Author: Bu Shuqing, Wall Street Insights

The results of the U.S. midterm elections are introducing an unexpected variable to the AI industry.

The Democratic Party achieved victories in several key states, with some candidates promising to pause or tighten the approval of data center construction. As anti-data center forces quietly rise, the market begins to worry that the expansion of AI infrastructure in the U.S. will face resistance, causing a massive shock in global chip stocks, with companies like Samsung Electronics and SK Hynix facing risks related to changes in demand expectations for server DRAM and high-bandwidth memory.

However, technology author Derek Thompson has put forward an intuitive counterpoint: the intensifying anti-AI politics in the U.S. may suppress AI stocks in the short term, but could help the entire industry avoid a bubble in the medium to long term. Thompson worked as a full-time writer for The Atlantic for 17 years, closely following technology, economics, labor markets, and social trends. He is also a co-author of the best-selling book "Abundance" and currently hosts the podcast "Plain English," where he analyzes U.S. technology and economic issues through his personal newsletter.

The key to this judgment is not that the anti-AI movement will eliminate AI demand, but that it may change the speed of AI supply expansion. Historical technology bubbles often accompany infrastructure investments that outpace real demand; however, the current AI computing power remains tight. If political resistance slows down data center construction, it might allow demand to catch up with supply, and the anti-AI movement could inadvertently step on the brakes for the AI industry.

Republican Setbacks and the Rise of Anti-Data Center Forces

After the U.S. midterm elections, the political risks of data center construction began to attract market attention.

The Democratic Party won in key states like Texas and Georgia, with some candidates explicitly proposing a moratorium on new data center construction. Texas, in particular, is noteworthy: GLJ Research estimates that the state accounts for about a quarter of the data center capacity planned for use in the U.S. by 2030. Democrat Gina Hinojosa defeated Republican incumbent Governor Greg Abbott, and her campaign promises included halting new data center approvals.

Georgia is also an important data center market. According to CBRE data, Atlanta is one of the most active markets for data center construction in the nation. Democrat Keisha Lance Bottoms' victory in the gubernatorial election has led the market to begin assessing potential changes in local data center construction policies.

The anti-data center sentiment is not merely a partisan conflict. Local residents oppose projects primarily due to concerns about noise, water consumption, massive energy demands, and potential increases in local electricity rates; at the same time, communities also question whether the jobs and tax revenue generated by data centers are sufficient to justify the incentives provided by the government.

This resistance is crossing party lines. As the expansion of data centers increasingly conflicts with local electricity, land, and water resources, AI infrastructure construction is no longer just a business decision for tech companies and investors but is becoming a local political issue.

The resulting market concern is: if more states implement moratoriums or raise approval thresholds, the pace of data center expansion in the U.S. may slow down, subsequently affecting demand expectations for the AI infrastructure supply chain including servers, storage chips, and high-bandwidth memory.

The Risk of an AI Bubble May Lie Precisely in The Supply Expansion Being Too Rapid

However, Derek Thompson believes that the impact of anti-AI politics on the AI industry may not be solely negative.

His logic is based on a common lesson from the history of technology: the technology itself has value, but that does not mean the capital invested around it is necessarily rational. Many tech bubbles eventually burst not because the technology loses significance, but because the pace of infrastructure construction far exceeds real demand.

In the 1870s, American railroads were built in areas lacking sufficient population and economic activity to support them; after the internet bubble burst, many fiber optic networks remained unused for extended periods. The growth in demand brought about by technological revolutions ultimately failed to absorb the supply that expanded too rapidly beforehand.

Thompson believes there’s an important distinction between the current AI industry and these historical cases: AI computing power supply is still tight, and demand is rapidly increasing.

In his judgment, the chips in data centers are still in short supply, and the demand for computing power continues to grow. In other words, the current risk for the AI industry is not necessarily insufficient demand but rather capital being invested too quickly, leading to potential future supply exceeding actual demand.

If data center construction continues to expand at a rapid pace, the industry might invest a large amount of capital in infrastructure before demand is realized. If the commercialization of AI applications does not proceed as expected, issues of excess capacity, equipment depreciation, and financing pressure could become concentrated.

And the anti-data center politics may very well alter this process from the supply side.

Political Resistance Slowing Construction Allows Demand to Catch Up With Supply

According to Thompson's analysis, if local government and residents' opposition results in stricter approval processes and elongated construction timelines for data centers, the growth rate of AI infrastructure supply may slow down.

This does not mean that AI demand will decrease in tandem. Model inference, AI applications, and intelligent agents may still drive the growth of computing power demand. If supply expansion slows while demand continues to grow, the gap between the two has the opportunity to gradually narrow.

This is precisely why he believes anti-AI politics may help avoid a bubble: it inadvertently limits the speed of capital investment, allowing the industry time to absorb the existing infrastructure.

From this perspective, the anti-AI movement's impact on the AI industry might present a contrast:

In the short term, data center projects face pauses or delays, putting pressure on tech stocks and the AI supply chain; in the medium to long term, if demand continues to grow while supply expansion slows, it might reduce the risk of infrastructure oversupply.

Of course, this does not mean that setbacks to data center construction are a benefit for all companies. Chip manufacturers, server providers, storage firms, and data center operators may still face pressures from project delays, order postponements, and slow revenue realization.

Anti-AI Regulation May Indeed Become the "Manager" of the AI Bubble

Economic historian Adam Tooze later offered a sardonic comment on Thompson's viewpoint: anti-AI regulatory politics could actually benefit AI by helping to "manage the bubble." In his view, this sounds like a spokesperson for the overall interests of capital.

This commentary highlights the most interesting aspect of this argument: the original intention of the anti-AI movement might be to limit the tech giants' occupation of local resources, or even to prevent the AI industry from continuing to expand; but if it indeed slows down data center construction, it may inadvertently help capital avoid overinvestment.

This also provides an alternative understanding of the market changes following the U.S. midterm elections. Democratic victories and the rise of anti-data center forces may increase uncertainty in AI infrastructure construction in the short term; however, from a longer time perspective, the market may need to assess not only how much demand is lost due to slowing construction, but also whether the previous rapid expansion of supply needs to cool off.

However, Thompson's viewpoint remains a projection rather than a verified conclusion. How much new supply political resistance will limit, whether AI demand can continue to grow, and whether companies can convert computing power investments into real income will all determine whether this logic holds true.

If the core risk of the AI bubble is that supply expansion is moving too fast, then could the anti-AI movement become the most unexpected brake in this capital frenzy?

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