LLMs Reshaping Global Power Dynamics

January 2025

In a bold initiative that could become the opening shot in the AI world race, U.S. President Donald Trump this week announced the Stargate Project. Backed by America's most influential technology innovators, the unprecedented $500 billion investment in the development of domestic AI infrastructure seeks to seal America's global lead in the field of artificial intelligence.

The basic direction of the Stargate Project is to ensure that fundamental ownership and control of key AI technologies remained in US hands. Trump emphasized the urgency of this effort, telling attendees, "We will not allow American innovation to slip through our fingers. We are building the infrastructure of tomorrow, and it will be built right here in the United States." This initiative is a direct response to growing concerns about falling behind global competitors like China, which has made significant advancements in AI.

To accelerate the project, Trump said he would declare emergencies in some areas that would let companies rapidly build out AI infrastructure such as data centers and energy facilities. Enormous data centers used for training and deployment of large-scale AI models use huge amounts of electricity, calling for new sources and upgrades to the power grid.

The Stargate Project shall have both physical and virtual infrastructure. It will be performed on large data centers and independent campuses. These will house not only the powerful computing resources but also become centers of research, development, and cultivation of AI talent. Importantly, the energy needs of these installations will drive the creation of new energy production technologies, including renewable sources and nuclear options that will supply huge power needs without burdening existing power grids.

This is close on the heels of an announcement by UK Prime Minister Keir Starmer in a speech exactly ten days earlier, on the transformative role of AI in the UK's future. Starmer framed AI not just as a technological leap but also one that could provoke growth, spur the building of new industries ready for the future, and enhance public services. The UK has been leading Europe in AI investment, publishing plans for AI growth zones, and accelerating the planning process for data centers.

Countries such as the UAE and South Korea are among several that have announced, or are slowly revealing, their AI strategies, which show that AI has emerged as a national priority. As countries jostle to take the lead in the race for AI dominance, investments in infrastructure, talent, and technology are set to reshape the global order.

As we run fast towards an AI-powered future, the technologies of value chains and systemic bottlenecks should be considered in as much detail as possible. It is in the comprehension of such dynamics that we can foresee with greater clarity the trajectories of innovation and their wide-ranging ramifications for nations and societies, and consider variant paths and strategies that could be followed given the current AI arms race.

So what are LLM's?

Large Language Models are a new generation in generative technology, specially developed to generate human-like text using advanced machine learning techniques and extensive training datasets comprising texts from books, websites, and other forms of material; this enables them to learn and mimic the complexity of human language.

Basically, LLMs generate text based on predictions of what comes after an input prompt. The foundation of such an ability at two pivotal stages in their training: pretraining and fine-tuning. While pretraining exposes the model to a wide variety of text data from which it picks up overarching language patterns, grammar, and semantics, the model then enters fine-tuning, where it is fitted with more specialized datasets to optimize its abilities for particular tasks such as summarization and question answering.

The architectural backbone of LLMs is the Transformer model, acclaimed for excellence in dealing with sequences such as text. Originally devised for language translation, Transformers have revealed outstanding proficiency to anticipate the ensuing token in a sequence, which would enable LLMs to create coherent and contextually relevant responses.

It starts with tokenization, where input text is divided into smaller units known as tokens; these can be whole words, subwords, or even single characters. Each of these tokens is then embedded-in other words, converted into a numerical representation, popularly known as a vector. This embedding represents meanings in a high-dimensional space where meanings that are similar are clustered together and relationships are represented directionally.

Once embedded, the vectors enter an attention mechanism block. This vital step prioritizes relevant parts of the input text, allowing the model to focus on specific words or phrases that significantly influence the meaning of surrounding text. The model then utilizes a Multi-Layer Perceptron (MLP) to further refine each vector representation, enabling a sophisticated understanding of the input.

This iterative process of passing vectors through attention and MLP blocks occurs multiple times, each repetition sharpening the model's comprehension and leading to more precise and context-aware outputs. Ultimately, the refined vectors are employed to predict the next token. This stage involves mapping the final sequences to a probability distribution over all possible tokens and utilizing the softmax function to select the most likely next token before repeating the process to construct longer text segments.

For output variability control, LLMs incorporate a parameter known as temperature. A higher temperature yields more creative but less predictable responses, while a lower temperature results in deterministic outputs that favor the most probable tokens. The mathematical framework underlying Transformers hinges upon critical operations such as matrix multiplications, which transform vectors and amalgamate information, weighted sums for refined updates, and dot products that measure the alignment between vectors—a fundamental aspect of the attention mechanism.

Training LLMs revolves around fine-tuning their parameters or weights to optimize predictions through data-driven learning, employing backpropagation to adjust weights based on prediction errors. This intricate process allows LLMs to encode contextual meanings effectively, enhancing their capacity to anticipate subsequent text.

The performance and behavior of LLMs are shaped by various components and parameters, including weights that determine data processing, an embedding matrix that correlates tokens with vectors, and context and vocabulary sizes that define the model's capacity to handle and recognize distinct tokens. Each of these elements contributes to the remarkable ability of LLMs to generate text that closely mirrors human language patterns.

Cost and Pricing Structure of LLMs

The LLM landscape is shaped not just by the strong capabilities and varied applications, but also by the significant costs associated with the usage of LLMs. Any person wanting to deploy Large Language Models needs an understanding of their pricing and cost structures for running such models. Their costs can radically differ based on the LLM type being engaged, the form of deployment intended, and required infrastructure.

According to the pricing models, there are mainly two kinds of LLMs: managed LLMs and open-source LLMs. Managed LLMs, run by providers OpenAI and Anthropic, generally have token consumption in their pricing. Tokens are units of text and may consist of one character, parts of a word, or even full words that these models operate on. These are influenced by factors such as model size, input context length, and the capability of the model. It also charges for both input and output tokens, meaning the complexity and length of queries matter much in costing. For instance, OpenAI has some of its models for which it charges: GPT-4 and GPT-3.5. Prices differ depending on the token count. Similarly, Anthropic uses token-based pricing for the models of Claude.

