Pull up a chart of who does not want a data center built nearby, and something looks off. Conservative Republicans oppose them more than moderate Republicans do, which puts the most conservative voters closer to liberal Democrats than to the middle of their own party. “I’m not sure I’ve ever seen a chart where conservative Republicans are closer to liberal Democrats,” said Anthony Leiserowitz of the Yale Program on Climate Change Communication. In a country that agrees on almost nothing, that alignment is worth a second look.

Not a fringe

The data center backlash is not a handful of angry neighbors. Seven in ten Americans say they do not want one built in their community, a higher share than opposes a nuclear plant, a pattern researchers at Harvard have been tracking as it spreads. More than 800 groups across 49 states are fighting roughly 1,500 planned projects, and tens of billions of dollars in builds have already been blocked or delayed this year. Of the politicians who have taken a public position against a project, 55 percent are Republican and 45 percent are Democrat.

Same enemy, opposite reasons

What looks like one coalition is really two, arriving at the same place through different doors. On the right, the objections are tax giveaways, strain on the grid, property rights, and a distant company reshaping a town without asking. On the left, they are water, emissions, and who pays when the bills climb. The motives do not match. The vote does. As one observer told Talking Points Memo, the draw is having “real, actual villains” both sides can see.

Already moving votes

The data center backlash has left the comment period and reached the ballot box. Two Democrats won landslide upsets for the Georgia Public Service Commission, the first since 2007, running on energy bills and data centers, even as some Georgia Republicans raised their own questions about the facilities’ water and power use. In Virginia, the country’s largest data-center market, candidates ran on making the facilities pay more, and a seat changed hands over it. Voters in Monterey Park, California banned data centers outright with 86 percent of the vote. Towns in Pennsylvania, Nevada, Rhode Island, Wisconsin, and Maryland have paused, capped, or put them to a referendum. Arizona, where the fight runs through the power bill and the water table, passed one of the country’s strictest tax-break moratoriums, and in Pima County voters rejected a project only to watch the state move to override them.

Why this one crosses the line

The usual reason an issue goes bipartisan is that it is small or symbolic. This one is neither. What puts the rural conservative and the urban progressive in the same room is not a shared philosophy. It is a shared position: someone far away decided their town would carry the cost of the AI boom, and the first they heard of it was a rezoning notice. That experience does not sort by party.

Whether it lasts

The people who study this are not betting on permanence. The unity could fray, they warn, as the midterms approach and the issue turns into something to win with rather than something to agree on. It is also running against a well-funded current: while towns fight projects one at a time, the AI industry is spending at record levels in Washington to freeze state regulation before the local victories can add up. A problem everyone shares is, for a campaign, a problem to be divided. The thing to watch is whether the backlash still looks bipartisan once candidates need it to belong to one side.

The opposition makes the headlines. The alignment is the part worth studying. When the most conservative and the most progressive voters land on the same side of anything in 2026, the useful question is what they are seeing that the people in the middle are not. The safe bet is that it does not survive the midterms intact, because a unifying issue is exactly the kind of thing a campaign exists to split. For now, the data center is the rare thing a divided country can point at together.

FAQ

Is opposition to data centers bipartisan?
Yes. Roughly seven in ten Americans oppose local data center construction, including 75 percent of Democrats and 63 percent of Republicans, and conservative Republicans oppose them at a higher rate than moderate Republicans. Of politicians taking public positions against projects, 55 percent are Republican and 45 percent Democrat.

How many data center projects have been blocked?
More than 800 groups across 49 states are fighting about 1,500 projects, and tens of billions of dollars in builds have been blocked or delayed in 2026.

Why do the left and right oppose data centers?
For different reasons that reach the same conclusion. The right cites tax breaks, grid strain, and property rights; the left cites water, emissions, and rising bills.

Will the bipartisan coalition last?
Analysts expect it to weaken as the 2026 midterms turn the issue into a partisan weapon.

