Earnings Call Transcript - AI Q1 2024

Operator, Operator

Good day and thank you for standing by. Welcome to the C3.AI's First Quarter Fiscal Year 2024 Conference Call. At this time, all participants are in a listen-only mode. After the speakers' presentation, there will be a question-and-answer session. Please be advised that today's conference is being recorded. I would now like to hand the conference over to your speaker today, Amit Berry. Please go ahead.

Amit Berry, Investor Relations

Good afternoon and welcome to C3.AI's Earnings Call for the First Quarter of Fiscal Year 2024, which ended on July 31st, 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 close today, we issued a press release with details regarding our first 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 this 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 on 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.

Thomas Siebel, CEO

Thank you, Amit. Good afternoon, everyone, and thank you for joining our call today. We're off to a strong start for fiscal year '24. Our revenue came in at the high end of our guidance, exceeded analyst consensus, and we're seeing significant traction across our business. This is the 11th consecutive quarter as a public company in which we have met or exceeded our revenue guidance. Following the release of ChatGPT in November 2022, we are seeing a dramatic increase in demand for enterprise AI adoption. In Q1, we experienced strong traction with our enterprise AI applications and especially strong traction with C3 Generative AI. Let's take a look at our revenue highlights for the first quarter. Total revenue for the quarter was $72.4 million, coming at the high end of guidance that was $70 million to $72.5 million and exceeding the analyst consensus. Subscription revenue for the quarter was $61.4 million, constituting 85% of total revenue. Gross profit for the quarter was $40.5 million, representing a 56% gross margin. Non-GAAP gross profit for the quarter was $49.6 million, representing a 69% non-GAAP gross margin. GAAP RPO was $334.6 million. Current RPO was $170.6 million. GAAP net loss per share was $0.56. Our non-GAAP net loss per share was $0.09, both exceeded analyst consensus expectations substantially. We finished the quarter with $809.6 million in cash, cash equivalents, and investments, exceeding the average analyst consensus of $774.3 million. Net cash provided by operating activities was $3.9 million and free cash flow was negative $8.9 million, significantly exceeding analyst consensus that was negative $38.7 million. The market interest in applying enterprise AI to business processes appears to be expanding exponentially, fueled by the interest in ChatGPT and other consumer generative AI tools initially released in late last year. CEOs, business leaders, military leaders, and investors are all focused on how they can take advantage of these powerful new tools to improve operational processes. In Q1, we entered into new and expanded agreements with Saudi Arabia's Smart City, NEOM, Nucor, Roche, Pantaleon, Ball Corporation, Cargill, Con Ed, Shell, Tyson Foods, and the US Department of Defense. Our partner ecosystem continues to expand. In Q1, we closed 60% of our agreements with and through our partner network, including Google Cloud, AWS, Microsoft, and Booz Allen Hamilton. Our qualified partner opportunity has increased by over 100% in the past year and our qualified pipeline with our cloud providers grew by 61% just from Q4 to Q1. C3.AI's federal business is showing significant strength with federal bookings up 39% compared with the year-ago quarter. The company continues to expand its work with the US Department of Defense with new and expanded projects with the Chief Digital and AI Office, US Marine Corps, US Air Force, Missile Defense Agency, and Defense Counterintelligence Security Agency. C3.AI commercial customers including Shell, Georgia-Pacific, Koch Industries, Bank of America, and others, continue to expand their C3 application footprints increasingly now including C3 Generative AI, realizing outsized economic benefit from digital transformations using C3's enterprise AI. Let's talk about a few of these. First, the Department of Defense. Our business relationships with the Department of Defense are extensive and rapidly expanding. The DoD uses the C3.AI platform and C3.AI applications across many services, components, and combatant commands to realize significant improvement in readiness and decision advantage. One example, beginning in 2017, we started to work for the US Air Force to improve the readiness and applied predictive maintenance for the E-3 Sentry, an aircraft that you probably know as the AWACS. By fusing the handwritten maintenance notes with the flight logs and historical inventory, and pilot logs, C3.AI readiness improved the Air Force's legacy maintenance procedure substantially. Following this initial project, the United States Air Force Rapid Sustainment Office selected C3.AI for an additional readiness project called Condition-Based Maintenance Plus, CBM+, to apply similar analytics-based predictive maintenance approaches to the B1 strategic bomber and other aircraft weapon systems. This configuration of C3.AI readiness for the United States Air Force called the Predictive Analytics and Decision Assistant or PANDA, went live into production and is now scaled out to over 16 Air Force aircraft weapon systems. This system PANDA was subsequently selected as the system of record for all United States Air Force predictive maintenance applications. This is the only system of record for an AI application in the Department of Defense that we're aware of. The goal of C3.AI PANDA is to