New-Tech Europe | Q3 2026 | Digital Edition

1. AI Is Rewriting the Economics of Chip Development For more than a decade, smartphones were the semiconductor industry’s primary growth engine. Today, that role belongs to artificial intelligence. The rapid expansion of generative AI, foundation models and high-performance computing has created unprecedented demand for advanced semiconductor devices. The effects extend far beyond AI accelerators themselves, influencing nearly every layer of the semiconductor value chain. Industry projections indicate that the four largest US cloud providers are expected to invest approximately $718 billion in computing infrastructure and data centres during 2026. Those investments include far more than AI processors. They encompass high-bandwidth memory, ultra-fast networking, advanced packaging technologies and the infrastructure required to deploy increasingly sophisticated AI systems at scale. The nature of processor development is changing as well. While general-purpose CPUs remain indispensable, the strongest growth is occurring in specialised hardware designed specifically for machine learning workloads, including GPUs and application-specific integrated circuits (ASICs). At the same time, demand continues to rise for high-speed interconnects, advanced storage technologies and high-bandwidth memory, all of which are essential for feeding AI systems with the enormous volumes of data they require. Another significant shift is taking place inside the world’s largest cloud companies. Rather than relying exclusively on merchant silicon vendors, hyperscalers are increasingly designing their own processors. Google’s Tensor Processing Units (TPUs), Amazon Web Services’ Trainium family and Microsoft’s Maia accelerators illustrate how semiconductor design has become a strategic capability for cloud infrastructure providers rather than remaining the exclusive domain of traditional chip manufacturers. The result is a profound change in industry priorities. Artificial intelligence is no longer simply another application for semiconductors. It has become the primary force directing investment across processor architectures, manufacturing technologies, memory development, packaging strategies and networking infrastructure. Meeting AI’s rapidly growing computational demands, however, requires more than larger data centres and increasingly powerful accelerators. It also depends on continued advances in semiconductor manufacturing, where each new process generation must deliver higher performance while operating within increasingly demanding power and thermal constraints.

2. Why 2 nm Is More Than Another Process Node This is precisely where the transition to 2 nm becomes significant. For decades, each new manufacturing node was largely associated with smaller transistors and higher transistor density. The move to 2 nm represents a much broader technological transition. Further progress now depends on combining several major innovations, including Gate-All-Around (GAA) nanosheet transistors, backside power delivery, advanced materials and increasingly sophisticated manufacturing techniques. Together, these technologies enable further improvements in performance and energy efficiency even as conventional transistor scaling becomes progressively more difficult and expensive. The first generation of 2 nm manufacturing is expected to deliver substantial gains in performance while reducing power consumption, particularly for AI accelerators, high-performance processors and hyperscale data centre applications. Achieving these improvements, however, comes at an extraordinary cost. Developing leading-edge manufacturing technologies now requires investments measured in tens of billions of dollars for a single fabrication facility, limiting participation to only a handful of companies worldwide. TSMC is widely expected to lead volume production of 2 nm devices for customers including Apple and NVIDIA, while Samsung and Intel continue accelerating their own advanced manufacturing roadmaps as competition intensifies across the global foundry market. The race is no longer focused solely on technological leadership. Equally important is the ability to attract the next generation of AI, cloud computing and high-performance computing customers. Not every application, however, requires the industry’s most advanced manufacturing node. Industrial automation, automotive electronics, communications infrastructure and many embedded systems will continue to rely on mature process technologies for years to come. These applications often place greater value on reliability, manufacturing stability, long product lifecycles and cost efficiency than on maximum transistor density. As a result, the semiconductor industry is evolving into a far more diverse manufacturing landscape. Leading-edge nodes will power the most demanding AI workloads, while mature technologies will remain essential across industrial, automotive and communications markets. Competitive leadership is therefore being redefined. Process technology remains a critical differentiator, but it is

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