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Microsoft is testing high-temperature superconductor (HTS) cables to power its AI data centers, a technology that eliminates electrical resistance entirely. Zero voltage drops. Zero heat generation from the cables themselves. VEIR, a Massachusetts-based startup backed by Microsoft, completed a successful test of its 3-megawatt superconducting cable powering a server rack in a simulated data center environment. The pitch is elegant: replace the copper arteries of a data center with superconducting ones, and the power delivery problem shrinks by an order of magnitude. The cables themselves can be more than 10x smaller and lighter than their copper equivalents.

It is a genuinely impressive piece of engineering. It also will not matter for years, and the communities whose power grids are being consumed by AI expansion right now cannot wait that long.

The Physics Works. The Calendar Does Not.

The science behind HTS cables is well established. Cool certain ceramic materials below a critical temperature using liquid nitrogen, and they conduct electricity with zero resistance. No energy lost as heat in transmission. No need for massive copper bus bars and the ventilation systems required to cool them. Microsoft’s research team envisions replacing overhead power line corridors with compact underground HTS trenches, collapsing the physical footprint of data center power infrastructure dramatically. American Superconductor (AMSC), traded on NASDAQ, is a primary supplier of the HTS wire and systems that make this possible.

VEIR closed a $75 million Series B round with Microsoft among the investors. The test validated that the technology performs as promised under data center load conditions. But validated performance in a controlled simulation and deployed performance at production scale are separated by years of engineering, regulatory approvals, and reliability testing. The current pilots are still evaluating long-term maintenance costs of the liquid nitrogen cooling systems that keep the cables in their superconducting state. This is pre-deployment work. Microsoft is not installing HTS cables in Azure data centers next quarter. It is studying whether HTS cables might be viable for Azure data centers in the future.

The Gap Between Innovation and Relief

Power is the single largest bottleneck constraining AI data center expansion. Not GPUs. Not talent. Not capital. Power. The electricity cost pressures that AI data centers are already imposing on local communities are not theoretical. They are showing up in utility rate filings, in city council debates, in the monthly bills of families who live near facilities they never asked for. When a hyperscaler breaks ground on a new campus, the local grid absorbs the impact immediately. Utility companies file for rate increases. Residential customers subsidize industrial consumption through higher bills and degraded grid reliability.

The scale of capital flowing into AI infrastructure makes this a structural problem, not a temporary one. Big Tech is pouring hundreds of billions into data center construction right now, using copper cables, drawing from existing grids, and socializing the costs onto local ratepayers. HTS cables might eventually reduce the power lost in delivery. They do nothing to reduce the total power consumed by the facilities themselves. A data center running on superconducting cables still demands the same megawatts from the grid. It just wastes fewer of them in transit.

This distinction matters. The coverage of Microsoft’s HTS testing has treated it as a solution to the widening gap between AI energy consumption and available supply. It is not. It is an efficiency improvement to the plumbing. An important one, potentially, but not a fix for the fact that the reservoir is running dry.

The counterargument deserves serious consideration. Efficiency improvements compound. If HTS cables eliminate even 5 to 8 percent of power losses in distribution within a data center campus, that represents meaningful megawatts recovered at scale. A facility drawing 500 megawatts could reclaim 25 to 40 megawatts through zero-resistance transmission alone. That is power equivalent to tens of thousands of homes, freed without building a single new generation source. Multiply this across every hyperscaler campus worldwide, and the aggregate impact is substantial. Dismissing efficiency gains because they do not solve the entire problem is a fallacy. Every grid technology we rely on today was once a pilot that skeptics called insufficient.

That critique is fair, and it still sidesteps the timing question. The communities absorbing the grid strain of AI expansion today are not helped by a technology that might deploy at scale in five to seven years. Efficiency gains that arrive after the damage is done are retrospective improvements, not solutions. Microsoft’s superconductor research is a long bet on future infrastructure. It does not address the present reality that data centers are pulling power from grids built for cities, not server farms, and that the people living in those cities are footing the bill through higher rates and reduced reliability.

