- Recent hardware sell-off treated as if a safety note were a demand shock; “pacing” does not mean a halt to training, nor does it imply a collapse in broader AI infrastructure spending demand
- Whether frontier models will slow their development pace remains an open question; AI is now both a commercial market share race and a national security contest
- The stated reason for the slowdown is itself an endorsement of the technology, rather than a demand miss, which reinforces the core bull case for compute rather than invalidating it
- Most AI spend does not depend on the next advanced model launch: infrastructure and construction projects do not reverse on a single call to “pace” future developments
- Stay allocated across upstream tech suppliers, power/grid beneficiaries, and those leveraged to inference and enterprise deployment
The recent sell-off in AI-linked names treats a safety-and-governance statement as a demand shock to the capex cycle, a view we believe is too blunt. In essence, Amodei’s call to “pace the frontier”, which was subsequently endorsed by Altman, Musk, and other tech leaders, is not a halt to training or technical progress. Rather, it is a proposal to slow the rate of capability gains so that evaluation, alignment, and governance can keep pace to form a more sustained future. Altman has since added that pacing “does not mean stopping”.
Indeed, here is a new context to consider: stricter evaluation, capability thresholds, and longer release cycles could defer the next giant pre-training run. That matters more for names whose earnings are still modelled on larger, more frequent frontier clusters. But this does not imply a collapse in the broader AI infrastructure spending demand, nor fading in the AI narrative. The recent rotation from chips and memory names towards software and cybersecurity companies is more consistent with a shift towards AI deployment and defence, and mission-critical frameworks, rather than the view that “AI is over”.
Now, whether frontier models will slow is an open question. AI now sits at the intersection of technological innovation, commercial competition for market share, and national security imperatives. Industry leaders in both the West and East will therefore continue to press ahead with developing proprietary AI capabilities and building durable user interfaces.
China’s response to tech leaders’ calls was unambiguous, describing such claims as “fearmongering” that would disrupt global governance, while state media framed the plan as a “Cold War playbook”. Trump’s commentary pointed in the same direction. Competition, sovereignty, and the value of being first remain pragmatic reasons to keep building and innovating.
If the slowdown is real, the stated reason is itself an endorsement. The trigger for this “pacing” is not a failed product cycle or a capex cut, but a claim that systems are approaching recursive self-improvement and that agent swarms could cause damage at a massive scale within months. This is a statement of power: the people closest to the frontier argue that the capability curve is outrunning the safety curve. That narrative reinforces the core bull case for compute rather than invalidating it. Guardrails would raise the cost of being first, rather than erasing the need for capacity behind the models already in production.
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