(Art direction and design; illustration
created with AI: Jean-Dominique Lavoix-Carli)
Aramco, the Saudi state-owned giant oil company has developed “Metabrain”, an extremely powerful Large Language Model (LLM), with 250 billion parameters, and a trillion parameters version planned for launch by the end of 2025. Metabrain is trained with the oil and geological data that the oil company has accumulated since the 1930s. However, as spectacular as it may be, “Metabrain” is just but an example of the multiple ways the oil industry integrates AI technologies ( Carrington Malin, “World’s largest industrial LLM revealed“, Middle East AI News, March 4, 2024 and « Aramco’s AI push drives four billion in technological impact », Middle East AI News, June 22, 2025).
As the artificial intelligence (AI) revolution unfolds globally, all the major oil companies, be they American, French, Saudi, Russian, Chinese, British, Russian, etc. integrate these technologies from upstream to downstream operations (Kelly Norways, Hermann Wang, Rosemary Griffin and Charle Michell, « Feature : Oil companies race for AI edge in upstream operations », S&P Global, July 10, 2025). AI becomes a tool to reinforce efficiency from geological exploration to the management of equipment without forgetting the financial side.
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Meanwhile, the oil & gas industry generates colossal revenues, reaching 5.95 trillion dollars in 2024 (« Global Oil&gas industry guide 2025 », Research Market, 18 April 2025). Those revenues also happen to finance the development of the AI sector.
Reciprocally, the global oil & gas sector plays a major role in powering the mammoth international growth of the AI sector. The energy needs of AI encompass the proliferation of data centers besides the uses of AI by billions of people. Then, one must add the development of the material and electrical infrastructures upon which it depends (« Energy and AI », International Energy Agency, April 2025 and Hélène Lavoix, “Uranium for the U.S. Nuclear Renaissance (2): Towards a global geopolitical race, The Red Team Analysis Society, 17 January 2025).
Those rapidly evolving reality of energy and AI companies beg the question of their interlocked, if not interdependent futures. Then, we also have to wonder about the new geopolitical situations that may arise from this reality.
AI for oil
Data for oil
The oil industry is data driven. Oil companies need geological data to look for new deposits as well as to manage the currently exploited fields. Refining oil necessitates the production of a very accurate and constant data stream. The same can be said for the storage of oil and oil products, and for the management of reserves. It is also true for transportation, by pipeline, tankers, carriers and truck fleets (Mehdi Mohammadpoor, Farshid Torabi, « Big data analytics in oil and gas industry: an emerging trend », Science Direct, December 2020). Then, the selling and swapping of oil and oil products at global, regional and local levels produce a fantastic amount of financial, legal and regulatory data.
In other terms, on a global scale, the oil industry is a massive data engine of immense complexity. That’s why the AI new family of technologies meets with the oil&gas industry: technologies as deep learning are based on the classification and interpretation of big data.
Thus, the centrality of data production, management and application, turns the oil industry into a perfect attractor and integrator for AI (Hélène Lavoix, « Sensors and actuators for AI(1): Inserting AI in Reality », The Red Team Analysis Society, January 14, 2019). The same applies to the need for anticipating demand and new reserves.
The great convergence
In this technological context, it is worth noting t the historical convergence between oil industry and AI. Indeed, this dynamic has started in the short years that followed the massive breakthrough during the 2010s.
That’s when neural network technology met with deep reinforcement learning, leading to success as the victory of Alphago, the Google Deepmind AI, defeated the world champion of go. During the same timeline, the development of deep learning and machine learning turned into usable tools with economic value ( Hélène Lavoix, “Portal to AI-Understanding AI and Foreseeing the AI powered world”, The Red Team Analysis Society).
The Huawei case
It is the case in China, with, for example, Huawei, the leading 5G and AI giant company that has developed a major expertise in “intelligentizing” pipelines.
This expertise has been developed over more than a decade, first in Central Asia. (Jean-Michel Valantin, “Artificial Intelligence on the Chinese New Silk Road”, The Red Team Analysis, December 4, 2017 and “World’s longest smart pipeline”, Huawei case study, 30/03/2013). For example, Huawei has coupled the mammoth 3600 km long Central Asia-China gas pipeline with an immense array of sensors. This giant infrastructure connects Turkmenistan, Azerbaijan, Uzbekistan and Kazakhstan to China with a giant fiber-optic cable network (“World’s longest smart pipeline”, Huawei case study, 30/03/2013).
This fiber-optic cable allows for the installation of a huge population of sensors. Those devices measure flows, temperatures, pressure and all the important variables of this immense infrastructure. This vast array of sensors is also integrated to a satellite surveillance and communication network.