The production timeline is the part nobody wants to talk about honestly. This is years away from deployment. Maybe a decade before it shows up in enough facilities to matter at scale. Meanwhile the data centers are going up right now, pulling power from grids that were sized for residential neighborhoods and small businesses, not for buildings that consume more electricity than some towns. Superconducting cables are a solution to a future version of this problem. The current version, where families in Virginia and Texas and Arizona are watching their utility bills climb because a hyperscaler moved in next door, does not get fixed by a lab test in Massachusetts.

What This Means for Everyday People

If you live near a data center or in a region where one is planned, superconducting cables are not coming to help you anytime soon. The technology is real, the timeline is long, and the power draw on your local grid is happening now. Utility rate increases driven by data center demand are already being approved in multiple states. The benefits of AI accrue to shareholders and users of cloud services. The costs accrue to the communities that host the physical infrastructure. Until that asymmetry is addressed through regulation, rate structures, or technology that actually reduces total consumption rather than just improving delivery efficiency, the people closest to the machines will continue paying the highest price for progress they did not choose., Tom’s Hardware reported

For inquiries and analysis contact laterstack@proton.me

There is a peculiar cognitive dissonance in how the technology industry discusses artificial intelligence. The conversation revolves around models, parameters, benchmarks, and breakthroughs. GPT-5 is coming. Claude gets smarter every quarter. Gemini scales to ever larger context windows. The assumption embedded in this discourse is that AI progress is fundamentally a software problem. Build better algorithms, train larger models, accumulate more data, and intelligence will continue its exponential climb.

The assumption is wrong. AI progress is increasingly a hardware problem, and the hardware problem is increasingly an infrastructure problem, and the infrastructure problem is increasingly a physics problem. You cannot conjure substations and power generation capacity on the same timeline you order GPUs. The grid does not scale on demand. Transformers take years to manufacture. Permitting for new power plants moves at the pace of bureaucracy, not venture capital.

Siemens Energy announced last week that it will invest approximately $1 billion to expand U.S. manufacturing of grid equipment and gas turbine components. The announcement was not framed as a response to AI. It was framed as a response to “surging electricity demand.” But the source of that demand is not mysterious. Data centers consumed approximately 4.4% of total U.S. electricity in 2025. Projections suggest this figure will reach 6% to 9% by 2030. The hyperscalers, Microsoft, Google, Amazon, Meta, are racing to build ever larger training clusters, and every cluster requires power that the existing grid cannot provide.

This is the bottleneck that will determine who wins the AI race. It is not compute, which can be purchased. It is not talent, which can be hired. It is not capital, which flows freely to credible teams. It is the physical infrastructure required to power and cool machines at unprecedented scale. Whoever solves this problem first will not merely succeed. They will become unfathomably wealthy.

The Grid Was Not Built for This

The American electrical grid was designed for a different era. It assumed distributed demand: factories here, homes there, commercial buildings elsewhere, all drawing power at predictable times in predictable quantities. Load balancing was a solved problem. Utilities built generation capacity, maintained transmission lines, and charged rates that covered costs plus regulated returns.

AI data centers obliterate these assumptions. A single large training cluster can consume as much electricity as a small city. The demand is concentrated geographically, often in regions chosen for real estate costs, tax incentives, or proximity to cloud customers rather than proximity to power generation. The load profiles are intense and sustained. Training runs continue for weeks or months, consuming maximum power continuously.

The result is that data center operators are discovering what semiconductor manufacturers discovered decades ago: you cannot simply buy your way out of infrastructure constraints. TSMC’s most advanced fabrication facilities require dedicated power plants. AI data centers are approaching similar scale.

Consider the math. Nvidia’s next-generation Blackwell systems consume approximately 1,200 watts per GPU. A training cluster with 100,000 GPUs requires 120 megawatts of continuous power, equivalent to the demand of roughly 90,000 homes. The largest planned clusters exceed this by multiples. xAI’s Colossus facility in Memphis reportedly operates over one million H100-equivalent GPUs. The power requirements approach gigawatt scale.

