The Truth Behind the Democratic Party's Opposition to AI Policies and Capital Power Struggles
Author: Bayesian Beauty
When political slogans meet industrial interests, only through the lens of the US two-party struggle can we see the true underlying logic of the AI capital market.
After the conclusion of the 2026 US midterm elections, a familiar narrative inertia reappeared in the secondary market: Democrats win, and the US AI industry will suffer; data center construction may face restrictions, tech giants' capital expenditures will be impacted, and the AI bull market thereby faces systemic risk.
This claim sounds logically self-consistent. But if we extend the historical cycle slightly, we will discover a highly worth-examining fact:
From 2022 to 2024, the US President was the Democrat Joe Biden. And what did the US AI industry experience from 2023 to 2024?
That period saw the full-blown explosion of generative AI, a surge in computing power demand, large-scale expansion of tech giants' capital expenditures, and historic profitability surges for core leading companies such as NVIDIA.
Did these epoch-making developments occur in another parallel universe? If the Democratic Party inherently opposes AI development, then why did the most critical commercialization boom of the US AI revolution precisely occur during a Democratic presidency?
Of course, market rallies do not equate to complete correctness in government industrial policies. However, historical facts at least remind us: equating Democratic governance directly with AI industry decline is too rough logically and lacks sufficient empirical support. More important than simply attaching partisan labels is a deep understanding of actual policies, economic interests, and industrial transmission mechanisms.
Honor History First: The Truth Behind the AI Bull and Bear Markets During Democratic Governance
Let's first look at a few sets of intuitive market data.
In 2022, the Nasdaq Composite Index fell by 33.10%.
In 2023, the Nasdaq staged a strong rally of 43.42%.
In 2024, the Nasdaq continued to rise by 28.64%.
Over just these two years from 2023 to 2024, the Nasdaq's cumulative gain reached approximately 84.5%.
Now consider the most representative leading company in the AI industry—NVIDIA.
According to the annual reports submitted by NVIDIA to the SEC, its data center revenue was:
FY2023: $15.005 billion;
FY2024: $47.525 billion;
FY2025: $115.186 billion.
Within just two fiscal years, NVIDIA's data center revenue grew by approximately 7.7 times. Here, FY2025 ended on January 26, 2025, primarily reflecting the business leap throughout 2024.
Meanwhile, Meta's capital expenditures (including finance lease principal payments) reached $39.23 billion in 2024, and Microsoft's cash expenditure for purchasing property and equipment in fiscal year 2024 reached $44.48 billion. Although not all of these expenditures were directed toward AI, they fully corroborate the massive investments made by large tech enterprises in infrastructure.
These figures are not speculative media claims, but officially disclosed audited financial data from publicly listed companies. It must be emphasized that from 2023 to 2024, the US political landscape featured a Democratic president alongside a divided Congress; the market rally cannot be entirely attributed to a single party. Similarly, in 2022 when Democrats simultaneously controlled the White House and both chambers of Congress, tech stocks still suffered heavy losses.
What does this indicate?
The bull and bear markets in tech stocks have never been a univariate function of political variables. The 2022 tech bear market was closely tied to the Fed's rapid interest rate hikes, soaring real interest rates, and severely compressed valuations; whereas the 2023–2024 AI bull market was jointly driven by foundational technological breakthroughs, continuous upward revisions in corporate earnings, expansion of capital expenditures, and recovery in risk appetite.
If a single political party could explain the ups and downs of tech stocks, then the severe plunge in 2022 and the explosive surge in 2023 during the same presidential term would be completely logically untenable.
The Biden Administration's Actual Actions: The CHIPS Act and Computing Infrastructure
Compared to secondary market gains, more worthy of in-depth research is the actual industrial policy implemented during Biden's term.
First, in August 2022, the CHIPS and Science Act was officially signed and promulgated.
