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.
Related Stories
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.
Related Stories
Phil Spencer retired from Microsoft on Thursday after 38 years. His last day is Monday. Sarah Bond, Xbox president and the person most people assumed would succeed him, also resigned. Both of them, gone within the same announcement. And neither of them is being replaced by anyone from gaming.
The press release calls it a retirement. Spencer’s own statement says he told Nadella last fall he was “thinking about stepping back and starting the next chapter.” That’s the kind of language you use when you want the exit to look calm. And maybe it was calm. But the timeline tells a different story if you’re paying attention.
The Sequence
Spencer orchestrated the $69 billion Activision Blizzard acquisition, the largest in gaming history and one of the largest in tech. That deal closed in October 2023 after a year-long regulatory fight with the FTC. Within three months of closing, Microsoft laid off 1,900 gaming employees. By January 2024, another 2,500 across the company. Studios got shuttered. Teams got folded. The kind of restructuring that happens after a mega-merger, where you cut the overlap and consolidate the headcount.
Spencer oversaw all of it. The deal. The layoffs. The integration. The reorganization of every studio under one umbrella. And then last fall, once the hard part was done, he told Nadella he was thinking about leaving.
That’s not a retirement. That’s an architect walking off a job site after the building is finished. He built the thing. He did the ugly work of merging it. And then he left before anyone could ask him to run it differently than he built it.
Bond’s Exit Is the Stranger One
Spencer at least gets the “38 years, I’m tired” narrative. Bond doesn’t. She was Xbox president. She was running the day-to-day. She was the obvious next CEO of Microsoft Gaming. And instead of taking the job, she resigned.
That’s the part nobody is spending enough time on. Bond didn’t get passed over and stay. She got passed over and left. When the person everyone expects to get promoted decides to walk instead, it usually means they saw something they didn’t want to be part of. Or they were told the direction was going somewhere they disagreed with. Either way, it’s not the behavior of someone who lost a title fight. It’s the behavior of someone who chose to leave.
Microsoft replaced both of them with Asha Sharma from CoreAI, who joined the company in 2024 from Instacart. Her first public statement included a promise not to “flood our ecosystem with soulless AI slop.” She felt the need to say that on day one. Draw your own conclusions about what the internal conversations looked like.
What the Timing Actually Says
Spencer built Xbox into a $25 billion annual revenue business. Game Pass has over 34 million subscribers. The studio portfolio, after Activision and Bethesda, is the largest in the industry. All of that was Spencer’s project.
But building the portfolio was one job. What Nadella wants to do with that portfolio is a different job. Microsoft has spent the last two years layering AI into every division. Azure, Office, GitHub, Windows. All got Copilot. All got AI integration as a strategic priority. Gaming was the last major division without an AI executive running it.
Spencer built a content empire. Nadella wants a platform. Those are different ambitions with different definitions of success. Content success means great games that sell and retain subscribers. Platform success means AI tools, procedural generation, dynamic monetization, ecosystem lock-in. Content people and platform people rarely see the world the same way.
Spencer telling Nadella last fall he wanted to leave makes more sense if you consider that last fall is also when Microsoft accelerated its AI integration roadmap across divisions. If the conversation shifted from “build the best game library in the world” to “now turn it into an AI platform,” Spencer may have decided he’d rather leave on his own terms than execute someone else’s vision for the thing he built.
Bond apparently reached the same conclusion.
What’s Left
Matt Booty got promoted to Chief Content Officer, which is Microsoft’s way of saying the studios will still make games. Sharma’s job is everything else. The platform layer. The AI strategy. The part that Nadella actually cares about.
The studios that make Halo, Elder Scrolls, Call of Duty, and Minecraft are still staffed with the same developers. Nobody is firing the game makers. But the person those game makers report to, through Booty through Sharma, has never shipped a game. And the person who spent 12 years protecting them from the platform side of Microsoft just walked out the door.
Spencer’s retirement statement was gracious. Bond’s departure was quiet. Together they read less like a transition and more like two people who decided the next chapter of Xbox wasn’t one they wanted to write.
For the first time, major corporations are saying the quiet part loud.
Amazon announced plans to reduce its corporate workforce by 16,000 employees. Pinterest cut up to 15% of its workforce. Meta’s Mark Zuckerberg declared that 2026 will be when “AI starts to dramatically change the way that we work.” Across the technology industry and beyond, companies explicitly cited artificial intelligence as a driver of layoffs that affected more than 50,000 workers in 2025 alone.