The U.S. has not built gigawatt-scale power infrastructure in decades. The expertise exists but lies dormant. The supply chains have atrophied. The permitting processes were designed to prevent construction, not enable it.

The Transformer Bottleneck

The specific constraint that has captured industry attention is the transformer. Not the neural network architecture, but the electrical device that steps voltage up and down as power moves from generation to transmission to distribution. Large power transformers are among the most complex manufactured goods in existence. They weigh hundreds of tons. They contain thousands of gallons of specialized oil. They require specialized steel that only a handful of facilities worldwide can produce.

Lead times for large power transformers have extended from 12 months to 36 months or longer. A data center operator who breaks ground today may wait three years for the transformers required to connect their facility to the grid. This is not a problem that money can solve in the short term. The manufacturing capacity does not exist to meet demand.

Siemens Energy’s $1 billion investment is explicitly aimed at this bottleneck. The company will expand production of grid equipment and gas turbine components at facilities in Charlotte, Houston, and other U.S. locations. But a billion dollars buys incremental capacity, not transformational capacity. The gap between AI industry ambitions and infrastructure reality remains vast.

The Emerging Opportunity

Where there is constraint, there is opportunity. The companies and technologies that solve the infrastructure bottleneck will capture extraordinary value.

Several approaches are competing for dominance.

Small modular nuclear reactors promise dedicated, baseload power for data centers without the construction timelines of traditional nuclear plants. Microsoft has announced partnerships to explore this approach. The technology remains unproven at commercial scale, but the economics are compelling if regulatory hurdles can be overcome.

On-site natural gas generation allows data centers to bypass the grid entirely, generating power where they consume it. This eliminates transmission losses and permitting delays for grid interconnection. The environmental implications are contested, but the operational advantages are real.

Advanced cooling technologies can reduce power consumption by data centers, effectively stretching existing grid capacity further. Liquid cooling, immersion cooling, and novel heat dissipation approaches all show promise.

Grid-scale battery storage can smooth demand, allowing data centers to draw power during off-peak hours and store it for training runs. This requires advances in battery chemistry and enormous capital investment, but it decouples data center operations from real-time grid capacity.

Space-based data centers, as proposed by SpaceX following its xAI acquisition, represent the most radical approach: escape terrestrial constraints entirely by moving compute to orbit. The technical challenges are formidable, but the logic is not absurd. Solar power in space is continuous and abundant. Cooling in vacuum presents different challenges than cooling in atmosphere, but not necessarily harder ones.

Each of these approaches has advocates. Each faces obstacles. The winner, or winners, will not merely profit from the AI boom. They will enable the AI boom to continue. Without solutions to the infrastructure bottleneck, AI progress will plateau not because the models stop improving but because there is no power to run them.

The Investment Thesis

The investment implications are substantial. For the past several years, AI investment has flowed primarily to model builders and application developers. OpenAI, Anthropic, Google DeepMind, and their peers have absorbed billions in capital. GPU manufacturers, primarily Nvidia, have captured the hardware value.

The infrastructure layer has received less attention. Utilities are regulated and slow-moving. Grid equipment manufacturers are industrial companies trading at industrial multiples. Construction firms are not glamorous.

This is beginning to change. Siemens Energy’s stock has appreciated 200% over the past two years as investors recognize the demand driver that AI represents. Nuclear startups are raising substantial rounds. Data center REITs command premium valuations.

But the opportunity extends beyond public markets. The entrepreneur or investor who identifies the breakthrough technology for AI infrastructure, the equivalent of what TSMC’s advanced packaging is to semiconductors, will capture value commensurate with the importance of the problem. This is not a billion-dollar opportunity. It is a multi-hundred-billion-dollar opportunity. The first person to solve the power constraint at scale may well become a trillionaire.

What This Means for Everyday People

For ordinary Americans, the AI infrastructure buildout has immediate consequences. Your electricity rates will rise. Utilities that serve regions with large data center deployments are already requesting rate increases to fund grid upgrades. The costs are being socialized even as the benefits accrue to technology companies and their shareholders.

