Wealth Strategies

UBS Favors Diversified AI Exposures, Including China

Amanda Cheesley Deputy Editor May 1, 2025

UBS Favors Diversified AI Exposures, Including China

Sundeep Gantori, tech strategist at UBS Global Wealth Management chief investment office, shares his insights into the artificial intelligence trend and the case for broadening exposures way beyond the big techs of the US.

With broadening AI trends, Sundeep Gantori at UBS Global Wealth Management recommends diversified exposure across both big tech companies and other fast-growing AI beneficiaries such as Chinese AI players.

Gantori believes that investors should not miss the forest for the trees – the AI market is broadening both from an adoption and spend point of view. He expects the share of AI spend beyond the Big 4 – Microsoft, Amazon, Alphabet, and Meta – to rise to over 40 per cent in 2025 from below 20 per cent in 2023, due to the emergence of new AI players in China, the rise of neoclouds, and other developments. Therefore, he believes that AI industry fundamentals are more resilient than the recent market volatility implies.

Gantori highlighted that new players are driving a $150 billion AI spending in 2025. With more supportive signs emerging, Gantori believes that AI monetization should improve sharply in 2025. AI revenues should continue to lag capex spend in 2025, investors who care more about “delta” will likely be pleased to see a narrowing in the gap between AI capex and revenues.

Late in January, Big Tech stocks in the US were slammed by revelations that the new China-based DeepSeek AI app had been - so it was claimed - produced for a fraction of its US counterparts, shaking assumptions about revenue growth. While some of the noise around this story appears to have calmed, it highlighted the level of exposure investors had around US tech stocks, a process that got into gear even before the April 2 tariff shock from President Donald Trump.

While Gantori recommends investors to monitor the management guidance from the Big 4 – Microsoft, Amazon, Alphabet, and Meta – he highlighted that other AI players are now spending at a faster pace and driving ongoing resilience. “These new players include those in China following the success of low-cost models like DeepSeek; neocloud providers like CoreWeave or Lambda, which run dedicated AI-only data centers; and other enterprise- and sovereign-based players like Oracle and Softbank,” he said. He expects the combined share of these companies as a percentage of AI spend to rise from less than 20 per cent in 2023 to more than 40 per cent in 2025. It is therefore imperative to also track the spending of these companies to assess the broader risk-reward for AI. “The good news is AI spending beyond the Big 4 should grow by more than 80 per cent in 2025 and more than 50 per cent in 2026, based on our estimates, meaning overall AI growth momentum should continue,” he said.

Consequently, he has expanded the scope of his AI estimates beyond the Big 4 to introduce global AI spend estimates. He expects global AI spend to increase by 60 per cent year-on-year in 2025 to reach $360 billion and 33 per cent in 2026 to reach $480 billion, with the Big 4’s share of AI spend to decline from 58 per cent in 2025 to 52 per cent in 2026. “The broadening of the AI spend is a healthy development for the AI theme, as reduced concentration should eventually lead to less market volatility,” he said. He expects the AI spend beyond the Big 4 to reach a $150 billion in 2025.

Globally, within the AI market, Gantori expects AI compute to remain the dominant spending item, but other segments such as high-bandwidth memory (HBM) and elsewhere in tech, for example networking, are gaining traction. Meanwhile, he expects industrial capex, which includes spend on cooling, power, and other infrastructure, to drive the rest of the spending.

Gantori believes diversified exposure across both big tech companies and other fast-growing AI beneficiaries such as China AI is the best way to participate in the broadening AI trend. He also continues to like AI semiconductors exposed to compute and memory spend.

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