.

Regarding quality, I think there’s only two ways to look at pilot quality. It’s going to be what’s the conversion rate and what’s -- and what are they going to consume. Based upon our best guess at this time, based upon looking at every pilot we have out there, going to look at what actually has converted and what we think we will convert, we think our guesstimate that we gave you 6 or 7 quarters ago, 70% is about right. So, there’s one indication of quality. The other indication of quality is how many CPU seconds are they consuming over -- as you go from quarter 0 to quarter 12. And it’s tracking right in line. I mean, it varies a little bit from one quarter to another, but it’s basically right in line with what we told you. The quality is pretty high. Now that being said, as we move now to mass markets and start dealing with hundreds of thousands of people just either kind of ordering this online and playing with it, you can expect that conversion rate from that level of pilot to be, I would say -- I mean, the quality there will be much lower. And I think we need to measure quality by conversion rate and consumption levels. A lot of those people will try it for 5 minutes and drop off. And that’s just the way that it is with free stuff. Now the rest of the question, I think, goes to you.

Yes, right. So your second part about subscription versus services. So we were 9.3% professional services this period, which is a little bit lighter than our expected long-term model of 10% to 20% on professional services. We continue to expect that we will be at that range on a go-forward basis. And then, I think you were asking about how we feel about the partners in a go-forward basis. And partners are hugely important for us. And we continue to believe that they’re the key part of our go-to-market approach going forward.

It looks like we have time for one last question. Our last question will be from Pat Walravens of JMP Securities.

This is Owen Hobbs, on for Pat. I guess first one for Tom. What would you say are the top one or two federal use cases for generative AI that you’re seeing with those new -- those five new federal degenerative AI deals this quarter?

Our largest federal use case, as you know, is predictive maintenance of the United States Air Force. This was chosen by the Chief of Staff. And we now are doing -- this is the PANDA system, which is the only AI system of record that we’re aware of in all of DoD. So, this is a system record for the Air Force for predictive maintenance for all assets. So far, we have loaded the data, I believe, from 22 weapon systems. F-15, F-16, F-18, F-35, KC-135, F-22, et cetera, into unified federated image. This is 100 terabytes of data. Some of it is maintenance data, sorted data, inventory data, flight data, flight history, telemetry and one aircraft like B-50 -- each B-1 bomber has 42,000 sensors on it, admitting telemetry, and I’m not sure what hurt cycles but pretty fast. So, this is a stack of data. I will be there on Monday -- that is by next Monday. I will be in Washington, D.C., showing this to our customers with a generative AI front end. So, think about this as a Mosaic browser front end where a general officer can ask any question about -- this a 100-terabyte production system. This is one of the largest production enterprise AI applications in existence. Okay? And that person will be able to ask on Monday, be able to ask any question that you could ask of the weapon system, for example, where aircraft are operative at Travis Air Force Base now? What is my cost of operating the B-1 bomber program in the last year? What is the -- as it relates to F-35, where are my largest part shortages? And rather than going through some cold war era, menu-based SAP or even -- I don’t want to take shots at SAP enterprise information system user interface that looks like your Bloomberg terminal, which is -- I have one I guess, it’s unusable. It’ll just be a Mosaic browser, you could ask any questions and get the answer related to any one of these weapon systems in the United States Air Force, I guess, their production data. And we will show it on Monday, on Tuesday, on Wednesday. And I am telling you we expect some light bulbs to flash.

And if I could sneak one last one in for you Juho. Can you please explain the dynamics between the increase in accounts receivable from last quarter to this quarter, despite revenues kind of staying flattish?

Well, accounts receivable is timing of invoicing. So obviously, when we drop an invoice, it shows up in the accounts receivable. So it’s just timing of invoicing.

Ladies and gentlemen, I think we’re at end of program. We appreciate your time and your attention. And thank you very much. And we look forward to talking with you next quarter. Standby, it does appear to be a game-on in the AI industry at global scale, and I can assure you we are very much in the game. So, thank you all. And we’re signing off.

Thank you. Ladies and gentlemen, this does conclude today’s conference. Thank you all for participating, and have a good night. You may now disconnect.