America's AI infrastructure crisis: When inflated expectations meet structural realities

Energy shortages in traditional tech centers and the hidden costs of the AI ​​boom

In the epicenter of the global AI revolution, the United States, a feverish gold rush mentality prevails. Billions of dollars in investments, groundbreaking technologies, and the promise of a new era of productivity and prosperity dominate the public image. Businesses and government alike are outdoing each other with visions of a future transformed by artificial intelligence. But behind this glittering facade of technological omnipotence, a fundamental crisis is brewing, one that threatens to shake the very foundations of the American AI boom. The dream of limitless growth is colliding with the harsh reality of an overburdened infrastructure.

A closer look behind the scenes reveals a cascade of systemic bottlenecks that reinforce each other. The Achilles' heel of the American AI strategy is not a lack of brilliant algorithms, but a failure to meet the most basic requirements: The power grid, designed for decades of stagnation, is facing a demand shock of historic proportions. At the same time, the need for millions of AI specialists is exploding, a number the education system cannot even begin to produce. Critical resources like water are becoming fiercely contested commodities in already drought-stricken regions, while supply chains for essential high-performance chips are groaning under global pressure.

Here we analyze the profound infrastructure crisis in the US and demonstrate how the discrepancy between inflated expectations and structural realities is becoming an existential threat to the AI ​​boom. From energy shortages and a lack of skilled workers to growing public resistance and the looming threat of a speculative bubble, a picture emerges of an industry that is on the verge of failing due to its own unmet needs. The question is no longer whether a correction will occur, but how profound the shock of disillusionment will be when the digital revolution encounters its physical limitations.

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Between gold rush fever and looming disillusionment shock

The United States is engaged in an unprecedented race for dominance in artificial intelligence. But behind the glittering facade of technological superiority and billions of dollars in investment lies a complex mix of structural challenges that are increasingly shaking the foundations of the American AI boom. While companies and governments tirelessly extol the transformative power of the technology, it is becoming ever clearer that the infrastructure cannot keep pace with these ambitions and that the vision for the future may be built on sand.

The central irony of the American AI revolution is that the very nation that sees itself as the undisputed technology leader is in danger of failing at the most basic levels. Electricity, personnel, physical infrastructure, and regulatory frameworks are becoming bottlenecks for an industry that takes exponential growth for granted. This discrepancy between technological vision and infrastructural reality could prove to be the Achilles' heel of the American AI strategy.

The energy paradox of the digital revolution

The energy question is emerging as perhaps the most fundamental challenge facing American AI development. After two decades of largely stagnant electricity consumption, the American energy system is facing a demand shock of historic proportions. Analysts at Deloitte predict that electricity demand from AI data centers could increase from the current four gigawatts to 123 gigawatts by 2035. This more than thirty-fold increase would fundamentally reshape the entire energy system of the United States.

The sheer scale of some projects defies previous understanding. While the largest existing data centers of leading hyperscalers currently draw less than 500 megawatts of power, facilities with two gigawatts of capacity are in the planning or construction phase. Particularly dramatic are projects in the early planning stages that are slated to be built on 50,000 acres and would require five gigawatts. These individual data centers would consume more electricity than the largest nuclear or gas-fired power plants in the US produce and could power five million homes.

The structural problem lies not only in the absolute amount of demand, but also in the nature of the load. AI data centers generate continuous, 24/7 base load demand, combined with massive spatial concentrations. In Virginia, the world's largest data center market, harmonic distortions in the power grid, load shedding warnings, near misses, and power plant shutdowns have already occurred. Waiting times for grid connections have stretched to as much as seven years, while the industry needs solutions in months, not years.

Power shortages are forcing companies to take drastic measures. xAI's data center in Memphis avoids months-long waits by using mobile, gas-powered generators, which are significantly more expensive to operate than grid-connected power plants. This emergency solution underscores the urgency with which companies must build up computing capacity, even if it is economically suboptimal. The speed of energy access has emerged as the most important location factor, surpassing traditional criteria such as electricity price or land availability.

