Policy-makers are being urged to focus on investments in artificial intelligence (AI) in a way that makes sense for the economy. The Rethinking AI sovereignty paper, published to coincide with the World Economic Forum (WEF) meeting in Davos, recommends that policy-makers reframe AI sovereignty as strategic interdependence, where localised investments are combined with trusted partnerships and alliances.

The paper, co-authored by the World Economic Forum and Bain & Co, presents data that illustrates the gap between AI infrastructure investment in the US and China compared with other countries.

For instance, it shows that the US and China dominate the investment landscape, capturing about 65% of aggregate global investment in the AI value chain. According to the report’s authors, the outsized presence of the US and China in every element of the AI value chain reflects a full-stack approach that few economies can match, given the scale of investment needed.

Given that AI infrastructure is the backbone of AI competitiveness, smaller and mid-sized economies can be at a disadvantage. However, WEF and Bain & Co believe there is an opportunity. The paper notes that economies that move quickly and strategically channel investments – through partnerships, focus or shared regional capacity – can secure a competitive foothold despite limited resources. However, slower responses risk widening capability gaps as hyperscalers and large economies continue to consolidate their dominance.

In a fireside chat at Davos, Nvidia chief Jensen Huang urged every country to build out their own AI infrastructure. “There’s not one country in the world where [you won’t] need to have AI as part of your infrastructure, because every country has its electricity, you have your roads, and you should have AI as part of your infrastructure,” he said.

Huang said AI buildouts will involve high-paid jobs for electricians, plumbers and network engineers, who will be needed to build AI datacentres. Rather than economies experiencing job losses arising from greater use of AI in the workforce, Huang described the AI boom as the “largest infrastructure buildout in human history”, which he claimed would create a lot of jobs.

“It’s wonderful that the jobs are related to tradecraft, and we’re going to have plumbers and electricians and construction and steel workers,” he said. “Everybody should be able to make a great living. You don’t need to have a PhD in computer science to do so.”

But, according to the WEF and Bain & Co paper, land, energy and water are critical constraints for scaling AI infrastructure within the economy. While some economies will inevitably find it difficult to source the highly skilled workforce they require, WEF and Bain & Co also believe local regulation is playing a part in slowing down AI infrastructure development.

However, in the longer term, some industry bodies do not see regulatory pressure as a constraint, but rather an opportunity to move ahead before other regions regulate datacentre build-outs due to their immense energy usage.

UKAI, the trade body representing the AI industry across the UK, believes high energy costs, grid constraints, complex planning systems and strong public scrutiny mean the UK is already operating under the conditions that many other AI economies will soon face. According to UKAI, these pressures are driving innovation in efficiency, system design and coordination, the foundations for greener AI.

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