The legislation allocated $52.7 billion to the US domestic semiconductor industry and related initiatives, with approximately $39 billion dedicated to semiconductor manufacturing incentives and roughly $11 billion for cutting-edge R&D. This bill received bipartisan support across party lines and was not solely pushed by Democrats, but it indeed completed its legislative passage and implementation during Biden's tenure. One of its core objectives is precisely to strengthen US advanced process manufacturing capabilities and reduce external dependencies on key computing supply chains.
Semiconductors are the core foundation of AI computing infrastructure. If the White House totally opposed AI industry development, it would be impossible to explain this multi-billion-dollar subsidy bill for advanced process nodes.
Second, in October 2023, Biden signed Executive Order 14110 on AI.
Many market participants today, when mentioning this executive order, often only recall model safety testing, algorithmic compliance, and government regulatory requirements. However, the complete document not only addresses security risk management but also explicitly includes provisions for supporting frontier AI innovation, fostering market competition, attracting global top talent, promoting scientific research, and accelerating adoption within federal government systems. This executive order reflects a policy combination where governance and development coexist: aiming to secure the technological high ground while simultaneously seeking to bring it under a regulatory framework.
We can debate whether its compliance costs are excessively high or whether certain security reviews affected R&D pacing, but we absolutely must not misinterpret "advancing AI governance" as "stifling technological development."
Third, in September 2024, the White House proactively coordinated efforts to address bottlenecks in AI data center construction.
On September 12, the Biden administration personally convened hyperscale cloud providers, AI laboratories, data center operators, and electric utility companies for closed-door discussions on hyperscale computing clusters, grid expansion, clean energy assurance, cross-agency project approvals, and labor shortages. Subsequently, the White House spearheaded the establishment of an AI Data Center Infrastructure Working Group, with direct federal intervention to clear construction bottlenecks. This clearly demonstrates that the federal government soberly recognizes: the competition for comprehensive national strength in AI is not merely a contest of algorithmic models, but fundamentally a physical engineering competition involving GPU supply chains, electrical energy, and infrastructure delivery capabilities.
Fourth, in October 2024, AI was comprehensively integrated into the core of US national security strategy.
The Biden administration officially issued National Security Memorandum 25 (NSM-25), requiring the US to maintain a leading advantage in advanced frontier AI fields and strengthening coordination among computing infrastructure, defense R&D, and national security applications. Even on the eve of Biden's departure on January 14, 2025, the White House once again signed Executive Order 14141, specifically aimed at accelerating AI infrastructure expansion, directing the Department of Defense and the Department of Energy to assess federal lands suitable for supercomputing center construction.
Of course, executive orders do not equate to project implementation, and policy blueprints cannot be directly equated with final outcomes. But at least looking at legislative texts and governance trajectories, the generalized claim of "Democrats completely opposing AI development" is severely detached from objective reality.
Supporting AI Industry Technology Does Not Equal Unconditional Support for Tech Giants
A crucial economic distinction must be introduced here: supporting the technological development of a strategic emerging industry has never been the same thing as supporting all industry giants in retaining monopoly profits.
While supporting semiconductor localization and computing infrastructure, the Biden administration indeed enforced extremely stringent regulations on tech antitrust, algorithmic governance, and advanced process export controls.
In January 2024, the US Federal Trade Commission (FTC) formally initiated antitrust reviews against the strategic partnerships between Microsoft and OpenAI, Amazon and Anthropic, and Google and Anthropic, focusing primarily on whether super-giants are leveraging computing resources, exclusive funding, and distribution channels to entrench market competition barriers.
In 2022 and 2023, the federal Department of Commerce also continuously tightened export controls on advanced process computing chips. These measures did indeed directly squeeze the current revenues and global expansion space of some chip manufacturers, representing undeniable policy costs. But the thrust of antitrust and export controls targets market concentration and national security dynamics, not representing any desire from the federal government to stifle domestic frontier innovation.
Examined from the perspective of institutional economics, there are three levels of competing demands here:
First, at the national level, the goal is to maintain absolute leadership in frontier technologies;
Second, commercial giants aim to maximize shareholder profits through technological barriers;
Third, the general public hopes that the social costs and benefits brought by technological development are fairly distributed.