The geographic distribution of energy shortages is highly uneven. Virginia, Texas, and California together account for an estimated 80 percent of American data center capacity. This concentration effect dramatically exacerbates regional grid strain. In Virginia, data centers consumed approximately 26 percent of the total electricity supply in 2023; similar concentrations are seen in North Dakota (15 percent), Nebraska (12 percent), Iowa (11 percent), and Oregon (11 percent). Local infrastructure is increasingly reaching its physical limits.

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The energy crisis reveals a deeper systemic problem. For decades, the energy infrastructure was geared towards moderately or even stagnant demand. The American system is structurally unsuited for rapid growth. Permitting, planning, and constructing new transmission lines takes five to ten years. New power plant capacity faces similar timeframes. The interconnection queues are 95 percent filled with renewable energy and storage projects, while baseload generation capacity is shrinking.

The energy situation is exacerbated by supply chain problems for critical grid components. Transformers, switches, and circuit breakers are experiencing unprecedented demand. Natural gas turbines are largely sold out until the end of the decade. Industry is pinning its hopes on advanced nuclear technologies, but these will not be commercially available until the 2030s at the earliest. The gap between the need for and availability of solutions is widening continuously.

The silent exodus inland

The energy shortage in traditional tech hubs is catalyzing a quiet geographical reorganization of America's AI infrastructure. The Midwest is experiencing an unprecedented boom as a data center location. Amazon Web Services is investing $7.8 billion in Ohio, Microsoft is pumping billions into the region, and Google is interested in Indiana. This shift doesn't primarily reflect cost-cutting strategies, but rather the desperate search for the four critical resources: land, energy, water, and connectivity.

The Midwest offers structural advantages that coastal regions cannot replicate. Electricity costs 20 to 40 percent less in Iowa, Nebraska, and South Dakota than on the coasts. The region generates over 60 percent of its electricity from renewable sources, primarily wind power. Industrially suitable land is available in virtually unlimited quantities. In addition, a cooler climate significantly reduces cooling costs and enables free cooling techniques that utilize ambient air for heat dissipation.

The political economy of location selection is undergoing a fundamental shift. Midwestern states and municipalities have developed streamlined permitting processes that reduce project timelines by six to twelve months compared to Tier 1 markets. Tax incentives, infrastructure guarantees, and workforce development programs further enhance the region's appeal. The contrast with coastal regions could hardly be greater, where organized resistance to data center projects is increasingly emerging.

This geographical shift, however, creates new challenges. Latency to key internet exchange points increases. The availability of highly specialized professionals is more limited than in established tech hubs. The social and economic infrastructure of rural regions is unprepared for the sudden influx of technology investments. The transformation is happening faster than local communities can adapt, leading to tensions.

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The personnel trap of the AI ​​industry

Alongside the energy crisis, a dramatic shortage of skilled workers is developing into a second fundamental challenge. A White House report puts the shortage of AI specialists at over four million. This figure is not a hypothetical projection, but reflects concrete needs. 36 percent of all AI-related positions in the US remain unfilled. In some specialized areas, companies can find virtually no qualified applicants.

The demand for AI skills is exploding at a breathtaking pace. Between 2015 and 2023, job postings requiring AI skills increased by 257 percent, while the total number of job openings grew by only 52 percent. In 2024, AI-related job postings reached 1.8 percent of all job openings in the US, a year-over-year increase of 28.6 percent. The supply of qualified professionals is nowhere near keeping pace with this growth.

Leading AI research organizations like OpenAI and Google DeepMind are in a constant state of recruitment. Training a single AI model can cost over $100 million. To attract the best talent, top AI labs allocate between 29 and 49 percent of their budgets to personnel. This competition for top talent is driving salaries to astronomical heights. Professionals with AI expertise command a 56 percent salary premium compared to similar positions without AI specialization.

