SLB’s Generative AI Strategy 2025: NVIDIA Partnership Drives Energy Sector Projects

SLB Commercial Scale Projects: Generative AI Adoption in the Energy Sector

SLB transitioned from foundational digital collaborations to developing targeted, domain-specific generative AI solutions for the energy sector, powered by NVIDIA’s compute infrastructure.

• Between 2021 and 2024, industry efforts focused on integrating existing AI and cloud platforms to digitize workflows and analyze operational data. These initiatives established the necessary data foundation for more advanced AI applications but did not yet involve custom, large-scale generative models.
• The strategy shifted significantly in September 2024 when SLB and NVIDIA announced a collaboration to develop generative AI specifically for the energy industry. This new phase moves beyond generic AI tools to create proprietary, high-value applications.
• The partnership’s central project is the creation of a domain-specific large language model (LLM). This model is being trained on SLB’s extensive internal data, enabling the development of AI copilots and other applications tailored to complex energy workflows like exploration and production.
• This progression from using off-the-shelf AI to building custom generative models indicates that the technology is now being applied to core, high-value business challenges. It signals a broader industry move toward creating defensible competitive advantages through proprietary AI.

SLB’s Strategic AI Partnership Analysis with NVIDIA

SLB’s core generative AI strategy relies on its long-standing and deepening partnership with NVIDIA to leverage advanced computing for domain-specific applications.

• The collaboration provides SLB with access to NVIDIA’s full technology stack, including its leading GPUs and software frameworks. This is essential for training and deploying the computationally intensive generative AI models required for the energy sector.
• The partnership, announced in September 2024, is not a new relationship but an evolution of a long-standing one. This history ensures a deep integration of technologies and a mutual understanding of the technical challenges involved.
• By developing a custom LLM with NVIDIA, SLB aims to transform its vast proprietary dataset into a unique asset. This creates AI copilots and applications that are not easily replicable by competitors who lack access to similar specialized data.

Table: SLB Partnership Detail

SLB’s Geographic Focus: Global AI Deployment for Energy Operations

SLB’s generative AI initiatives with NVIDIA are global in design, reflecting the company’s international operational footprint and targeting deployment across its worldwide energy projects.

• Between 2021 and 2024, digital transformation in the energy sector was often region-specific, tied to individual assets or business units. The focus was on digitizing local operations rather than deploying a single, global AI platform.
• The 2024 partnership with NVIDIA supports a centralized AI development strategy with the intent of global deployment. The AI copilots and applications are designed to be used across SLB’s operations, from North American shale plays to offshore projects in Europe and the Middle East.
• While development is likely concentrated in technology hubs where both SLB and NVIDIA have a significant presence, the strategy mirrors NVIDIA’s “Sovereign AI” model. This approach, seen in the UK and UAE, involves creating powerful, centralized AI infrastructure that can serve national or corporate-wide strategic objectives.

Technology Status: SLB’s Generative AI Moves from R&D to Commercial Application

Exponential Growth in AI Training Compute Demands.

The compute required for large AI training runs has been doubling rapidly, illustrating the massive hardware needs of modern generative AI. This trend, shown in charts explaining the AI boom, necessitates partnerships with leading compute providers.

(Source: Understanding AI)

SLB’s generative AI technology, supported by NVIDIA, has advanced from the research stage to the active development of commercial applications intended for operational deployment.

• During the 2021-2024 period, AI applications in energy were primarily analytical, using machine learning to optimize existing processes. The technology was a tool for enhancement, not a platform for creating new, interactive capabilities.
• The announcement in September 2024 marks a critical shift in technological maturity. The joint effort to build a domain-specific LLM and AI copilots is a move from proof-of-concept R&D toward building a scalable, commercial product.
• The use of NVIDIA’s advanced hardware and software stack validates the technical feasibility and commercial intent of the project. This level of computational resource is allocated for production-grade systems, not purely experimental research, signaling that SLB is preparing for deployment.

SWOT Analysis: SLB’s Competitive Position in AI for Energy 2025

Visualizing NVIDIA’s Dominance in AI Chip Sales.

This comparison of AI chip sales demonstrates NVIDIA’s commanding market leadership over competitors like Intel and AMD. This market control solidifies its position as a key strategic partner and a source of competitive advantage.

(Source: Visual Capitalist)

SLB’s most significant competitive strength is its vast proprietary energy dataset, which, when combined with NVIDIA’s compute leadership, creates a formidable advantage in developing custom AI solutions.

• The company’s primary strength, its unique dataset, has been amplified into a key strategic asset through its partnership with NVIDIA.
• A critical weakness is the high dependency on NVIDIA’s hardware and software ecosystem, which could introduce supply chain and cost risks.
• The main opportunity lies in creating new, high-margin revenue streams by offering AI-driven insights and services to the entire energy industry.
• The most significant threat comes from other major energy operators or large technology companies that could form similar alliances to develop competing generative AI tools.

Table: SWOT Analysis for SLB’s AI Strategy

Future Outlook: What to Expect from SLB’s AI Strategy

SLB’s immediate strategic priority is the successful deployment and monetization of its first generative AI copilots to prove the technology’s value in enhancing operational efficiency and creating new revenue.

• The most critical signal to monitor is the transition of the generative AI copilots from development to active field deployment within SLB and its first external customers. This will validate the commercial viability of the entire strategy.
• Based on the September 2024 announcement, progress is gaining traction in the creation of domain-specific models. The focus is shifting away from generalized digital platforms toward highly specialized, valuable AI tools.
• Expect SLB to use the NVIDIA partnership as a blueprint to expand its AI offerings. Initial success in exploration and production will likely lead to the development of similar generative AI solutions for other parts of the energy value chain, such as new energy systems and carbon management.

Frequently Asked Questions

What is the main purpose of the SLB and NVIDIA partnership?
The primary goal is to develop custom generative AI solutions for the energy sector. The partnership is focused on creating a domain-specific large language model (LLM) trained on SLB’s proprietary data to build AI copilots for complex workflows like exploration and production.

How did SLB’s AI strategy change in 2024?
Before 2024, SLB focused on using existing AI to digitize workflows. In September 2024, the strategy shifted significantly to a partnership with NVIDIA to build proprietary, domain-specific generative AI models. This marks a move from using off-the-shelf tools to creating custom, high-value AI applications.

What gives SLB a competitive advantage in developing AI for the energy industry?
SLB’s key competitive advantage is its vast and unique proprietary dataset gathered over decades of global energy operations. By using this data to train a custom LLM with NVIDIA’s technology, SLB can create specialized AI tools that competitors cannot easily replicate because they lack access to similar specialized data.

Are SLB’s new AI tools just for research or for real-world use?
The technology has advanced beyond the research stage into the active development of commercial applications. The project with NVIDIA is focused on building scalable, production-grade AI copilots that are intended for operational deployment in real-world energy projects to enhance efficiency and create new revenue.

What is a major risk in SLB’s generative AI strategy?
A critical weakness identified is the deep dependency on a single technology partner, NVIDIA. This reliance on NVIDIA’s hardware and software ecosystem could introduce supply chain and cost risks, and any disruption in the partnership could impede progress on the AI initiatives.