realize up to a 25% increase in overall aircraft mission capability and when rolled out to all aircraft in the United States Air Force, this is budgeted to realize a $3 billion cost savings in maintenance and readiness. Let’s talk about the CDAO. The Department of Defense Chief Digital and AI Office is the organization that is chartered with selecting the AI platform of record for all DoD. We began working with them less than a year ago, initially to bring the C3.AI platform into production across a number of unclassified, secret, and top-secret enclaves as part of CDAO's Advanta ecosystem, a centralized data repository for the entire Department of Defense. Our first project showed how nodal analysis and contested logistics can radically improve when AI systems are applied to US Transportation Command or TRANSCOM data. This application took a simulation-based approach to provide options in response to global logistics disruptions. We're able to accelerate the time it takes to conduct this kind of nodal analysis from days to minutes. C3.AI has now been engaged less than a year later in a dozen projects through CDAO including contested logistics, strategic force readiness, supply chain visibility, commander's dashboards, and combined joint all-domain command and control. Let's take a look at Shell. Shell has been an important customer since 2018. The C3.AI applications are continuing to expand across the entire Shell asset base, including upstream, downstream, integrated gas, renewables, and retail to address asset integrity, optimization, ESG, and predictive maintenance. Today, Shell's C3.AI predictive maintenance program monitors almost 20,000 pieces of equipment and because C3.AI can identify failure in advance with very high levels of accuracy, this can both increase production and prevent potential disasters such as offshore oil rig failures, the cost of which may be incalculable. The economic benefit for Shell is enormous and they have given presentations at Bank of America and other conferences, where they estimate it to be in excess of $2 billion per year. In the past three months, Shell and C3.AI have further expanded deployments, applying AI-based estimation techniques in subsurface reservoir management, deployed a new C3.AI-based Shell oil condition monitoring application for its customers to reduce unplanned downtime and optimized maintenance of heavy-duty assets, and expanded Shell's use of the C3.AI ESG solution. Let's switch to Koch Industries. We continued to expand our partnership with Koch, particularly at Georgia-Pacific and Flint Hills Resources. We generated almost $4 million monthly predictions across 300 plus assets using our reliability and C3.AI supply chain applications. Georgia-Pacific is realizing up to 5% improvements in overall equipment effectiveness. Koch also initiated two Generative AI projects to help process data, documents, and files. Georgia-Pacific is improving efficiency in triaging and resolving equipment and production maintenance issues to automate processing for paper manufacturing. Flint Hills Resources is using C3 Generative AI to increase efficiency and improve information access in commodity trading operations. At Bank of America, our C3 applications are deployed to deliver customer insights, optimize business workflows, and provide recommendations to its liquidity product specialists and treasury sales officers. The liquidity team is responsible for managing the bank's cash flows. Every day, over 500 liquidity and sales users log in to the C3.AI applications. The bank is applying AI-based techniques to access client responsiveness and sensitivity in a fluctuating interest rate environment. Three applications are in production today. Bank of America and others are in development. All are expected to generate significant annual benefits, especially in a higher interest rate environment, where balanced retention, optimal pricing of interest rates, and efficiency of sales and operations become important drivers of profitability and expense reduction. Let's talk for a minute about C3 Generative AI because ladies and gentlemen, this is big. Now by combining the power of the tried, tested, and proven C3.AI platform that we've built in the course of the last 14 years with large language models, C3 Generative AI enables immediate interaction with the relevant and frequently massive corpus of data, documents, and signals associated with enterprise domains. For example, machines, factories, systems, supply chains, natural phenomena, biological systems, and operating divisions. We use a natural language interface to rapidly locate, retrieve, and present relevant data across an entire enterprise's information systems, allowing users to utilize the full power of AI to optimize productivity, monitor systems, forecast demand, and in general, understand what is happening, what will happen, how to plan, and how to maximize efficiency. The production adoption and customer success since our initial March 2023 C3 Generative AI release has been immediate. In the last quarter, C3.AI closed eight new agreements for C3 Generative AI, addressing use cases across multiple industries, including agriculture, consumer packaged goods, defense, intelligence, manufacturing, state and local government, oil and gas, and utilities. To date, we have closed 12 generative AI agreements and have a pipeline of more than 140 qualified C3 Generative AI enterprise opportunities. Over 140 in less than six months. So putting this in perspective, our qualified pipeline of Generative AI sales opportunities exceeds that of any other product in our product line that we've introduced in the last 14 years. This is significant. To meet market