The hardware sector is suffering from similar talent shortages. Data centers and semiconductor supply chains require highly specialized engineers. In 2021, investments in American data centers reached $48 billion, yet the annual demand for talent is growing by three percent. The majority of these positions require advanced academic degrees, but the education system is not producing enough graduates. The semiconductor supply chain is particularly affected, as design, manufacturing, packaging, and testing demand highly specialized expertise. Over 50 percent of the workforce requires at least a bachelor's or graduate degree.

Educational institutions cannot keep pace with the speed of technological development. AI is evolving faster than curricula can be adapted. The World Economic Forum estimates that 40 percent of the world's required workforce skills will become obsolete within the next five years. Traditional curricula are structurally incapable of providing the necessary flexibility. The gap between industrial demand and academic output is constantly widening.

The United States is structurally dependent on foreign talent. Over 50 percent of computer scientists with graduate degrees employed in the US were born abroad. Nearly 70 percent of enrolled PhD students in computer science are of foreign origin. Approximately 80 percent of PhD students in AI-related fields trained in the US remain in the country. This dependency creates vulnerability. Stricter immigration policies or increased competition from other countries for this talent could fundamentally weaken the American position.

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The chip shortage as a growth inhibitor

GPU shortages are emerging as the third critical bottleneck. Exploding demand for AI computing power is colliding with fundamental supply chain constraints. Lead times for high-end accelerators have stretched to six to nine months. Cloud costs vary by up to 95 percent between traditional providers and new alternatives. Companies without hyperscaler budgets have virtually no access to sufficient computing capacity.

The causes of this scarcity are multifaceted. The unprecedented demand from tech giants seeking to train increasingly larger AI models is the most obvious factor. The devastating earthquake in Taiwan in 2025 damaged critical semiconductor wafers, dramatically exacerbating the situation. Geopolitical tensions led to disruptive tariffs and export controls, fragmenting established manufacturing flows. Computing power has transformed from a technical resource into a strategic competitive advantage.

Nvidia's near-monopoly on the AI ​​GPU market is largely unified by its CUDA ecosystem. This reliance on a single vendor significantly exacerbates supply shortages. Production utilizes cutting-edge 5-nanometer or 7-nanometer processes, but available wafer capacity is limited. Advanced packaging technologies like high-bandwidth memory integration and CoWoS packaging create additional bottlenecks. Nvidia's next-generation Blackwell GPUs are already booked out for a year or more, with hyperscalers like Microsoft, Google, and Meta dominating allocations.

The high-bandwidth memory market is experiencing its own dramatic bottlenecks. HBM3, the memory standard for data-hungry AI accelerators, is produced by only three manufacturers: SK Hynix, Samsung, and Micron. These companies are operating at near full capacity and reporting lead times of six to twelve months. Combined with specialized packaging requirements, especially for TSMC's CoWoS integration, lead times are sometimes extended even further. Prices for HBM3 have already risen by 20 to 30 percent compared to the previous year, a trend expected to continue into 2025.

Foundry capacity is under extreme pressure. While TSMC is expanding aggressively, new fabs take years to become operational and cost tens of billions of dollars. Short-term capacity bottlenecks were reported in 2024 and 2025, with deliveries further hampered by design flaws in chips. This situation typically leads to demand overshoots and shortage gaming in the supply chain. TSMC is expected to extend its capacity investments beyond the strictly necessary short-term needs. This could lead to temporary overcapacity, followed by renewed bottlenecks a few years later when pent-up demand eases.

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The water issue as an underestimated conflict

While energy and chips receive media attention, water is emerging as an underestimated third resource crisis. AI data centers consume enormous amounts of water for server cooling. A typical 100-megawatt data center requires up to two million liters of water daily, equivalent to the consumption of 6,500 households. Meta's data center in Georgia consumes about 500,000 gallons per day. New facilities designed for AI are expected to require millions of gallons daily.

