Good morning, thank you for joining us for SLB's Digital Investor Day. I'm James McDonald, Senior Vice President of Investor Relations and Industry Affairs. This is an exciting time for our industry. Digital is reshaping the way energy is planned, produced, and optimized, and AI is accelerating that shift. At SLB, we operate at the intersection of energy and AI, and as digital scales across the energy value chain, it creates new opportunities for our customers and SLB. Over the course of the morning, you will hear from several of our leaders who will take you through our digital journey, our portfolio, and our strategy, and how this business will continue to support our long-term growth endeavors. We will conclude with a Q&A session, then we will host a lunch, where you will have the opportunity to engage directly with our leadership team. Before we begin, I'd like to remind you that today's remarks will include forward-looking statements. These statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied. The presentations will also include certain non-GAAP financial measures. Please refer to our SEC filings and the materials posted on our investor relations website for additional information. Please note that in the event of an emergency here today, NYSE personnel will be on hand to direct you to the nearest exit and provide further instructions. Let's begin the show. What does it take to shift an industry? It takes vision, it takes capability, it takes the courage to be first. SLB has been shaping the digital backbone of our industry for decades, we've had a lot of firsts. The first to simulate and predict reservoir performance. The first service company to build a global computing network. The first to create software with an open architecture. The first to move upstream platforms to the cloud. The first to drill and steer a well autonomously. The first to deploy agentic AI for upstream operations. Being first is one thing. Going further is another. Going further means building data-driven platforms designed for scale, with insights collected, curated, and connected across operations. Where agents become teammates. Combining domain expertise with digital intelligence. Where equipment is connected, intelligent, and autonomous, where decisions are made with confidence for every well, every barrel, every customer. This is the future we are building, not just as a vision, but as a reality delivered at scale. A future where intelligence is embedded everywhere energy is. Where technology doesn't just optimize performance, it delivers impact. This is how we go further. This is how we lead. This is the next chapter of SLB. Ladies and gentlemen, good morning, and thank you for joining us today. As you have seen, progress in our industry belongs out to those willing to be first, to go further, and to turn vision into reality. That same spirit of innovation is what brings us here today. This morning, we discuss a force that is reshaping how energy is discovered, developed, and produced. That force is digital. For many years, our industry viewed digital as an enabler, a bolt-on tool to improve workflows and to create pockets of value. Today, that has changed. Digital has become foundational. It unlocks performance, efficiency, and returns across every aspect of energy operations. For SLB, it is redefining how we grow, how we differentiate, how we create value. This is not a cycle. It is a structural shift in how this industry will operate. SLB is positioned to lead it. To understand the opportunities ahead, let's begin by discussing the challenge we must address. Energy is the foundation of modern life. Without it, societies cannot prosper, economies cannot grow, and progress cannot be sustained. Yet, as the world enters a new phase of demand, the role of energy is becoming even more important. From advanced manufacturing to cloud computing, transportation networks to AI models, urban growth to national resilience, the modern economy is becoming more energy-intensive. At the same time, expectation around energy are rising. Not only does the world need more energy, it also demands reliability, affordability, and sustainability. To meet this, our industry must achieve new levels of performance and efficiency. This is where digital changes the equation. Digital enables us to produce more intelligently, improving decision-making, automating workflows, and increasing recovery. While energy transformed the world, digital transforms energy. This is the next chapter of value creation in our industry. This is why we have positioned SLB at the forefront. Unlocking the full potential of digital energy requires domain expertise, global scale, trusted relationships, and a digital platform foundation that connects the full life cycle of energy operation. SLB is bringing this capability together in a way few others can. At the moment when the industry needs them most. Today, we're navigating a complex environment, one where energy security has become more critical. Assets are becoming more mature. Customer remains disciplined in how they allocate capital. Against this backdrop, four structural priorities are driving investment across this industry. Improving operational performance, increasing recovery, reducing cycle time, and delivering greater capital efficiency. These priorities are durable, they are investable, and they increasingly favor digital. First is operational performance. Customers need to perform with greater speed, consistency, and precision across increasingly complex operations. That means reducing non-productive time, improving reliability, and using technology to deliver better outcomes. In this environment, operational performance is no longer just a measure of execution. It is a source of competitive advantage. Second is increasing recovery. As the resource base is becoming more mature and complex, more of the next source of value creation will come from existing assets themselves. Customer needs to understand reservoir more deeply, manage production more dynamically, and apply technology that improve recovery over time. It is no longer enough to bring production online. The greater value lies in maximizing recovery throughout the life cycle of an asset. Third is cycle time reduction. Customer needs to move faster from planning to production. From discovery to first oil and gas. This requires technology and workflows that shorten project timelines, improve coordination, and accelerate decision-making across the value chain. The fourth is capital efficiency. Across all basins, our customers remain focused on cash flow and returns. They need more value from every dollar invested, and that requires solutions that improve productivity, reduce total cost of ownership, and deliver measurable impact at scale. Underpinning each of these priorities is a common enabler, AI and digital transformation. Increasingly, this is how performance will be achieved through better data, faster decision, and intelligent automation from planning to production. Customers expect equipment to be connected, workflows to be digital first, and decision to be informed by data. This is why the opportunity ahead is structural. Because even as the market continues to change, the need for energy and returns will not. Digital is key to both. At SLB, we have been helping to shape the digital fabric of this industry for years. That matters because the capabilities our customer requires cannot be built overnight. They need trusted platform, proven workflows, and a partner that can deploy globally. Very few company can do this. We can. Our digital advantage is built across four reinforcing areas. Domain expertise, a platform approach, partnerships, and scale. It all starts with deep domain expertise. In our industry, you can't leave anything to chance. Decisions depend on a deep understanding of physics, the workflows, and the operational constraints. That expertise is embedded in our people, our models, and our platforms. It cannot be outsourced. It cannot be bought off the shelf, and it cannot be recreated by digital-only third-party provider. Second is our platform approach. Products create value, but platforms are what make them scale. In the age of AI, platforms are becoming even more valuable because they are the control layer through which models, agents, and workflows operate together. This is why we have invested in architecture that is open and built to operate in environments our customer manage every day, from subsurface test planning to production operation, and from on-prem to the cloud. This wasn't built in a quarter. It was built over decades, and it is extremely difficult to replicate. Third, our partnerships. We work across operators, technology partners, and geographies to bring customers the best capability of the broader ecosystem to our platform. In digital, no company can do it alone. The key is knowing what to build, where to partner, and how to make those technologies work in the realities of an energy operation. This is what SLB does. We connect leading technology with the data, science, and workflows of our industry to unlock performance and efficiency. Finally, our scale. SLB is funded across the major energy basins with the people, the infrastructure, and operational capability to support customers locally. That matters because digital and AI must work securely and reliably across all assets and operating environment. Our footprint allows us to learn globally, deploy locally, and extend what works across the energy system. This combination is what brings our AI advantage to life. Energy is among the most compelling environments for AI, with complex physics, high-value decision, and vast amounts of operational data. The technology is only as powerful as the data and the domain experts behind it. SLB has a unique ability to bring together platforms, connected assets, partner scale, and deep domain expertise. Individually, this capability matter. Together, they create a differential digital offering that is increasingly important and difficult to match. That is what we bring to our customers, and it is how SLB is taking digital and AI further. What does it mean in practice? It means we can go beyond software, collecting digital intelligence to hardware and sensors in the field so that insight become action and every decision improves the next. This is where science matters, where integration with all key technology makes an impact, and where our differentiation is the strongest. In planning, digital is already accelerating the prediction and improving model quality. AI can create a new growth curve in this market by automating workflows and personalizing our projects as they grow. In drilling, digital is enabling automation and real-time optimization. This lowers cost per mile. It accelerates access to resource, and it can significantly reduce the industry's carbon footprint. In production, digital is increasing uptime by predicting issues before they occur. This cuts maintenance costs and extends asset life. It also optimize the reservoir production potential. These example are here, and they are happening today, and you will hear throughout this presentation this morning. Moving forward, as the industry advance more autonomous operation, customer will simplify who they work with, prioritizing partners who can deliver across the full ecosystem. That dynamic strengthens our core business, creates new revenue opportunities, and expand the strategic value of our platform. This is how digital drives growth, not only within digital itself, but increasingly across all of SLB as we move from being first in digital to becoming digital first. Across every well, every mile, every customer. This is not only a secondary story. It is a growth story. It is a margin story and a return story. Digital is already a powerful earnings engine for SLB. For every $1 of revenue, digital generates 1.5x the adjusted EBITDA compared to the rest of our portfolio. This is also one of the fastest-growing parts of our business, and its margins have continued to expand over time. The value of digital extends beyond the segment itself. This technology are increasingly embedded in the rest of our portfolio, helping customer move faster, produce more efficiently, and recover more from existing assets. When our customer perform better, SLB becomes more valuable to them, increasing retention and expanding our total addressable market. The story is not simply digital as a division. The story is how digital lifts the earnings power of the entire company. This is a far larger opportunity, and today you will hear how we plan to capture it. Throughout this morning, our leadership team takes you deeper into the opportunity, the strategy, the financial frameworks behind this business. First, you hear about our flagship platforms, comprehensive digital offering and competitive advantage. You will see why our position is strengthening as adoption scales and why our platform becomes more valuable as customers move from digital pilots to enterprise-wide deployment. You'll hear about the race to scale digital operation and AI. This is where applications, connected equipment, automation, and AI come together to transform how we sense and manage in real time, and we believe this can become an important new growth engine at SLB. Finally, we discuss key performance indicators. This time, how we are monetizing significant opportunities ahead and share our 2030 financial ambitions for this business. As you listen, I encourage you to keep this in mind. Digital is becoming central to how this industry drives performance, unlocks efficiency, and creates value. With our platforms, domain expertise, and global scale, SLB is well-positioned to lead this next chapter. Thank you again for being here with us. We're excited to share the momentum we have built and the opportunities ahead. Before I welcome Rakesh to the stage, let's hear from some industry leaders as they share their own perspective on SLB's contribution to their digital journeys. In 2019, Chevron, SLB, and Microsoft formed a strategic collaboration to accelerate petrotechnical digital solutions anchored in the Delfi platform. By combining a century of SLB's domain experience with Microsoft's cloud infrastructure and Chevron's experience and operating scale, we've moved from pilot to measurable performance, delivering sustained value across our global operations. Together with SLB's continued commitment to innovation, that's positioned them strongly to build and deploy secure, agentic workflows that are disruptive to our industry. One of the main leverages that we need to use is artificial intelligence and digital. In order to do that, we've decided to partner with SLB on a partnership on subsurface called Arena. It's a 10-year partnership which couples the know-how of our reservoir engineers, along with the digital and AI capabilities of SLB. The SLB Delfi digital platform allows us to seamlessly integrate subsurface evaluation, well planning, and field development, enhancing collaboration, enabling our teams to work concurrently rather than sequentially. As a result, we shorten planning cycles from months to days, significantly accelerating time from discovery to first production. A key part of our 2024 strategy is to implement and maintain world-class standards of operational excellence by embedding digital intelligence with SLB and partners. We are well on the way to achieving this with AI initiatives running across the full E&P value chain. Wow. I've seen this video multiple times, every time I see this video, I feel that we're onto something. What the future holds for us gets me even more excited. Of course, we are very grateful for these messages, and a big thank you to all our customers who challenge us to go further every day. I'm Rakesh Jaggi, and I have the privilege of running the digital business at SLB. Along with Trygve Randen, the Senior Vice President of Digital Products and Solutions, we will highlight SLB's unique and compounding advantage at the exciting intersection of digital and energy. Before I go there, allow me to take you on a tour through the upstream value chain. These are the big questions our customers must answer. We start by asking, "Where should I look for oil and gas? Which basins and geologies offer the best potential for discovery and extraction of commercially viable hydrocarbons? How do I allocate capital across frontier exploration, proven undeveloped resources, and also the aging fields I have in my portfolio? How do I ensure that every asset is producing at its full potential, that I'm leaving nothing in the ground and nothing