The three can overlap at certain stages, but structural tensions inevitably exist within specific interest mechanisms.
Therefore, a government can absolutely provide tens of billions in subsidies for domestic chip manufacturing while imposing strict restrictions on the flow of cutting-edge chips; it can also push forward computing center construction while requiring tech companies to bear more public grid retrofitting costs. Reducing this multi-dimensional complex game into simplistic black-and-white "support" or "opposition" will only mislead investors' value judgments.
The Focus of Controversy Shifts: Who Ultimately Pays for AI Data Center Expansion?
Why, in 2026, did computing data centers suddenly become the core agenda item in US local elections and congressional debates?
The most direct reason lies in the fact: the large-scale expansion of AI infrastructure has begun generating negative externality costs that are difficult to ignore. A research report published by the US Department of Energy in 2024 showed that nationwide data centers consumed approximately 176 terawatt-hours annually in 2023, accounting for 4.4% of total national electricity consumption. The report projects that by 2028, data center electricity usage could surge to 325 to 580 terawatt-hours, causing its share to skyrocket to 6.7% to 12.0%.
Although these are predictive values based on scenario modeling, they intuitively outline the grim scale of computing consuming resources. Hyperscale data centers require not only NVIDIA compute cards but also supporting natural gas or nuclear units, ultra-high-voltage transmission lines, giant substations, closed-circuit cooling towers, water resource quotas, and contiguous industrial land.
Thus, the core contradiction surfaces: who ultimately bears such massive infrastructure expansion costs?
If tech giants exclusively reap the high market cap and commercial profits brought by AI productivity, while the costs of power grid expansion and surging electricity loads are passed on to ordinary households through utility rates, local community backlash will rapidly evolve into an electoral political storm. This resistance does not stem from opposition to AI technology itself, but rather from practical anxieties over rising residential electricity bills, water resource encroachment, environmental pollution, and unfair distribution of public subsidies.
In January 2026, Democratic Senator Cory Booker and others submitted the "Power for the People Act," whose core demand is precisely to require large data center developers to independently bear all incremental costs associated with grid expansion, firmly cutting off the channels for passing these costs onto ordinary residents.
Notably, however, this regulatory stance is by no means exclusive to the Democratic camp. In September 2026, the US House of Representatives overwhelmingly passed the Republican-sponsored "Ratepayer Protection Act" with 417 votes in favor and 3 against, with many Democratic legislators also casting affirmative votes. The bill subsequently secured a 57-to-43 vote in the Senate's procedural vote, failing to cross the 60-vote cloture threshold. Opposing Democratic lawmakers mainly argued that the bill lacks enforceable mandates across states, while Republicans insist on preserving state-level autonomous regulatory authority.
This legislative maneuvering fully indicates: both parties hold extremely high consensus on the basic livelihood goal of "protecting local residents from being burdened with passed-on high electricity prices"; the core of the dispute lies solely in the execution boundaries between strong federal intervention and state autonomy. The complexity of the true political landscape is far beyond what simple two-party labels can summarize.
The Substantive Impact of Data Center Restriction Proposals on Corporate Costs
Rational industry observation must similarly avoid sliding into the opposite extreme of dismissively attributing all statements regarding restrictions on computing infrastructure merely to campaign rhetoric.
After the midterm elections settled on November 3, 2026, Reuters reported that Democratic candidate Gina Hinojosa, who won the Texas gubernatorial race, had publicly pledged to delay approving new large-scale data center connections until clear community rights protection and consumer cost rules are established. Given that Texas holds extremely high weight in the US's pending computing capacity, such approval stalls will inevitably disrupt project delivery timelines, cross-regional asset allocation, and internal rate of return (IRR) models, even if they do not alter ultimate computing demand.
However, another undeniable fact is: long before this election, Republican former Texas Governor Greg Abbott had already rolled out measures suspending data center grid access permits in certain areas and launched special review procedures. By September 2026, Republican candidates in at least five states nationwide had explicitly advocated for strict control over data center expansion. This indicates that the grassroots trend of restricting computing infrastructure is a product of shared energy pressures across all US states, and blaming it solely on a single party's ideology does not align with facts.