demand, C3.AI today announced the immediate availability of the new C3 Generative AI suite, including 28 new domain-specific Generative AI solutions for industries, business processes, and enterprise systems. C3 Generative AI provides fine-tuned tailored domain-specific Generative AI solutions that mitigate the crippling problems that prevent the widespread industry adoption of LLMs. The market response to our Generative AI offerings is simply staggering. We believe that the advent of Generative AI may more than double the immediately addressable market opportunity available to C3.AI. And now with our generative suite of Generative AI products at the door, you can expect that we will be investing in the coming quarters to promote market and support these initiatives. The 28 applications that we released today are available in three categories: C3 Generative AI for industries, which includes Generative AI for aerospace, defense, financial services, healthcare, intelligence, manufacturing, oil and gas, telecommunications, and utilities; C3 Generative AI for business processes, including customer service, energy management, ESG, finance, human resources, process optimization, reliability, and supply chain; and importantly, C3 Generative AI for enterprise systems. Ladies and gentlemen, this is not just slide-ware that's being offered by software vendors. This is production software available to order today, ready to ship now, and available to solve problems tomorrow, with a live deployment in 12 weeks. These products include C3 Generative AI for Databricks, Microsoft Dynamics 365, Oracle ERP, Oracle NetSuite, Palantir, Salesforce, SAP, ServiceNow, Snowflake, and Workday. LLM support is immediately available in these products for Falcon 40B, Llama 2, Flan-T5, Azure GPT-3.5, AWS Bedrock Claude 2, Google PaLM 2, OpenAI GPT-3.5, and MPT-7B. Additional support will be announced for leading LLMs as the market develops. By combining the power of LLMs and Generative AI with the tried, tested, and proven C3.AI platform, we believe C3 Generative AI addresses the troubling problems endemic to all other Generative AI solutions currently proposed. Firstly, the answers from C3 Generative AI are deterministic, not random. Every time you ask the same question, you receive the same answer. All answers are immediately traceable with one click to ground truth. Unlike LLMs you're familiar with, such as ChatGPT and Google Bard, C3.AI provides links that direct you to the source of the information used in the answers, ensuring complete transparency. With C3.AI, LLMs are shielded from data, minimizing the risks of data exfiltration and other cyber security issues. Moreover, C3.AI’s Generative AI solution enforces enterprise access and cyber security controls while ensuring end-to-end encryption of data both in motion and at rest. LLM reasoning is limited to enterprise-owned and licensed data, effectively mitigating risks associated with LLM solutions in the marketplace. This structure virtually eliminates any risk of hallucination in the generated responses. All C3 Generative AI applications can be fully deployed within 12 weeks for $250,000 and they are available today through the AWS marketplace, Google Cloud marketplace, and Azure marketplace. The licensing model is straightforward. C3.AI supports customers in bringing their Generative AI applications into production. Following deployment, customers continue to pay according to CPU or CPU hour with volume discounts available. Bloomberg Intelligence predicts the Generative AI market will reach $1.3 trillion by 2032, much of which will go to chip manufacturers, cloud service providers, and professional service providers, while a significant portion is expected to benefit Generative AI applications as well. Within this, we expect Generative AI applications to reach $280 billion during the same timeframe, with the majority accruing to providers of software that enable businesses to utilize LLMs for enhanced business processes and decision-making. Numerous start-ups are currently proposing specialized Generative AI solutions targeting specific industry niches. In contrast, C3.AI already offers comprehensive solutions supported by a well-capitalized company, with nearly 1,000 skilled professionals and a robust partner ecosystem. The market opportunity appears vast. We've proven solid management and expense controls in recent quarters. In Q4 of last year, cash flow from operations was positive at $27 million. In Q1 of '24, cash flow from operations was $3.9 million. Our non-GAAP operating loss significantly beat market expectations in both Q4 of '23 and Q1 of '24. We finished Q1 of '24 with $809.6 million in cash and investments, down $2.8 million from the previous quarter. After careful consideration with our leadership team and marketing partners, we have decided to invest in Generative AI, lead generation, branding, market awareness, and customer success related to our Generative AI solutions. This is an immediate market opportunity that we intend to seize. While we still expect to be cash positive in Q4 this year and in fiscal year '25, we will be investing in our Generative AI solutions and at this time do not expect to be non-GAAP profitable in Q4, '24. Expect to see updates on this as we assess market activities in the next couple of quarters. We maintain a strict focus on financial controls. Our disciplined approach to business reinforces our confidence that investing in Generative AI aligns with shareholder interests. C3.AI was ahead of its time in predicting the potential of enterprise AI applications, and we have spent the last 14 years preparing for this opportunity, which is now arriving at our doorstep.

