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 10-Q for the quarter ended June 30th, 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 10-Q. 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 19, 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 10-Q 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. 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. 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 a managed Kubernetes orchestration layer in 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 are also focused on addressing emerging pain points in the inference market. Now we will touch on why our business model works. I will answer this question in three parts: where the demand is, why customers pick Corvex, and why we expect the economics to hold. I will 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 will 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 write 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 unlocked 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. 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. Second, we seek to solve the customer's problem, not just our own. For example, a model builder moved a workload onto our H200 systems, and it ran slower than they expected. Most operators would confirm the infrastructure was healthy and close the ticket. Instead, we took the workload, profiled it, found some code in need of optimization, and we patched it. 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. Third, security changes who we can sell to. Almost all AI compute today runs unprotected inside the CPU and GPU. Model weights are exposed, training data is exposed, prompts are exposed. Every provider in this market asks you to trust their policies, their access controls, and their people. Policies can fail through error, through misconfiguration, or through a bad actor. Our goal is to remove trust from the equation and to prove it with cryptography instead. We've been leveraging a portion of our capabilities for more than a year with certain customers, and we believe our offerings enhance our positioning with regulated enterprises, which we believe are an attractive customer segment. 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 take or 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 customers' 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 would rather report a smaller contracted number to you than a larger one that is not positioned to exceed its cost of capital. Now we will touch on where we are going and how to measure us. I will be direct about the second quarter. It is a small quarter. We recognized $3.8 million of revenue. That number does not describe the underlying business activity that actually occurred in 2Q 2026. 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 join Brian Cullinan, who chairs our audit oversight work, and Emily Fairbairn. Patrick is the Chief Financial Officer of TeraWulf. He has 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 will turn it over to Seth to share updates on our quarterly progress on the technology front. Seth? Thanks, Jay. Corvex Token Factory version one is now live in closed alpha. We spent the quarter shipping and adding to the team, and we have further releases planned for the third and fourth quarters as items move from roadmap into production. We completed planning for version two of our cloud management software during the second quarter and have moved into execution. It is designed to improve the automation, reliability, and scalability of the platform as we grow. With that, I will turn it over to Chance. Chance? Thank you, Jay and Seth. Before I begin, one framing point. We completed the merger on March 19th, 2026. Corvex, Inc. is the accounting acquirer. Our results include the AI cloud computing business only from March 19th forward. Prior year periods contain only the legacy healthcare business. So year-over-year comparisons are not meaningful this quarter, and I will focus on the current period. Total revenue for the second quarter was $3.8 million. All of it came from the AI platform and services. For the six months and for the acquired business for the period from March 19th through June 30, total revenue was $4.3 million. As Jay noted, contracted annualized recurring revenue on live compute is approximately $22 million as of today. Let me define it precisely because we intend to report on it on the same basis every quarter. It is the annualized value of fixed contractual fees on capacity that has been delivered, accepted by the customer, and is generating revenue as of the stated date. It excludes contracted capacity that is not yet live. It is a point-in-time operating metric, not a forecast and not a GAAP measure, and it will not equal annualized GAAP revenue in any quarter when capacity comes online mid-period or after a given reporting period. For example, as Jay noted earlier, the $22 million today includes the impact of the compute delivery announced last week on August 4th. One other point before we move on. Because contracted ARR is a point-in-time measure, it may vary in future periods as we strategically look to reposition assets. As one example, as we approach contract renewal discussions with certain customers, we may elect to optimize that compute through the Corvex Token Factory rather than the open market. As utilization needs to achieve a certain rate before the economics are equivalent to bare metal reservations, we may see a dip in contracted revenue that we hope is transitory. Cost of compute for the AI platform and services was $2.1 million in the quarter, excluding depreciation and amortization. This consists mainly of data center rent, power, network access, and directly attributable labor. D epreciation and amortization was $2.7 million in the quarter. This reflects primarily our servers, network equipment, and computing hardware, plus finance lease amortization and amortization of acquired intangibles. On the operating expense front, technology and infrastructure was $1.4 million, sales and marketing was $700,000, and G&A was $12.1 million. I want to unpack that G&A number because it is the largest single item on the page, and it is mostly non-cash. Total stock-based compensation in the quarter was $9.4 million, and $7.6 million of that sat in G&A. This is driven by the replacement equity awards we issued for Corvex OpCo options and RSUs at the merger. The total fair value of those assumed awards was approximately $148.5 million. Approximately $4.9 million went into the purchase price, and the remaining approximately $143.6 million is recognized as compensation expense through 2030. You should expect a large, non-cash, and reasonably predictable stock compensation charge in every quarter for several years. Net