Whether the new governor's campaign pledges materialize depends on multiple checks and balances from state legislative powers, Public Utility Commission (PUC) regulatory frameworks, existing compliant contracts, and judicial litigation. Media declarations of victory do not equate to immediate administrative restriction enforcement. Opposing location subsidies for specific computing parks is a completely different proposition from negating AI industry development.
Yet market investors must also not harbor wishful thinking: even if long-term AI demand remains robust, forced delays in project timelines will bring tangible interest and depreciation losses. For third-party data center developers operating with high leverage and relying on rapid commissioning to recycle cash flow, this asset delivery risk is particularly lethal.
Common Economic Constraints Faced by Both US Parties on AI Issues
AI at the current stage has completely broken free from the realm of pure software applications.
It is deeply intertwined with advanced wafer manufacturing, national cybersecurity, defense military applications, cloud supercomputing hubs, energy power construction, frontier scientific computing, and the global pricing power of transnational tech leaders. This means that whichever party holds office, while steering administrative power, must confront highly consistent objective economic constraints.
On one hand, frontier AI is widely recognized as a core lever for improving total factor productivity, driving capital expenditures, and reshaping national competitiveness; on the other hand, gargantuan computing clusters impose severe pressure on power supply, water resource consumption, public fiscal subsidies, and workforce employment structures.
Both parties emphasize different policy preferences and governance tools at various levels, but no single political executive can eliminate these objective physical and economic constraints relying solely on campaign speeches.
Investors in the secondary market frequently use individual congressional members' personal holdings as directional indicators. For example, investment disclosures showing the Pelosi family and several congressmembers holding significant tech stock exposure is often cited as grounds that policies will not be overly harsh. But personal holdings suffer from numerous limitations including delayed disclosure, broad position ranges, and independent operation of trust accounts. One absolutely cannot infer that a legislator would necessarily block public grid regulatory legislation merely because their family holds NVIDIA stock.
Individual asset allocation can only reflect personal economic incentives and cannot replace systematic tracking of congressional voting records, federal executive orders, and industry regulatory details. Rather than focusing on what assets politicians hold, it is far more important to discern which interest landscapes are truly being reconstructed by finalized policy clauses.
Common Pitfalls in Capital Markets: Equating Policy Narratives with Earnings Changes
From a professional investment perspective, for a macro political news piece to truly impact a stock's intrinsic value, it must undergo a rigorous causal transmission chain: macro political event → formation of policy text → implementation of administrative procedures → alteration of project microeconomic models → reshaping of future corporate cash flow expectations → repricing of discount rates and valuation premiums.
In this chain, if any single link experiences delay or deviation, the final market outcome will diverge significantly from initial emotional expectations. For example, if a state increases special fees for data center grid connection, it will directly lower the project internal rate of return (IRR) for data center heavy-asset developers; but if finally overscale cloud vendors absorb this premium in full, then real procurement demand for underlying GPUs will not see substantial contraction.
Similarly, project approval delays, while negative for newly built developers in specific regions, conversely elevate the scarcity premium for existing computing assets that have already secured ample power quotas and interconnection agreements. If the capital market broadly labels all political statements as "major negatives for the AI sector," it will generate severe mispricing across different sub-sectors.
Within a causal attribution analytical framework, we can decompose current market risk into three competitive hypotheses:
Hypothesis One (Demand Suppression): End-user AI commercialization demand faces substantive curbs at the policy level. This hypothesis requires verifying with direct financial evidence such as tech giants downgrading full-year capex, widespread server order cancellations, and front-model training compute budgets being slashed in half;
Hypothesis Two (Cost Reconstruction): Underlying AI demand remains elevated, but infrastructure construction costs and energy bills undergo reallocation across upstream and downstream segments of the industrial chain. This requires closely tracking grid interconnection fee trends, compute delivery cycles, developer gross margin levels, and cloud service contract quoting;
Hypothesis Three (Valuation Clearing): Political events merely serve as the spark triggering repricing of crowded trades. The true core reason behind sharp stock price pullbacks lies in previously extremely high valuation multiples, excessively crowded long positions, liquidation of leveraged funds, and liquidity squeezing caused by derivative hedging.