Communities near planned data center facilities face decisions about land use, water consumption, and noise. These facilities are not neighbors. They are industrial installations disguised as technology campuses.

Employment effects are mixed. Construction of data centers creates short-term jobs. Manufacturing of grid equipment creates longer-term jobs. But the facilities themselves require minimal labor to operate. A gigawatt-scale data center might employ a few hundred people. A semiconductor fabrication facility of similar power consumption would employ thousands.

The broader economic question is whether AI delivers productivity gains that justify the infrastructure investment being made on its behalf. If artificial intelligence transforms work as profoundly as its advocates claim, the infrastructure buildout will prove prescient. If AI proves more incremental than transformational, we will have rebuilt the grid for a revolution that never arrived.

Either way, the physical constraints are real. The opportunity to solve them is real. And the race to do so is only beginning.

For inquiries and analysis contact laterstack@proton.me

Frequently Asked Questions

Why can’t AI companies just buy more power?

The electrical grid has limited capacity in any given region, and expanding that capacity requires building new generation plants, transmission lines, and substations. Lead times for large power transformers alone have extended to 36 months or more. Data center operators can order GPUs faster than they can secure the power to run them.

How much electricity do AI data centers consume?

Data centers consumed approximately 4.4% of total U.S. electricity in 2025, projected to reach 6% to 9% by 2030. A single large AI training cluster can consume 120 megawatts or more, equivalent to the demand of 90,000 homes. The largest planned facilities approach gigawatt scale.

What is Siemens Energy investing in?

Siemens Energy announced approximately $1 billion in investment to expand U.S. manufacturing of grid equipment and gas turbine components. The investment responds to surging electricity demand driven largely by data center construction and aims to address bottlenecks in transformer and grid equipment supply chains.

While the world argues about AI, another transformation is unfolding far from the usual tech centers. solar startup Africa is expanding rapidly as local founders build energy systems designed for people who have spent decades waiting for reliable power. This shift is not theory. It is visible in homes, clinics, markets, and schools that once went dark every evening.

The growth of solar startup Africa comes from simple forces. The cost of panels has dropped. Mobile payments work everywhere. Community trust accelerates adoption. Most importantly, the builders grew up with the problems they are solving. They remember outages that lasted days. They know what a broken grid means for a small shop or a rural clinic.

A ground level Solarpunk reality

This is Solarpunk without the aesthetic filter. It is not a design trend or a social media fantasy. It is real infrastructure for communities that cannot wait for national grids to catch up.

Shops can stay open at night. Clinics can preserve vaccines. Farmers can cool crops instead of watching them spoil. Students can study without burning candles. And people can work remotely because their power no longer cuts out every afternoon.

solar startup Africa creates opportunity by making electricity stable. That one change shifts everything else.

Why this acceleration is happening now

A few key shifts pushed this movement forward.

Affordable solar panels made small scale grids possible.
Mobile money created a simple pay as you go system for families and businesses.
Local founders understood exactly where the grid fails and why diesel generators drain income.
Communities saw results quickly, which gave the model legitimacy.

The result is a wave of village scale and neighborhood scale solar grids that operate independently from national systems that move far slower.

The next phase of energy growth

The future of solar startup Africa is already visible in early projects.

Smart community grids allow operators to track usage remotely.
Solar bundles pair panels with fridges, pumps, or tools to raise income.
Energy payments evolve into financial identities that help residents access credit.
Local assembly plants begin to keep more of the value inside the region.
AI supports maintenance and prediction quietly in the background instead of becoming the primary story.

These developments point toward a world where reliable power is not something communities hope for. It is something they build and own.

Why the rest of the world should pay attention

The rise of solar startup Africa offers a model for regions with weak infrastructure. It demonstrates that large grids are not the only path to progress. It shows that communities can build upward using energy systems designed for their own needs rather than imported assumptions.

This is progress without permission. It is change without waiting for someone else to deliver it.

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