The open-source models, such as Llama2, Llama3, and Mistral, are free to use per query. However, a user would be in charge of infrastructure-related costs, including hardware like GPUs and cloud computing resources. Larger models will require serious resources to run-for example, the model Llama3-70B requires 160GB of memory and eight GPUs to operate, but Mistral-7B-v0.3 is more modest and can function on a single GPU with only 24GB of memory.

Regarding the cost elements of managed LLMs, token pricing is the most important factor. While the managed providers charge for the number of input and output tokens processed, the costs are still hooked on the model complexities. Variable costs will vary with the length and complexity of input prompts and generated outputs. Longer and more complex requests consume more tokens, which raise the expenses.

In the case of open-source LLMs, costs can be broadly divided into infrastructure and compute costs. Since hosting these models requires high memory and GPU resources, monthly costs could run into the thousands, especially for larger models. While spot instances may reduce costs, they may not be suitable for applications requiring high availability. Models with more limited power, such as Mistral-7B, can run more affordably while others with far higher capability and more ambition in this arena-Llama3-70B for one-cost considerable resource investments to even process and answer requests. Scale appropriately; based on user volume, site load, there might also be increased, especially latent traffic loads.

Several factors contribute to the overall costs of LLMs. For instance, token size is critical; longer input prompts and generated outputs consume more tokens, thereby impacting costs. Latency is another factor, as applications requiring rapid responses may necessitate more advanced and, therefore, more costly hardware. The complexity of the model heavily influences the computational resources required, as larger models with more parameters demand significantly more power. Issues around prompt consistency can complicate cost estimation and resource allocation, and organizations with stringent data security measures may choose self-hosted LLMs, incurring additional infrastructure costs. The quest for accuracy in model output can result in the need for even more resource-intensive setups, and the scalability of the service can have a significant effect on both managed and open-source deployments.

Cost estimation for managed LLMs involves calculating the number of input and output tokens processed per request, which is then multiplied by the frequency of those requests and the cost per million tokens applicable to the selected model. In the case of open-source deployments, estimation encompasses hardware requirements for hosting the model, considerations for latency, throughput, and concurrency to effectively scale the service, as well as additional costs related to data storage, preprocessing, and optimization.

Not to mention, there are more fees involved. For instance, RAG frameworks, which support external data sources, may further add costs to host or access these sources. The adoption of additional support tools-sentiment analysis, security mechanisms, or gateways for dealing with a fleet of multiple LLMs-are bound to drive the costs up. Hardware itself remains one of the major costs. High-power GPUs, such as those from Nvidia, dominate AI infrastructure spending. Data centers are by no means inexpensive, seeing as building them often exceeds more than $10-15 million per megawatt, not taking into account power usage, constituting about 90% of their operational cost.

Finally, the high computational requirements for running LLMs cannot be understated. Training large models often requires weeks or even months of processing time on thousands of GPUs, leading to expenses that can reach millions of dollars. While less expensive in comparison, the inference-actually creating the outputs-is still substantial in accumulation, and hence careful planning and budgeting are required for anyone involved in their deployment.

Cost Curves and Economics of LLMs

The rapid declines in the cost of AI inferences, notably the ones featuring large language models such as GPT-4, represent the technological inflection point. Historically, the cost of access to LLMs has decreased extremely rapidly, or rather more precisely, faster than historical rates of decrease evidenced in computing and networking. For instance, the cost of accessing GPT-4 equivalent intelligence has decreased from $180 to less than $1 per million tokens within just 18 months, which shows a striking 240-fold decline. Moreover, in three years, the cost of models reaching an MMLU score of 42 came down by a factor of 1,000. Arguably more impressively, models scoring higher-such as those reaching an MMLU score of 83-have seen costs reduced 62 times in the few months since early 2023.

This precipitous drop in the cost of inference is a continuation of history, with other trends such as Moore's Law, which predicted exponential growth in computational power, and Edholm's Law, a document on the rapid enhancement in data bandwidth. But the velocity of cost reduction for AI is unprecedentedly rapid.

The number of reasons that surround the LLM for dramatic decreases in these costs include improvements in the core technologies, market trends, GPU performance, increased efficiency from more advanced architecture of processors, power and enhanced design in particular of the Nvidia GPU designs accelerating computation with declining costs.

Model quantization also contributes significantly to this development. Quantization increases computational speed and reduces memory consumption by reducing model weight precision from 16-bit to 4-bit, at some cost in terms of reduced accuracy. This, however, saves so much that it becomes irresistible; this practice indeed will become mainstream with the arrival of next-gen GPUs like the Nvidia Blackwell series.

However, in reality, a raft of software optimizations has largely smoothed over such performance bottlenecks to much better utilization of the processor and memory bandwidth. These improvements reduce operations costs when running large LLMs with no additional investment in hardware.

The landscape of AI has, therefore, changed with recent smaller and more efficient models. Today's 1-billion-parameter models often outperform their predecessors, like the 175-billion-parameter models from just a few years ago, because of improved scaling laws that guide the balance between model size and dataset size.

Reinforcing the learning techniques in itself, such as RLHF (Reinforcement Learning from Human Feedback) and DPO, have brought improvements within fine-tuning phases with improved model performance and without necessarily large model sizes. More open-source options continue to join the fray with competition heating up, especially those provided by the likes of Meta and Mistral, offering numerous cost-effective options. Free licensing agreements, such as the community license for Llama, further open the technology to smaller organizations that otherwise would not have access to advanced AI technologies due to proprietary systems.

Other on-device models run right at the device, for instance on laptops or even smartphones. Indeed, the combination of better-powered devices and increasingly miniaturized models means on-device AI provides natural, cost-effective ways in which several needs are served each day without using massive centralized cloud-based resources.

The implications of such a trend in LLM inference costs are immense and far-reaching. Businesses can now embed these powerful models in their operations without the burden of prohibitive expenses, thus allowing applications that were previously unfeasible, such as personalized AI assistants, real-time data analysis, and tailored content generation.

