Concerns about the pace and scale of the AI buildout have been amplified by fears that regulatory friction and other hurdles could challenge the economics and timing of expected returns. Yet the debate largely centers on the conditions under which new data centers can be built, rather than whether they should be built at all. Importantly, this does not signal the end of the AI cycle. Instead, heightened regulatory friction will likely drive greater differentiation across projects, making selectivity key as the cycle matures.

Concerns about the AI capex cycle have been building for quarters, long before the midterm elections, driven by lingering uncertainty about the pace, size and eventual returns associated with the buildout. Democratic victories in key congressional and gubernatorial races have added political and regulatory uncertainty to a secular story that was already being reassessed. Yet, the policy debate is more nuanced. 

While political opposition to data centers is real, the strategic case for development remains strong across both parties, as AI is viewed as important to U.S. economic competitiveness and national security. As such, the discussion is less about whether AI infrastructure should be built and more about the conditions under which new projects can move forward. 

Importantly, these pressures do not mean the AI cycle has run its course. Rather, heightened scrutiny will likely drive greater differentiation across companies, projects, and geographies. Tighter conditions for new development may raise the value of established data center operators, while projects that can align computing demand with infrastructure readiness, economic viability and community acceptance will be rewarded. For investors, this argues for greater selectivity rather than abandoning the AI theme altogether. 

The election has increased the probability of greater regulatory friction around AI infrastructure. It has not demonstrated that the underlying AI investment cycle is breaking.