Key takeaways:
- The watershed Democratic midterm election victory at the state level was fueled, in part, by growing concern about the environmental and social impact of massive data center investment.
- We believe artificial intelligence (AI) companies will continue their community engagement, and in cases where an impasse is reached, potentially shift projects for 2028 and beyond to friendlier jurisdictions.
- Should greater data center regulation take hold, we think hyperscalers and established AI companies with strong business models would fare better than neoclouds, startups, or parts of the ecosystem that have only benefited from recent strong pricing conditions.
On November 5, the tech-heavy NASDAQ Composite Index registered its worst day since April 2025 as investors continue to parse the significance of the U.S. midterm elections. The catalyst for the sell-off, in our view, isn’t found in divided government returning to Washington but in the states where governorships – and in some cases legislative chambers – switched from Republican to Democratic control.
While affordability and the war with Iran were key issues on the federal level, within states, many of the victorious Democrat candidates incorporated rhetoric that questioned the environmental and societal costs of the ongoing AI data center buildout.
According to the Wall Street Journal, Texas, Georgia, Michigan, and Ohio will all soon have occupants of their governors’ mansions who campaigned against data centers. This matters, as much of the regulatory, permitting, and zoning framework governing these massive infrastructure projects occurs at the state and local level.
Even prior to the 2024 presidential election, we closely monitored the degree to which regulatory risk could impact the AI infrastructure buildout. That threat has grown over the past two years as NIMBYism1 has taken root in many communities. All along, our research team has reviewed developments on the local level and on a company-by-company basis and assigned an appropriate risk premium to reflect the threat of a more strenuous regulatory framework.
The November 3 result, in our view, creates a thicker veil of policy uncertainty across the entire AI infrastructure value chain. Importantly, however, we believe the AI buildout will continue, but the locations in which it will be concentrated, and the pace of the rollout, are now in question.
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An existential matter?
As we anticipated, the midterm elections returned divided government to Washington. The Trump administration remains in control of the executive branch, and from both a geopolitical and economic perspective, it views maintaining AI supremacy among the highest of national priorities. Similarly, the hyperscalers funding this buildout view the continued advancement and integration of AI as fundamental to their businesses.
We believe the gigawatts of AI capacity slated to come online in 2027 will largely continue according to plan. Longer term, projects that run into new regulatory hurdles may end up shopping for friendlier jurisdictions. One could realistically surmise, however, that even in states with a certain degree of data-center hostility, concessions and compromises will be made that address environmental, water usage, and power concerns without having the location give up potential tax revenue. Indeed, since the emergence of NIMBYism, data center companies have dedicated significant resources to community engagement on the state and local level with respect to those concerns.
Reshuffling the landscape
Within technology, a higher level of regulation tends to favor the incumbents. Large companies typically have the financial firepower and staff to withstand and comply with more rigorous oversight. We believe this would be the case should the data center buildout face such headwinds. Hyperscalers, with their existing torrent of cash flows from software sales, cloud services, and digital advertising, would likely be advantaged over AI-centric neocloud providers.
This week’s broad-based, AI-ecosystem sell-off – which hit semiconductor stocks particularly hard – along with the specter of more regulation, presents investors with the opportunity to seek out those business models that have multiple business lines. In our view, these companies are likely to be better positioned compared to single-product companies and those that have only seen an earnings lift through pricing power during this period of acute supply/demand imbalances.
Also potentially disadvantaged are smaller and newer companies and those that are less efficient on a token-per-watt basis. Should data center backlash manifest itself in more restrictive policy, spread to other states, or take root at the federal level (possibly after the 2028 election cycle), we could expect to see compute capacity shift to other regions. This includes countries that, thus far, have little to no exposure to the AI buildout but would likely welcome it for economic and geopolitical reasons.
A clear destination but a winding path
In closing, as we discussed prior to Tuesday’s election, the AI buildout will unfold across multiple election cycles. The path, not only from a policy perspective but also technological and funding, will invariably be uneven. Still, with the transformative potential of AI becoming more evident every day, especially with respect to productivity gains and the advancement of knowledge, we believe there is no turning back from what we consider the most transformative technology advancement in generations.
A shift in regulatory regime will impact winners and losers. But by a much larger degree, so will the willingness of companies to push innovation and alter their business models to incorporate AI. All these variables are in play, and this election is, in our view, proof that the landscape and ultimate winners can change quickly.
Denny Fish, Portfolio Manager
Definations
Important Information
Artificial intelligence (“AI”) focused companies, including those that develop or utilize AI technologies, may face rapid product obsolescence, intense competition, and increased regulatory scrutiny. These companies often rely heavily on intellectual property, invest significantly in research and development, and depend on maintaining and growing consumer demand. Their securities may be more volatile than those of companies offering more established technologies and may be affected by risks tied to the use of AI in business operations, including legal liability or reputational harm.
Cash flows: The net balance of cash that moves in and out of a company. Positive cash flow shows more money is moving in than out, while negative cash flow means more money is moving out than into the company.
Equity securities are subject to risks including market risk. Returns will fluctuate in response to issuer, political and economic developments.
NIMBYism: “Not In My Back Yard.” It describes opposition to a development or infrastructure project located nearby.
Hyperscaler: A large technology company that operates extensive cloud computing and data center infrastructure at global scale.
Investment Cycle: The purchasing of fixed assets by a company for the purpose of supporting day-to-day operations.
NASDAQ Composite Index: a market capitalization-weighted stock market index that tracks almost all common equities, American Depositary Receipts (ADRs), and real estate investment trusts (REITs) listed on the Nasdaq stock market. Larger companies have a greater influence on its overall performance, and it is commonly used as a benchmark to gauge the health and trends of the technology-driven segments of the American economy.
Neoclouds: A specialized cloud computing structure, supplying large clusters of high-end processing units, built specifically for artificial intelligence (AI) and machine learning workloads, rather than general applications.
Risk premium: The additional return an investment is expected to provide in excess of the risk-free rate. The riskier an asset is deemed to be, the higher its risk premium to compensate investors for the additional risk.
Token-per-watt: A measure of how much artificial intelligence (AI) output – in the form of tokens – an AI system or data center produces for every watt of electrical power it consumes. A token is the basic unit of data/text processed or generated by a large-language AI model.
Volatility: A measure of risk using the dispersion of returns for a given investment.
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