Furthermore, the changing market conditions exhibit a two-way division in the AI terrain. High-value applications are served by premium models, while open-source models dominate low-cost, mass-market use. In this regard, OpenAI has managed to keep pricing for its "o1" model closer to GPT-3's launch price, focusing on keeping quality intact.

Although the trend seems impressive today, not everyone thinks this level of pace will continue; with specific gains having their eventual limit, such things as quantization and software optimisation can become saturated in that their potential will become fully utilized, and thus a smaller model might also be developed when further scaling gives reduced benefits.

Despite these challenges, this general trend of costs continuing to fall will most likely persist, driven by ongoing innovation and fierce competition. As on-device models become more common, architectures improve, and open-source technologies proliferate, the democratization of AI will continue to make state-of-the-art advancements accessible to an even greater number of people.

Generative AI Value Chain & Technologies

The Generative AI stack represents a very complex framework that is made up of four layers, each interdependent on others for the development, deployment, and usage of large language models, among other AI applications. These would involve Foundation, Model, Tooling, and Application-all working in conjunction to support the creation of generative AI solutions while addressing challenges related to infrastructure, energy consumption, and sustainability.

The base is the Foundation Layer, which forms the backbone of AI infrastructure. This layer includes all the hardware and services that power generative AI: powerful GPUs, data centers, and cloud services. Nvidia dominates this space with an impressive 80% market share, largely due to its advanced GPU models optimized for AI workloads. While AI rests on data centers, increasingly, it also represents a growing challenge for energy efficiency and sustainability. In contrast, large cloud providers such as AWS, Azure, and Google Cloud are rapidly integrating AI into their offerings to capture enterprise business moving to the cloud for AI infrastructure. At the same time, a new wave of startups, such as CoreWeave and Lambda Labs, democratizes access to high-performance computing, letting smaller AI startups thrive by providing access to affordable GPU resources. Innovations also emanate from companies like Baseten and Modal, which are developing serverless environments to scale AI models with much more ease, thus removing obstacles to innovation.

Next is the Model Layer, which is the home of the foundational AI models powering generative AI applications. The model layer consists of LLMs, GANs, or Generative Adversarial Networks, and diffusion models, each serving as the computational engine for various applications. Recent breakthroughs in model efficiency, including RL-AIF and SMoE techniques, are improving performance while reducing computational costs. Localized LLMs, such as those by Wiz AI, address diverse regional markets by supporting a range of languages, from Hindi to Bahasa Indonesia, in an effort to help AI reach underrepresented populations. Their success heavily relies on the strong, preprocessed data underneath, facilitated by data pipelines, labeling services, vector databases, metadata management-end to end data ecosystem that is required to train or fine-tune such models.

Tooling Layer represents the translation of development and deployment into working mechanisms, backed by tools and frameworks for efficiently dealing with models. Key functionalities here include model hubs, where platforms like Hugging Face offer repositories for sharing and collaborating on models. Monitoring tools make sure that models act as they should, with companies like Calypso AI and Credal focusing on security, governance, and explainability. Furthermore, prompt optimization is a must; firms like PromptLayer and LangSmith refine prompts to extend the capabilities of LLMs. Governance and observability are of particular importance in this layer, as it will make sure that AI models adhere to regulatory standards and their decisions are understandable.

At the top of it all sits the Application Layer, dedicated to the delivery of generative AI capabilities through industry-specific applications to end-users. This will include not only vertical SaaS solutions tailor-made for areas like health care, financials, and education but also horizontal platforms built around embedding AI in the most everyday work processes. These further enhance productivity and creativity at all levels; hence, this class enables smooth performance, better decision-making, and unparalleled user experiences, from real-time customer support to personalized content generation.

Advancements in AI architecture, including transformer models, state-space models, and small mixtures of experts, are improving deployment efficiency. Quantum computing research by IBM and Google holds promise for faster training and solving complex problems. At the same time, on-device models are enabling localized AI processing, with companies like Apple and Qualcomm leading the charge in edge hardware and energy-efficient computing. Techniques such as model quantization and improved memory bandwidth further enhance efficiency, alongside renewable energy integration at data centers.

Generative AI technologies, including diffusion models and GANs, are advancing image generation and synthetic data creation. To mitigate environmental impact, companies are adopting sustainable practices, such as energy-efficient designs, innovative cooling technologies, and emerging carbon capture solutions.

The compute market, long dominated by Nvidia, is diversifying with competitors like AMD and startups developing specialized AI inference architectures. Networking technologies, essential for seamless data flow, are advancing through innovations like Nvidia's InfiniBand and Arista Networks' Ethernet solutions. As AI models grow in complexity, these high-performance networks play a crucial role in avoiding bottlenecks.

AI inference, the process of using models for predictions, is now outpacing training workloads. Companies are deploying inference solutions at the edge, reducing latency and expanding AI's integration into real-time applications. Apple, Qualcomm, and Intel are leading this decentralized approach, optimizing AI capabilities for everyday use.

Generative AI and Data Centers

Data centers running AI applications already use 1-2% of all power consumed globally, a number which will increase to 3-4% by 2030. The power consumed by generating responses from models such as ChatGPT by a factor is cause for alarm as carbon emissions continue to rise. In addition, the need for sustainable solutions regarding the growing carbon footprint of AI operations opens avenues toward renewable energy adoption and advanced cooling systems that will lessen environmental impacts.

Further, most data center expansions put a lot of stress on the local power grids and water resources, resulting in communal resistance in some areas. Financial viability is a major challenge: several generative AI companies are yet to find a way out of high infrastructure costs and model training expenses. For instance, OpenAI, though leading in the industry, has shown considerable financial losses. As the call for sustainability increases, companies are finding creative ways to balance growth with environmental care, and the way ahead is not straightforward.

AI data centers have become key facilities in supporting the increasing workloads of artificial intelligence, which requires much higher processing power and energy use compared to traditional computing tasks. This is the justification for the sudden rise in construction and expansion projects for data centers around the world. With AI demand still growing, data centers are being developed to address the singular challenges inherent in high-density, power-intensive operations.

