Corvex, Inc.

Hello, everyone. Thank you for joining us and welcome to the Corvex Q2 2026 Financial Results Conference Call. I will now hand the conference over to Chance Moreland, Chief Financial Officer. Chance, please go ahead.

Good afternoon and welcome to Corvex's second quarter 2026 Earnings Conference Call. Joining me are Jay Crystal, co-founder and co-chief executive officer, and Seth Demsey, co-founder and co-chief executive officer. Before we begin, I remind you that our remarks today include forward-looking statements. Actual results can differ materially from these statements. The factors that could cause this difference are in today's earnings press release and in our quarterly report on Form 10Q for the quarter ended June 30, 2026, which we filed today with the SEC. Our forward-looking statements use assumptions as of today. We do not undertake to update them. On this call, we discuss both GAAP and non-GAAP financial measures. We use adjusted EBITDA, and we refer to contracted annualized recurring revenue on live compute, which is an operating metric rather than a financial measure. A reconciliation of adjusted EBITDA to net loss is in today's press release and in our Form 10Q. Adjusted EBITDA is not a substitute for GAAP results. Other companies calculate similar measures in different ways. The press release and this call replay are on our investor relations site at investors.corvex.ai. Corvex completed the merger with Corvex Legacy Holdings on March 19th, 2026. The second quarter of 2026 is our first full reporting period that includes the AI cloud computing business. Comparisons to prior periods are limited. We encourage you to read the form 10Q for the full picture. I will now turn the call over to Jay.