The geographical distribution significantly exacerbates the problem. A Bloomberg analysis found that over two-thirds of new data centers built since 2022 are located in water-stressed regions. Approximately 160 new AI-focused data centers have been built in the US in the last three years, a 70 percent increase over the previous three years. States like Texas and Arizona, already experiencing historic droughts, are seeing massive new data center projects, including a $100 billion OpenAI campus in Abilene, Texas.

The International Energy Agency warns that data centers worldwide already consume approximately 560 billion liters of water annually. This figure could double by 2030. AI-specialized data centers contribute disproportionately, with consumption rising from 30 billion to 338 billion liters by 2030. The average water consumption rate will increase from 0.36 liters per kilowatt-hour in 2023 to 0.48 liters per kilowatt-hour in 2030, driven by the higher power densities of AI data centers.

Newton County, Georgia, exemplifies the local impact. After the construction of Meta's $750 million data center, wells in the surrounding area ran dry. A report predicted the county could face a water deficit by 2030. Unless the local water authority upgrades its infrastructure, residents may have to ration their water. Water prices are slated to rise by 33 percent over the next two years, compared to the usual two percent annually. Similar problems are emerging in Texas, Arizona, Louisiana, and the United Arab Emirates.

The water crisis reveals a deeper governance failure. While municipalities can expand energy capacity through new solar, wind, or nuclear power, water resources are fundamentally limited. In Newton County, the supply depends on a nearby reservoir that is replenished only by rainfall. Tech companies prioritize locations with low energy costs, even when these regions experience droughts. Water remains an afterthought for tech firms; the attitude is: Someone will sort it out later.

Organized resistance to data center expansion

The combination of resource pressures and local impacts is catalyzing growing community resistance. Over $64 billion in data center projects have been blocked or delayed in the last two years. Approximately $18 billion of projects have been canceled entirely, and another $46 billion has been delayed. Data Center Watch identified 142 local activist groups dedicated to slowing development. The resistance spans two dozen states and unites a broad political spectrum.

The opposition is remarkably bipartisan. Roughly 55 percent of public officials who oppose data centers are Republicans, 45 percent Democrats. This rare bipartisan phenomenon reflects the fact that local impacts transcend ideological boundaries. Residents are organizing around concerns about noise, water consumption, network congestion, traffic, light pollution, and environmental impact. The criticism is rarely one-dimensional but combines multiple factors.

Concrete examples illustrate the scale of the problem. Tract's $14 billion project in Arizona was withdrawn in May 2024 after residents pressured local officials not to approve the necessary rezoning. Culpeper Acquisitions' $12 billion project in Virginia was unanimously rejected by the Planning Commission, citing concerns about rural preservation and impacts on state parks. Amazon's project in Warrenton, Virginia, drew over 500 people to a town council meeting, including Oscar-winning actor Robert Duvall. Every town council member who supported the project subsequently lost their reelection bid.

Legal battles are becoming increasingly sophisticated. In Fairfax County, Virginia, a citizens' group is fighting a $12 billion project through multiple lawsuits concerning permitting processes, withheld emails, and appeals. A court ordered the project halted for at least a year. These precedents are encouraging resistance elsewhere. Organizational structures are becoming more professional, with coordinated campaigns, legal expertise, and media outreach.

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The climate impact of the AI ​​boom

The environmental impact of AI infrastructure extends far beyond water consumption. Data centers contributed about 1.5 percent to global electricity consumption in 2024, but this share could double to 945 terawatt-hours by 2030, equivalent to Japan's total electricity consumption. In the US, data centers already account for 4.4 percent of energy consumption. This could rise to nine percent by 2030, exceeding the Energy Information Administration's baseline projections by 150 terawatt-hours.

Greenhouse gas emissions are growing accordingly. Data centers currently contribute about one percent to global energy-related emissions and are among the fastest-growing emission sources. By 2035, increased data center energy consumption could lead to an additional 0.4 to 1.6 gigatons of CO2 equivalent. Global CO2 emissions from data centers could rise from 212 million tons in 2023 to 355 million tons in 2030. AI-specific infrastructure will increase particularly dramatically, from 29 million tons to 166 million tons, and will overtake traditional data centers by 2030.

