Why we remain negative on AI names
Why we retain key AI names in our short calls
We continue to advise being very cautiously positioned with our long picks mainly focused on some promising laggards left behind from the AI rally while holding on to some key short positions among AI names. Investors should remain wary of potential relief rallies where positioning will be prove crucial. But given the fundamental fragility of these AI-heavy valuations, we suspect any such counter-trend spikes are likely to be transient and should be viewed as opportunities to re-evaluate exposures rather than signs of a structural bottom.
As we outlined in our last December note, we view the massive AI infrastructure buildout as an inflationary menace rather than a disinflationary productivity boon and one we now refer to as a secular inflation within the AI supply chain. This is now playing out in real-time as the marginal cost of compute surges—driven by acute power, water, and industrial commodity bottlenecks—while the market price of compute is simultaneously under sustained downward pressure. This structural margin squeeze is the ultimate indictment of current LLM business models, where soaring infrastructure requirements are decoupled from any realistic path to pricing power or profitability.
A long overdue recalibration as reality sets in
We are seeing a major, overdue recalibration in the market as investors look beyond quarterly results. While competition between OpenAI, Anthropic, Google, and Grok has intensified at the high end, Chinese LLMs—which charge a fraction of their token fees—are capturing market share in less demanding segments and starting to challenge their American rivals for more complex applications.
Moonshot AI’s release of Kimi K3 last week, as the world’s largest open-weight AI model, serves as a prime example of this growing threat from the East. We remain convinced that the Trump administration is poised to ban the usage of Chinese models by US corporates to protect homegrown AI firms from this market share assault. While this may not stop Chinese players from securing a substantial portion of the global market, US government meddling could isolate their own firms from international arenas.
Moreover, data centre expansion in the US faces mounting challenges as regulatory and local community opposition to such monoliths grows exponentially, creating obstacles regarding access to local power grids and water. Furthermore, for projects employing onsite generators, growing awareness regarding the low-frequency noise they generate—which has rendered neighbouring homes effectively worthless—has become a significant headache.
Given that these issues are unlikely to prove significant obstacles to data centre construction in China—which is aggressively adding nuclear capacity, constructing large dams, and designating strategic locations—it is difficult to see how US compute costs can remain competitive. We contend that their primary advantage of early access to advanced silicon is being eroded rapidly as other critical resources become the bottleneck.
Finally, secular inflation across the AI supply chain is accelerating, raising the costs of building data centres and straining the massive budgets of hyperscalers. From foundry quotes and memory chips to multilayer PCBs, fibre optics, gas turbines, construction machinery, and high-grade steel, nearly everything we have seen from the ground up has experienced price hikes of 20% or more. With total global budgets for data centre spending estimated at $450bn this year, this secular inflation—fuelled by AI’s inelastic markets where securing supply has been the sole consideration—is beginning to hurt.