on the table? Most importantly, how do I operate safely and efficiently across a complex hardware landscape where a single failure can be catastrophic, where decisions cannot be left to chance, because in our industry, probably right is absolutely wrong?" These are some of the questions that define the upstream oil and gas, getting answers to these questions takes us right to the heart of our digital offering. Our ability to serve the upstream market rests on four areas of differentiation. Each of them position us uniquely, but taken together, they represent a wide and deep moat. I know Olivier already introduced these in his opening presentation this morning, but I'd like to take you a level deeper. The first is domain expertise. SLB has spent a century measuring, modeling, and interpreting the subsurface. That science is not peripheral to our digital business. It is the very foundation of it. It is encoded in our software and embedded in the data on which our models are trained. The second is platforms. We have built and commercialized enterprise-grade cloud-native platforms, Delfi for workflows and Lumi for data and AI. These are purpose-built for our industry. They are designed for the specific data types, security and uptime requirements, and scientific workflows that the upstream operators depend on. Trygve will give you a more in-depth look at our technology stack and why is it that it is so special. The third differentiation is partners. Our platforms are open and host a best-in-class tech ecosystem. We are deliberate about what we build and what we integrate. Cloud infrastructure from the leading hyperscalers, operational data capability and AI tooling from specialized technology players, large language models or LLMs from leading AI providers. Our platforms are enriched by the technology of others in areas where we choose not to compete. You will hear directly from some of these partners in a bit. The fourth key area of differentiation is scale. In many ways, it is the outcome of the other three. Domain expertise gives us the right to play, platforms give us the means to deliver, partners give us the speed to market, scale allows us to deliver for our customers across all geographies and resource plays. These four areas, domain, platform, partners, and scale, are mutually reinforcing. They allow us to compete in a way that other technology companies or traditional oil field services and equipment companies cannot. All of this did not happen overnight. SLB has a history of disruption embedded in our DNA. We began collecting computer-ready data in the field in 1952. Since then, we have seen a succession of technology shifts from mainframe to workstations to personal computers, then onto the cloud. With each of these shifts, we deployed the same playbook. Each time a new computing architecture emerges, we use it not only to modernize the existing tools, but to fundamentally expand what our customers can do. Another shift is underway, this, ladies and gentlemen, is truly different. Artificial intelligence isn't just changing how software is delivered and consumed. It promises to be the most fundamental and revolutionary shift we've ever seen. agentic AI, in particular, changes what software can do. With agentic AI, we are creating systems that observe, reason, act, and learn. Dare I say that while others have been fast followers, when it comes to our digital capabilities, we've always been first. Just two weeks ago, as some of you would have noticed, the AI-Driven Enterprise Institute awarded SLB a perfect score for AI adoption. A score achieved by only three other companies, NVIDIA, Amazon, and Meta. We are a company whose entire digital history has been converging on this moment, where domain science, trusted data, and intelligent systems meet in a single stack. I want to give you an analogy. The banking sector has undergone a very similar journey. The way my father banks, and God bless his soul, he's going to turn 92 day after tomorrow, and the way I bank are very different. The banking sector, three to four decades ago, decided to digitize each of the steps that required a customer to visit the bank. I don't remember the last time I went to the bank. This is exactly what we have done for our industry. Let me illustrate how our domain applications help our customers along the industry value chain, just like the banking sector. All of our domain offerings can be classified in two broad categories: planning and operations. There are steps that you have to take to get to your destination as a petrotechnical or operational expert. We have a product that will help our customers perform each of these steps digitally. We do not want them to work manually like my father did decades ago. They never have to bring manual skills to bear if the job can be done successfully, more efficiently, and more accurately by software. Let me go through the steps a petrotechnical expert undertakes in the planning phase. The workflow in planning begins with raw seismic data. This is the aggregation of sound waves that are sent into the Earth and reflected back. It's transforming billions of acoustic signals into a usable image of the subsurface. Think of it like the MRI scan of the Earth. Geophysical interpretation maps the layers and faults. Structural modeling and well interpretation then reveal how subsurface layers were formed and enable us to construct a 3D model of the subsurface. Reservoir and geological modeling predict properties like porosity and permeability and identify where hydrocarbons are likely to accumulate. Reservoir engineering quantifies the flow of fluids through rock formations and how the field will produce over time, incorporating the surface infrastructure into that equation, too. Next, field development planning or FDP comes in. Every technical step is overlaid with economic considerations, oil prices, capital outlay, operating costs, each element with its own uncertainties. Field development planning determines the returns to access the hydrocarbons underground. What you've just seen is a whirlwind tour of what a petrotechnical expert lives daily. We have an application for each of these steps. Omega, Petrel, Techlog, Intersect, FD Plan. These offerings enable our customers to complete their work anytime, anywhere, across every stage of the planning process. Once a development is sanctioned, the focus must shift to operations. Drilling planning is where operators engineer the well that will access the reservoir, defining trajectory for every section of that well. Drilling operations is execution of that plan, managing the real-time complexity of putting a wellbore through thousands of meters of rock. Production operations is the management of flowing wells and production networks. It includes the optimization of hydrocarbons to the surface. For aging fields with declining pressure, artificial lift is employed. Asset performance then encompasses the surface infrastructure, including facilities, processing equipment, pipelines, et cetera, that must operate continuously because unplanned downtime has consequences measured in millions of dollars a day. What we just saw is a quick tour of the operations. DrillOps, OptiFlow, OptiLift, OptiSite, powered by our Agora edge AI platform. Just as in planning, SLB Digital is increasingly serving each of these core operating processes, too. We are uniquely present across the entire value chain, from exploration through development and production, both in planning and operations from the edge to the office. I'm sure my daughters would like to bank differently compared to me, and we too are preparing for the agentic AI future for our industry. Besides planning and operations, we also have a market segment of data and AI. This framework on the slide now will provide insights into a key part of our digital strategy. If planning and operations are where the decisions are made, the data layer is where the raw materials for those decisions is organized and made accessible. Upstream operations generate extraordinary volumes of data. A single deep water well through its lifetime will generate around 10 petabytes of data, which is equivalent to nearly half a million of the 4K movies that you and I enjoy. This is a distinct and new market with new buyers for us. I've described planning and operations as two different worlds. As many of you would have already guessed, there is huge value in bringing them together, our digital tools make that possible today. Connecting these worlds for data is what SLB's Lumi and data and AI platform makes possible. It is a single trusted layer which connects planning data to the operations data seamlessly. Just like the banking sector, the Delfi platform has digitized the workflows for both planning and operations on the cloud. We are the only company that plays in all three of these market segments, planning, operations, and data and AI. The value we generate is clear. In planning, we reduce cycle time and risk. In operations, we enable greater production and superior efficiency. With Lumi, we help unleash the power of AI. Data from operations helps us plan better, which optimizes future operations. This becomes an exponential loop, bringing significant improvements in efficiency. From a commercial perspective, this is the flywheel that drives our commercial model too. More integrated workflows means more platform usage. Richer data means more AI workloads. Better AI means customers do more analysis, run more scenarios, deploy more agents. The circle turns, and with every rotation, the outcomes improve for our customers, and the value of our partnership deepens. Finally, let me put this in a context that will speak to all of you. To illustrate this, I will use Microsoft's product architecture as a comparison. We all know about the Microsoft stack, with tools like Word, Excel, and PowerPoint. You're also aware of the OneDrive and how you access and share files in your organization. Petrel, Techlog, DrillPlan, and OptiFlow are applications just like Word, Excel, and PowerPoint. Delfi is the Office 365 equivalent that binds them together architecturally and commercially in a cloud-native digital platform. It is the environment which our planning and operation software is accessed. Petrel, Techlog, DrillPlan, OptiFlow, all delivered through a single secure experience. Delfi is more than a hosting layer. It is an integration environment, the place where decisions flow between disciplines without manual handoffs. A subsurface model built by a geoscientist in Petrel can be consumed directly by a drilling engineer in DrillPlan. Real-time production data in OptiFlow can feed back into a reservoir simulation in Intersect. The transition from planning to operations that we described earlier, that seamless handoff between the work of deciding where to drill and the physical work of actually drilling that well, Delfi is where that becomes real. Lumi is our data and AI infrastructure, just like OneDrive and Azure AI Foundry is for Microsoft. If Delfi is where workflows run, Lumi provides the scalable, governed environment to ingest, contextualize, and deliver the data so that the right data in the right shape reaches the right workflow at the right time. It is also the home of our agentic AI workflow. We have things like domain foundation models, our agentic AI framework, and digital twins as a part of it as well. Working in sync across both Delfi and Lumi is Tela, our agentic AI, the parallel is Copilot in Microsoft. Shashi will elaborate on this exciting technology later. Briefly put, Tela is an agentic AI mesh that operates within the workflows and data environments our customers already use. Its architecture follows a continuous loop: observe, plan, generate, act, and learn. It is grounded in domain models and industry-specific guardrails that SLB has built. Before I hand it to Trygve to share more details on our platform approach, let's hear from a key customer in the Middle East. At the core of ADNOC's subsurface AI strategy is ENERGYai, which brings agentic AI into upstream workflow. Built with technology partners, including SLB, ENERGYai uses digital platforms, including Lumi and Delfi to enable integrated workflows and accelerate deployment at scale. This represents the world's first private cloud deployment, enabling intelligence and integrated workflows across the enterprise. Starting with 42 agentic AI-driven subsurface use cases, spanning from seismic interpretation to reservoir simulation, ADNOC can accelerate reservoir understanding, enhance field development planning, and identify new resource opportunities. For productions and operations, the strategy is driven by AI PSO, delivering more than 25 connected workflows that enable smart, autonomous operations. These capabilities support greater operational efficiency, improved decision-making, and increased performance at scale. The opportunities ahead are significant, and together with technology partners like SLB, we are well on our way to meet our ambitions. Congratulations to SLB on 100 years of leadership and innovation. We are proud of our partnership and excited about what the future holds. Shukran, Ali, and thank you, Rakesh. The SLB's platform approach has been a cornerstone of our digital strategy for over two decades. To start, it's worth grounding what we mean by platforms because the term can be used loosely. Our platform must do two things, provide and make an enterprise trusted data available and accessible And provide an open environment in where that data is consumed by applications, by workflows, and increasingly, by AI. It is the layer that makes everything work together at scale. In our industry, that bar is unusually high. As Rakesh explained, data in our industry is complex, domain-specific, and often business and safety-critical. The workflows span multiple scientific and engineering disciplines. The operating environments are global and frequently constrained by data residency and increasingly, technology sovereignty constraints. A horizontal multipurpose cloud platform does not meet these needs. What is required are platforms built for the domain that understand the data, understand the workflows, and can operate at enterprise scale for the most demanding customers. Our customers operate in a world of multiple vendors and proprietary data. We accommodate customers who wish to bring their intellectual property and run it alongside ours in a governed environment. We even partner with many of our customers to co-develop technology. Through open APIs and an extensible application framework, a concept we pioneered 20 years ago with the industry's first open API and plug-in environment, customers and third-party developers can deploy their own technology, their own algorithms, alongside ours in Delfi, Lumi, and Tela. They bring their workflows, we provide the platform. Turning to security, our platforms operate under the tightest standards with data encrypted in transit and at rest, multi-factor authentication, and role-based access control. For an industry that deals in competitive sensitive data and that operates under regulatory oversight, this is not a feature but a prerequisite. There is no doubt we have the data, but one of the structural constraints that has held this industry back is the physical limitations of traditional computing infrastructure. Our reservoir simulation that takes three weeks to run on a workstation can run overnight using elastic cloud resources. A seismic processing job that would require a dedicated data center can be scaled on demand and released when complete. Lumi provides scalable storage and governance for petabytes of operational and subsurface data. Delfi provides on-demand compute for simulation, processing, model training with no ceiling and capacity. The shift from evaluating three development scenarios to evaluating 300 has a dramatic impact on our customers' understanding of development risk, and is only possible when compute is no longer the constraints. Our platforms remove that constraints. Rakesh has explained Tela and the domain foundation models within Lumi, but it is worth stating plainly what this means. Every agentic AI deployed in this industry depends on the quality of the models, the quality of the data on which these models are trained, and the quality of the domain science that governs their output. We have all three. Our models are trained on all that we know and allow our customers to enrich them with all that they know. They are grounded in physics, not just pattern recognition. They are deployed within an agentic framework that can act, not just advise. When