The global data center market is well-positioned for high growth in 2025, with strong demand for AI infrastructure and the associated power requirements. Large hyperscalers are taking a balanced approach to creating self-built facilities and engaging with third-party providers to meet growing demand: AWS by Amazon, Microsoft Azure, and Google Cloud. The advent of generative AI and large language models puts extreme pressure on the existing grid for exponential capacity addition at a fast speed. New market participants-investors through energy companies-emerge to play a crucial role in the changing market dynamics with radical solutions and path-breaking innovations to answer the growing demands for innovation.

Regionally, North America is leading demand for AI data centers. The trend is toward larger builds, including gigawatt-scale facilities, with site selection favoring areas offering abundant power supplies-in particular, the central U.S., where wind resources are greater. Natural gas is also becoming a major source of power, supplemented by other newer ones such as hydrogen and small modular reactors.

It would also be helpful to remember that data centers also play a significant role in this revolution, acting like the backbone to these compute, networking, and storage systems engaged in AI workloads. Generally, there have been two fundamental sorts of data centers: Hyperscale Data Centers are large, generally cloud-provided data centers that are tuned for high-scale performance; Edge data centers are generally small centers, sited very close to a user for latency-sensitive applications in vehicles and home automation.

In Europe, AI deployments are well on the increase, with the Nordic region being particularly favored due to lower energy costs and access to renewable sources. It said data centers in this region are upgrading to Tier 3 infrastructure to support the higher power and reliability demands of AI workloads. However, with building and material costs on the rise, driving up prices, new concepts such as "data center sovereignty" are being put forward-these would be embassy-like arrangements designed to cut costs.

The Latin American subcontinent is building off the core capabilities of Brazil, which is expected to achieve an incredible 97% capacity for renewable energy before the end of the coming year. Even though import tariffs remain a hindrance, their rollback can help AI capacities grow very fast in this region. Most of the data centers are moving beyond metropolitan locations due to lesser ground coverages and power supplies.

AI data centers are in a league of their own when it comes to their demands for energy consumption and power density. Indeed, a single query to ChatGPT uses nearly ten times the amount of electricity required by a typical Google search. Data center electricity demand will more than double between 2022 and 2026, with AI workloads driving much of the increase. Projections say this could be 3-4% of the power consumption in the world by 2030. Such demands require very high power density per rack, and AI servers often resort to direct-to-chip liquid cooling and special equipment for efficiency maintenance.

AI data centers have to be located where power is ample and cheap for maximum efficiency. Some of the hotspots are central parts of the United States, vast swaths of Latin America, and pockets in Europe with access to renewable energy sources and lower electricity tariffs. Developers often build their own energy solutions as a way of mitigating dependence on local grids, which usually face capacity constraints.

The environmental impacts of AI data centers are of growing importance. It already comprises more than 1% of the total global consumption of electricity, and by 2030, this sector is expected to contribute even more significantly. Water consumption is also put under urgent consideration in such a way that these facilities may use over one trillion gallons of freshwater each year by 2027. By 2030, the CO2 released from the global data center industry is expected to be approximately 2.5 billion tons. This shows how badly sustainability measures are needed.

The high capital expenditures that involve land acquisition to establish AI data centers, construction, and energy solutions financially make profitability hard to achieve. The high demand and low supply are likely to increase lease pricing, further straining operational expenses with increased construction and material costs.

These challenges are encouraging the way toward more effective operation with technological advances and innovation. Other innovative features include the two-phase cooling and metal foam for energy usage decrease while increasing the power density of the servers. Among the options, direct to chip liquid cooling has become an increasingly favored technique to manage artificially intelligent servers high power densities.

Eventually, and with everything pertaining to energy solutions, it would appear that renewables like wind, solar, and hydro are indeed finding their ways into the operations of data centers, with regions like Latin America leading in that respect. Hydrogen-powered data centers and SMRs are in trial stages; however, the issue of scalability is not yet resolved.

With this in mind, data centers have invested heavily in connectivity infrastructure to ensure seamless operations; proposals for gigawatt-scale projects are moving into view to meet long-term demands created by AI development and deployment. The geographic spread in demand for data centers is changing relentlessly, matching the dynamic landscape of AI-driven innovations.

Generative AI and Energy Consumption

Artificial Intelligence is rapidly transforming industries, driving technological advancements while escalating energy demands. The shift from conventional to AI-driven computation has significantly increased pressure on global energy resources, raising urgent questions about sustainability, ecological impact, and scalability. 

AI applications, particularly those powered by large language models, are especially energy-intensive. For instance, a single query to ChatGPT consumes approximately ten times the electricity of a typical Google search. This energy demand is projected to grow exponentially, with data center power consumption expected to surge by 160% by 2030 due to increasing AI workloads. Currently, data centers account for 1-2% of global electricity consumption, a figure that could reach 3-4% by the decade's end. 

The computational power required for training AI models doubles approximately every nine months, driving a relentless increase in energy usage. This "insatiable appetite" has spurred a boom in data center construction. The International Energy Agency predicts global electricity demand for data centers will more than double between 2022 and 2026, with AI as the primary growth driver. This demand comes at a high environmental cost: data centers are projected to emit 2.5 billion tons of CO2 annually by 2030 and strain water resources, with cooling systems alone accounting for 40% of their electricity usage in the US. 

Local power grids are struggling to cope with these demands. Regions hosting large clusters of data centers face heightened risks of grid strain and brownouts. While gains in efficiency temporarily mitigated these issues, the rapid expansion of AI workloads is reversing that trend. Notably, the geographical spread of AI-supportive infrastructure is uneven. Countries like Saudi Arabia and Brazil are scaling renewable energy-powered data centers, while Europe faces the challenge of upgrading an aging power grid, requiring an estimated €1.65 trillion in investments to support AI growth. 

