Signal over Noise with Ulrike Hoffmann-Burchardi
The desk interprets UBS Wealth Management's recent commentary, as Ulrike Hoffmann-Burchardi highlights a shift in sentiment regarding U.S. tech investments, downgrading from overweight to neutral. This viewpoint underscores a belief that as artificial intelligence (AI) matures, the risks may begin to outweigh the rewards, particularly at a time when AI capital expenditures have reached significant levels, amounting to 2% of U.S. GDP and comparable to 15 years of cloud computing expenditure. Per the full note source, this nuanced sentiment around tech could influence wider market behaviors, including FX pairs linked to economic activity in the tech sector.
What the desk is arguing
The desk asserts that UBS’s fractional downgrade of tech stocks reflects broader challenges within the AI sector as real economic impacts begin to manifest. This move suggests shifting market dynamics that could favor currency pairs sensitive to tech sector performance.
Hoffmann-Burchardi noted that the profitability of AI investments remains uncertain, with significant CapEx that compels a reevaluation of expectations. This is particularly compelling given the potential for a reduced pool of companies that will emerge as long-term winners in this evolving landscape.
Where it sits in our coverage
Our consensus target for the relevant currencies is 1.075, with a range between 1.04 and 1.12. Specific targets from firms in our coverage include: - jpmorgan: 1.10 (targeting Mar 26) - bofa: 1.04 (targeting Mar 26)
This view is slightly divergent, as the bofa stance appears more conservative compared to the consensus, positioning them at the lower end of the spread.
How other firms see it
Firms aligned with a positive view on tech, such as jpmorgan, show optimism, while bofa presents a contrary stance, potentially reflecting fears about the profitability of AI investments.
Markets should keep an eye on how the EUR/USD might react to developments in the tech sector, as this could be critical to understanding the broader implications for currency movements.
What the calendar says
With no major scheduled events affecting these dynamics in the next month, traders may find themselves navigating based on corporate earnings announcements and market sentiment regarding tech's profitability rather than significant macroeconomic data.
How firms align with this view
Aligned with the desk view
Contrary positioning
Key takeaways
- 01UBS's downgrade of U.S. tech from overweight to neutral reflects growing concerns about the profitability of AI.
- 02AI capital expenditures currently constitute 2% of U.S. GDP, raising questions about sustainable growth.
- 03The market is narrowing toward a few leading AI companies, all vying for dominance in profitability.
- 04Implications for FX traders include potential shifts in tech-sensitive currency pairs as the sector’s dynamics evolve.
Market implications
Traders should watch for potential resistance levels around 1.10 in USD/EUR, especially as market sentiment adjusts to tech earnings and the evolving narrative around AI profitability.
Risks to this view
A reversal in this viewpoint could emerge if leading AI firms demonstrate substantial profitability, or if macroeconomic indicators highlight stronger-than-expected performance in the tech sector.
Hello and welcome to Signal Over Noise. I'm Ulrike Hofmann-Borchati, CIO for the Americas and Global Head of Equities for UBS Wealth Management. We downgraded U.S. tech last week from overweight to neutral.
Being neutral on tech is a bit like swimming upstream. It's a contrarian position and one not to take lightly. Over the last 40 years, tech has outperformed in roughly two out of three years.
So what drives our conviction that the risk reward in tech is getting less favorable? We believe that we are entering a new chapter in AI. One that I would call the face of young adulthood where economic realities will start to sink in.
We're now getting to a level of AI CapEx where the math is getting more challenging. Not only because we have reached a level of annual CapEx that is equivalent to 15 years of CapEx for cloud computing or that it now reached 2% of U.S. GDP, but because the key question is getting more difficult to answer.
How is AI spent going to turn into profits? Revenues are simple to reason through. AI addresses 30 trillion in global knowledge worker wages.
But profits? That's not so simple. The most important question is, how many winners will there be?
And with winners, I mean those at the frontier of artificial intelligence. Those companies that are driving this new wave of generative AI and are competing for the top spots on the AI league tables. The answer to this question determines the market structure and profit pools.
Right now we have about a dozen frontier model apps such as OpenAI, Anthropic, Gemini, XAI, Meta, Mistral, Alibaba, DeepSeek, Moonshot, Cohere, Baidu, and Tencent. And new ones that have been formed more recently. Safe Superintelligence, Thinking Machines, AMI Labs, Sakana AI, just to name a few.
And there will be more. Microsoft's head of AI, Mustafa Suleyman, said last week that Microsoft will be coming out with its own frontier model sometime this year. So at this stage, the frontier model market looks like the textbook definition of perfect competition.
A dozen competing labs, new entrants on the horizon, low switching costs, and performance transparency. In perfect competition, price equals marginal costs and companies earn zero economic profits. This describes what we're seeing right now.
The largest private frontier model apps do not seem to be profitable based on reports from recent funding rounds. There are two reasons frontier models need to spend on compute. For training, that is spend that is required to be at the forefront of intelligence.
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