we describe our digital platforms, we are describing something quite specific. Not a collection of point solutions, not desktop applications moved to the cloud. An integrated open platform environment underpinned by the industry's deepest data architecture, powered by domain-native AI, and designed to serve the full life cycle of an upstream asset. There is no other platform in the energy industry that offers this combination of workflow depth, data breadth, domain intelligence, extensibility, and enterprise-grade infrastructure. That is the position we have created, and it is the position we will extend. The final aspect is our partner ecosystem. Let me dive deeper into that. I should be clear that we did not build all of this alone. We have more than 40 strategic partners are contributing capability across our platform. I will talk about them in a moment, but the ecosystem extends well beyond our strategic partnerships. More than 175 commercial plug-ins are available on our platforms. Developed by third parties who are built on our open APIs and frameworks, and more than 110 third-party applications are hosted on the platform, accessible to our customers alongside our own. This matters for two reasons. First, it is evident that openness is a reality, not just a philosophy. Developers and technology companies are investing their own money and resources building on our platforms because the customer reach, the data environment, the commercial opportunity justify that investment. That is the hallmark of a genuine platform ecosystem. Second, it deepens the moat. Every third-party application, every partner integration increases the value of the platform. The ecosystem compounds our own investments and makes the platform more valuable in ways we do not have to build or fund ourselves. In addition to the other vendors who have brought their IP to our platform, our strategic partner ecosystem above that network is structured in six layers, each serving a distinct function in the platform. We have foundational partnerships with AWS, Google, and Microsoft, the hyperscalers. They are drawn to us as the market leader in the domain, and we are drawn to them for their modern cloud infrastructure on which Delfi and Lumi run. Multi-cloud support is not a convenience but a requirement. Our customers operate all over the world, many with strict data residency constraints. Being cloud agnostic means we can deploy wherever the customer needs us. We can also deploy on the edge using our Agora edge AI platform. Agora addresses the real-time demands of remote environments where connectivity, latency, cybersecurity, and operational continuity affect performance. I won't expand upon the other layers in this. The point is not the number of logos. We could have added many more. It is the architecture. Every layer is deliberate. Every partner is best in breed in their domain, and together they create a platform that is comprehensive without being closed. Let's hear from some of these valued partners. Microsoft and SLB have worked together for decades, evolving alongside some of the biggest shifts the energy industry has seen. That collaboration sets the foundation for how we work together, combining deep domain expertise with powerful platforms. As SLB enters its second century of technical leadership, Google Cloud is ready to anchor your vision with our own pioneering investments in advanced energy. Together, SLB and AWS are building the digital backbone so our joint customers can perform at an AI-accelerated level. We are entering a new industrial era powered by AI, and energy sits at the center of it. We focus very strongly on strategic partnerships like the one with SLB. SLB brings digital and domain expertise across subsurface, subsea, and topside production systems. 80% faster in competition cycles, earning success rates that are changing the economics of natural assets. Together, we deliver grounded real-time insights that drive action. One joint customer of ours increased production by 100,000 barrels and also saved $4.3 million in operating expenses. Together, we are creating a foundation that helps SLB move faster, operate smarter, and deliver more value to customers around the world. Quite some heavy hitters that gave their video statements there. I want to emphasize on something that comes from these videos, and that is one of the most significant barriers to entry in our industry, trust. Our customers trust us with their most competitively sensitive data. Seismic surveys that cost hundreds of millions of dollars to acquire. Reservoir models that underpin the multi-billion development decisions and real-time operational data from producing assets. This is data that governments regulate, that boards scrutinize, and that our customers' competitors would love to see. We deliver our trusted platforms across more than 80 countries, essentially everywhere that oil and gas is found. Each with its own regulatory framework, its own data and technology requirements, and in many cases, its own constraints on which cloud infrastructure is permissible or even available. Solving for all these constraints at scale requires multi-cloud deployment capabilities, on-premise options, and a deep operational understanding of the legal and political landscape in every market we serve. A national oil company in the Middle East has fundamentally different requirements from an independent operating in the U.S. or a multinational operating in deep water Brazil. We serve all of them, whatever their infrastructure constraints. In addition to sovereignty, reliability reinforces our customers' trust in SLB. We are well in excess of 99.5% uptime. In many cases, we continue delivering our services even when the hyperscaler goes down. It is possible precisely because we operate across multiple cloud providers. If one goes down, we move over to another. Our customers' workflows do not stop because a data center in a single region has an outage. For an operator running real-time production surveillance or time-critical drilling operations, that resilience is a condition of adoption. If trust is the foundation, scale is the outcome. A scale we have achieved thanks to more than $3 billion of R&D spend since 2016 and 390 digital U.S. patents granted in the last five years. That scale of what we have built is worth dwelling on for a moment, because these numbers are not projections, they are the current state of the business. At the center of this slide, more than 90% of global production is simulated or modeled using at least one of our digital solutions. I'll let that sink in for a moment. That is not a market share statistics, but a measure of how deeply embedded our technology is in the decisions that Rakesh talked about that govern the hydrocarbon output of the world. Around it, the operational footprint. More than half a million feet drilled each quarter using SLB automation technology. Over 2 billion API calls across the platform in 2025, a proxy for the volume of machine-to-machine interaction, executing continuously across our infrastructure. Over 45 million CPU hours in Q1 of this year alone and growing as customers are unlocking the simulation processing and AI workloads we discussed earlier. Supported by over 2,600 petrotechnical experts, the largest team in the industry. The position is built. The question is now how fast and how far we can grow from it. Well, let me hand back to Rakesh to describe the market opportunity. Thank you, Trygve. I would now like to define and quantify the market we play in, ladies and gentlemen. This chart from Gartner shows the digital spend as a percentage of the total expenditure across major industries. Oil and gas digital spend, 4%-5%, considerably less than manufacturing and natural resources that you might expect to closely correlate. The point is simple. Oil and gas is one of the most data-intensive, technically complex, and capital-heavy industries on Earth. Yet it spends proportionally less on digital technology than almost any comparable sector. However, this is not a market where we are merely fighting for a share of a fixed pie. The pie itself is growing, and it is growing because the industry is underinvesting relative to its complexity, and the technology to close that gap now exists. Less than half of that investment supports the technical workloads that we've been talking about this morning. That is where we play and where emerging tech disruptions will bring significant value to our industry. According to Rystad Energy, this market in 2025 represented about $25 billion. Looking at the breakdown of this digital TAM, customers are allocating digital spend across planning and development, operations, and their enterprise digital infrastructure. Furthermore, the capabilities I've been talking about have only recently matured to the point where they will become compelling. For us, this means the total addressable market has significant room to expand. Another way to look at this market is through the lens of the main player in this TAM. On the top right here are the traditional oilfield services and equipment competitors. They compete with us in domain-specific software, particularly in planning and drilling. We are unmatched in our investment in platform modernization and in artificial intelligence. Next, the industrial technology companies. These are very credible players in operational technology, particularly in surface automation, process control, and equipment monitoring. They bring strong capabilities in the industrial IoT and facilities layer. We compete with them in operations. They lack subsurface domain expertise. The enterprise technology companies, which serve the industry's broader IT needs, networking, databases, communications infrastructure, et cetera. They operate horizontally across many industries, but without domain specialization. The system integrators. These firms provide implementation services, custom development, and data migration. They compete with us in services around the data, but they do not own platforms, domain science, nor proprietary AI models. We have the hyperscalers and horizontal platform providers who bring cloud infrastructure, compute, and data storage. They are essential to the ecosystem, but they are not competitors in the domain software. As we've described, many are already partners providing the infrastructure on which our platforms run. We are the only company equipped to address a majority of this market. Rather than competing with the hyperscalers and system integrators, we have made them a part of our architecture. Their infrastructure powers our platforms. Their compute is used to fuel our AI models, and their services assure rapid market adoption of our platforms. In other words, we convert potential competitors into distribution and capability partners. On the other side, our open platform architecture means the technology of others can integrate into our environment. We do not require the customers to choose between us and these companies. We provide the platforms on which they coexist. Other companies occupy a segment, we occupy the entire space, more than two-thirds of the market. Our openness turns these companies into participants in our ecosystem rather than obstacles to our growth. I now want to spend a few minutes talking about how this market is expected to evolve. By 2030, the expectation is that another $10 billion in annual spend will be added as digital spend becomes further decoupled from the overall industry CapEx and OpEx. This growth is driven by our customers' ambition to secure greater value from digital, especially in operations where significant value is expected. You will hear more about this in the next section. The most important number is the one on the top right. With accelerated AI adoption, the total market could reach as much as $50 billion by 2030. This reflects what happens when AI fundamentally changes the nature of digital work. When interpretations become exponentially faster, customers do more of them. When simulations are no longer constrained by hardware limitations, teams run hundreds of scenarios instead of just a few. When agentic workflows automate routine surveillance across thousands of wells, digital spend expands. It does not just make existing work more efficient, it unlocks possibilities that were previously uneconomic. For us, this is the most important dynamic. We are not competing for a larger share of a static opportunity. The opportunity itself is accelerating, driven by the same AI capabilities that we are building into our platforms. That, ladies and gentlemen, is our digital advantage. With that, let's now zoom in onto two parts of this market that are growing the fastest, digital operations and AI. These will unlock the possible doubling of this market. You're about to hear from Cecilia and Shashi, who will share how our digital capabilities are transforming operations and how AI is expected to disrupt our industry. Before Cecilia takes the stage, let's hear from a few more of our customers. Thank you. Our digital collaboration with SLB brings together deep domain expertise, scale, and capability to address the specific challenges across the subsurface, well construction, and production. Through this collaboration, we have strengthened our ability to understand subsurface complexity, optimize drilling, and make better decisions faster. Analysis that previously took months can now be completed in weeks or even days. With SLB solutions, particularly OptiFlow, OptiSite, and Agora, YTS will comprehensively optimize production and the facilities, improving performance, efficiency, and operational integrity. We are now building on this strong foundation by expanding our collaboration into production, including the pilot of AI-driven tools for real-time optimization. We deploy SLB technology on the majority of our wells through artificial lift data transformation, through chemical data transformation. While that process has often been manual in how we optimize, we're starting to breach the world of automation and machine learning. We implemented the SLB Agora solution as a pilot approximately six months or so ago, and we saw an immediate 15%-20% uplift relative to how we were doing it. Delfi has become the platform on which we bring new technology into our subsurface workflows. We keep adding these features to our main process, which means we are able to work together more easily, grow as needed, and deliver more value across the company. Thank you, Rakesh. I'm Cecilia Prieto, what I'm going to do today is to take you to the physical world, where digital meets operations and delivers results. Our industry is very clear about where we're going. Autonomous operations. That's the destination. The challenge isn't the feasibility. We've already proven that autonomy works. The question is, how do we scale? This can only happen when we are applying digital in every operation. In the next few minutes, I'll tell you how digital has a material impact on our customers' production and lifting costs, and why SLB is best positioned to truly transform the way it is done today. To understand what we can offer and how fast we're scaling, let's take this story from the beginning. We have a vast footprint of services and equipment in the field delivered by our reservoir performance, well construction, and production system divisions. This is our sandbox. Our first step is to connect this footprint. That's how we collect data and enable surveillance and control. Our customers pay for this value. Here's just one of many examples. Today, 35% of our electrical submersible pumps are connected and monitored. By 2030, we aim to reach 60%. The next tier is intelligent solutions and services. This is where operational data is turned into actionable insights. Another example, today, about 14% of our formation evaluation operations run with a digital insight add-on. By 2030, our ambition is to drive 60% of adoption amongst our customers. The final destination, autonomy. It is not a dream. It is happening today. 