loss for the second quarter was $12.8 million. Net loss attributable to common stockholders was $12.9 million. Other income included a $2.5 million gain on disposal of assets. On June 30, we completed the transfer of the legacy healthcare assets to the lender in full satisfaction of the bridge loan, including $1.5 million of principal and a $3 million repayment premium. The gain represents the excess of the carrying value of the bridge loan over the assets transferred. It is non-recurring, and it is non-cash. Adjusted EBITDA was - $3.2 million for the quarter and - $4.8 million for the six months. Adjusted EBITDA for the AI platform was - $2.3 million for the quarter and - $2.4 million for the six months. We define adjusted EBITDA as net loss, excluding D&A, stock-based compensation, merger transaction costs, interests, gains and losses on disposal of assets, and income taxes. The reconciliation is in the press release and in the 10-Q. On the balance sheet, we ended the quarter with $21.7 million of cash in cash equivalents. Property and equipment net was $31.4 million. Goodwill was $519 million, and intangible assets net were $15 million, both arising from the merger. Following the extinguishment of the bridge loan associated with the pre-merger business, there was no funded debt outstanding. Finance lease liabilities were $9.5 million, and total operating lease liabilities were $5.5 million. Deferred revenue was $3.7 million in total, $1.8 million current, and $1.9 million non-current. Total stockholders' equity was $573 million. On the cash flow front, for the six months, we used $9.6 million of cash in operating activities. $4.3 million of that was in the first quarter, with $5.3 million in the second quarter. Before we move on, I'd like to call out a few components of that figure. First, approximately $1.9 million was associated with vendor payments tied to the continued wind down of the pre-merger healthcare business. Second, approximately $1.6 million was associated with non-recurring accounting, legal, and other costs associated with the merger. Lastly, $2.8 million was paid to a vendor as a deposit for an intended capital investment and was refunded to the company in July 2026. Investing activities provided $29.8 million, consisting of $36.7 million of the cash acquired in the merger, less $6.5 million of property and equipment purchases, and $700,000 of capitalized software. Financing activities used $1.8 million, primarily finance lease payments. Scaling this business will require additional capital, and our funding requirements depend on the pace of our infrastructure expansion. As one recent example, the Blackwell expansion we announced on August 4th was funded through a debt financing, a customer prepayment, and existing cash on hand. On our capital structure, we simplified it materially after quarter end. On July 1st, our stockholders approved a series of proposals which had the following end result: full conversion of Series A preferred stock to common stock, full conversion of Series C preferred stock to common stock, and partial conversion of Series D preferred stock to common stock. Following these conversions, as of July 8th, we had 27.6 million shares of common stock outstanding, no shares of Series A preferred stock outstanding, no shares of Series B preferred stock outstanding, no shares of Series C preferred stock outstanding, and 28,930 shares of Series D preferred stock outstanding, convertible into roughly 28.9 million shares of common. Taken together, that is approximately 56.6 million common shares on an as-converted basis. On July 10th, we filed a resale registration statement covering up to 53.4 million shares held or issuable to existing holders. It is not a primary offering by Corvex, and we will not receive proceeds from those resales. I say this plainly because dilution math matters to you and because it is easier to model when someone says it out loud. The details are in Note 15 of our 10-Q. Lastly, I'll touch on internal controls. We disclosed material weaknesses in our 2025 Form 10-K, and they remain unremediated in this quarter's filing. I'm not going to skip past that. We are a company that closed a reverse merger, changed its entire business, and is now building a public company finance function around an operating platform. Building that function is core to my mandate here. We acknowledge there is significant work to do to remediate these material weaknesses, and we have a multi-step plan in place to accomplish this goal. We have engaged a top 10 U.S. accounting and advisory firm to support the scoping, implementation, and testing of our Sarbanes-Oxley compliance program. Work is already underway on the risk assessment and scoping with the goal of completing that during this fiscal quarter. Focus will shift then towards developing and documenting internal controls over financial reporting, followed by testing those internal controls. We look forward to updating you on our progress as this work stream proceeds. With that, I'll hand the call back to Jay. Thanks, Chance. Let me close where I started. Seth and I have built companies together before, and we did it the same way each time. We focused on a compelling market opportunity where we saw a clear right to compete, and we focused on building better engineering and out-executing the competition. AI infrastructure is that place right now. The bottleneck is not demand. The bottleneck is not even chips. The bottleneck is the ability to put power, land, hardware, software, and a creditworthy customer in the same place at the same time, and to do it faster than the next company. That's the whole job. That's what we do. The second quarter was small on the income statement. It was not small on the business. We closed a merger, we cleaned up a capital structure, we exited a legacy business, we brought new clusters into revenue, and we built a pipeline we are focused on converting. We have a lot of work in front of us. Our capacity needs to grow by multiples. Our revenue is concentrated in a small number of customers. We need to rapidly iterate and grow Corvex Token Factory from its present alpha state. We need to enhance our controls and streamline our back-office operations. None of that is hidden, and none of it is news to us. So measure us on contracted power, on contracted annualized recurring revenue on live compute, and on the capacity we bring into service. Those are the core operating metrics we intend to use to help investors measure our progress, and we will expand our reporting around them as the underlying figures become meaningful and appropriate for disclosure. Thank you for your time today and for your interest in Corvex. This concludes today's call. Thank you for attending. You may now disconnect.