Daily Asia

14 September 2026, 23:00 UTC Chief Investment Office GWM Investment Research

Frontier “pacing” calls may not spell an end to AI capex cycle UBS House View - Daily Asia Min Lan Tan, Head Chief Investment Office APAC, UBS AG Singapore Branch Mark Haefele, Global Wealth Management Chief Investment Officer, UBS Switzerland AG Delwin Kurnia Limas, CFA, CIO Equity Strategist, UBS AG Singapore Branch Kevin Dennean, CFA, CIO Equity Strategist, US Technology & Telecom, UBS Financial Services Inc. (UBS FS) Vincent Heaney, Strategist, UBS AG London Branch Christopher Swann, Strategist, UBS Switzerland AG Daisy Tseng, Strategist, UBS AG Singapore Branch

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Thought of the day AI-linked stocks came under pressure on Monday, after several leading US frontier artificial intelligence labs publicly backed efforts to pace development of the most advanced models. Leading North Asian memory stocks fell 2.9-6.4%, while AI-focused investor SoftBank fell 10.7%.

Leaders from frontier labs over the weekend made the public case for stronger safeguards and coordination. This follows closed-door talks between these labs to form an “industry-led AI safety standards body,” according to The Information. The safety debate has intensified in recent weeks as departing “alignment’ researchers at leading labs have warned that advanced AI development could threaten human survival.

A bipartisan US Senate effort to establish national AI rules is also progressing, according to Reuters, and California enacted standards for independent AI auditors last week. US President Donald Trump pushed back against calls for a slowdown over the weekend, emphasizing the need to maintain US leadership in AI, while leaving room for some safeguards. He told reporters that “whoever wins AI, wins.”

The high-profile, coordinated calls to pace AI development are certainly notable. But we would caution against equating stronger safeguards with an end to the AI capex cycle:

• Pacing does not necessarily imply lower capex. First, AI compute demand stems from both training models and running them, a process known as inference, with the latter estimated to account for roughly

This report has been prepared by UBS AG Singapore Branch, UBS Switzerland AG, UBS Financial Services Inc. (UBS FS), UBS AG London Branch. Please see important disclaimers and disclosures at the end of the document.

two-thirds of demand. Importantly, inference demand is driven primarily by real-world adoption and monetization, in our view, with the practical challenges of integrating AI into existing enterprise operations a greater constraint than current model capabilities. Training demand, by contrast, is more directly tied to the costs and expected returns of developing more capable models. The safety proposal also explicitly distinguishes pacing from halting model training, and xAI’s Elon Musk confirmed on Sunday that further training was underway for its leading edge Grok 4.8 model. OpenAI also recently reiterated its expectations for continued growth in memory demand. We retain our 2027 AI industry capex forecast of USD 1.2tr, a rise of 33% from our estimate of USD 900bn this year.

• Regulation could reshape competition, not just development. This may represent the industry acknowledging a more challenging political backdrop into the US mid-term elections, with voter concerns spanning employment, and data-center electricity and water consumption. The push to address safety concerns also appears to reflect growing internal pressure from employees within frontier AI labs, rather than political positioning alone. The labs’ efforts to establish common standards may reflect a desire to shape future AI regulation, such as limits on the liability of platforms for what their users do with AI.…