In response, technology companies and utilities are investing nearly $1 trillion in AI infrastructure and renewable energy, including building new data centers and upgrading power grids. Efforts to mitigate AI's environmental impact are focusing on alternative energy sources such as solar, wind, hydrogen, and small modular reactors (SMRs). SMRs, in particular, offer promising potential with their ability to provide stable, low-cost, zero-emission energy for 24/7 operations. Cloud hyperscalers like AWS and Microsoft are already partnering with nuclear power providers for long-term clean energy contracts. 

Natural gas is also emerging as a transitional energy source, offering cost-effective and flexible solutions for data centers. Gas-fired power plants can be deployed quickly and efficiently, supporting hybrid energy models that integrate renewables. Cogeneration systems using natural gas further enhance efficiency by capturing waste heat for additional uses. 

Under Trump’s strategy, the US appears poised to prioritize low-cost energy sources, including natural gas, to accelerate its AI development and outpace global competitors. This approach underscores the urgent need for faster infrastructural evolution to meet the energy challenges associated with AI growth. From nuclear power to natural gas, the future of AI infrastructure will hinge on balancing technological demands with sustainability considerations, shaping the trajectory of this transformative era.

Generative AI & Capital Deployment

The financial health of tech companies has demonstrated remarkable resilience and growth since the COVID era, becoming a cornerstone of the AI revolution. U.S. companies alone have seen their cash reserves grow from $2.8 trillion in 2010 to an impressive $3.7 trillion by 2024. This substantial liquidity highlights the strength of corporate balance sheets and their ability to deploy diverse financial strategies. These reserves provide a crucial buffer and serve as a foundation for investments in cutting-edge technology and infrastructure, enabling businesses to stay competitive in the rapidly evolving AI landscape.

Major tech firms like Apple, Microsoft, Amazon, Alphabet, Tesla, Nvidia, and Meta are heavily investing in AI, with spending projected to surpass $275 billion by 2025. These investments focus on data center expansion, GPU procurement, custom silicon development, and advancing AI research.

Another critical driver of AI's growth is the vitality of global equity markets, which collectively hold valuations exceeding $120 trillion. These markets serve as vast reservoirs of funding for mergers, acquisitions, and other strategic initiatives. The United States leads with a market capitalization of $51.4 trillion, followed by China at $10.8 trillion, and other developed economies. Additionally, the U.S. corporate credit market has more than doubled since the Global Financial Crisis, reaching $11 trillion in 2024. This surge reflects the growing reliance on debt markets to finance transformative projects. Notably, 13% of investment-grade bonds issued in 2024—valued at $107 billion—were allocated to M&A activity, underscoring the vibrant and dynamic investment climate.

Sovereign Wealth Funds (SWFs) have emerged as significant players in the global investment arena. The ten largest SWFs collectively manage $12 trillion in assets, led by the Norwegian Government Pension Fund Global with $1.634 trillion under management. Resource-rich GCC (Gulf Cooperation Council) funds are among the most active participants in cross-border M&A, often collaborating with private equity and venture capital firms to amplify their reach and impact in areas of AI.

Private markets have also proven to be a resilient and thriving channel for capital allocation. Since 2013, private markets have grown at an annual rate of 14%, with assets under management reaching $13.1 trillion by mid-2023. Private equity funds in the United States alone raised $77 billion in the first quarter of 2024, continuing a trend of record-breaking fundraising. The private equity sector now holds $2.6 trillion in "dry powder," or unallocated capital, signaling immense potential for future investments. Similarly, venture capital dry powder stands at $296 billion, further fueling innovation and growth in AI and adjacent industries.

The rapid expansion of the technology landscape has positioned artificial intelligence at the forefront of a new frontier in infrastructure investment. This shift has given rise to an emerging asset class: AI infrastructure. Comprising data centers, power grids, and advanced networking solutions, the global cost of building AI infrastructure is projected to reach $900 billion over the next decade. In the United States alone, estimates suggest an investment of up to $1 trillion by 2030, highlighting the monumental scale of this transformation.

The energy demands of AI workloads are equally staggering. By 2029, an additional 128 gigawatts of electricity capacity will be required in the United States to support AI activities. This surge in energy consumption underscores the urgent need for innovative and sustainable energy solutions to power the next generation of AI applications.

Data centers are at the heart of this infrastructure revolution. These facilities act as the nerve centers for computing, networking, and storage, enabling the high-intensity workloads required by advanced AI models. Hyperscale data centers, operated by tech giants like Amazon, Google, and Microsoft, are optimized for training and deploying large-scale AI systems. Meanwhile, edge data centers, strategically located closer to end-users, are facilitating real-time applications by reducing latency and supporting decentralized processing.

The investment in AI infrastructure extends beyond data centers to encompass critical advancements in power generation and networking. Renewable energy sources, hydrogen power, and small modular reactors (SMRs) are being explored to meet the escalating energy demands sustainably. High-performance networking technologies, such as Nvidia's InfiniBand and Arista Networks' Ethernet solutions, are ensuring seamless data transfer across AI ecosystems.

The financial commitment to AI infrastructure is further underscored by major partnerships, such as the $30 billion initiative by BlackRock and Global Infrastructure Partners and the $50 billion collaboration between KKR and Energy Capital Partners and recently the $500 billion Stargate project. These investments are not only reshaping the physical and technological landscape but also setting the stage for the projected $20 trillion economic impact of AI by 2030.

The Geopolitics of AI

When the internet first appeared, few countries and politicians truly predicted how it would manifest itself with societal transformation. Over the last 30 years, Big Tech has risen meteorically, primarily in the United States. This has reshaped not only the global economy but also politics, culture, and national security. Silicon Valley, the epicenter of this transformation, has become to the modern world what Rome once was to the ancient one—a hub of infrastructure which dictates the rules of engagement for a global empire.

The Roman road network in ancient Rome was more than physical infrastructure; it was also a tool of power, which allowed Rome to move troops, administer its provinces, and-what's crucial-collect taxes from the most distant parts of its territory. It was through the roads that the distant regions were connected to the capital, and it was through them that tributes and produce flowed consistently.

