AI & the Technology Race

Artificial intelligence has become the defining arena of great power competition. The race to develop, deploy, and control frontier AI systems is reshaping economies, alliances, and the global balance of power.

Why AI Sits at the Centre of the Technology Cold War

Artificial intelligence is no longer a futuristic abstraction. It is a strategic asset — one that both Washington and Beijing treat as central to national security, economic competitiveness, and global influence. The competition to build the most capable AI systems has become inseparable from the broader contest over semiconductors, critical minerals, and the architecture of the global technology stack.

The stakes are structural. Whichever nation — or bloc — leads in AI development will shape the standards, platforms, and infrastructure that underpin the next generation of industries: from autonomous systems and precision medicine to financial markets and defence. Control over AI is, in practical terms, control over the operating system of the 21st-century economy.

This is not a competition that can be measured by a single benchmark or a single model release. It is a contest across multiple dimensions: raw capability, deployment at scale, open-source influence, hardware access, regulatory frameworks, and the ability to attract and retain talent.

Two Ecosystems, Two Philosophies

The AI landscape in 2026 is defined by the emergence of two increasingly distinct ecosystems — American and Chinese — each operating under different constraints and with different strategic logic.

The American ecosystem is anchored by proprietary models backed by unprecedented capital investment. OpenAI, Anthropic, Google DeepMind, and Meta lead the development of frontier large language models. Infrastructure spending has accelerated sharply: Goldman Sachs projects that AI companies may invest more than $500 billion in 2026 alone. Nvidia's valuation surpassed $5 trillion in 2025, reflecting the market's conviction that compute-intensive models will continue to dominate.

The Chinese ecosystem operates under the constraints imposed by US semiconductor export controls — and has turned those constraints into a strategic discipline. Facing restricted access to the most advanced chips, Chinese developers have prioritised algorithmic efficiency, open-source distribution, and rapid deployment into commercial applications. DeepSeek, Alibaba's Qwen, ByteDance's Doubao, and Tencent's Yuanbao have become embedded in daily life across China, often offered free to individual users.

The divergence extends beyond business models. It reflects fundamentally different assumptions about how AI should be developed, distributed, and governed — assumptions with consequences for every nation navigating between the two poles.

Read our full analysis: The AI Arms Race — From GPT to DeepSeek →

The Open-Source Battleground

One of the most consequential dimensions of the AI competition is the contest over open-source models. China's strategy of releasing powerful models freely — led by DeepSeek and Alibaba's Qwen — carries particular geopolitical weight. By making frontier-competitive AI available to developers worldwide at no cost, Chinese labs are building a parallel infrastructure that does not depend on American platforms.

The adoption pattern is striking. Since August 2025, cumulative downloads of Chinese open-source AI models have surpassed those of Western competitors globally. Alibaba's Qwen family alone has exceeded 700 million downloads. DeepSeek has seen two-to-four times higher adoption interest in Africa than in other regions. Several major US technology companies now use Chinese open-source models in their own applications.

For developing nations with limited access to cloud infrastructure and high-end processors, Chinese open-source models offer an accessible entry point into AI capabilities — one that carries implications for technology standards, data governance, and long-term alignment.

Efficiency as Strategy: The DeepSeek Factor

The release of DeepSeek's R1 model in January 2025 marked a watershed moment. By matching the performance of OpenAI's o1 on key benchmarks at a fraction of the training cost, DeepSeek demonstrated that algorithmic efficiency could compensate — at least partially — for hardware constraints. Nvidia's stock fell 17 percent in a single session. The shockwave was as much strategic as financial.

DeepSeek's subsequent development trajectory has deepened the challenge. Its V4 model, anticipated in early 2026, introduces architectural innovations — including the Engram conditional memory system — that may reduce dependency on GPU High-Bandwidth Memory, the scarcest component in the current AI hardware stack and the primary target of US export controls.

Read our technical analysis: DeepSeek V4 — Inside the "CodeKiller" Architecture →

Read our architectural deep-dive: DeepSeek V4 Set to Drive Structural Change in LLM Development →

The implication is significant: if frontier AI performance becomes achievable on commodity hardware, the economic and strategic assumptions underpinning Western AI investment — and Western export control policy — may require fundamental reassessment.

The Hardware–Software Nexus

The AI race cannot be understood in isolation from the semiconductor competition. Models require chips. Chips require advanced manufacturing. Advanced manufacturing requires equipment that only a handful of companies — ASML, Tokyo Electron, Applied Materials — can provide. This interdependence makes AI development inseparable from the broader technology cold war.

US export controls have restricted China's access to the most advanced AI processors. But the relationship between hardware and AI capability is more complex than a simple linear function. DeepSeek's success has demonstrated that architectural innovation can partially offset compute limitations. The deeper question — whether hardware superiority still translates cleanly into AI superiority — remains open.

Europe's Position

Europe has adopted a regulatory-first approach through the AI Act, positioning the EU as a standard-setter for safety and transparency. But regulation alone does not produce capability. European AI companies remain significantly smaller and less well-funded than their American and Chinese counterparts. The challenge for Europe is to ensure that its regulatory framework enables rather than constrains innovation — while maintaining the strategic autonomy to avoid dependence on either superpower's AI infrastructure.

The Year of AI Agents

The AI competition in 2026 is entering a new phase. The industry is shifting from conversational chatbots to AI agents — autonomous systems capable of executing complex, multi-step tasks over extended periods without human intervention. This transition will increase computing demands per user significantly, sustaining the infrastructure investment boom while raising new questions about safety, accountability, and control.

At the same time, the competition is becoming more plural. India, the Gulf states, Japan, and South Korea are all making significant investments in AI capabilities. The era of US-China duopoly in frontier AI may be giving way to a more distributed landscape — though the two superpowers will continue to exert the greatest influence.

What We Cover

Tech Cold War tracks the AI competition across its full scope: frontier model development, open-source dynamics, export controls and their consequences, architectural innovation, hardware dependencies, regulatory approaches, and the geopolitical implications of AI deployment at global scale.

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