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China and the US Vie for AI Supremacy, Europe Falls Behind
The rapid advancement of Artificial Intelligence (AI) today is intrinsically linked to cutting-edge semiconductors, chips, and integrated circuits. However, the true determinant of leadership in this field extends beyond mere hardware components. The most critical factor is the entire technological ecosystem, often referred to as the “technological stack” – a comprehensive package of hardware and software originating from a single producer.
For decades, the United States has held a dominant position in this domain. Yet, Chinese companies have recently emerged as formidable challengers, aggressively vying for a share of this crucial technological landscape. Amidst this escalating rivalry, Europe appears conspicuously disengaged, seemingly oblivious to the high-stakes competition unfolding.
The Technological Stack: The Core of Modern AI Competition
The technological stack, which combines physical hardware with its foundational software, represents the most significant source of competitive advantage in the modern era. Analysts at the Brussels-based think tank Bruegel, cited by Biznes Enter, emphasize that whoever successfully builds such an ecosystem and establishes it as a market standard will ultimately win the global AI competition.
NVIDIA’s Dominance and the CUDA Ecosystem
NVIDIA is arguably the closest to fully realizing this scenario, recently ushering in what it calls “a new era of robots.” The company has long been a leading producer of highly sought-after integrated circuits. Currently, it accounts for approximately half of the world’s installed AI chip resources, providing an astounding two-thirds of the total computational power globally. This monumental success, however, would be incomplete without one pivotal component: CUDA.
CUDA, an acronym for Compute Unified Device Architecture, is a proprietary platform designed for developers. It enables them to harness the full potential of the American giant’s powerful chip architectures. Crucially, NVIDIA offers CUDA for free, a strategy that has allowed it to rapidly dominate the market. The reason is straightforward: even if a competitor develops a more efficient or cheaper processor, it often lacks compatibility with the existing CUDA ecosystem. For more insights into NVIDIA’s recent advancements, refer to our article on NVIDIA’s AI Trillion-Dollar Shift and GTC Announcements.
To use a non-NVIDIA chip, developers would need to rewrite their code from scratch, adapt their tools, and likely accept a potential decrease in performance. Furthermore, NVIDIA’s platform integrates seamlessly with PyTorch, one of the most popular tools for developing AI models, further solidifying its ecosystem’s appeal.
China’s Ambitious Bid to Compete in AI
Despite NVIDIA’s stronghold, concerted efforts are being made to challenge its leadership. Huawei’s technological stack is at the forefront of these attempts. While Bruegel suggests that Huawei’s processors still lag behind NVIDIA’s, the “signs of Chinese progress are real and significant.” In response to CUDA, Huawei has been vigorously developing its own platform, CANN (Compute Architecture for Neural Networks).
In 2025, the company plans to make the first components of its CANN tool available as open-source. Although initial reactions from developers have been mixed, and CANN currently trails its main competitor, experts largely agree that the Chinese platform is improving rapidly month by month. The broader landscape of semiconductor manufacturing also plays a role in these advancements, as seen in developments like Samsung’s Semiconductor Opportunity amid TSMC Backlog.
A potential game-changer could be a product named `torch_npu`. This small plugin is designed to allow code written in PyTorch to run on Huawei Ascend processors. If Chinese developers succeed in widely releasing this tool, it could represent a significant breakthrough, enabling programmers to more easily transition from CUDA to CANN.
It’s also important to mention DeepSeek, a company from the People’s Republic of China responsible for a large language model that caused a stir among American tech giants upon its release. DeepSeek has announced that its chip models can be optimized for both NVIDIA’s technological stack and their native Chinese counterparts, demonstrating a dual-pronged approach to market penetration.
Europe: An Observer in the Global AI Technology Race
As the AI market witnesses an intense contest between an established leader and an ambitious challenger – a dynamic cherished by sports enthusiasts – Europe appears largely unaware of the competition at hand.
While the European continent is home to technologies vital for chip production, such as advanced lithography machines, this contribution doesn’t translate into an overarching strategy for building a complete technological ecosystem. Instead, Europe often limits itself to the role of a supplier of tools and components.
There are currently no significant European players engaged in creating their own comprehensive technological stacks. According to Bruegel analysts, it may now be too late for Europe to fully catch up on lost time. However, this doesn’t mean Europe is entirely excluded from the competition.
Europe’s opportunity could lie in a more specialized approach, perhaps through a well-considered open-source strategy. Additionally, it would be beneficial for local European corporations to focus AI development in sectors where the continent still holds a competitive edge, including:
- Automotive industry
- Climate modeling and environmental technologies
- Development of safe and trustworthy AI systems
Frequently Asked Questions (FAQ)
The technological stack, comprising integrated hardware and software from a single producer, is crucial because it creates a seamless, optimized ecosystem. This synergy allows for maximum efficiency and performance, giving developers powerful tools and making it difficult for competitors to easily switch or match capabilities without significant investment in re-engineering. It fosters a lock-in effect, making the dominant stack the industry standard.
NVIDIA maintains its strong position primarily through its highly performant GPUs and the ubiquitous CUDA platform. By offering CUDA for free, NVIDIA has created an extensive developer ecosystem and a significant barrier to entry for competitors. CUDA allows developers to fully leverage NVIDIA’s hardware, making it difficult for them to switch to alternative chip architectures without rewriting code and potentially sacrificing performance.
Europe’s primary challenge is its lack of a fully integrated technological stack and major players that develop both AI hardware and software ecosystems. It primarily acts as a supplier of tools rather than an ecosystem builder. Opportunities lie in strategic specialization, such as a focused open-source strategy or concentrating AI development in sectors where Europe already excels, like the automotive industry, climate modeling, and the creation of ethical and trustworthy AI systems.
Source: Biznes Enter, Bruegel analysts
Opening photo: Gemini
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