Similarly, the digital infrastructure of Silicon Valley-Amazon, Google, and Microsoft-provides today's Roman roads. This too has become an infrastructural foundation on which an immense number of businesses in the world today have been built: A Bangladeshi merchant selling products on Amazon, an Ethiopian company placing advertisements on Google and a Singapore-based company running its applications over AWS or Microsoft Azure. Like Roman provinces paying their tribute to the empire, these businesses pay their due in the form of fees, commissions, or revenue shares to Silicon Valley companies.

Silicon Valley has captured a disproportionate share of the value created by the internet revolution. Companies like Amazon, Google, Apple, and Facebook have built ecosystems that are not only indispensable but also near-monopolistic. This was made possible through technological innovation and beating national governments and global institutions at understanding and exploiting the potential of the internet.

Before nations could even make sense of what this new technology would mean to them or develop their own digital infrastructure, the game's rules were set by Silicon Valley. From search engines to cloud computing, from social media to e-commerce, platforms coming out of Silicon Valley set a worldwide standard. This is where it has led many countries to depend on American tech giants for their needs in the digital sphere, rather than the fostering of local industries and even the very sovereignty of those countries.

We stand at an epochal juncture-a moment when our world is abutting a radical technological shift, a second big turning of our time: the AI revolution. The first turning was exponential growth of the Internet; it had cemented the United States' position as an undisputed leader in technology. It was powered through a buzzy innovation ecosystem, oodles of venture capital, and a network of companies laying the ground for what would become Big Tech. In this, the AI era, it would appear that the U.S. once again has created a wedge for itself from its global competitors.

This supremacy of America in AI is not an accident but rather a consequence of the Matthew Effect-the principle that the rich and successful are going to gather more wealth and success. And that created a self-reinforcing cycle within the American tech industry. The U.S. features unparalleled infrastructure, with the most advanced cloud computing capabilities. This infrastructure allows companies to train and deploy large-scale AI models at a speed and efficiency that few competitors in other countries can match. American firms lead in key technologies, such as GPUs, with Nvidia in the lead, and lead in cloud platforms like AWS, Azure, and Google Cloud. They have also pioneered foundational models, including advanced language models like GPT.

Innovation flows from the U.S. in torrents, supported by a dense ecosystem superior to others in research output, patent filings, and the development of pioneering AI technologies. Prestigious universities, including Stanford, MIT, and Carnegie Mellon, have been incubating AI research into the best talent that feeds further improvements continuously.

In this narrative, financing also plays a big role. For no other country can compete with the American venture capital landscape, which was providing funding in staggering amounts to AI startups, often exceeding the GDP of smaller countries. Companies like OpenAI, Anthropic, and Cohere attracted billions in investments supported both by the U.S. government and by private sector backers who want to enhance their AI capabilities.

The density of talent, technology, and capital in Silicon Valley exerts powerful network effects. The more data US-based companies process, the better their AI algorithms perform-which makes their advantage appear self-reinforcing.

The gap between the U.S. and the rest of the world in AI is stark; even compared to combined Europe, the difference in magnitude is by a factor of ten, and for many nations, let alone catching up, having some sort of balance feels like an insurmountable challenge. This raises an urgent question: where does this leave other nations in the grand scheme of things?

With the election of Donald Trump by the American people, the signal was clear for a strategic reorientation toward an "America First" approach, placing national concerns at the center and readjusting America's role on the world stage. In considering Trump's approach, he wants to make the U.S. stronger internally while reassessing the nation's commitments globally. This was demonstrated in his persuading allies to shoulder more of the financial and strategic burden of alliances that American taxpayers had long subsidized.

The United States has moved from an international enforcer responsible for the maintenance of world trade, security, and governance to a selective orchestrator of world order. This transactional approach expects countries desiring U.S. support to provide specific quid pro quo incentives-for instance, investments, lower taxation, or strategic concessions.

The policy of the United States has turned inward, in contrast to the global ambitions of its technology sector. Companies from Silicon Valley, which dominates the world's digital infrastructure, continue to expand their reach internationally, creating a paradoxical situation. While the U.S. government adopts a more restrained and transactional approach to globalization, its technology giants remain deeply embedded in global supply chains and markets.

This divergence creates many problems. For one thing, trade and taxation problems abound. Countries whose companies and state administrations rely on U.S.-based tech infrastructure will increasingly clash with the U.S. over issues like data sovereignty, tax payments, and local self-determination.

Besides, there are important security considerations. The reliance on American-made AI and cloud systems leaves other nations vulnerable to geopolitical shifts and economic pressures, as the control of critical infrastructure remains firmly in American hands.

Moreover, there are crucial security concerns. The reliance on American-made AI and cloud systems leaves other nations vulnerable to geopolitical shifts and economic pressures, as the control of critical infrastructure remains firmly in American hands.

Culturally, these dynamics pose additional challenges. Generative AI systems, which are predominantly trained on Western datasets, inherently carry a Western bias. This could result in conflicts with local cultures, traditions, and societal values, further complicating the relationship between technology and global diversity.

The competition for generative AI supremacy is marked by two primary aspects: the development of cutting-edge technologies and their widespread adoption across various sectors. In the United States, the landscape is characterized by notable strengths. The U.S. leads in foundational technologies such as graphics processing units (GPUs), with companies like Nvidia at the forefront, as well as in cloud computing through major players like Amazon Web Services, Microsoft Azure, and Google Cloud. Furthermore, American firms have established dominance in the realm of large-scale language models, exemplified by innovations like GPT and its competitors. The robust venture capital investments in the U.S. equip these companies with the necessary resources to maintain a competitive edge in technological innovation.

The federal government is also playing a crucial role by increasing its budget focused on artificial intelligence, which supports sustained growth in critical areas such as defense, cybersecurity, and healthcare. However, American companies face challenges, particularly due to high input costs that could hinder their ability to compete globally, especially against low-cost firms from China.