3% of the footage we drill is done autonomously. By 2030, we aim to reach 25%. This is the most advanced form of digital operations and where the industry will unlock the biggest value. Here's the reality. Drilling a well is extremely challenging. The wells we drill keep getting longer, and our reservoir targets are miles and miles away from the wellhead, with lots of unknown on the way. Unknown rock properties, unknown pressure levels, unknown fractures, porosities, and so much more. Every day, something goes wrong. The industry wastes about $4 billion every year to remediate high-impact events. It can take a few days or even a few weeks to regain control and resume operations. Complexity is multiplied because drilling involves several service companies and rig contractors spanning many individual workflows. Two decades ago, we began digitalizing drilling by collecting and interpreting data in remote operations centers where SLB and our customers work side by side. We needed less personnel at rig sites, preserved key expertise in those centers. Until recently, manual lab measurements and Excel files were still the norm. Directional drillers, subsurface experts, and fluid experts were all each receiving more data to interpret and act on. It was a step forward, but they were still working in silos. More integration was needed. That is exactly what we did. We integrated the data and workflows into what we call drilling insights. They provide intelligent recommendations in a standalone workflow or across multiple ones, and this is where we generate 80% of our digital drilling revenue today, on top of SLB drilling services or as a generic application. Here comes the big finale. When we combine drilling insights with bottom hole and surface automation, drilling autonomy becomes a reality. The most optimal decisions are recommended and executed in real time by the system without slowing drilling down. Many decisions can be taken and executed simultaneously. This is simply not humanly possible. Drilling autonomy is how we drill wells faster and better, always placing them in the production sweet spot. It's how we improve drilling efficiency by 25%-40% and help our customers produce more and reduce lifting costs. Let me tell you about a real example. In Libya, we have been working with Sirte, a subsidiary of the National Oil Corporation. To accelerate well development, autonomous drilling was the answer. Today, Sirte produces around 110,000 barrels of oil per day with ambition to increase this further and fast. This is critical to the country's national production and economical development. We deployed DrillOps automation with all the drilling insights, orchestration, autonomous well placement, and bottom hole automation for directional control. Here, the autonomous drilling system decides on all directional changes to remain in the best zone of the reservoir, all while optimizing speed and safety parameters. It only needs 15 seconds to interpret data, decide to change the drilling plan, and send the change command to the bottom hole assembly. All of this would have taken 45 minutes without automation. If you were drilling at 100 feet per hour, it took 75 feet before the course of your well could be updated. It's like missing your exit when driving at full speed on the highway and only realizing it miles later. Tela, our agentic AI assistant, is already embedded in the system to help users who may decide to go back to manual mode. Here are the results. We doubled drilling efficiency and placed the well 100% in the reservoir. Our preferred monetization for a full drilling autonomy project like this is a performance model where we capture a portion of our customers' cost savings. As you can imagine, the revenue impact can be meaningful. In 2023, SLB was the first company in the world to drill a well with autonomy in Brazil. We could only deploy drilling autonomy on rigs equipped with our own control systems. To scale, we needed to develop interfaces to enable connections to a wide range of rig control systems. That is why we partnered with rig companies such as Nabors and H&P for land rigs, and Transocean and NOV for offshore operations. Thanks to this, we have the potential to automate 25% of rigs worldwide today. By 2030, we will be able to connect to 85% of the rigs. Today, we drill autonomously for 15 customers in every type of environment and geography, and we hold the most patents by far. We continue to innovate our drilling assemblies to bring new levels of control, precision, and speed. Speaking about the value we create for our customers, let's hear from one of them. Today, we operate in an increasingly complex environment with growing challenges across assets and geographies and higher expectations of safety, efficiency, and performance. In this context, delivering reliable, affordable, and sustainable energy is our priority, technologies plays a key role in making this possible. Digital solutions, data, AI, and automation help us simplify complexities, improve decision-making, and accelerate execution. Technology alone is not enough. Strong partnerships are essential, SLB has proven to be a strong partner for Eni across multiple areas. Drilling is a clear example. By adopting a digital drilling model built around the SLB solution that integrates planning, real-time execution, and automation, we achieved up to 35% reduction in drilling time with safer and more predictable operations. In Congo, this approach enabled us industry-critical level of full drilling automation. We also deploy a wider set of SLB applications across the value chain, from retrievable ESP system in Mexico with reduction of downtime and cost savings, to the application of geosteering technologies in Ivory Coast to navigate within the reservoir formation and maximize well productivity. These results show how the pragmatic use of technology and innovation at scale, combined with trusted industrial collaboration like the one we have with SLB, create tangible and measurable value today and open the way to further joint opportunities across new development areas. Hearing from one of our major customers talk like this about our collaboration makes me very proud, I really look forward to seeing what else we can achieve together in the drilling space. Now, let me take you to the world of production. Complexity is heightened here. We battle disconnected equipment installed by different companies across multiple decades. A lot of it is still analog. This fragmented physical reality is found at the well level, across surface, production systems, along pipelines, and in facilities. The monitoring process will require a human to travel to the field to collect measurements. This is why connectivity is the foundation. It enables basic surveillance that generates data from SLB equipment or other providers' hardware. That's how we bring production into the digital age. Once the data stream is enabled, we can optimize equipment with digital twins. We combine physics-based models with AI and our domain intelligence to identify equipment constraints sooner and deliver real-time insights to take the right action at the right time. This means more equipment uptime, leading to more production. Going further, digital enables optimization at a system level. It breaks the silos between all the equipment that coexists in a production operation, and finally connects all the elements from reservoir to point of sale to maximize production and ultimate recovery. Just like drilling, production is moving towards full autonomy, where technology not only informs intelligent decisions but also makes them. Imagine a production agent that is scanning equipment operating parameters continuously. Imagine how it could intuitively understand the impact a single change has on the entire production system. It acts autonomously and ensures that a single set point optimization continues to trickle through the production system to optimize it entirely, from reservoir to wells to surface equipment, pipelines, and to facilities. It sounds simple when you say it like that, but in fact, it's a very highly complex multivariable and cross-domain workflow. This is what we're actively working towards. This future is not so far ahead of us. In the U.S. Permian Basin, all wells are equipped with pumps that help lift oil to the surface. Production can decline rapidly due to the dynamic reservoir changes, and as a result, operations require continuous monitoring of artificial lift equipment. We worked with a major operator to provide real-time optimization recommendations that can be deployed on SLB and other providers' electrical submersible pumps. These recommendations are transmitted instantly to their innovative closed-loop control technology. The system was deployed on an initial 26-well pilot. It continuously monitors well conditions, generates optimal operating set points, validates them, and implements the adjustments in a fully automated cycle. Full optimization, which initially took 29 days manually, was cut down to three days. That's 90% improvement. Equipment downtime was reduced by half. Working hours needed to monitor wells and optimize them were significantly reduced as well. Production per well was increased by 10% based on Permian average. After the success of this pilot, we signed a three-year enterprise agreement with all of the Permian ESPs and to monitor all the other lift systems in the U.S., including the gas lifts, plungers, and rod lift systems. As of today, 800 wells are actively using this closed-loop system, and we're running surveillance and optimization workflows on their 11,000 wells in the U.S. With the integration of ChampionX, we expanded our reach with the additional footprint of production equipment. Today, our install base includes 200,000 physical equipment that is either already connected or can be in the future, from artificial lift systems, which we talked about, to flow meters, chemical tanks, processing equipment, well heads, and completion hardware. All of this is our initial playground to deploy more digital production solutions. We're on a clear path to scale and this SLB install base and beyond. Looking at production and system optimization, I would like to talk to you about a digital solution we're very excited about. It truly showcases we innovate every single day. We built our existing OptiFlow tech offering, which unifies reservoir and wells into a single intelligent ecosystem to create a module exclusively available on our intelligent completion hardware. We're piloting it with three of our largest customers in deep water West Africa, in the Middle East, and the Caspian region. What we give them is production insights they would not have dreamed of before without overengineering their completions. Customers can see water or gas breakthrough data in real time and zone-by-zone productivity index. With the active inflow control provided by our electrical completions, which are the higher tier of our intelligent completions, they can act on these insights immediately. Production is optimized in minutes by closing, opening, or regulating from individual producing zones, all without additional intervention or workover. No more guessing and waiting, which often results in production loss. For high-producing wells like deep water wells, it promises to be a game changer. It's like wearing a smartwatch and continuously monitoring your heart rate and blood pressure, getting alerts and recommendations without having to go and see a doctor to get your ECG measured. OptiFlow is patent protected. It is one of a kind because it leverages our production domain understanding and digital expertise combined with truly differentiating completion equipment. Simply put, it will be hard for our competition to replicate. According to Kimberlite research, the intelligent completion market will double in the next two to three years. SLB will quadruple installations of electrical completion specifically. Amongst our top 15 completions customers, eight of them have already adopted them with immediate reservoir control benefits. Our mission is to upsell OptiFlow on more than 80% of our electrical completions, and we know we can do it because from early pilots, all customers have already signed the subscriptions. Traditionally, operators follow a longer adoption path from proof of concept to proof of value before committing to long-term commercial contracts. The speed of adoption we are seeing with OptiFlow is unprecedented. After all this, what are the key takeaways? It's that SLB has a unique advantage to capture the rapid growth in digital operations, and this is why we're confident. First, we have the industry's broadest operational footprint across all key environments and geographies. Every year, we drill or complete 20,000 wells, execute 100,000 intervention operations, and install more than 8,000 ESP pumps. Every drilling or production operation is an opportunity to introduce and upsell a digital solution. Second, SLB has a technology portfolio no other company in the sector can replicate. From our physical products and services to our digital platforms and solutions covering all the industry workflows. Third, we leverage our digital platforms. All our digital operation solutions run on Delfi. It means that they inherit robust cybersecurity standards, cloud integration, data management, and a common edge infrastructure. This speeds up deployment and provides the foundation to scale AI in all our operations. Finally, we never stop innovating. Our process is symbiotic between innovation in hardware and software. Innovation projects are often linked, as we demonstrated with the intelligent completions example. As the digital operations mature and ingest more data, our systems are getting more intelligent. Our leadership position gets stronger, and the gap with our competition widens. The race to scale is on, and we are leading it. Let me hand over to Shashi, who will tell you more about AI, its transformative power, and where our growth efforts are focused. Thank you. Every molecule of oil and gas ever produced began with a question about what lies beneath the surface, answered with incomplete data, fragmented visibility, and the limits of human know-how. The industry spent decades digitizing and built real value doing it. The tools it built, the workflows, the simulations, the data systems, were designed to support decisions, not make them. They capture. They store. They model. What has been missing is the intelligence layer that connects insight to action. Agentic AI continuously reasons, interprets, and responds subsurface to surface, grounded in the physics of the domain. What once required weeks of specialist analysis now happens in hours, not by replacing expertise, but by extending it across every well, every facility, every enterprise. This only works if the AI thinks like the industry. Platforms have to be purpose-built for that trusted data, domain foundation models, and decades of industry expertise. AI that talks like an expert and analyzes like an engineer. The result, every reservoir, every well, every pump, every facility continuously optimized. When conditions change, the system responds. Operational costs no longer scale with complexity. Assets run around the clock, proactively managed, continuously learning. The future belongs to those who add intelligence to what they've already built. We're already leading the way. Thank you, Cecilia. Good morning, ladies and gentlemen. I am Shashi Menon, and I lead digital technology development for SLB. Rakesh and Trygve have outlined our platforms and our market opportunities. As Rakesh was looking back through our digital history, it reminded me of my own digital journey at SLB. I had the opportunity to lead the development of our first digital platform around Excel, GPU computing with NVIDIA, cloud computing with Google, and data platforms with Microsoft. Now here I am to tell you what we are doing in this exciting world of AI that we are all living in. Today, I'm going to show you our proprietary AI technology stack. I will tell you why this is entirely unique, how we have developed specialized domain foundation models, and the secret behind why nobody else can replicate this. Let's get going. The foundation of our AI capability is an industrial platform configured specifically for the physics and data complexities of the energy sector. These are the three key elements that I want you to take away. I can tell you that these three elements are unique in our industry. No one has been successful in building these to date, not just in our industry, but across any industrial sector. They are the ones that will make or break AI in our industry. Let me explain each of these three pillars and show you how they will drive our AI transformation. Number one, the AI-ready technology stack. Let's take a look under the hood. The data layer. I think you will all agree that there is no AI without data. In our industry, more so than in any other, data is all over the place. Public versus private, on-prem, on the edge, on the cloud, and pretty much everywhere. We work with mission-critical technical data like seismic, reservoir, and real-time