Individual projects create significant local air pollution. xAI's data center in Memphis emits an estimated 1,200 to 2,000 tons of nitrogen oxides annually and is among the largest regional emitters. High concentrations of nitrogen oxides damage human health and natural ecosystems. Some companies circumvent regulations through clever structuring. This practice undermines emissions targets and climate policy commitments.

Chip production itself contributes significantly to environmental pollution. Manufacturing facilities require massive amounts of water and energy. Most factories are located in regions with fossil fuel-based energy supplies. New semiconductor plants worldwide are leading to additional gas-based energy infrastructure. The manufacturing process involves complex steps from raw material extraction to chip production, each contributing to greenhouse gas emissions. The GPU carbon footprint is further exacerbated by transportation and product manufacturing.

The overall cost of AI training is staggering. Research from the University of Massachusetts shows that training a single AI model generates over 626,000 pounds of CO2, equivalent to five cars over their lifetimes. GPT-3's training phase consumed 1,287 megawatt-hours of electricity and produced 502 tons of carbon emissions, comparable to 112 gasoline-powered cars driving for a year. The inference operations generate continuous environmental burdens. A single ChatGPT query consumes 100 times more energy than a typical Google search.

A speculative game with an uncertain outcome

As infrastructure problems worsen, doubts are growing about the economic sustainability of the AI ​​boom. Global AI spending is projected to reach $375 billion in 2025 and climb to $500 billion in 2026. This unprecedented concentration of capital reflects investor confidence in AI transformation, but market selectivity has increased significantly. Funding is increasingly focused on later development stages and proven business models. The days of easy early-stage financing are over.

The analogies to the dot-com bubble are striking. Over 1,300 AI startups currently boast valuations exceeding $100 million, including 498 unicorns with valuations over $1 billion. These figures are reminiscent of the late 1990s. Unlike the dot-com era, however, today's AI leaders generate substantial cash flows and profits. Amazon, Meta, and Microsoft are investing billions in expanding their data centers using operating income. The fundamental stability of leading companies stands in stark contrast to the speculation of the turn of the millennium.

Nevertheless, warning voices are growing louder. An MIT report shows that approximately 95 percent of generative AI business efforts fail, with only five percent achieving significant revenue growth. Between 70 and 85 percent of current AI initiatives fall short of their expected results. While 78 percent of companies report using generative AI, the majority report no significant bottom-line effect. This gap between adoption and results underscores the GenAI paradox: widespread use, but limited measurable value.

Productivity gains are proving elusive. A UK government study by Microsoft's M365 Copilot found no discernible productivity gains, with some tasks accelerating but others slowing down. US research showed that companies invested $35 to $40 billion in generative AI initiatives, yet 95 percent saw zero returns. Stanford research indicates a 13 percent decline in entry-level positions in customer service, accounting, and software development since 2022, but the hoped-for broad productivity revolution has not materialized.

Stock valuations are reaching dangerous levels. The S&P 500 is trading at 23 times forward earnings, while the FTSE 100 is trading at 14 times. The Shiller price-to-earnings CAPE ratio has exceeded 40, for the first time since the dot-com crash. The five largest tech companies now account for 20 percent of the MSCI World Index, double the amount they held during the dot-com bubble. Historically, periods of such extreme concentration have shown poor future returns. Since 1957, the top 10 stocks in the S&P 500 have underperformed the rest of the index by an average of 2.4 percent annually.

Capital Economics predicts that the AI-driven stock market bubble will burst in 2026, with rising interest rates and increased inflation putting pressure on valuations. Morgan Stanley Wealth Management's CIO Lisa Shalett warned of a "Cisco moment" similar to the dot-com crash, possibly within the next 24 months. Paul Kedrosky speaks of financial wizardry, with hyperscalers using accounting tricks to reduce infrastructure spending and inflate profits, as well as shifting massive expenditures into special-purpose vehicles.