On the other hand, China boasts its own array of strengths in the AI arena. The country leads in the number of AI patent applications, a feat driven by a strategic, state-backed approach that emphasizes subsidies, domestic innovation, and targeted investments. Major companies like Baidu, Alibaba, and Tencent are at the forefront of AI adoption, making significant strides across various sectors, including healthcare, IT infrastructure, and social media platforms. Moreover, China has successfully established a self-reliant tech infrastructure, which minimizes its dependency on foreign systems. This independence enhances its position as a formidable rival to the U.S.-led AI ecosystems.

Nonetheless, China faces its own set of challenges. Although governmental support for AI is robust, its global adoption is met with skepticism in regions that are wary of Beijing's increasing influence. This complex landscape of strengths and challenges continues to shape the ongoing rivalry between the U.S. and China in the realm of generative AI.

As generative AI reshapes global dynamics, a clear division is forming between two distinct ecosystems: the American and the Chinese. On one side, we have the American ecosystem, which comprises wealthier nations with advanced economies. These countries are aligning themselves with AI systems led by the United States, benefiting from cutting-edge technology that promises to enhance their capabilities. However, this alignment does not come without its challenges. As they adopt these sophisticated systems, they may grapple with issues surrounding cost and control, as the dynamics of reliance on U.S. technology become increasingly complex.

In contrast, the Chinese AI ecosystem will be drawing in developing nations, particularly those in Asia, Africa, and parts of Latin America. These countries are attracted to the affordability and accessibility of Chinese AI infrastructure, which often presents a more manageable path for technological advancement.

Generative AI, by its nature, is shaped by the data it is trained on. Western-trained AI or Chinese trained AI models will carry biases, reflecting the cultural, linguistic, and societal norms of the datasets they use. This creates unique challenges for nations that rely on these models but wish to preserve their own cultural identity.

In an age characterized by geopolitical uncertainty, the concept of technological sovereignty has emerged as a vital component of national security and economic resilience. To mitigate reliance on foreign artificial intelligence systems while safeguarding cultural integrity, countries may consider implementing a two-layered framework for artificial intelligence. This strategy combines foundational AI models rooted in local contexts with a dynamic ecosystem of applications designed to meet domestic needs.

The first layer focuses on developing localized models that embody a nation’s unique identity. A key aspect of this foundational layer is the creation of proprietary datasets that ensure cultural fidelity. To achieve this, nations would gather data from domestic sources such as government archives, historical texts, local media, literature, and multilingual content. By doing so, AI models can better reflect the linguistic diversity, social norms, and ethical frameworks intrinsic to each country. For instance, a Southeast Asian nation might train its large language model on indigenous languages and folklore that are often overlooked by global models like GPT-4 or Gemini.

Another crucial element is context-aware reasoning, where the models prioritize local problem-solving. This might involve interpreting legal systems influenced by customary law or addressing region-specific challenges in agriculture or healthcare. An example could include an African language model optimized for predicting crop yields by incorporating hyperlocal climate data alongside traditional farming practices that larger global agritech platforms might neglect.

To effectively implement this localized model development, it is essential to mitigate cultural bias. By relying less on foreign datasets, nations can lessen the risks of AI systems adopting biases that clash with local values, such as the differences between Western individualism and collectivist perspectives. For example, a Middle Eastern model trained with regional discourse would have a more nuanced understanding of gender roles, religious contexts, and family structures, rather than defaulting to norms imported from other cultures.

Equally important is the investment in infrastructure and talent development. Governments could support the establishment of domestic computing infrastructures, such as sovereign cloud clusters, and cultivate AI talent through universities and public-private partnerships. The "Bhashini" initiative in India, which aims to create large language models for its 22 official languages while enhancing the skills of local developers, serves as a notable example of this approach.

 The second layer of this framework focuses on fostering an ecosystem of AI applications. This layer empowers businesses, startups, and developers to create specialized AI services based on the foundational models. It encourages innovation while ensuring that these developments align with national priorities. Businesses can leverage the base large language models to devise industry-specific solutions in sectors such as healthcare, education, governance, and defense. For instance, a fintech startup in South Korea might design AI-driven financial advisors tailored to the country’s high savings rate and pension systems.

Additionally, governments can establish regulatory sandboxes to provide frameworks that support ethical AI deployment, balancing innovation with protections against misuse, including regulations on deepfakes and data privacy. Localized versions of existing frameworks, such as the EU’s AI Act, can ensure that new applications adhere to cultural sensitivities and democratic values.

The framework also promotes economic democratization by giving local developers access to state-supported APIs and tools. This reduces dependence on foreign technology giants, consequently boosting homegrown entrepreneurship and job creation. For example, startups in Indonesia could develop AI tutors for rural schools by utilizing a government-funded large language model proficient in Bahasa and regional dialects.

Moreover, sovereign AI systems designed for specific regional challenges could grow into global niche exports, further enhancing a nation’s geopolitical influence. For instance, climate resilience models developed for Pacific Island nations could find international markets.

However, while this two-layered approach offers numerous advantages, it also presents various challenges. Smaller nations may struggle with data scarcity, necessitating collaborations among regional partners, such as ASEAN nations pooling resources. Additionally, the costs associated with building large language models can be significant, although modular and open-source frameworks can help mitigate these barriers.

Lastly, while striving for self-reliance, nations must ensure that their sovereign AI systems can still interact with international systems. This will require establishing standards for cross-border data flows and model compatibility.

Ultimately, this two-layered AI strategy allows nations to strike a balance between self-sufficiency and global engagement. By anchoring AI development in local contexts, countries can maintain cultural autonomy while creating a competitive edge in strategic sectors. The applications layer ensures that AI acts as a democratized tool for economic growth rather than merely a centralized asset. Over time, this approach could redefine global AI governance, shifting power from a small number of tech monopolies to a more multipolar ecosystem composed of sovereign yet interconnected systems.