operations data. They are stored on customer systems of record that are often proprietary, and many outside of the industry do not even know that they exist. Our customers do not want us to move or duplicate their data from their systems of record. In fact, they cannot, as data is core to their business processes, and any missteps that we make can cause serious issues. We have implemented a unique exploration and production data bridge from the ground up to honor all those customer constraints. Our data bridge is a fabric that allows us to connect to customer data sources without moving or replicating data. It is architected to work with the many, many data sources and cloud setups of our customers. It is what allows us to search, discover, access, and consume data in our AI workflows. The AI layer, it is arguably the most important. It is the magic dust on how SLB's AI differentiates. It just works for our industry. Everyone here is familiar with large language models and the weekly developments that we hear from frontier AI companies. We have seen many of our peers and our customers adopt and force-fit these models into their AI implementations. I can tell you this, that is an uphill battle to fight. Trying to decide which LLM to use, and worse, using these generic LLMs for technical workflows is really trying to boil the ocean. To uniquely solve this, we have built proprietary domain foundation models. Not one model, but models for several of our petrotechnical domains. These models are special in three ways. One, they don't replace those generic LLMs. They actually work in tandem with any customer preferred LLM. We solve for the domain specifics while these LLMs solve for the generic. Two, our domain foundation models are purpose-built. Purpose-built because we know exactly how domain data is structured, what to look for in that data, and how to use it to deliver on the user's intent. Three, because we know which parameters are important, we know what data to feed it. We have augmented our publicly available datasets with proprietary SLB data. We have absorbed the IP and knowledge that come from decades of oil field services into these models. What does it mean for our customers? It is simple. Our domain foundation models give them a powerful base model that is continuously updated. They can even refine these models within our platforms with their own data to customize them for their own assets. Three, the user experience layer. The key here is the Tela Canvas. Think of it as the ultimate industry Copilot. You all know how ingrained ChatGPT, Claude, Gemini have become in our day-to-day activities. Our users will soon find Tela indispensable because it is just as easy to use, and it is already integrated into the products that they use daily. Now, even more so because Tela understands their technical context, has strong guardrails that ensures that it never goes off-rails, and is built using domain benchmarks to ensure that it stays within the bounds of domain science. The second key element is our domain AI. Our AI implementations are structured to meet our customers wherever they are in their AI readiness. For organizations beginning their AI transition, we offer conversational experiences that extract insights from their data, from their project histories, and from their operational context. This is powered by an energy-specific LLM infrastructure that is trained on SLB's technical data, documentation, intellectual property, and essentially, our oil field experience. The barrier for adoption to our customers is very low, and the value for them is immediate. For those customers who are further along in their technical journey, our software embeds dedicated AI agents powered by our domain foundation models, skills, and tools. These provide high-value engineering recommendations while keeping the user in control of the decisions. For those customers that are advanced operators, our agentic framework runs fully autonomous workflows. They observe, plan, generate, act, and learn loop, but they are fully constrained at every step by domain science and physical guardrails. These agents are trained on validated domain data, they offer an assurance and accuracy that is unique to SLB. The third key element is our technology partnership. You heard from Trygve earlier about the breadth of our partner ecosystem. I want to go deeper into one in particular. Our partnership with NVIDIA is special. It is nearly a 20-year-old joint engineering program. We have direct access to their top engineers, and they choose to work with us for one specific reason. We bring the unique domain physics that is needed to push the boundaries of digital in energy. Together, we are building the Tela AI factory for energy to bring the most powerful set of agentic AI implementations in the industry to our customers. Let me be clear what this integration means. Guaranteed peak performance. Every domain foundation model we produce is optimized to be the absolute highest performing model in the industry. NVIDIA engineers are actively taking our source code and tuning it to run optimally for today's Blackwell chips, and they're already future-proofing it for tomorrow's Vera Rubin architecture. We are fusing the world's leading AI computing architecture directly with our unparalleled domain data and science. No one else can do this at scale today. It creates a competitive moat that simply cannot be replicated. We deliver these AI capabilities through two distinct user experiences, each one designed for a different mode of working. Tela Embedded integrates agentic AI directly in our existing widely deployed software, Petrel, Techlog, Trillo, OptiFlow, et cetera. Our users access conversational and agentic tools natively within these applications that they use every day. This drives immediate productivity gains and reinforces the value of our software for our users. This provides that indispensability that I talked about earlier. It is simply there for them to use. Tela Canvas is a standalone experience designed for broader cross-functional use. It works across application boundaries and data silos, orchestrating complex end-to-end technical workflows by combining AI with the physics-based domain science that forms the core of our software portfolio. Where Tela Embedded enhances individual application workflows, Tela Canvas connects them. Our customers can choose between rapid transaction-focused interactions via Tela Canvas or deep, immersive engineering workflows within our core applications. Both run on identical agentic AI backbone, the same domain foundation models, the same physical guardrails, and the same data infrastructure. Now let me show you what this looks like in practice, starting with planning. Our planning solutions and workflows are driven by an extensive portfolio of subsurface agents, models, and specialized tools that cover the full spectrum of technical workflows used in exploration and field development. They provide a very comprehensive coverage of the key domains across geophysics, petrophysics, geology, and reservoir engineering that Rakesh talked about earlier. Let's see an example of these planning agents in action within the Tela Canvas and connecting to Petrel, our leading subsurface platform. Log then. Tela immediately notifies them of new well data available for the southern part of Block C14. With a single click, they choose to view the data. The data is loaded into the IVAAP log canvas. Tela recognizes that this data has not been reviewed yet and offers to run a quality control check. The user agrees, and the well data is conditioned and QC'd automatically. With the data now ready, the user requests a porosity prediction for the reservoir interval. Using the domain foundation model, Tela predicts porosity and updates the canvas with a new log. The user asks for a list of seismic data in the area and guidance on which dataset is most suitable for structural interpretation and trap detection. Tela quickly provides a list of seismic cubes and highlights the BO Carry dataset as the best option for this task. With the dataset selected, the user asks Tela to identify a structural trap. Leveraging the domain foundation model, Tela detects a structural trap in the Vinton Dome area and displays the section. The structural trap is visualized, and Tela suggests performing a more detailed fault analysis. However, the user decides to handle the fault analysis manually and instead requests a search for fluid contacts within the trap. Using the anomaly detector agent, Tela identifies a pay zone and presents a 3D visualization of the area, providing critical insights for further evaluation. To refine the structural interpretation and prospect analysis, the user requests to load the new well and seismic data into Petrel. Tela seamlessly transfers all data, enabling the user to continue their work with ML-assisted seismic interpretation tools. In the Petrel application, the user has access to Tela in the side panel, and the conversation can continue. You just saw a glimpse of Tela in action, both as a Tela Canvas as well as Tela Embedded in Petrel. It shows how a combination of domain foundation models and other AI models integrated into a project workflow can completely transform them. I want you to think why this is so differentiating, why this demo could not have been done just six months ago. A typical exploration workflow to directly detect hydrocarbon-rich areas in the subsurface takes a team of geoscientists weeks to execute. We are transforming these complex nine steps into two simple clicks, almost magically. We are compressing weeks of complex analysis into a few hours, while covering a broader range of scenarios than was previously practical. Let me tell you how this works. You've heard me say domain foundation models several times. Let me explain what they are and the key role that they play in our AI implementations by using the seismic domain foundation model as an example. Seismic data is core to most exploration and field development workflows. However, seismic data modalities, its structure, and its format are very unique to our industry, and large language models are unable to work with them. What we did was we started with a base vision transformer model. We adapted it to handle those seismic data modalities. We incorporated seismic and geoscience domain priors and context, we trained it with public and SLB multi-client seismic datasets. The outcome is a rich and capable seismic foundation model with less than 1 billion parameters. For reference, leading frontier models are well beyond 1 trillion parameters today. The small parameter count means our models are cost-effective to develop and to operationalize. It will also allow our customers to then fine-tune these models with their data for their continued use in Delfi and Lumi platforms. As in planning, our operational execution relies on a dedicated, scalable portfolio of operational agents, models, and tools. These cover our drilling operations in our DrillOps family of products. They enable the increasing autonomy of complex operations, such as real-time geosteering that Cecilia talked about. On the production side, agentic workflows form the core of our Opti Suite of production offerings. These range from lift operations in wells to pipelines and networks to complex facility operations like FPSOs. Let me exemplify this from a deployment that we did for a customer in the Middle East. In traditional operating models, engineers must manually trigger, review, and process data to manage facility of equipment. This is an incredibly frustrating and monotonous activity for a production engineer, while for the company, this means operational adjustments can only be made when there is someone at the desk. What Tela does is that it converts these manual actions into autonomous evergreen loops, reserving human intervention only for high-value capital decisions. You might say, "So what?" When we talk about a production asset, we are talking about a lot of equipment. These come from different vendors, are of different vintages, each with different sensors, working with different parameters and data formats. All of them are being used differently in different conditions. Leveraging agentic AI is the only way to reach operational autonomy at scale while still retaining human oversight. We can do this. We can do this because our equipment digital twins are trained on real-world physics and are validated through continuous iterations. We can do this because we are OEM-agnostic and can support equipment from a wide range of providers. We can do this because we can model and simulate at the asset or system level. These enable round-the-clock facility optimization, directly lowering operating risks, minimizing unplanned downtime, and maximizing barrels produced. You will soon hear from Stéphane our broader monetization strategy, but let me be a bit bombastic for a moment. Every single agent, every model, every tool that a customer uses within our ecosystem, we have implemented the platform so that we can track their consumption every single time at scale. That tracking and provenance is exactly why and how we can monetize AI. We do this through four distinct channels. We already talked about Tela Embedded into our existing widely deployed software platforms. We monetize through upselling Tela subscriptions and then on the consumption of agentic workflows. Tela Canvas opens a new channel for monetization, where a base subscription paired with consumption-based pricing allows us to cross-sell to the existing customer base. It also creates new sales opportunities with those customers that find adopting platforms like Petrel to be a heavy lift. Going beyond these two channels, we also develop fit-for-purpose AI workflows for our customers through our innovation factory model, a network of seven AI centers of excellences around the world. These solutions are then deployed with the customer's Lumi and Delfi environments, creating long-term platform stickiness and ongoing consumption. Finally, the digital marketplace that we announced on Monday. It enables a platform business model for SLB and verified third-party developers to offer specialized agents, models, and applications that can be deployed in our digital ecosystem. We monetize through revenue sharing driven by consumption, a high margin, scalable channel that will grow with the ecosystem itself. As I conclude, I want to emphasize one critical truth. Today, no other company in our industry can do what SLB has accomplished. We did not ride the wave of generic AI models. We built proprietary domain foundation models from the ground up. We did not ask the industry to rewrite their operations. We embedded intelligence directly into the applications that tens of thousands of geoscientists and engineers trust every day. We have created an agentic framework that is open for our customers to extend and yet rigorous to operate autonomously within physical constraints. This combination of proprietary models, trusted platforms, deep domain science, augmented by strong digital partnership is what positions SLB to lead the commercialization of AI in our industry. It is a position that will allow us to be first and go further once again. Now, let me welcome Stéphane to the stage to discuss the financial impact of our digital business. Thank you. Good morning, everyone, and thank you for joining us today. Before we start, let me briefly step back and review the story you've heard so far. We have discussed the pivotal role of digital in our industry and the differentiated position SLB has built over time. You have seen how we are leveraging our platforms and applications across planning and operations workflows. You have heard about the opportunity to scale AI across our portfolio to unlock even greater value. What I would like to do now is bring that story together through a financial lens. Over the next few minutes, I will focus on three areas. First, the digital profile of our digital business. The financial profile of our digital business. Second, the significant market opportunity ahead of us and how we plan to monetize it. Finally, our 2030 financial ambitions. The key takeaway is this: digital has become a meaningful contributor to SLB's financial performance in the past few years, and we continue to see significant runway ahead with accretive growth and continued margin expansion. Let me begin with where the business stands today. In 2025, digital generated approximately $2.7 billion of revenue, more than $900 million of adjusted EBITDA, and an adjusted EBITDA margin of 35%. It also