In his article, John Werner explains that the next big leap in artificial intelligence will depend less on the models themselves and more on specialized hardware: the chips designed to power training, inference, memory-handling, and energy-efficient scaling. He argues that as AI systems move from research labs into wide-scale deployment, the constraints around power consumption, heat dissipation, cost per token, and speed of development will become decisive.
Werner highlights how today’s architecture challenges are not simply incremental. For example, traditional CPUs and even many GPUs are hitting physical limits—while future demands require chips optimised for sparse computation, on-chip memory, massive parallelism, and ultra-low latency. He points to emerging players and specialized designs (e.g., AI accelerators, memory-centric chips) as the front-line of the next hardware era.
Another key insight: the competitive landscape of AI hardware will increasingly influence who wins in AI services and platforms. Firms that can design, manufacture and deploy high-performance chips at scale will gain an advantage in cost per operation and energy efficiency—this translates into faster time to market, lower operating cost and better margins for AI applications. The article notes that reliance solely on off-the-shelf GPUs may no longer suffice as the economy of scale swings toward tailor-made silicon.
Finally, Werner stresses that for governments, investors and technology leaders (including those in India and other emerging markets), the recommendation is clear: invest not just in model development, but in the hardware ecosystem—design-capable foundries, supply chains for chip materials, memory systems, cooling infrastructure and skills in chip design. Without that, AI ambitions may be limited by bottlenecks even if the algorithmic breakthroughs continue.