This is new. Previous waves of technology-driven displacement were discussed euphemistically. Automation. Efficiency gains. Workforce optimization. The machines were taking jobs, but companies rarely said so directly. The social contract held that corporations would not publicly attribute job losses to technology, even when technology was clearly responsible.
That contract has broken.
But before accepting the narrative that AI is now actively replacing human workers at scale, we must ask a harder question: Is it true? Are these layoffs genuinely driven by AI capabilities that now exceed human performance? Or is something else happening, something that tells us as much about corporate communications and investor relations as it does about artificial intelligence?
The Case That AI Is Real
Let us begin with the bull case, because it is not without merit.
Generative AI has advanced remarkably over the past three years. Large language models can now write code, draft marketing copy, summarize documents, conduct research, and perform dozens of tasks that previously required human labor. Tools like GitHub Copilot demonstrably increase developer productivity. Customer service chatbots handle inquiries that once required human agents. Content generation systems produce text at scales impossible for human writers.
Companies deploying these tools are reporting productivity gains. Klarna, the payments company, has stated that its AI assistant performs the work equivalent of 700 full-time customer service agents. If AI can do the work of hundreds of employees, why would companies continue paying those employees?
The logic is straightforward. Technology substitutes for labor. This has been true since the spinning jenny. AI is the latest instance of a pattern that has repeated throughout the history of capitalism.
From this perspective, the 50,000 layoffs explicitly attributed to AI represent an honest acknowledgment of technological progress. Companies are admitting what has always been true but was previously impolite to say: technology replaces workers.
The Case for Skepticism
The bear case is equally compelling.
Forrester, the technology research firm, published a report in January arguing that many companies announcing AI-related layoffs “do not have mature, vetted AI applications ready to fill those roles.” The report identifies a trend of “AI-washing,” companies attributing financially motivated cuts to future AI implementation that may or may not materialize.
Molly Kinder, a senior research fellow at the Brookings Institution, noted that saying layoffs were caused by AI is a “very investor-friendly message.” The alternative, admitting that a business is struggling or that management made poor decisions, is far less palatable to shareholders.
Consider the incentives. Wall Street loves AI narratives. Companies that credibly claim to be AI-forward receive valuation premiums. Stock prices respond positively to announcements about AI adoption. In this environment, attributing layoffs to AI efficiency gains rather than business weakness is not merely acceptable. It is strategically optimal.
The skeptical view holds that AI is being used as cover. Some portion of these layoffs would have happened regardless of AI capabilities. Economic conditions, competitive pressures, overcapacity from pandemic hiring, and strategic missteps all contribute to workforce reductions. AI provides a socially acceptable explanation that simultaneously signals technological sophistication and deflects blame from management.
The Truth Is Probably Both
Reality rarely conforms to clean narratives. The honest assessment is that both factors are at work simultaneously.
Some jobs are genuinely being replaced by AI systems that now perform tasks better, faster, or cheaper than humans. The customer service representative whose role consisted primarily of answering routine questions from a knowledge base is vulnerable. The junior copywriter producing formulaic content at volume is vulnerable. The data entry clerk transcribing information from one system to another is vulnerable.
Simultaneously, some layoffs attributed to AI are opportunistic. Companies facing pressure to reduce headcount can now cite AI as the reason rather than acknowledging overcapacity or strategic failure. The narrative is convenient. Investors reward it. The workers lose their jobs either way, but the framing matters for corporate reputation and stock price.
Distinguishing genuine AI displacement from AI-washing in any specific case is difficult. Companies do not provide the detailed productivity data that would allow external observers to verify their claims. We see the announcements. We do not see the internal analysis, if any, that preceded them.
The Policy Question
For policymakers, the uncertainty creates a dilemma. If AI is genuinely displacing workers at scale, policy responses are urgent. Workforce retraining programs, social safety net expansions, and potentially more radical interventions may be necessary to manage a transition whose speed exceeds historical precedents.
If AI displacement is overstated, different interventions are appropriate. The focus should be on counter-cyclical employment policy, supporting workers affected by economic downturns rather than technological transformation, and resisting the temptation to create programs addressing a problem that exists more in corporate communications than in labor market reality.