Juho Parkkinen, CFO

Thank you, Tom. I will now provide a recap of our financial results, some color around the expected drivers of our financial results for the remainder of the year, and walk you through our second quarter and full year fiscal '24 guidance. First quarter revenue increased 10.8% year-over-year to $72.4 million, with subscription revenue up 7.6%, representing 85% of total revenues. Gross profit for the first quarter was $49.6 million, leading to a gross margin of 68.6%. I want to remind everyone that we expect short-term pressure on our gross margin due to a higher mix of pilots, which carry a higher cost of revenue during the pilot phase. We are pleased with our progress in managing expenses and getting the entire employee base bought into expense discipline. Our expense management success is shown in our first quarter operating loss of $20.7 million, which was better than our guidance of a loss of $25 million to $30 million. Operating loss margin was 28.6%. As Tom mentioned, the Generative AI opportunity is massive, and we believe it is in the best interests of our company and shareholders to make incremental investments in sales, marketing, and customer success. Consequently, we are revising our 2024 expense guidance to reflect these investments. Turning to RPO and bookings. We reported GAAP RPO of $334.6 million, which is down 27% from last year, expected as we transition to consumption-based agreements. Current RPO was $170.6 million, down 1.7% from last year. We see positive trends in diversifying our pilot bookings, with Q1 pilots representing eight industry sectors. Operating cash flow was $3.9 million in the quarter, while free cash flow was negative $8.9 million, reflecting expenses related to the build-out of our new corporate headquarters. We closed the quarter with strong cash and investment balances amounting to $809.6 million, decreased by only $2.8 million from last quarter. As it relates to our consumption business model, I would like to provide two key updates. First, we previously communicated a 70% conversion rate of pilot phase engagements to production phase. At quarter-end, we had signed 73 pilots, of which 70 are active, meaning they were either converted in their original six-month term, extended for one to two months, or are currently under negotiation for production licenses. Second, concerning consumption data, our actual vCPU consumption over the past three quarters is slightly higher than our original estimates. Finally, our customer engagement increased to 334 from 287 in Q4 '23. Now regarding guidance. We're guiding Q2 revenue to a range of $72 million to $76.5 million. We expect our non-GAAP loss from operations to range from negative $27 million to negative $40 million. As before, the Generative AI opportunity is enormous, leading us to invest for success. Therefore, we expect to achieve non-GAAP profitability in FY'25. For FY'24, we are maintaining our previous guidance for revenue in the range of $295 million to $320 million, while increasing the non-GAAP loss from operations to between negative $70 million and negative $100 million.