Regulatory fragmentation as a brake on innovation

The regulatory environment further exacerbates the challenges. Unlike the centralized EU regulation through the AI ​​Act, the US has developed a multi-layered framework of federal executive orders and landmark state legislation. This patchwork approach means that organizations must navigate an increasingly complex web of requirements that vary across jurisdictions.

In the past two years, over 60 federal AI laws have been passed. More than ten states considered legislation on algorithmic harm and discrimination. All 50 states were considering AI-related measures in 2025. Colorado passed the most comprehensive regime, which will take effect in February 2026. Utah, Texas, and California each developed their own frameworks. These divergent policies create compliance costs for companies operating across state lines.

The federal level does not pursue a coherent legislative approach, but rather regulates through existing laws and agency directives. The Trump administration emphasized the removal of barriers to American AI leadership. The Executive Order "Removing Barriers to American Leadership in Artificial Intelligence" directed federal agencies to review and rescind policies that allegedly hinder AI innovation, to prioritize American competitiveness in global AI dominance, and to expedite approvals for AI infrastructure.

This governance-risk management approach, based on strict regulatory mechanisms, prioritizes rapid adoption. The plan emphasizes that the bottleneck to fully leveraging AI's potential is not model availability, but rather limited and slow adoption, particularly in large, established organizations. A lack of trust in or understanding of the technology, complex regulatory landscapes, and a lack of clear governance standards are identified as the main obstacles.

Tensions between states and the federal government are intensifying. The Trump administration may attempt to overrule states, similar to previous conflicts over net neutrality or vehicle emissions. California spent at least $41 million during Trump's first term defending policies in court. The unclear federal direction is forcing states to assume larger roles in AI policy, leading to patchwork governance and a weakening of the US position internationally.

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Monopolization by Big Tech

Market concentration is further exacerbating structural problems. Between 2017 and 2025, the combined revenue share of the top five digital companies doubled from 21 to 48 percent. Their share of total assets rose from 17 to 35 percent. This dominance is reflected across the entire AI value chain, from chips and cloud services to model development and deployment tools. The barriers to entry for smaller players are continuously increasing.

Generative AI requires massive computing power, chips, cloud services, talent, and data, all controlled by tech giants. Microsoft, Google, and Amazon are positioning themselves as essential AI service providers through their cloud platforms. AWS, Azure, and Google Cloud have become central to the AI ​​supply chain, providing the computing power, data centers, and specialized tools for training and deployment. The sheer scale of investment by these companies far surpasses that of smaller businesses and startups.

Strategic partnerships are increasing market concentration. Microsoft's partnership with OpenAI, Google's investments in Anthropic, and Amazon's stakes in AI startups are creating a network of dependencies. Over 90 partnerships and strategic investments between Google, Apple, Microsoft, Meta, Amazon, and Nvidia in the generative AI foundation model market have been identified. These interconnections limit the independence of smaller players and concentrate decision-making power.

AI startups attracted $89.4 billion in global venture capital in 2025, representing 34 percent of all VC investments, despite representing only 18 percent of the funded companies. This unprecedented concentration of capital reflects investor confidence, but market selectivity has increased significantly. Funding is increasingly focused on late-stage companies and proven business models. Startups without access to cloud computing, data, and capital from major players struggle to scale. Some are acquired by Big Tech, further consolidating control.

The efficiency limits of AI architecture

The technical challenges extend beyond resource scarcity. The cooling requirements of modern AI hardware are reaching physical limits. Traditional air-based CRAC and CRAH systems cannot handle the thermal loads of AI hardware. The industry is undergoing a rapid shift to advanced liquid cooling technologies, including direct-to-chip cooling and immersion cooling, where entire servers are submerged in thermally conductive liquids.