The policy response depends on accurate diagnosis. That diagnosis is currently impossible because companies have incentives to exaggerate AI’s role and no obligation to provide verifiable data.
A pragmatic approach acknowledges uncertainty while preparing for multiple scenarios. Invest in workforce adaptation programs that help workers transition regardless of whether the cause is AI or traditional economic displacement. Monitor labor market data for patterns that would distinguish AI-driven structural change from cyclical fluctuations. Require more transparency from companies making AI-related layoff claims, perhaps through enhanced disclosure requirements in securities filings.
The worst outcome would be either complacency (assuming AI displacement is overstated and being caught unprepared) or panic (overreacting to a narrative that serves corporate interests more than it reflects reality).
The Moral Question
Beyond policy, there is a moral question that both left and right should consider.
From the left, the concern is worker welfare. Fifty thousand people lost their jobs. Whether AI or management failure or economic conditions caused those job losses, the human impact is real. Families face disruption. Communities lose economic activity. The psychological toll of unemployment falls on individuals regardless of the cause.
From the right, the concern is honesty. If companies are citing AI as cover for ordinary business failures, they are misleading investors and the public. Market efficiency depends on accurate information. AI-washing, if widespread, distorts capital allocation and undermines the integrity of corporate communications.
Both perspectives converge on a demand for truth. Workers deserve to know why they lost their jobs. Investors deserve accurate explanations for corporate decisions. Policymakers deserve data that reflects reality rather than narratives constructed for strategic advantage.
The current situation satisfies none of these demands. Companies make claims. Workers lose jobs. The truth remains obscured by incentives that favor narrative over accuracy.
What the Data Actually Shows
The available evidence is mixed.
Challenger, Gray & Christmas, the employment consulting firm, documented that AI was cited in connection with approximately 50,000 layoffs in 2025. This represents a significant increase from previous years when AI was rarely mentioned explicitly.
However, total layoffs in the technology sector have remained elevated since the post-pandemic correction began in 2022. The pattern predates the current AI narrative. Companies hired aggressively during the pandemic, discovered they had overcapacity as growth normalized, and have been reducing headcount through multiple rounds of layoffs.
AI may be accelerating this correction. It may be providing cover for a correction that would have happened anyway. The aggregate data cannot distinguish between these possibilities.
What is notable is the change in rhetoric. Companies previously avoided explicitly blaming technology for job losses. Now they embrace it. This shift reflects changed incentives in capital markets, where AI capability is rewarded, rather than necessarily changed conditions in labor markets.
What This Means for Everyday People
For workers, the practical implications are the same regardless of whether AI displacement is real or exaggerated. The jobs are gone. The skills that secured previous employment may not secure future employment. Adaptation is required.
The strategic response for individuals is to develop capabilities that complement AI rather than compete with it. Tasks requiring judgment, creativity, interpersonal connection, and physical presence are less vulnerable than tasks that are routine, digital, and scalable. This advice would be valid even if AI were not a factor. It becomes more urgent if AI displacement proves real.
For citizens evaluating public policy, the appropriate stance is skepticism toward corporate narratives combined with preparation for genuine disruption. Companies have incentives to exaggerate AI’s impact. But the technology is real, and its capabilities are expanding. Prudent policy prepares for scenarios that may not materialize rather than assuming benign outcomes.
The 50,000 layoffs attributed to AI may be the beginning of a transformation that reshapes labor markets over the next decade. They may also be a convenient narrative that serves corporate interests while obscuring ordinary business dynamics.
The honest answer is that we do not know. What we know is that companies are now willing to say what they previously would not: that technology is taking jobs. Whether that statement is accurate remains to be determined.
For inquiries and analysis contact laterstack@proton.me
Frequently Asked Questions
Did AI really cause 50,000 layoffs?
Companies explicitly cited AI as a factor in over 50,000 layoffs in 2025, including major cuts at Amazon, Pinterest, and others. However, analysts debate whether AI actually replaced these workers or whether companies are “AI-washing,” using AI as a convenient explanation for cuts driven by overcapacity, economic conditions, or strategic failures. The truth likely involves both genuine displacement and opportunistic framing.
What is AI-washing in the context of layoffs?