Operator, Operator

Thank you. Our first question comes from Patrick Walravens with JMP Securities. You may proceed.

Patrick Walravens, Analyst

Great. Thank you very much. So it's great to hear about the demand levels and all the activity. Tom, can you talk a little bit about the linearity in the quarter, how that was and how things closed out? At your investor event, you told us that you had closed 16 agreements, and you ended up with 32. But if you look back a quarter, you had 10 in the middle and you ended up with 43%. It makes it seem like maybe the second half wasn't quite as good as you would have hoped, but I don't know. Maybe I'm interpreting that wrong, too?

Thomas Siebel, CEO

Or maybe the first half was great. I would say that this might have been our best quarter ever in terms of linearity. I'm not entirely sure, but it was very predictable. The business volume throughout the quarter was quite consistent.

Patrick Walravens, Analyst

Okay. And then if we multiply the average TCV times the number of deals right then we get a total TCV number, which I mean, you guys are the only ones to disclose it, so thank you for that transparency. That was around $26 million this quarter, and last quarter again it was $52 million, almost twice as much. I just want to make sure we understand what's going on here; is TCV not a good indication of what you're actually doing in the quarter?

Thomas Siebel, CEO

Well, we used to compensate people on TCV, and that's back when we used to do $10 million, $20 million, $30 million, $40 million, $50 million deals, Pat. Now we're doing $250,000 projects in Generative AI and $0.5 million projects for our other enterprise products. The Generative AI products last 12 weeks, while the other pilots generally last up to six months. So it follows that TCV goes down, RPO goes down. And by the way, gross margins go down in the short run, okay? Because the gross margin when we're doing these Generative AI pilots for $250,000, we are going to ensure success on the first 50 of these projects. If we have to over-invest to make that pilot successful, we'll do it. I'm not certain that RPO is meaningful going forward.

Patrick Walravens, Analyst

Okay, great. And then lastly, Juho, probably for you. You have a footnote on the balance sheet where there is a related party presumed to be Baker Hughes that still has an account receivable of $75 million, which is the same as last quarter. Are you guys okay with that?

Thomas Siebel, CEO

It's a lot bigger than $75 million.

Juho Parkkinen, CFO

No, total. All right. Yes, we are okay with that. I'm not entirely sure how to interpret your question. We have no collection concerns from any of our customers. Our bad debt reserve is only $359,000, and we have no concerns regarding collections.

Patrick Walravens, Analyst

Okay, thank you.

Operator, Operator

Thank you. One moment for questions. Our next question comes from Mike Cikos with Needham. You may proceed.

Mike Cikos, Analyst

Hey, I appreciate the new pronunciation of the last name. Thanks for taking the questions here, guys. A couple of questions: first on the guidance. I appreciate this pivot you guys are trying to take advantage of this opportunity, where it really feels like Generative AI has come online. I think my question is more around the guidance, and where I'm going with this is, given the increase that we're talking about in the go-to-market investments, which is obviously acting as a drag on your operating losses, no question about it, why aren't we seeing some sort of benefit when looking at the fiscal '24 revenues? Why maintain that guidance as we sit here today?

Thomas Siebel, CEO

Hi, Mike. I think we've been doing the best we could do since we've been a public company to be credible in setting expectations and we have met or exceeded expectations in every quarter. Now, we are in uncharted territory still with the consumption pricing model and with Generative AI. If I were to take all the spreads of all my product groups and business plans, you can be sure that they come to larger numbers than we've talked about in guidance. However, our position is we feel comfortable with the guidance that's out there today. We are planning on significant accelerated growth, but I don’t want to do it prematurely. I don't want to lose credibility, and I think this is the responsible thing to do.

Mike Cikos, Analyst

All right. Thank you for clarifying that out; I appreciate it. Another question, I understand the commentary on RPO and even CRPO declining, but for Juho, with the transition to the consumption model, shouldn't we be seeing CRPO remain more resilient as these consumption pilots start to convert? Or are consumption pilots, even when they move to production, not necessarily going to show up in CRPO? Can you provide any more color on that, please?