reached approximately $1 billion in annual recurring revenue on a trailing 12-month basis. What is most important is the quality of this growth. Since 2021, digital revenue has grown at a 16% compound annual growth rate, well above the oilfield services market and SLB's overall growth during the same period. Adjusted EBITDA grew even faster at a 23% CAGR compared with approximately 14% for SLB overall, demonstrating digital's strong operating leverage and differentiated earnings power. The margin profile you see here already reflects a meaningful share of the cost required to support growth as the research and engineering spend is directly expensed. That translates into very strong cash generation and effectively makes digital the division with the highest return on capital employed in the company. What digital brings to SLB is very clear. It adds growth, it lifts margins, and it delivers very attractive returns. Let me now describe our digital revenue footprint. One of the defining strengths of this business is that it is diversified across geographies, customer types and revenue categories. That matters because it gives us a broader opportunity set, greater resilience and multiple paths to growth. Today, our digital business serves more than 1,500 customers, including more than 90 of the world's top 100 oil and gas producers. That is a strong install base and a solid foundation for growth. Geographically, the business has broad exposure across the Middle East and Asia, Europe and Africa, Latin America and North America. Our customer mix is also well-balanced across national oil companies, independents and majors. That mix is important. With national oil company and independents, we already see strong digital adoption, particularly in planning workflows. With the majors, we see a meaningful runway. As some customers move away from internally developed systems towards scalable enterprise-grade platforms that can support broader digital transformation. Finally, from a revenue category perspective, platform as an application represents approximately 40% of digital revenue, followed by professional services, digital operations and digital exploration. That mix will evolve as digital operations continue to scale. We expect it to become the largest part of the business over time, as I will describe momentarily. Overall, this is a well-balanced business. It is not dependent on one geography, one customer or one product line. It is also supported by the breadth of the broader SLB portfolio and our global reach. Taken together, that gives us confidence in both the durability of the business and the opportunity ahead. Next, building on what Rakesh outlined earlier, let me turn to the market opportunity and where we see the strongest growth. Recent third-party analysis shows the total addressable market for our digital business growing to approximately $35 billion by 2030. That view aligns closely with SLB's internal analysis. When we map the market by category, we expect the strongest growth to come from digital operations, where the market is expected to grow at an 11% CAGR through 2030. This is compared with about 8% for the overall digital market. As I highlighted earlier this morning, there is meaningful upside to this outlook. If adoption of AI solution moves faster than currently forecasted, the digital market could expand to as much as $50 billion by 2030, representing a 15% CAGR. Together these trends, along with our differentiated market position, give us confidence that we can grow digital revenue at a 10%-15% CAGR through the end of the decade. With the higher end of this range based on accelerated AI adoption. This revenue trajectory would outpace both oil and gas upstream investment, as well as the industry's digital spend, as we believe we can leverage our digital platforms, customer footprints and AI capabilities to continue growing ahead of the market. Notably, we expect to deliver this level of revenue growth without significant M&A activity, although we will continue to consider bolt-on technology acquisition that can further strengthen our offering. With that as the backdrop, let me now turn to how we will monetize that opportunity. Our digital offerings are monetized through several commercial models, each contributing differently to growth, margins, and recurring revenue. Platforms and applications are largely recurring. They are sold through software subscriptions or perpetual licenses with annual maintenance. Digital operations has a different model. It is generally sold as an incremental digital line item connected to our core services or equipment. Revenue in this category is repeatable or sometimes recurring, and typically delivers high incremental margins. Digital exploration represents our exploration data business, which consists of a differentiated library of seismic surveys and other subsurface data covering key basins worldwide. This is usually highly profitable but non-recurring in nature, with revenue generated primarily through one-time license sales. Our success in producing and selling high-quality data is highly dependent on the use of our platforms and applications, enhanced by our domain foundation models. Finally, professional services is more project-based. It includes consulting and technology services required to support our clients' digital transformations. Although this category has lower relative profitability than the other digital categories, it remains strategically important because it helps drive adoption and creates pull-through across the broader portfolio. In short, we have multiple ways to monetize the digital opportunity. More importantly, these various models reinforce one another, and combined, they create a business with growth, resilience, and flexibility. Let me now go 1 level deeper into the 2 areas with the strongest growth potential, namely platforms and applications and digital operations, and explain how we will unlock further growth and value. In platforms and applications, we see 3 key levers for increasing monetization. First, gradually transitioning on-premises customers from perpetual licenses with maintenance to subscription models. This allows for better tiering of our commercial offering based on the features our customers choose to consume. Second, migrating more customers from on-premises offerings to the cloud. Third, monetizing consumption across the portfolio as customers expand usage of our platforms and applications, data environment, and AI solutions. As you can see, growth in platforms and applications is not only about adding customers. It is also about shifting the mix towards more recurring subscription and consumption or outcome-based models. This will improve revenue predictability, reduce sales volatility, increase contract lifetime value, and improve customer retention. The opportunity in digital operations is of a different nature and scale. Here, we believe we can increase the size of the market, if not create the market, by scaling connected equipment and autonomous workflows across customer operations with new AI capabilities further accelerating that trend. Today, those digital services only represent about 1.5% of our core equipment and services revenue, despite delivering significant results in the field. As customers increasingly recognize the benefits of these solutions, we see the potential for spending in this category to grow at an elevated rate, potentially tripling by 2030, supported by digital add-ons and increased outcome-based pricing. Taken together, the evolution of platforms and applications and digital operations are expected to drive a majority of the growth in our digital business. The value generated from these offerings will continue to compound. As platform usage increases, more data is organized and activated. As more assets and operations become connected, the opportunity to automate workflows expands. As AI becomes embedded in those workflows, the value we create for customer increases. All in all, this will support our ability to continue delivering attractive digital growth with margins that are highly accretive to SLB. To make this more explicit, let me now close by sharing our 2030 financial ambitions. Based on market growth and the trends we are seeing in terms of adoption and monetization, we expect to double digital annual recurring revenue to approximately $2 billion by 2030. This is supported by the assumption I shared earlier that digital revenue will grow at a 10%-15% CAGR from 2025 through 2030. We also see a path to approximately double our current adjusted EBITDA for digital to between $1.8 billion and $2 billion by 2030. With margins expanding to a range of 38%-42% towards the end of the decade. Our ability to achieve margins towards the higher end of this range will depend on our success in increasing the share of subscription-based revenue in our mix, the continued expansion of digital operations, and the addition of AI-driven capabilities that create incremental value for customers and support better monetization of the outcomes we help enable. In summary, digital is already helping to accelerate SLB's growth with accretive margins and compelling returns. As adoption continues to expand across platforms, operations, data, and AI, we see a clear path to sustained double-digit growth, continued margin expansion, and increasing contribution to SLB's overall returns over time. Thank you for your attention. I will now turn it back to Olivier. Thank you, Stéphane. Ladies and gentlemen, as we conclude, let me leave you with this. Digital is becoming central to how this industry plans, operates, and creates value. What you have heard today reflects a leading position SLB has built over many years. It is one that is powered by science, accelerated by AI, and built for the complexity of energy operations. We are the only company that brings together the domain expertise, the technology, the partnerships, and global execution to redefine what is possible where it matters most. This is just the beginning. In the age of artificial intelligence, new opportunities are being unlocked across all industries, and we are pursuing them not only through the digital frame we discussed today, but also through our data center solution business. There, we are extending our work with hyperscalers, the same partners we work with and collaborate in our digital upstream business to deliver the physical infrastructure required to scale AI. In that sense, SLB is uniquely positioned to benefit from the secular growth of AI in two ways: through the platform and digital solutions that transform energy operations and through the infrastructure that enables AI to scale. If there is one takeaway, it is this: Our digital leadership is real, it is differentiated, and it is creating long-term value for SLB and its shareholders. Thank you for joining us today and for your engagement throughout the session. With that, I would like to invite today's speakers to come with me on stage for the Q&A session. Thank you again for your attendance. It is now my pleasure to open it up to questions. If you have a question, please raise your hand and we will come to you with a microphone. Please introduce yourselves and ask only one question so that we can get to as many of you as possible. As you're thinking about your question, allow me to kick it off by asking Olivier about something we've been hearing a lot lately. Olivier, as we think about SLB's digital next phase, what gives you the confidence that this business can evolve into a scaled higher multiple engine, distinct from traditional oil field services, and what proof points should investors focus on today? Thank you, James. Indeed, I think I would state first thing is that we are not building our digital capability anymore. We have built it. We're here to scale it. If you look at the proof point of where we stand today, we are already going at double digit with expanding margins, and we are seeing a mix further evolving towards increased recurring and consumption-based revenue. What makes me confident is that we have a clear path forward. The clear path forward is resilient on digital operation and AI solution. The sandbox is a total SLB OFE footprint. That is unique. The capability we have together, the domain, the platform, including AI-ready stack, the partnership ecosystem we have developed, and the global reach, as we said, is unique. When you combine all of this, as we continue to scale, the ARR will shift upwards. The consumption base on our platform will start to be clear, and our margins will resemble software-like margins. When you put all this together, I believe this will deserve a higher multiple. I think it's no more physical growth. It is durable growth that will compound and create value for the company. Thank you, Olivier. Let's take questions now from the audience. We have one right up here up front. Hey, thank you. Marc Bianchi with TD Cowen. Thank you for the presentation. I'm curious to achieve these targets, I think you talked about $3 billion of R&D spend since 2016. Can you talk about what additional R&D spend is contemplated to get to these targets? Related to that, how do you see this initiative sort of helping the capital intensity of the overall business? Do we see a reduction in capital per dollar of revenue, for instance, as time goes on and you're able to implement more of these capabilities? Thank you, Marc, for the question. Stéphane, can I pass that one to you? Yes, of course. Thank you, Marc. Look, as Olivier mentioned, the foundations are built. We've spent actually decades and increased R&D in the last few years to get there. We are not going to stop there. We will always need to enrich the platform. In terms of R&D, you've seen the numbers over the last 10 years. I would expect this, of course, not to increase as fast as the revenue, if it ever increases. You will gain operating leverage from this, but we will continue to enhance the platform and invest into it. I have a question right here in the middle. Scott? Yes. Scott Gruber from Citigroup. Thanks for the presentation this morning. Super impressive. I'm curious about the pricing strategy for some of these services. Thinking back to the digital operations examples where autonomous drilling can save 25%-40% on the drilling time of a well. If you think about that in the context of a deep water well, it could be like $25 million. Which is a huge amount of savings. How do you guys think about what is the fair share of that savings for Schlumberger, SLB, sorry, I'm old school, for SLB to capture versus how much you share with the client? Obviously, you want to push the adoption of these services and scale it up, but there's a huge amount of value creation there. How do you think about the pricing strategy with that value creation potential? Rakesh, would you like to kick off that question and perhaps, Cecilia, on digital operations, you can have a follow-up? Thank you. I think for different categories of revenue, as we've reported, the pricing strategies, of course, vary. For the operations, as you rightly point out, significant value for our customers, and we will therefore be in a very strong position to be able to scale. In the operations, as I think Stéphane briefly mentioned, we are talking about almost very little new investment for us to be able to provide this value addition because of the fact that we are utilizing the existing hardware already, and we are just bringing new algorithms to be able to bring the value for our customers. Of course, we expect, therefore, the margins to be very, very significantly accretive, as I think mentioned by Stéphane. For the other categories, for example, in the platforms and applications, again, I think the fact that we have a very distinctive and a very strong offering, we expect to scale that. Therefore, the additional scaling would not cost us very much, which is why the confidence that we have in terms of even stronger margins in the years ahead. Then, of course, the agentic AI, that will bring significant value on top of what we are already charging, and that should bring significant margins for us going forward as well. If I look at those, each one of those categories has distinct advantages, which will continue to bring more margins for us going forward. Cecilia, perhaps you want to elaborate on operations? A couple of points other than what Rakesh has said. First of all, many and most of our contracts are performance-based contracts. When we get this additional digital add-on service, we actually increase revenue not just from digital, but also from our general operations. Second is many