AI-washing refers to companies attributing layoffs to AI capabilities and efficiency gains when the actual drivers may be unrelated to AI. According to Forrester research, many companies announcing AI-related layoffs “do not have mature, vetted AI applications ready to fill those roles.” Citing AI for layoffs is an investor-friendly message that can make ordinary business problems appear like technological progress.
What jobs are most at risk from AI?
Tasks that are routine, digital, and scalable are most vulnerable to AI displacement: customer service handling standard inquiries, formulaic content generation, data entry and transcription, and basic research and summarization. Jobs requiring judgment, creativity, interpersonal skills, and physical presence are less vulnerable. However, AI capabilities are expanding, and the boundary between vulnerable and protected work continues to shift.
Consider the audacity. A deepfake candidate applied for a security researcher position at Evoke, an AI security company whose entire business is threat modeling for artificial intelligence systems. The CEO, Rebholz, has spent years researching deepfakes. He has used them in presentations. He has built a career on understanding synthetic media.
And he almost hired the deepfake.
“Everything in me, everything I know about deepfakes was screaming at me: this is a deepfake,” Rebholz told The Register last week. “But there was something blocking me, the one percent chance that I’m wrong, this is actually a good candidate, and he’s going to think poorly of me if I confront him.”
This is the moment when an emerging threat becomes a systemic crisis. When a deepfake expert, interviewing for his own security company, experiences “inner turmoil” about whether to trust his expertise, the verification systems that underpin remote work have failed. The question is no longer whether deepfakes will compromise hiring processes. The question is what comes next.
The Anatomy of the Attack
The incident began ordinarily. Rebholz posted job openings on LinkedIn. Within hours, someone he did not know messaged him, recommending a candidate for the security researcher role. The referral itself was not suspicious. People refer candidates. Networks operate through introductions.
The first red flag was the candidate’s profile picture: not a photograph but something resembling an anime character. In the security community, this is not automatically disqualifying. Privacy concerns lead many professionals to avoid displaying their real faces online. Aliases and stylized avatars are common.
Rebholz gave the candidate the benefit of the doubt.
When the video interview began, the candidate sat in front of a virtual background. His face appeared “a bit blurry and plastic.” There was a greenscreen reflection visible in his glasses. At one point, dimples appeared on his face and then disappeared as he moved. The “softness of his face” came and went.
Rebholz noticed behavioral indicators as well. The candidate repeated interview questions back before answering them, a technique that buys processing time for systems generating responses. Many of his answers were nearly word-for-word quotes of things Rebholz himself had said or written publicly.
“It was almost an out-of-body experience where I felt like I was talking to myself,” Rebholz said.
Despite all of this, the deepfake expert experienced doubt. The candidate might be real. The visual artifacts might be compression issues. The familiar answers might be coincidence or good research. The social pressure not to accuse someone of being fake competed with the technical evidence that something was wrong.
After the interview, Rebholz sent video clips to a colleague at Moveris, which develops deepfake detection technology. The analysis confirmed what his expertise already told him: the candidate was synthetic.
The Implications for Remote Work
The remote work revolution that accelerated during the pandemic created enormous value. Talent could be hired from anywhere. Geographic constraints on labor markets loosened. Companies accessed skills that were previously unavailable in their local markets. Workers gained flexibility that many describe as transformational for their quality of life.
The same structural changes that enabled remote work also created vulnerabilities that are now being exploited.
Remote hiring relies on video interviews conducted over platforms that were not designed with identity verification in mind. Zoom, Teams, and Google Meet assume that the person on the video feed is who they claim to be. There is no cryptographic proof of identity. There is no biometric verification. There is trust, backed by the assumption that creating a convincing synthetic identity is difficult.
That assumption is no longer valid.
Experian’s fraud forecast for 2026 identified deepfake job candidates as a top threat. Nearly every major technology company, from Amazon to small startups, has encountered fake IT workers applying for positions. Some have been hired. Some have passed background checks. Some have gained access to internal systems before being detected.
The underlying threat is not merely fraudulent employment. It is infiltration. An attacker who passes an interview, receives credentials, and gains access to internal systems can exfiltrate data, compromise code repositories, establish persistent access, or conduct espionage. Remote work and global hiring widen talent pools, but they also weaken the signals of identity verification that previously protected organizations.
The Scale of the Problem
Industry data suggests the problem is larger than isolated incidents.