Juho Parkkinen, CFO

Yeah, absolutely. So effectively, the CRPO is flat, right, and the way the consumption-based business model works is that we start with a pilot phase; that pilot amount would be RPO in the quarter that we signed that deal. The consumption phase, unless the customer were to sign up for volume discounts, will never be in RPO because it is after consumption invoicing, so you will only see that in revenue.

Thomas Siebel, CEO

So if it were a 100% consumption model, RPO would be zero.

Juho Parkkinen, CFO

That is exactly right.

Mike Cikos, Analyst

Okay. And the expectation is that most of these customers would not be signing up for those larger volume commitments, which is going to be an expected drag on the RPO and CRPO.

Juho Parkkinen, CFO

Yeah.

Thomas Siebel, CEO

Yeah.

Mike Cikos, Analyst

Okay, all right, thank you for that.

Thomas Siebel, CEO

And it's quite easier to buy rather than saying 10, 20, 30, 40, 50. One deal we did was $0.5 billion if I'm not mistaken; well, it was $300 million plus a couple of things. We're saying, hey, it's a $0.5 million; if you like it, keep it; okay? After they pay their $0.5 million if it goes that way, there is no RPO.

Juho Parkkinen, CFO

That's right.

Mike Cikos, Analyst

Got it, got it. And maybe just one more if I could, sorry for taking all the time here, but I did just want to circle up. I know that you guys are talking about the C3 Generative AI pilots being $250,000, lasting 12 weeks, and the remaining product lines typically have about six months for those pilots. Can you help us understand what the significant value is in these Gen AI pilots that allows for this much quicker conversion?

Thomas Siebel, CEO

It is quicker, Mike. In some cases, we might have to load all the data, model supply chains, and build machine learning models that fit the structure of Cargill, which is roughly a $100 billion business, or the United States Air Force, which is a substantial organization. With Generative AI, we only need to load their data into a deep learning model and it takes the learnings from those data, storing them in a vector store. We excel at aggregating structured data, non-structured data, sensor data, enterprise data, and images into a consolidated format. We've been doing this for 14 years; we are exceptionally proficient at that, so this process is much simpler. The time to value is faster, the implementation effort is easier, and technically, it's an order of magnitude easier.

Mike Cikos, Analyst

Awesome. Thank you very much, guys; I appreciate it.

Thomas Siebel, CEO

And there is nobody who doesn't want to talk about it.

Operator, Operator

Thank you. One moment for questions. Our next question comes from Kingsley Crane with Canaccord Genuity. You may proceed.

Kingsley Crane, Analyst

Thanks for taking the question, and congrats on the result. It sounds like your plan is to invest more in lead generation, branding, market awareness, customer success. You've mentioned that you have more than 140 qualified leads in Generative AI, so it seems like you've done tremendously well generating leads. As we think about the incremental change to the profit guidance, are you balancing investments between customer success and pilot conversion without lead generation and brand awareness?

Thomas Siebel, CEO

I'm sorry; could you clarify your question again? A lot of this is branding and lead generation, Kingsley, what we're focusing on. Just like we established our brand for enterprise AI that worked quite well, we are planting a flag on the Generative AI market, and we're going to communicate that. We're first to market; how many companies out there have 28 enterprise Generative AI solutions? The answer is exactly one. At the same time, if we have a customer in any market needing additional resources to ensure their pilot's success, we will allocate those resources. As we advance down the learning curve, we will achieve increasing efficiency, which will improve gross margins.

Kingsley Crane, Analyst

Okay, thanks, Tom; that makes a lot of sense. If I could ask one more question regarding the 28 domain-specific Generative AI solutions: for example, if you're serving an oil and gas customer that requires a solution in sales, will this ultimately be linked into Salesforce? Will this require three separate apps or how will that be consumed and priced?

Thomas Siebel, CEO

That will be in one; basically, it's priced per CPU. It's going to depend on whether it's discrete projects or whether they're united into one generative AI application. If they are united, it will be a $0.25 million to bring it live in 12 weeks, and thereafter pay $0.35 per VCPU hour or VGPU hour.