These solutions require entirely new facility designs, installations, and operational protocols. The integration of cooling systems with IT workloads must be dynamic. When a GPU cluster powers up for model training, the cooling system must respond instantly to prevent overheating. Intelligent data center management platforms link workload activity with environmental controls, enabling automated responses and reducing energy waste. Cooling can account for up to 60 percent of a data center's total energy consumption.

The 48-volt architecture is gaining importance in response to efficiency requirements. Increasing the voltage from 12 to 48 volts reduces the required current by the same factor. Line losses decrease by a factor of 16, as they are proportional to the square of the current. This improves efficiency, reduces heat dissipation, and allows for smaller busbars. At the same time, many systems and components still require regulated 12-volt power. Transforming power distribution within data centers requires massive infrastructure investments.

Latency requirements add further complexity. AI inference increasingly demands real-time responses. Edge computing and distributed data center architectures aim to minimize latency, but this multiplies the number of locations and the complexity of coordination. Geographic load shifting between data centers requires advanced predictions and global data, hardly reflecting the real-world situation of most operators. Load shifting models themselves require significant computation time and are not well-suited for real-time scheduling requirements.

The looming market crash and consolidation

The economic sustainability of the current AI boom is increasingly being questioned. AI investments are currently the only thing keeping the US economy out of recession, with data center infrastructure and model development offsetting high borrowing costs. Apollo Global Management's chief economist noted that there is virtually no growth in corporate capital expenditure outside of AI. Contrary to typical investment patterns, AI spending has not declined despite Fed interest rate hikes, as data center investments are ultimately financed by rising stock valuations of the Magnificent Seven.

The dependence appears dangerous. A September 2025 analysis by Deutsche Bank argued that without AI-related investments, the US economy could already be in recession. GDP growth is almost entirely driven by AI capital expenditures. Jason Furman, economist and former deputy director of the National Economic Council, estimated that 92 percent of economic demand in the first two quarters of 2025 came from information processing equipment and software. The S&P 500 is quite unbalanced, creating the risk of an investment collapse.

The return to investment remains uncertain. Although companies are directing impressive portions of their operating cash flows, around 50 percent, into AI initiatives, actual returns may not be apparent for more than a year. OpenAI has committed roughly one trillion dollars to AI transactions, including a 500-billion-dollar data center project, but is projected to generate only 13 billion dollars in revenue. The remarkable gap between expected earnings and current investments appears bubble-like.

Gartner predicts AI market consolidation, as the number of AI providers now exceeds demand. Consolidation is likely over the next two to three years due to reduced venture capital funding and more exits to well-capitalized leaders. ABI Research believes consolidation in the AI ​​software landscape is inevitable, as single-service providers dominate and large players acquire startups to facilitate market entry and solution consolidation. The development of end-to-end MLOPS platforms will drive M&A spending.

The historical parallels to previous AI winters are undeniable. The history of artificial intelligence already includes several periods in which enthusiasm for machine learning waned and investments in AI products, companies, and research dried up. The last of these winters ended in the 1990s. If another comes, it could involve polar vortex-like pain, given that the generative AI boom is worth hundreds of billions of dollars, far more than previous cycles.

The unequal distribution of the burden

Regional disparities in the US exacerbate the problem. While the Midwest benefits from investment, Virginia bears a disproportionate burden. The Dominion Energy Service territory in Northern Virginia secured contracts for 40 gigawatts of data center capacity through the end of 2024, a significant increase of 21 gigawatts six months earlier. The utility proposed new rate structures for high-load customers to reduce the financial burden on residential customers, as well as electricity price increases for other customers to cover costs.

Concentration creates local crises. In Virginia, resource adequacy constraints could severely limit planned growth. EirGrid in Ireland and Dominion in the US have been identified as particularly vulnerable. Geographic concentration intensifies regional network stress. Fifteen states, especially Virginia, Texas, and California, recorded an estimated 80 percent of the national data center load in 2023. This concentration effect exacerbates local network strain.