The third scenario deserves particular high attention under the current market environment.
History from 2022 to 2024 sufficiently proves: under an identical macro political landscape, variations in corporate earnings cycles, tightening or easing of monetary liquidity, are enough to sculpt dramatically divergent market trends.
The AI sector in 2026 is exactly the same. Even though the industry's long-term growth potential remains vast, sky-high capital expenditures, expensive financing rates, and highly concentrated holdings with high Beta inherently contain tremendous technical correction pressure.
Therefore, maintaining a long-term bullish view on AI does not mean blindly bottom-fishing during every correction, nor does it imply that macro risk exposure across the entire portfolio can be ignored.
Truly successful contrarian investing must be firmly grounded in independent examination of underlying fundamentals, liquidity conditions, and valuation rationality, rather than betting on simplistic optimism or pessimism toward a specific political party.
Beyond Partisan Labels: Piercing Through Industrial Laws and Valuation Realities
Looking across the entire AI industry cycle, the cognitive trap capital markets most easily fall into is: habitually using extremely simplified political narratives to explain highly complex industrial evolution.
Democrats win elections, markets worry about tighter regulation, prompting tech stock sell-offs; Republicans win elections, markets blind-wager on deregulation dividends, subsequently driving up asset prices.
This emotional narrative possesses immense viral penetration on social media, but within rigorous asset pricing models, it often overlooks the most critical intermediate variables.
The majestic AI bull market from 2023 to 2024 did not halt because the Biden administration released the AI safety executive order; similarly, the energy constraints facing computing infrastructure in 2026 will not dissipate out of thin air merely because Republicans emphasize technological hegemony.
Policies will affect grid connection and land approval progressions, but cannot magically erase enterprises' rigid demand for intelligent productivity; policies may squeeze monopoly windfall profits in specific intermediate links, but do not hinder the penetration of advanced processes across society; policy-caused project cycle delays are genuine risk variables, but absolutely cannot be equated with the overturning of industry logic.
A mature and in-depth investment analytical framework must hierarchically deconstruct and independently model partisan campaign slogans, institutional/legal boundaries, energy micro-models, corporate cash flow performance, and asset valuations.
Returning to the original core proposition: Do Democrats truly oppose AI?
From the detailed policy track records and legislative archives spanning 2022 to 2024, it is utterly impossible to derive the crude assertion that Democrats seek to completely suppress AI development. But this does not mean that regulatory forces advocating taming the giants due to environmental concerns, household electricity pricing, or antitrust worries are absent within political circles. A more precise reality is: the expansion of the US AI industry has stepped from initial technological revelry into the complex deep waters where computing infrastructure externalities and social wealth distribution are profoundly intertwined.
For secondary market investors, what determines the life and death of investment performance has never been slapping cheap pro or con labels on political parties, but rather calmly verifying whether technological demand can continuously materialize, whether tech enterprises can accumulate abundant free cash flow, and precisely how institutional transformations reshape the profit cake across each segment of the industrial chain.
Do not misread campaign slogans as industrial facts, do not directly equate short-term electoral victories with earnings guidance, and do not mistake extreme market sentiment for rational investment conclusions.
If the bull and bear transitions of recent years have left the market with its most profound lesson, it is this:
The magnificent evolution of frontier technology can traverse prolonged political cycles, but the valuation multiples and profit quality of any tech giant must accept the harshest reality tests within every concrete cycle.
True independent thinking does not involve wishfully believing a specific party will forever nurture tech bull markets, but rather possessing the ability to peel back layers to identify technological trends under any policy environment, verify data evidence using first principles, and ultimately perceive precisely where capital will flow.