Kingsley Crane, Analyst

Okay, very helpful. Keep up the good work. Thank you.

Thomas Siebel, CEO

As it pertains to runtime pricing, it won't matter if it's one application or three; it will be the same overall runtime charge.

Operator, Operator

Thank you. One moment for our next question. Our next question comes from Pinjalim Bora at JPMorgan. You may proceed.

Noah Herman, Analyst

Hey, this is Noah on for Pinjalim; thanks for taking our questions. On the semi-pilots that are active at the moment, if we exclude the pilots that have been extended for one or two months, is there any way to parse how many of these pilots are under production licenses? I have a quick follow-up.

Thomas Siebel, CEO

Thanks for the question. At this point, we are looking at 73 pilot deals that we've been managing; 70 are either converted in their original term or are in the process of negotiating production licenses. Take away from this that out of 73 pilots, only three have not progressed. We feel very comfortable and optimistic about the pilot program's current progress.

Noah Herman, Analyst

Understood. A quick follow-up on the gross margins. I know you commented that with the transition to function, but can you provide any further clarity on the gross margins for our models moving forward?

Juho Parkkinen, CFO

I think, Noah, the key takeaway is we are expecting continued margin pressure due to more pilots being introduced. This will likely impact margins until consumption becomes a more substantial portion of the revenue stream, at which point we expect margins to begin to recover.

Operator, Operator

Thank you. One moment for our next question. Our next question comes from Sanjit Singh with Morgan Stanley. You may proceed.

Sanjit Singh, Analyst

Thank you for taking the questions. I had one for Tom and one for Juho. Tom, what's the vision around multimodal? I know there's a lot of interest around the language models. As you think about the different diffusion models—video, audio, image—what's the vision for supporting those types of models if multimodal becomes the dominant deployment architecture for enterprise AI?

Thomas Siebel, CEO

Are you referring to data, Sanjit? I'm not sure I understand the question.

Sanjit Singh, Analyst

What I meant is that obviously LLMs have become very popular and pervasive, but there are other AI models that address image, audio, and video as well. As we think about the market landscape moving forward, how does C3.AI envisage capitalizing on this?

Thomas Siebel, CEO

So you're noting that large language models mostly deal with text, HTML, and code. Excellent point. C3.AI excels at ingesting various types of multimodal data—images, telemetry, enterprise data, and more—and we leverage our standard architecture to accommodate this data. Our deep learning models organize this data and store insights in a vector data store. The large language model is utilized for interaction with users and interpreting queries while extracting information from the underlying data.

Sanjit Singh, Analyst

That makes perfect sense. Thank you.

Juho Parkkinen, CFO

As I mentioned, we are starting to see very positive indicators related to actual vCPU consumption from customers currently in the pilot phase. This transition is essential to achieving revenue neutrality and ultimately revenue acceleration.

Operator, Operator

Thank you. And we have time for one final question. Our final question comes from Michael Turits with KeyBanc Capital Markets. You may proceed.

Eric Heath, Analyst

I wanted to ask about Baker Hughes. Can you give us some color on what changed with the relationship that they are no longer considered a related party? Secondly, if I take the $16.5 million of Baker's revenue contribution for two months in the quarter and extrapolate that for an additional month, I arrive at around $24 million, which is what we expected at around $20 million. How did Baker's contribution in the quarter compare to expectations? Is there any way to understand how the non-Baker Hughes side did in relation to your guidance?

Thomas Siebel, CEO

Baker Hughes is no longer a related party because they sold parts of their stake, going below the required 5% threshold. We provided details of this on our investor relations site. Regarding Baker Hughes revenue, it met exactly our expectations.

Juho Parkkinen, CFO

Right.

Eric Heath, Analyst

All right, thank you.

Amit Berry, Investor Relations

That concludes our call. Thank you for your time. We look forward to providing an update at the end of our second quarter. Thank you all and stay tuned for some exciting things to report.

Operator, Operator

Thank you. This concludes today's conference call. Thank you for participating. You may now disconnect.