The pursuit of sovereign AI is not constrained by a nation’s energy resources or technological limitations; rather, it presents an opportunity to catalyze innovative geopolitical partnerships and to redefine traditional notions of infrastructure ownership. By expanding the concept of sovereign AI to encompass energy-resource alliances and diplomatically protected data hubs, countries can overcome resource limitations while maintaining control over their digital futures.

One fundamental aspect of this vision is the recognition of energy as a geopolitical currency for sovereign AI. Many nations face challenges in establishing the necessary energy infrastructure to power AI data centers, which require vast amounts of electricity. In contrast, resource-rich countries such as Saudi Arabia, Norway, and Chile could position themselves as AI energy partners. They could offer renewable or fossil-fuel-powered computing capacity in exchange for strategic advantages.

For instance, Saudi Arabia, with its abundant solar energy and sovereign wealth funds, could host massive data centers, referred to as "AI oases," powered by solar farms. These facilities could be established through international treaties and operate under a shared sovereignty model. The host nation would provide land, energy, and physical security, while client nations—such as smaller states in Africa or Asia—would retain legal ownership of their data and algorithms. In this model, servers could be granted diplomatic immunity similar to that of embassies, ensuring protection from local surveillance or seizure. Revenue-sharing agreements would allow host countries to benefit from energy exports while providing client nations with autonomy away from foreign tech giants.

Similarly, Nordic countries like Iceland or Norway, with their surplus geothermal and hydropower, could create energy-efficient data centers for AI training, serving EU nations that seek to reduce their reliance on U.S. or Chinese cloud providers.

To formalize collaborations, nations could establish AI Infrastructure Treaties, which would blend principles of energy diplomacy, data sovereignty, and mutual defense. These treaties could include provisions such as granting diplomatic immunity for data, thereby treating servers hosted in partner countries as the sovereign territory of the client nation. Resource-poor yet technologically adept nations, like India or South Korea, could exchange AI software expertise for discounted energy access. Additionally, collective security measures could be implemented, much like NATO’s Article 5, to protect shared infrastructure from cyber threats.

An illustrative case could be Saudi Arabia’s NEOM megacity, which might host a neutral AI zone where nations can lease server clusters powered by a $5 billion green hydrogen plant. Participating countries, such as Egypt, Pakistan, and Singapore, would co-govern the hub through a treaty organization, ensuring that no single entity dominates decision-making.

This model not only fosters regional collaboration but also promotes equity by transforming AI infrastructure into a vehicle for cooperation and integration. For example, Southeast Asian nations could pool their resources to build distributed data centers across the Malay Archipelago, connected by undersea cables. Indonesia’s geothermal energy, Malaysia’s semiconductor expertise, and Singapore’s capital could unite to create a shared AI backbone. In Africa, solar-rich Sahel nations like Niger and Chad could host data centers for Francophone Africa, while Kenya’s geothermal resources could support AI needs in anglophone East Africa.

Ultimately, this revised understanding of sovereignty in the AI age decouples it from physical infrastructure. A nation’s AI systems could be hosted globally while remaining legally and culturally autonomous, akin to international waters. For example, the Maldives could operate its language models on wind-powered servers in Scotland, ensuring its linguistic and climate resilience models continue to thrive even if the islands are submerged. Indigenous nations, like the Navajo Nation, might host their AI systems in Canadian hydro-powered data centers under treaties that recognize their digital self-determination. By treating energy and computing capacity as shared global resources, nations can reconfigure the landscape of AI, facilitating equitable access and fostering sustainable partnerships, ultimately moving towards a multipolar AI world order.

Conclusion

The rise of generative AI and Large Language Models (LLMs) represents a transformative shift in technology, with profound implications for global power dynamics, economic growth, and cultural evolution. From the United States' Stargate Project to China's state-backed innovations, nations are investing heavily in AI infrastructure to secure strategic advantages. This competition highlights the critical importance of energy-efficient systems, sustainable data centers, and advanced networking technologies in meeting the demands of this new era.

While generative AI offers unprecedented opportunities to enhance productivity, creativity, and decision-making, it also poses significant challenges, including escalating energy consumption, cultural biases, and geopolitical dependencies. The dual ecosystems emerging from the United States and China reflect contrasting approaches to AI development and adoption, raising questions about global equity, AI sovereignty, and the future of innovation.

The geopolitics of artificial intelligence (AI) is reminiscent of the transformative impact the internet had on society, with the U.S. taking the lead through unmatched infrastructure, innovation, and investment. Silicon Valley's dominance can be compared to the ancient Roman roads, establishing a global economy that heavily relies on American tech giants like Google, Amazon, and Microsoft for digital infrastructure. As we enter the AI era, the U.S. continues to assert its supremacy by leveraging advanced cloud computing, foundational AI models, and a rich ecosystem of talent, venture capital, and government support.

Generative AI further deepens the divide between the U.S. and China, with Western-trained models often aligning with wealthier nations while Chinese systems tend to attract interest from developing regions. Both AI ecosystems reflect the cultural biases present in their training data, posing challenges for countries that aim to preserve their cultural autonomy.

To mitigate dependence on foreign AI, various nations are exploring "sovereign AI" strategies. This approach has two main layers: building localized AI models grounded in proprietary data and fostering ecosystems of AI applications that cater to national priorities. Examples include India's "Bhashini" initiative and region-specific AI solutions in areas like agriculture, healthcare, and education. Collaborative efforts, such as pooling resources regionally, can help tackle issues like data scarcity and high costs.

A new element in this landscape involves utilizing energy diplomacy for AI sovereignty. Resource-rich countries like Saudi Arabia and Norway could establish AI data centers in exchange for strategic advantages, effectively creating "AI oases" powered by renewable energy. Through the establishment of AI Infrastructure Treaties, nations can formalize partnerships that ensure data sovereignty, energy sharing, and collective security.

This multipolar AI framework envisions equitable access to AI while respecting cultural diversity and national autonomy. It aims to redistribute power away from a few tech monopolies, fostering a decentralized yet interconnected global AI ecosystem. By combining technology, energy diplomacy, and cooperation, countries can redefine sovereignty and governance in the AI age.