Thank you, Chance. Good afternoon, everyone, and thank you for joining us. Many of you are new to Corvex, so I want to do three things today. First, I'll tell you what we do. Second, I'll tell you why it works. And third, I'll tell you where we're going and how you should measure us. Then Seth will share updates on our quarterly progress on the technology front, and Chance will take you through the numbers. So first, I'll start with what we do. Corvex builds and operates AI infrastructure. We deliver secure, reliable, and scalable computing for AI workloads. We're focused on three products. The first is AI factories and GPU clusters. We combine accelerators, networking, power, cooling, and system software into one integrated platform that customers use for training the next generation of AI models, as well as running those models for end users in production. Our systems can be delivered as bare metal or with the managed Kubernetes orchestration layer and multi-tenant or single-tenant configurations that meet SOC 2, Type 2, and HIPAA requirements at a minimum and can optionally be designed with up to nation-state grade security protections. The second product is Corvex Token Factory, our inference platform, which is now live in closed alpha. It gives customers access to leading open-weight models through a standard API running on our performance-tuned inference engine. The third product is confidential computing. Our patent-pending capabilities combine trusted execution environments, post-quantum key exchange, and remote attestation to enable model builders to secure their models' weights so they can safely run inference on third-party infrastructure. We're also focused on addressing emerging pain points in the inference market. Now we'll touch on why our business model works. I'll answer this question in three parts. Where the demand is, why customers picked Corvex, and why we expect the economics to hold. So I'll start with where the demand is. We serve distinct markets. The AI factory market is for customers who want to directly operate on large-scale AI infrastructure. The Corvex token factory is for customers who want to programmatically access intelligence through an API. Finally, Corvex's confidential computing offering is a competitive differentiator for both markets, and we also plan to offer it as a standalone product for third-party AI factories. Each of these markets is large, growing, and growing for a different reason. That distinction matters, so I'll take them separately. On the AI factory side, demand exceeds supply and the binding constraint is power. The industry does not have a shortage of interest. It has a shortage of energized, permitted, cooled, and connected capacity. That gap is what we sell into. On the token factory side, there are two tailwinds, not one. The first is the one everybody sees. Inference is now the majority of AI workloads, and it keeps compounding as AI moves from experimentation into production. But frontier intelligence is expensive. Customers can burn their entire annual token budget in the first quarter of the year. That second tailwind is the one we think is underappreciated. We believe workloads are shifting from expensive closed-weight models to open-weight models. The capability gap has narrowed to the point where, for many production tasks, a frontier model is not necessary in our opinion. We work this way ourselves when building software internally. We route a small share of our own requests to frontier models for planning and genuinely hard problems, and we route the large majority of routine execution to open-weight models we run in the Corvex token factory. The outcomes are equivalent for that work, and the cost is a mere fraction. Many sophisticated teams are now doing the same thing. Every workload that makes that shift needs somewhere to run, and we intend to be that somewhere. Finally, on confidential computing, we see security lagging the AI build out, and that gap represents the market opportunity. Model weights represent concentrated high value intellectual property. In addition to the vast sums that traditional model builders spend on developing their model weights, we also believe every enterprise can be a model builder by fine-tuning open weight models with their internal data. Recent security incidents, as well as the increasing use of open weight models from outside the United States, are raising awareness of the need for enhanced security on the inference side of the market as well. The products we're developing are designed to address these pain points in addition to unlock demand from regulated and security-conscious enterprises. Now I'll touch on why customers pick Corvex. On the AI factory side, it's often a combination of three things. The first is speed to value. Whether inference demand exceeds a customer's available supply of compute or they need to train the next generation of their model to maintain competitiveness, we believe customers value the speed with which they can obtain incremental computing resources. To meet this demand for compute closer to customers' timelines, we carefully select power sites, partners, and technology. Two examples of how we do that. In our first data center, we added liquid cooling support in approximately two weeks, which let us serve a customer closer to their schedule on the latest generation of compute rather than the industry standard timeline. And we can also pre-position long lead equipment through our construction partners. So an air cooled site can be converted to high density liquid cooled racks faster than traditional procurement timelines allow. Thank you for joining us. We handed it back to the customer in under two days, running hundreds of times faster than where it started I highlight this because customers in this market talk to each other Reputation in this market compounds faster than capacity does Thank you very much. We believe our offerings enhance our positioning with regulated enterprises, which we believe are an attractive customer segment. And even when customers don't take our highest security offering, the discussion is an opportunity to expose potential buyers to our software and security capabilities, which builds credibility and trust in Corvex. With respect to the Corvex Token Factory, we'll have a lot more to say about why customers choose us in future quarters. For the moment, we'll say that we're focused on going beyond delivering reliable, scalable access to intelligence to also engineer efficiency and security advantages into the platform. Now I'll touch on our AI factory economics. First, our AI factory contracts are taker pay. 100% of our AI platform revenue today comes from fixed term contracts. The customer pays a fixed fee for reserved compute and storage capacity across the term, regardless of how much of that capacity they use. Our revenue does not depend on customer's utilization. What we report is contracted revenue, not spot revenue. Second, we underwrite before we sign. Every new cluster deployment clears a return threshold at the project level before it is approved. Power cost, hardware cost, financing cost, contract term, and residual value all go into that model. We assess counterparty credit as part of the same work, and where it is appropriate, we build reasonable prepayment terms into the contract. If a deal does not clear the threshold, we do not sign it. I'd rather report a smaller contracted number to you than a larger one that's not positioned to exceed its cost of capital. Now we'll touch on where we're going and how to measure us. I will be direct about the second quarter. It's a small quarter. We recognized 3.8 million of revenue. That number does not describe the underlying business activity that actually occurred in 2Q26. Instead, it describes how much capacity was live and handed over to customers during a three-month window. I want to be very clear about this mechanic. A signed contract does not produce revenue. Live compute produces revenue. As of today, inclusive of the compute delivery announced last week on August 4th, contracted annualized revenue on live compute is approximately $22 million. We define that as the annualized value of fixed contractual fees on capacity that is delivered, accepted, and generating revenue as of today. It excludes contracted capacity that is not yet live, and it is not a forecast. Chance will share more about this. A signed contract with a customer requires Corvex to line up inputs like power, GPUs, and related equipment and capital. A major portion of our quarter was spent in service of lining up these resources to meet the demands of new prospective customers and the market writ large. Our business development efforts around securing additional power advanced significantly. We anticipate being able to share more with you in the near future about growth in our contracted power capacity as well as how we are building out our team to continue to scale our power footprint. We also made announcements about new hires during the quarter that provide useful guidance into the capabilities we are building to architect and procure new computing clusters and to capitalize these projects. In July, we announced the appointment of Michael Craig as Vice President of Architecture and Site Operations. Michael has more than 25 years of experience with large-scale computing infrastructure, including building Crusoe's Global Site Operations Organization and leading GPU hardware procurement and supplier strategy. Michael also architected and delivered high-performance computing systems at Hewlett Packard Enterprise and Supermicro. Michael now leads how we architect, source, and operationalize new clusters. In June, we appointed Chance Moreland as chief financial officer. Chance leads capital formation for our AI infrastructure platform. He joined us from Morgan Stanley, where he was a managing director who structured and executed more than 150 complex financings across loan and bond markets. That work included multi-billion dollar financings in digital infrastructure, power and energy, and asset leasing. Chance now builds our capital formation engine at both the corporate and the project level. We also grew our board since our last earnings call. We announced two new directors, Nick D'Onofrio and Patrick Fleury. They joined Brian Cullinan, who chairs our audit oversight work, and Emily Fairbairn. Patrick is the chief financial officer of TerraWolf. He's led more than $8 billion of financings for data center infrastructure. He also brings direct experience with data center transactions, from powered land acquisition through structured leases. That experience supports our power footprint and our capital plan. Nick is a 44-year IBM veteran who led IBM's semiconductor, microprocessor, storage, and server development organizations. He also served on the board of AMD and is a trustee of the MITRE Corporation. That experience supports current and prospective customers across AI labs, enterprise, and federal customers. Brian is a 35-year-plus PwC veteran advising leading public companies and boards on audit, strategy, and financial oversight. Brian served on PWC's global board and as lead director on its U.S. board. All four of these directors bring deep public company governance experience. This board gives Corvex the oversight we need as we scale. I could not be more thrilled to welcome Chance, Michael, Patrick, and Nick to Corvex. At this stage of a company, the team matters as much as the plan. Between Seth and our technology and operations teams and our newest function builders and board members, Corvex has assembled exceptional operators within their respective fields to help us grow the essential pillars of our business, power, technology and operations, capital, and customers. On behalf of our team, I want to convey our gratitude for the trust that you have placed in us. We could not be more excited about the opportunity for Corvex as we execute in the AI infrastructure as a service market. With that, I'll turn it over to Seth to share updates on our quarterly progress on the technology front. Seth? Thanks, Jay.