of our digital operations that we sell actually are agnostic. As in the completions example, when we merge the digital piece with our innovative hardware, that's when we see a step change in performance. It is also an enabler to bring additional pull-through revenue for the locations where we're not having operations there. Thank you, Cecilia. James. See you right here. Not James, yeah. Yeah. Sorry. James, question about the changing dynamics that we've seen, how it impacts the digital adoption in this space. Energy and power has changed a lot in the last 110 days. Of course, that change with energy security started in 2022 as well but has become more pronounced. Olivier, you're having a CEO to CEO conversation, and I'm curious what the feedback is from the customer base about the security of their operations as they move more and more information to the cloud and go more digital. Do they worry about cybersecurity? Do they worry about hacks, things like that? Does that limit, or have you created a platform where they're very comfortable that you can protect their data? Olivier, would you like to take the first part of the question, then perhaps we can pass it to Shashi for the second part? Yeah. The first thing I would say is, Canon, that what is happening today with energy security, the need for supply diversification, the need to secure and accelerate supply management is all playing to the strengths of the impact of digital in our industry. Anything I'm hearing from customers, the same way we heard back in 2020, is that digital is becoming more critical and more essential to unlock the performance efficiency to fast-track the cycle of first oil, first gas, and to improve recovery for the market, for the assets that can be deployed securely in the world. This is the trend that we see is only accelerating, is a secular trend that we believe that this crisis is only reinforcing. The role of digital going forward will be a shift and a critical transition for the industry. That is happening, and I think this is only accelerating. That's the feedback we're getting, and we are seeing it in adoption. We are seeing the pilots. If any mention of the impact, actually, our digital business in the Middle East has been extremely resilient against this backdrop of crisis. Let me add two points here. I think when we talk about customers and their concerns around their assets, I would put them into one aspect, which is around data. We implemented our digital platforms in a way that we can meet the customers where they are. For those customers that are comfortable with a traditional SaaS offering, great, we support all the three hyperscalers. There are customers for whom we have implemented what we call private SaaS, which means deployed solutions onto their tenant, which means it is managed by their own IT and security organization. That's one facet. Of course, there is a set of customers that want everything on-prem. We cover that entire spectrum to say wherever the customer is and their data are, we can deliver a solution there. The second angle I would say is from a cybersecurity point of view. We run one of the largest cybersec ops operations across the industry, and we work very closely with leading hyperscalers, plus also security companies like Palo Alto Networks, et cetera, on those, right? We are adopting and using the latest frontier models to test to validate our implementations or any kind of loopholes that might be existing. Then, of course, we work very hand in hand with the customer's own IT and security organizations as well. At the end of the day, for our customers to use our stack, they need to be comfortable that the implementations that we have meet their standards, and that's what we go with. Thank you, Shashi. Right here in second row in the middle. Dave, please. Thanks. David Anderson, Barclays. Stéphane, just a real quick point of clarification. On your 2030 targets, was that based on the $50 billion TAM or the $35 billion TAM? Dave, it's a range. This is why we have a range of EBITDA as well. The revenue itself is between 10%-15% CAGR through that period, right? The market overall, if you take the low end of the TAM we've given you, the $35 billion, that would be 8% CAGR. The $50 billion would be 15% CAGR. It's based on the entire range, if you want. Got it. Understood. Olivier, SLB has made a big point today about your mode in digital. You're really the only OFS company doing this. You've been doing this longer than anybody. The foundational models, the domain expertise gives you all a head start or a lead in AI. Your customers are also adopting AI. They're adopting agentic AI, all sorts of platforms as well. Where is that line today? Are you concerned about that line moving? In other words, your customers are going to be adopting some of this in-house. You're going to be providing other things, but is there a concern that that line could shift? What is the concern that some of them are going to be start adopting what you're doing? As you heard before, we meet our customer where they are in their digital journey. I think if you look back at the history of digital, 30 years ago, most of the reservoir simulators were owned and developed by our customers. Some of the basic interpretation was done the same way. Over time, the emergence of platform, industrial-grade platform, has replaced those developments. Nowadays, some customers are willing to enter the development of AI model, if not development of agentic AI, using models. What we offer is an open platform. We offer Delfi, Lumi, the data and AI open platform, and Tela as agentic agent framework that our customer can use to extend their agent team, connect to their agentic workflow, connect to their third-party applications, and also embed our domain foundation model or retrain our domain foundation model to their own data set. That's what is happening in a pilot we have with several customers, and they see a huge benefit of doing so because they have a starting base that is a step change from what they can do by themselves. As we said, the relationship with NVIDIA give us the guarantee that you have peak performance on the domain foundation model. We have designed it from the ground up, not using the existing Frontier model. We are designing using our science, our technology from the ground up with the guardrails that you integrate it from NVIDIA, from other provider into it. The starting point is very strong. The framework we have give them the freedom to extend, and that's what is attractive into our offering to the customer today. Yes. Right back here. Right here in the middle. Can someone pass the microphone? Yeah. Hi there. Sebastian Erskine from Rothschild & Co. Just a question. In one of the presentations, you mentioned about the performance-based contract in Libya, actually trying to buy in a bit to the efficiencies that customers can gain. Obviously, that's interesting to me. When we look at U.S. land, one of the big stories was the deflation services, the fact that E&Ps could do more with less. How much as a% of these performance-based contracts or pricing-based, outcome-based models do you see and a scope for that in digital operations going forward? Thank you. Cecilia, I think you answered part of that question earlier. Yeah. It's a large% of our contracts are performance-based contracts. I believe you were asking specifically on the U.S. market. In U.S. market, we have a very flexible go-to-market approach. We rent and sell our equipment as well as do the services. Many of our services are performance-based, then the rental and the sale of our equipment is through a third-party competitor. Yes. Right back here. Thanks. Heath Terry, Citi. Really appreciate you taking the time on all of this, particularly the level of detail around some of your technology partnerships. The reliance that you have on the cloud providers, they've obviously been very vocal about the issues that they're dealing with from a supply perspective and the constraints with demand increasing the way that it is. That's showing up in pricing. It's showing up in this whole issue around token costs going up as we've started referring to as token maxing. I'm curious if you're seeing any of those kind of issues showing up in your relationships, either with the hyperscalers or with your customers as those underlying costs start to go up and how you're planning longer term against the constraints that seem like they're going to be around for a while in this space. Trygve, you explained to the audience the digital advantage and the partnership model. Why don't we pass this question to you? Yeah. As you say, we have a close relationship with all the hyperscalers, all the major cloud providers. We, of course, secure ourselves for our own direct expenses. We secure ourselves with long-term contracts with these providers to ensure that we have cost-competitive access to the technologies. We also work very actively with them, particularly on securing capacity, where we have a well-established playbook for securing that we have the right capacity. As our workloads will be sometimes demanding the same type of capacity they use for other workloads, so make sure that we can continue providing continuity to our customers in operating. Rakesh, would you like to elaborate further? Heath, actually, you do make a good point. There is clearly a transition happening, and the industry is getting used to the changes that are happening. I'll say there is a very interesting trend that is happening right now. Instead of going from cloud first, many of our customers are going to what they call hybrid cloud for elasticity. They want to keep on-prem for consistency, and then they go on the edge for immediacy. They are moving in a direction where they will actually have infrastructure which encompasses all of them, so that they're able to take the benefit of what the cloud compute brings as well. They are also prepared so that they are able to get the maximum benefit from what they have in-house already, and also from the edge operations where it is required. Thank you, Rakesh. Olivier? What is important to this is that to offer our customers the ability to navigate through this tenant hybrid cloud for elasticity of cloud compute and edge at the same time, doesn't come in a quarter. It has taken us years of deployment, of tuning, of testing and validation, and certification for customer. Proud ourselves to be the only one that can do this complex environment at scale, industry grade, complex architecture that combine the benefits that you heard about, that allow our customers to use the cloud when and as necessary, and remain in that tenant where they believe it is more secure and they have the capacity they can to develop their workflows. That's unique. Thank you. In the very back, I see a hand up. Hi. Thanks. Stephen Gengaro, Stifel. When we think about digital and we think about maybe the last few years and then now through 2030, how do you think that impacts your growth versus history in the core business? Stéphane, let me go ahead and pass this one to you. Did you hear the question? Yeah. Actually, if you don't mind rephrasing. Yeah, very good. Stéphane? maybe relative, unless you want to tell us what you think the market does for the next 5 years, but relative to the market through 2030, how do you think digital impacts the growth in your core operations versus the peer group? Okay, got it. Sorry for that. Look, first, the growth we are portraying here for digital, and we've said this before, we believe is at least partially de-correlated from the growth of our core services and equipment, which as you know, are more cyclical. If we are confident to give that 10%-15% CAGR there for digital only, is that we think this is really secular, structural, and triggered more recently by the acceleration of AI. That gives us confidence that there is this pot of digital, if you want, that can grow a bit regardless of what can happen in the rest of the E&P upstream sector. Particularly because it remains a very small% of the total spend, as you've seen. Now, can that influence the size of the overall E&P spend? Yes, it can. What it can do, at least for us, is that it can bring more, first more digital. Because, as Cecilia highlighted, it's not just about the software and the platforms, but it is the connection with the hardware, is that more digital is going to pull through more core services as well. We want them to gain in efficiencies and generate cost savings. This is not going to happen in the core services and equipment we provide. To the contrary, we are going to have a boost from the advent of more digital operations. Thank you, Stéphane. Yes, Doug. We'll get you a microphone right here. Thank you. Doug Becker with Capital One. Curious about, as autonomous operations really start to scale, how are you thinking about risk management? What safeguards are in place from a suboptimal decision made by an autonomous operation or maybe in an extreme example, a well control incident that was really triggered by an autonomous decision? Thank you, Doug. We're going to pass that one to Cecilia. Just like a self-driving car like Tesla, the system can go into manual mode at any time, and the user can decide whether to go autonomous or if the recommendation needs to be approved by the user. It's a very easy on/off, and ultimately, there's always going to be a user that makes the final call, which is going to be our customer. Thank you, Cecilia. Saurabh, did you have your hand up? Yeah, right up here in the front, please. Hi, Saurabh Pant, Bank of America. One thing, Olivier, when you took over as the CEO back in 2019, you were talking about the fit-for-basin at that point of time. I think I heard the word fit-for-basin once in Shashi's remarks. How do you think about fit-for-basin from a digital perspective? I know you talked about seven, I think, innovation factories across the globe. Maybe talk to how are you thinking about that? What are you doing differently in different parts of the world? Olivier, why don't you go ahead. Yeah. Great question. I think if digital brings us one thing, is ability to customize, to tailor, and to fit our digital frame to the basin challenge that we are facing. I think the concept we have put together with the industrial factory, and we have seven of them in the world, were to provide the digital backbone, the digital domain expert, the digital platform, close to our customer to collaborate on what could be done locally to make fit technology, digital technology solution. Now, with the advent of digital operation, the advent of agentic AI, we are going to the next level. The next level of putting together, stitching together OFE operation with digital capability and creating unique set of fit operation with a fit domain foundation model, with fit set of workflows that are stitched together through an agentic AI, and with a fit set of equipment or services provided back to back. The best example actually happening today that we can refer to, it's what you heard about at ADNOC, referring to it as AI PSO. An AI PSO is production optimization using AI. We are co-developing the agents. We are fitting this agent to work on the specific asset of ADNOC, and we are lifting and enhancing the production performance through this. It's a fit application of AI capability tailored to the OEM equipment that they use, tailored to the reservoir characteristics that they have, using our Delfi and our Lumi platform to make it work together. That's the principle, that's what we want to extend. That's what we want to repeat from basin to basin. Trygve, did you wish to add any additional color? Just there's one more color to fit for basin as well that is increasingly being important now and which is underpinned by our platform investment over the last few years, and that is the technology sovereignty. A lot of operators around the world are increasingly concerned about their sovereignty, their ability to operate their digital environments. This is exactly what our platform has been built for and enabled for the last few years, and I would say we are uniquely positioned to be able to guarantee our customers this type of sovereignty as well. That's the additional thing in addition to the particular operational and geological challenges they have as well. Cecilia. I want to add a different angle to the question. Every geography is going to be different, and the system needs to learn what are the parameters for that geography. For example, a deep water operation is directional drilling is going to look completely different than U.S. land. What the customer