Furthermore, all together these systems of sensors produce immense flows of data. And those flows demand a very high bandwidth capacity – only allowed by fiber-optic cables – in order to be transmitted to the AI data treatment center built and operated by Huawei. This mammoth structure is now called the “intelligent pipeline” because of the way it is intrinsically AI-powered.
The Huawei company, is in charge of the connectivity and electronic management of the Central Asia-China gas pipeline.
Huawei is also one of the several Chinese companies that are currently developing AI in China, Alibaba, Tencent, Huawei, Sensetime, DeepSeek, Bytedance, … (Jean-Michel Valantin, “The Chinese artificial intelligence revolution”, The Red Team Analysis Society, November 13, 2017).
With the development of this “intelligent pipeline”, Huawei is installing itself as one of the major actors of the “artificial intelligentization” of the oil and gas industry (Juan Pedro Tomas, “Huawei targets the oil and gas industry”, IoT insights-enterprise, November 14, 2016 and “Asia gas pipeline implements advanced systems communications in Central Asia”, Huawei, Dirk Van der Play, “China diversifies in Central Asia”, Nov 23, 2020).
This « oil and AI » convergence has profound consequences for both sectors, as it drives profound and accelerating transformations.
Towards hyper oil?
The major evolution of this dynamic is the integration of the oil sector in what Helene Lavoix defines as « AI world ». Indeed, there is a current “conversion” of our world to AI. In this “New World”, everything that can be managed through the production of data by sensors is going through AI capabilities (Hélène Lavoix, “Exploring cascading impacts with AI”, The Red Team Analysis Society, May 17, 2023 and “Portal to AI-Understanding AI and Foreseeing the AI powered world”, “Portal to Quantum Information Science and Technology- Towards a Quantum AI World ?” The Red Team Analysis Society). As It happens, those sensors are deployed at a global and multidimensional level.
Oil for AI world
In the oil sector, those sensors numerous. They can be seismic wave exploration devices, networks of pressure gauges along continental pipelines. There are also satellite-based radars and geospatial imagery for geological exploration, high frequency trading of oil based financial assets, etc. Today, most often, AI capabilities manage and treat the data flows that those networks of sensors produce.
For example, Aramco, the Saudi Arabian state-owned oil company, is integrating artificial intelligence technology into all of its operations ( (“AI and Digital economy development key part of Saudi-China partnership plan”, Arab News, December 9, 2022 and Jean-Michel Valantin, “China, Saudi Arabia and the Arab AI Rise“, The Red Team Analysis Society, 31 January, 2023). Simultaneously, as highlighted above, the company has built and uses «Metabrain», the world’s biggest industrial Large Language Model.
Aramco trains Metabrain with its 90 years trove of oil data. The Saudi giant has also hired hundreds of AI specialists ( Carrington Malin, « Aramco’s AI push drives four billion in technological impact », Middle East AI News, June 22, 2025).
Exxon Mobil, the U.S. giant, uses AI to optimize its own energy consumption, especially at the refinery level. British Petroleum integrates AI algorithms to is exploration service. Shell is using predictive AI to improve the uses and efficiency of its equipment while reducing downtime equipment. Through this predictive maintenance approach AI algorithms analyze sensor data from equipment and machinery at refineries and drilling sites to predict failures, enabling timely maintenance and preventing costly unplanned downtime.
This approach has cut operational losses by millions and helped save costs at individual sites. (« How is AI transforming the Oil&Gas industry ? 10 case studies, benefits and examples», Intelegain, August 4 2025). Rosneft, the Russian state-owned giant company, is also incorporating AI from its upstream to its downstream operations (Julien Noceti, « The Outsider: Russia in the race for artificial intelligence », IFRI, December 2020).
In China, Sinopec, CNOOC and Petrochina integrate DeepSeek AI model. They use it for research as well as for predicting the evolution of demand. For PetroChina has completed a similar private deployment, embedding DeepSeek models into its proprietary large language models, tailored for oil and gas sector intelligence. ( Xu Yihe, « How DeepSeek AI powers next-gen digital operations for Chinese oil majors », Upstream, 26 February 2025, Xialo Wang and Weijia Song, « Artificial intelligence in petroleum companies: a patent landscape review », Science Direct, 2025 and Mia Nurmamat, «China targets AI-dominance in AI-driven energy sector by 2030 », South China Morning Post, 8 September, 2025).
As a result, the oil industry has also become a driver of the AI sector. It also boosts the AI sector through massive financial investments. It is the case, among others, of the relationship between Saudi Arabia and the Silicon Valley. The Saudi government has launched « Humain », an AI company dedicated to the development of the AI sector in Saudi Arabia. « Humain » is financed by the the mammoth Saudi Arabia Public Investment Fund (PFI). PFI is worth 940 billion dollars, generated by the kingdom’s oil rent (Ahmed Al Omran and Andrew England, « Saudi Arabia launches AI venture Humain ahead of Trump visit », Financial Times, May 12, 2025).
