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In London on February 4, European advocates of artificial intelligence are revisiting a 160-year-old economic theory to support the idea that the surge in AI stocks might continue, despite the emergence of China's cost-effective AI model.

The tech market experienced a significant shift on January 27 following the introduction of DeepSeek, a new AI model priced well below its competitors and with reduced chip requirements, raising concerns about the substantial investments made by the West in chip manufacturers and data facilities.

The drop in tech stocks mainly affected U.S. chipmaker Nvidia, which experienced a historic one-day 17% decline, amounting to nearly $600 billion in lost market value.

Following the turbulence, tech stocks recovered, with European markets reaching new highs. The sudden resurgence of interest in the 19th-century economic theory known as the Jevons Paradox, named after English economist William Stanley Jevons, is now a topic of widespread discussion.

Helen Jewell, Chief Investment Officer at BlackRock Fundamental Equities, EMEA, noted the growing recognition of the paradox, emphasizing the uncertainty surrounding the future demand for data centers and their suppliers.

The downturn impacted both direct and indirect players in the AI sector, such as Dutch semiconductor company ASML and Siemens Energy. CEO Satya Nadella of Microsoft commented on the potential of the Jevons Paradox in fueling increased AI adoption.

Regarding the implications of lower AI costs, portfolio managers highlighted the potential for a surge in AI investments, with opportunities in software and inference technologies.

Despite differing opinions, with Mizuho EMEA's Jordan Rochester expressing skepticism, the overall sentiment among industry experts is that the evolving landscape of AI and its reduced costs could lead to increased chip demand and power consumption.

In conclusion, Kasper Elmgreen, CIO of fixed income and equities at Nordea Asset Management, emphasized the importance of software advancements in challenging traditional hardware requirements for AI implementation.