wants is going to be completely different. In deep water, it's about landing the operation per the plan in the sweet spot with a minimal amount of risk. In the U.S., it's about drilling as fast as possible. As long as you're in the tunnel, you're fine. The system learns and gets smarter depending on which geography and what type of operations you're running, hence why it's very important to have this wide footprint that we have at SLB. In the very back. Yeah, please keep your hand raised. Thank you. Ati Modak from Goldman Sachs. I wanted to connect a few dots. I think Shashi, you mentioned generic LLMs are challenging to do. Olivier, you mentioned at the beginning that it's important to know what to build. We've been hearing customers trying to build their own applications. Where are we in that evolution of that dynamic? I'm curious how that evolution is factored into or affects the sensitivity on your 2030 guidance. Shashi, would you like to take the first part of the question? Yeah. I think, we talked about LLMs because they are very powerful tools, but they're very statistical in nature. They build and they generate the next response based on the context you provide. We cannot take that risk when we are talking about technical workflows where customers are making high-value decisions or high-risk decisions, right? What we want to do is to say we will leverage the large language models where they bring value, which is converting the context into an outcome. When the context is set by us, by providing the domain. That means when we are working with a well log foundation model or a seismic foundation model, that absorbs the knowledge that comes from that domain and does the handoff between the foundation model and the large language model to aggregate the information and serve it out, right? That way, we don't ask the large language model to figure out how to work with seismic data. It has no clue, but we do. We work with that balance of us providing the domain context and informing everything based on the domain, and use the large language model for where it is best suited, which is to aggregate and summarize and provide the outcome to the user. I'll pass it to Rakesh. Yeah. I think, I want to also bring in Dave's point that I think you were alluding to. Many of our customers have actually tried, absolutely they will continue to try to go down that alley as well. They're realizing more and more that the changes are happening at such a rapid pace that unless you really have the expertise and you're engaged in it on a regular basis, this is not a pace that you will be able to keep up with. More and more, we are seeing that the customers are actually aligning with partners that they realize are going to be in this for the long game. The other comment I want to make, we're talking about LLMs a little bit. LLMs are based only on text. The data that we have in our industry is in very other different formats. seismic formats have nothing to do with text. Logs are completely different, and therefore the models, the domain foundation models, naturally, the LLMs cannot do anything with the data that we have in our industry. The domain foundation models have a very distinct application that will continue to bring value to our industry specifically, and only companies who can handle that kind of data will be able to benefit from it as well. I just wanted to give you those two colors. Thank you for that, Rakesh. Right here. Dan? Hey, thanks. Good morning. I just wanted to ask a question on labor and kind of talent retention. The catalyst for the question was, I noticed in one of the earlier partner testimonials, it was someone who had actually been at SLB for a couple of decades, and then most recently was at one of your biggest competitors. Yeah. Can you just talk about to what extent attracting talent, retaining talent is a bottleneck or any type of impediment to growth for SLB? Also, if it's something when you speak with your customers, if training and attracting the right talent is a bottleneck for their digital adoption as well? Thanks. Olivier, why don't you? I think we all compete for the same talent pool. I think we have demonstrated for the last decade that I think we have still the foundation, the culture, the training framework to attract talent, digital talent, geoscience talent, people, technical experts, engineer talent that we train. We co-train in AI and in data science as well as in geoscience domain. We have been able to attract from every region, top talents across the best university. Occasionally, we compete with those hyperscalers. We compete with some other horizontal player. I think the talent we have in our team has allowed us to build what you have seen today, to build the Tela infrastructure, to build the Delfi, to build the Lumi. I think it speaks volume to the talent we have that Shashi is leading and our team is leading. I'm very proud of what we have as a talent pool in our team. I'm convinced we'll continue to attract, I think, these events and what we are publishing every day and the path to autonomy is what is exciting the most new and future employees and prospects that are joining us. They love what they can see when they enter the company. They see that we are becoming a digital-first company, and I think that is very attractive, and I think that is the magnet we are putting for digital talent throughout the next few years. I'm not concerned. I'm excited about the future can give us with this talent pool we are attracting. Thank you, Olivier. Hey, Keith Beckmann from Pickering Energy Partners. It sounded like M&A is probably not a key way to grow. You guys got a lot of internal things going on. On that front, is there anything within the digital portfolio that you think you're missing? Maybe what are some of the key characteristics you're looking for when evaluating potential opportunities? Thank you, Keith, for your question. I'll pass that to Rakesh. Keith, clearly, we are always on the lookout for bolt-on technologies that will bring value. We've announced a couple, I think, over the last few months that I'm sure you are aware of. I'm not going to sit here and tell you this is the weakness we have in our system. We are always on the lookout for technologies which will complement what we have or for bolt-ons that we decide we will not develop in that particular domain or that particular part of the technology. I think both extending our partnerships with companies that have complementary skills that we will either integrate or we've decided not to compete. Occasionally, where we see that is a good fit into our own organization as we have done, we will continue to look out for opportunities. Thank you, Rakesh. I think Destiny Global and Tigo's are good examples of that. Derek. Right here first. Thank you. Derek Podhaizer, Piper Sandler. I found it interesting when you split apart the customer type for digital. I think you had 37% NOC, 37% independents, 21% for the majors. Maybe could you talk about the opportunities to capture more share with the majors or on the flip side, some of the limitations and headwinds to continue to drive adoption in with the majors? Go ahead. I think you have seen three statements from Eni, from Chevron, from TotalEnergies, and from Shell. I forgot about Shell in this statement. They're very, very clear of the benefit they've seen partnering with us. They all collaborate with us on a different scope, utilizing digital operation, trying to get the most of autonomy for drilling operations. Chevron is the historical partner that has helped us develop and accelerate our platform at scale with Microsoft. Both Shell and TotalEnergies have entered a collaboration agreement with us to develop fit subsurface and adapt their workflows to the benefits of the organization. I don't see any limitation on this. I see organization on the customer side that are keen and eager to leverage and to work side by side with us so that they can leverage agentic AI environment. They can leverage the powerful platform so that they can deploy to the complex environment they will always want to deploy to match security requirements they have, sovereignty when operate in certain country, and leverage of their own IP, which our platform allows us to plug in. I don't see a cycle. I see a big runway with all the major and the ones that were mentioned into this to continue to work with them for adoption at scale. Very good. Right here. Thanks. Phillip Jungwirth with BMO. Can you talk about the drivers behind the margin improvement by 2030, 38%-42% is quite a bit higher than 35% in 2025, and I think you guided a similar level here in 2026, despite the 9% growth. Is it mainly just mix shift with platforms and applications, digital operations growing more, or is there more behind it? If so, could you please expand upon that? Thanks. Very good. Stéphane, I'm going to pass this one right to you. Look, first, I'm quite confident we can reach that range towards the end of the decade, if not earlier. I think actually margins will increase year after year into 2030 to reach these levels. The key driver is a few things. First, you have the simple operating leverage. We've mentioned R&D before. R&D, if you want, is the biggest cost to grow. Again, the heavy lifting is done, and if we increase R&D a little bit, it's not going to increase for sure as much as the revenue growth. You get margin expansion from there, you have that shift in pricing model. Some of it is enabled by AI. We believe we will be able to increase the subscription-based revenue, which allows us to tier better, if you want, the levels of pricing, depending on the features each customer use. More consumption-based, more outcome-based pricing should help us lift the margins as well. It's the combination of all this that really gives us the confidence that 38%-42%, the midpoint of 40%, if you want, is quite a good ambition I think we can reach. Thank you for that perspective. Allow me to come back to this side of the audience. Yes, right here. Hi. Noah Naparstek from Goldman Sachs. I have another question on the mix. If we look at the 60% or so of revenues that's recurring and repeating, just wondering how weighted it is to pure SaaS. Do you plan to increase SaaS mix over time? How do you plan to do that? Yes. I'll pass it back to Stéphane. Yeah. Definitely we do, yes. It's part of the driver is indeed the SaaS mix. Again, we are not betting everything on the cloud, right? Because as we mentioned before, we leave the customers where they are. Still, we are seeing that shift. Is it going as fast as we want it to be? Maybe not, but it is going. Today, we have, if you want, a bit less than 50% on the cloud, and we could very much go to around 75% at one stage of SaaS and cloud. It's part of it. Olivier. The other element is the consumption model as part of the science. I think the use of AI, the use of agent, as you have seen a demo, as was shown by Shashi earlier today, you can imagine the compounding effect of deploying agents that can then run autonomously part of our engines that are either on the cloud or on the tenants, and consumption is based on the frequency and intensity of use of this application. That's this compounding effect that we believe will drive the way forward. Do we have any further questions from the audience? Yes. Right here. Thank you. Keith Mackey with RBC. The digital operations TAM expansion is certainly key to the growth metrics here. Can you just talk about what some of the key customer impediments to adopting digital operations has been, and how do you mitigate that to drive the further adoption going forward? Thank you, Keith, for the question. Cecilia? Sure. Thanks for the question. Excellent question, in fact. What we see is that customers like to pilot and test the system first to really understand the value it brings and to ensure that it's a safe operations and it fits everything that they would like to see out of the tool. Just to give you an idea, the last 6 months, we've done as much autonomous feats drilled than the first 2 and a half years. It's taken us quite some time to get those pilots, to get our customers to feel comfortable with it. Now we're starting to see quite a lot of uptake and an acceleration of uptake. The second thing is that a lot of customers are waiting to see who's going to go first. Now we have enough pilots that we're actually seeing customers almost not wanting to be left behind, and they're starting to be very interested in what we have to offer. We have time for one final question. Yes. Right there. Thank you. Heath Terry again from Citi. You obviously have operated for a very long time in some of the most geopolitically sensitive parts of the world. This past weekend, we got a bit of a wake-up call with the U.S. government's decision to effectively ban access to one of the large language models. How does that potentially impact the way that you and your customers are operating around this? Does it lead you to want to use more open source? Does it lead you to want to have more distributed systems in terms of where your own technology or where your customer technology is sitting? Thank you for that final question. Shashi, why don't you go ahead and take the question, and then we'll leave it to Olivier for closing remarks. Yeah. It's a very good question. I think when we started this journey, the LLM providers was few and select, but now the level of capabilities that we will need from an LLM to integrate into our technical solutions is getting to a point that you can get it from a large number of providers. What we have done is that while we leave this choice of a specific LLM to a customer because they may have an internal enterprise-level choice, we also make sure that we implement our technology stack from the point of view of supporting open models. We partner with NVIDIA, we have NVIDIA's Nemotron models as the models that we can deploy ourselves. We don't have to wait for a CSP to provide it in a particular area. It can be deployed on-prem or within a customer's environment. Similarly, we have models from Mistral. We have several options. We keep that options open. Even our own domain foundation models start from a base model that is open source, so that we are not tied down to a particular provider and get our hands in a bind at some point in time. It matters to our customer. They realize when they are walking with us through and discover the way we have built this model, the way we have factored open source or open protocol into our Tela framework, into our Lumi, into our GenAI, reinforce the attractiveness and the confidence they can bet on this technology platform for the future. Just to conclude, I think we had run through for the last more than two hours. I hope we convince you that I think we have a unique moat as a digital leader in our industry. We are building it on four clearly distinct combined capabilities, deep domain expertise that is rooted 100 years ago, a platform approach that includes an AI-ready stack, an ecosystem with partners that you have heard about that is unique and are willing to, and making every effort to work with us. Finally, ability to scale. Not only to scale AI, but to scale in digital operation and to scale and use the footprint and sandbox of our oilfield services and equipment potential. To reach all of our customers and to then help transform this industry to be digital first. That's the way we are willing to lead the future, to be recognized as digital-first company that help transform and unlock new level of efficiency, performance, and value for this industry. We believe we are there to lead this, to create this shift that industry needs for energy security, for energy affordability, and for the future of growth in our societies. That's where we believe we have a role to play, and that's what we wanted to share with you today. Again, thank you for joining us. I hope that you got enough information to help you model the future and recognize what you believe will be an elevated multiple for the company going forward. Thank you very much. Thank you, everyone. That completes the formal portion of our program today. On behalf of the entire team, we thank you for your time, your thoughtful questions, and your continued engagement. We hope today's session clearly demonstrated not just the current strength of our digital business, but the distinct competitive advantages that will drive our next phase of growth. We are incredibly excited about the opportunities ahead and our ability to deliver long-term value for our shareholders. With that, we will conclude today's livestream.