From Saudi oil to AI
On 13 May 2025, during the Saudi Investment Forum, all the major American players in the AI sector visited the Kingdom’s capital, and multiplied their investments and projects with the Saudi PIF and the company Humain (Reem Walid, “PIF’s HUMAIN to launch $10bn AI fund in global tech push“, Arab News, May 28, 2025).
Among others deals, Nvidia and Humain announced plans to build AI factories and data centers across the Kingdom. The deal starts with the sell of 18 000 Blackwell ultra-efficient GPUs to Humain. Nvidia and Humain established a landmark strategic partnership in May 2025 to create hyperscale AI infrastructure and build “AI factories” across Saudi Arabia, backed by the country’s Public Investment Fund (PIF).
Humain will invest billions to deploy up to 500 megawatts of AI compute capacity powered by hundreds of thousands of Nvidia’s most advanced GPUs over five years, beginning with an 18,000-unit GB300 Grace Blackwell supercomputer installation in the first phase. AMD, Qualcomm, Salesforce, Oracle, Google, Uber also announced crossed dealsUS and Saudi investments. Then, the White House also announced that the Kingdom would invest USD 600 billion in 5 years in the U.S. AI sector (Tyler Durden, « Nvidia, AMD, Nvidia, AMD Secure Saudi AI Data Center Deals During Trump’s Gulf States Tour », Zerohedge, May 14, 2025).
Thus, while AI technologies bolster the efficiency of oil companies, oil revenues have become an importantr driver for the expansion of AI. For the oil sector, this race towards new levels of efficiency is a strategic stake. Indeed, nowadays, the geopolitics of oil collides with the tensions associated with the global geopolitical fragmentation in a time of global energy transition and climate change (Jean-Michel Valantin, « What are climate wars ? », The Red Team Analysis Society, November 2, 2025 and Helene Lavoix, « Climate breakdown: towards wars to reduce CO2 emissions ? », The Red Team Analysis Society, September 19, 2023).
The geopolitics of AI oil
It is interesting to note that, since the 2000s, the U.S. has been the country where the twin revolutions of the shale oil and gas industry and of AI have happened. Furthermore they followed parallel timelines (Loretta Napoleoni, Technocapitalism: the rise of the new robber barons and the fight for the common good, Seven Stories Press, 2024).
As it happens, the shale oil technological revolution of horizontal drilling and hydraulic fracturing turns the U.S. into a net producer and exporter of oil and gas (Daniel Yergin, The New Map, Energy, Climate, and the Clash of Nations, 2020).
AI for fracking
However, this new approach relies on heavy equipment that necessitates large and continuous investments. Shale oil companies have to face very complex hurdles, such as optimization of extraction, reservoir modelling, production forecasting, evolution of demand, etc. Integrating AI allows those companies to optimize the different aspects of their activities, thus securing their revenues while mitigating risks.
The U.S shale oil and gas being technology intensive, it integrates machine learning, especially for accelerating drilling operations (David Whete, “U.S. oil companies use AI for faster shale drilling, mitigating OPEC production cuts”, From Bloomberg-World Oil, March 14, 2024).
This alliance between the U.S. AI and shale oil sectors is also securing the oil and gas production for the US domestic market. This evolution happens to be of strategic importance for the growth of the AI sector. Indeed, the proliferation of data centers, the training of AI models and the massification of the use of AI generates a growing electricity demand (See Hélène Lavoix, “Uranium for the U.S. Nuclear Renaissance (2): Towards a global geopolitical race”; Goldman Sachs, “GS Sustain: Generational Growth: AI, Data Centres and the Coming US Power Surge”, April 29, 2024).
Interlocked geopolitics
At the international level, the oil and AI sectors convergence means that the oil sector is operating in a transforming international arena. Indeed, the U.S./China race to reach dominance in the AI field affects all the dimensions of the sector.
For example, the U.S. administration tries to slow down Chinese progress through technological export bans. Beijing retaliates by restricting exports of rare earths, while boosting its own R&D and productivity in the GPU field.
The oil industry, which is, historically, a major driver of international tensions, becomes dependent for is own development and persistence upon AI. Meanwhile, AI is quickly becoming a driver of an international civil-military competition.
Reciprocally, if the tensions that accumulate in the energy sector, due to the geopolitics of energy transition, climate change and competition for access to oil turbo-charge or trigger new conflicts, then it is probable that those tensions will have consequences for the AI sector. This trend could be enhanced by the militarization of AI, which is quickly accelerating.
So, we now have to see how AI and the different dimensions of the energy sector are going to interact.