The socioeconomic impacts are unevenly distributed. Wealthier regions benefit from tech jobs and tax revenue, while more rural areas bear environmental burdens without proportional benefits. Black communities in the Southern US are particularly struggling with the hidden costs of data centers. There are 1,200 data centers in the South, with $200 billion worth of additional projects under development. These communities experience disproportionate environmental burdens from air pollution, water consumption, and network strain.

The effects on the labor market vary significantly by region. Regions with established tech ecosystems benefit from high-paying AI jobs. Rural regions with new data centers primarily see construction jobs and low-skilled operational positions. The transformation of employment through AI reveals regional differences. In developed regions with a high skill bias, the employment structure optimizes in favor of highly skilled workers. In other regions, AI leads to job losses without adequate new opportunities.

The future between consolidation and realignment

The convergence of these challenges paints a complex picture of America's AI future. Infrastructural, personnel, regulatory, and economic problems reinforce one another. The energy crisis limits geographic options, labor shortages slow development, regulatory fragmentation increases costs, and economic uncertainty dampens investment. The sum of these factors could fundamentally challenge American AI dominance.

The most likely future lies between the extremes of catastrophic collapse and uninterrupted growth. Market consolidation appears inevitable. Weaker players, overvalued startups without clear business models, and projects without a measurable ROI will be eliminated. This shakeout will be painful for those affected, but it could pave the way for more sustainable development. The remaining players will be those who solve genuine business problems and deliver measurable value.

The geographic redistribution will continue. The Midwest and other previously underdeveloped regions will gain further importance. This decentralization could increase the resilience of the American AI ecosystem by spreading risks and unlocking new talent pools. At the same time, established hubs like Silicon Valley and Northern Virginia will retain their significance through network effects and talent concentration, albeit in a modified form.

Technological development will increasingly focus on efficiency. The era of ever-larger models with exponentially growing resource demands is approaching physical and economic limits. Innovations in model architecture, quantization, distillation, and specialized chips will be prioritized. Industry will learn to achieve more with less, driven not by environmental awareness but by economic necessity.

The regulatory landscape will need to be clarified. The current patchwork is unsustainable in the long term. Either a federal framework legislation will be established that balances state diversity with national coherence, or the fragmentation will become entrenched, with all the negative consequences for compliance costs and international competitiveness. The political economy of this decision remains uncertain, but industry will increasingly demand clarity.

Public acceptance is becoming a critical variable. Organized resistance to data centers reflects deeper concerns about distributive justice, environmental impact, and democratic participation in technological decisions. Tech companies must learn to treat local communities as stakeholders, not obstacles. This requires cultural transformation and genuine participation, not just PR exercises.

The international dimension remains crucial. While the US grapples with internal problems, China is investing heavily in AI infrastructure. Last year, China added over 400 gigawatts of new power plant capacity to the grid, compared to several dozen gigawatts in the US. This gap in infrastructure deployment speed could have strategic implications. America's ability to maintain AI leadership hinges on resolving its internal challenges.

The ultimate question is not whether the US can overcome the current challenges, but at what cost and with what consequences. The necessary infrastructure investments will amount to trillions over the next decade. The societal transformations resulting from AI deployment will be profound. The environmental impacts require serious consideration. The questions of distribution regarding democratic participation and economic gains remain unresolved.

The American AI boom is at a turning point. The phase of uncritical enthusiasm and seemingly limitless resources is coming to an end. What follows is a period of consolidation, realignment, and potentially painful adjustments. The technology itself will survive and evolve. The question is which companies, regions, and business models will weather the transformation and what the resulting landscape will look like. The decisions made in the coming years will shape the architecture of the AI-driven economy for decades to come.

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Industry focus: B2B, digitalization (from AI to XR), mechanical engineering, logistics, renewable energies and industry

More about it here:

• Xpert Business Hub

A topic hub with insights and expertise:

• Knowledge platform on the global and regional economy, innovation and industry-specific trends
• Collection of analyses, impulses and background information from our focus areas
• A place for expertise and information on current developments in business and technology
• Topic hub for companies that want to learn about markets, digitalization and industry innovations