A 2024 survey found that 15% of high school students had encountered explicit deepfake imagery of their peers. The same technology that creates fake intimate images creates fake job candidates. The underlying models are general-purpose.
Challenger, Gray & Christmas, the employment consulting firm, has documented cases of synthetic candidates at scale. North Korean operatives have been identified using fake identities to obtain remote IT positions at American companies, earning salaries that fund the regime while potentially conducting espionage.
The FBI has documented cases where deepfake extortion, using synthetic intimate imagery created from social media photographs, has led to self-harm among victims, including minors. The same technology ecosystem enables job fraud, identity theft, and targeted harassment.
What makes the Evoke incident notable is not that a deepfake attempted to infiltrate a company. It is that the target was a company specifically focused on AI security threats, the interviewer was a deepfake expert, and the attack nearly succeeded anyway.
If experts cannot reliably detect deepfakes in real-time video interviews, what hope do ordinary hiring managers have?
The Emerging Response
The market is responding with verification technologies, but adoption lags threat development.
Companies like Moveris and others offer deepfake detection as a service. The technology analyzes video feeds for artifacts: inconsistent lighting, unnatural facial movements, audio-visual synchronization errors, and other indicators of synthetic generation. These tools can be integrated into hiring workflows.
Biometric verification services offer alternatives to video interviews for identity confirmation. A candidate can be required to verify their identity through a trusted third party before an interview begins. This does not prevent the interview itself from using deepfakes, but it establishes that the person claiming the identity is who they claim to be.
Some organizations are returning to in-person interviews for sensitive positions. The geographic flexibility of remote hiring is sacrificed for the security of physical presence. A deepfake cannot shake your hand.
Others are implementing multi-stage verification: initial video screening, followed by live coding exercises observed in real-time, followed by reference checks conducted through established professional networks rather than provided contacts. The friction increases. The security improves. The talent pool shrinks.
There is no costless solution. Remote work created value by reducing friction. The friction was also security. Restoring security means restoring friction.
The Policy Dimension
This is not merely a corporate security problem. It is a labor market problem with policy implications.
Employment verification systems, background check infrastructure, and credential validation processes were designed for a world where identity documents were difficult to forge and video communication did not exist. The entire apparatus assumes that physical presence or documented history establishes identity.
Deepfakes undermine these assumptions systematically. A synthetic candidate can provide fake credentials, appear convincingly in video interviews, and pass initial screening processes. The verification happens after hiring, when access has already been granted.
Policymakers face difficult tradeoffs. Mandating biometric verification raises privacy concerns. Requiring in-person verification restricts labor market flexibility. Imposing liability on employers for deepfake infiltration may be unfair when detection is genuinely difficult.
The honest answer is that no policy solution currently exists that preserves the benefits of remote hiring while eliminating the risks of synthetic candidates. Technology created this problem. Technology may eventually solve it. In the interim, organizations must make risk decisions with imperfect information and imperfect tools.
What This Means for Everyday People
For job seekers, the deepfake threat creates a new burden: proving that you are real. Legitimate candidates may face increased scrutiny, additional verification steps, and suspicion that would have been absent in earlier eras. The friction imposed by security measures falls on everyone, not just attackers.
For workers in remote positions, the question is whether their employers can distinguish them from synthetic impostors. If not, what prevents an attacker from impersonating a current employee, attending meetings in their place, or redirecting their communications?
For society broadly, the erosion of identity verification extends beyond employment. If you cannot trust that the person on a video call is who they claim to be, the implications ripple through every domain that relies on remote communication: telemedicine, legal proceedings, financial services, family relationships.
The Evoke incident is a warning. The CEO of an AI security company, an expert in exactly this threat, experienced doubt about whether to trust his own expertise. He almost hired a synthetic candidate because the social pressure not to accuse someone of fraud competed with the technical evidence that something was wrong.
This is the future of identity in the age of generative AI. The signals we relied upon to verify that people are who they claim to be are failing. What replaces them remains uncertain.
For inquiries and analysis contact laterstack@proton.me
Frequently Asked Questions
What happened with the deepfake job applicant at Evoke?
A synthetic identity using deepfake video technology applied for a security researcher position at Evoke, an AI security company. The CEO, who has years of experience researching deepfakes, conducted the video interview and noticed multiple red flags including visual artifacts and answers that quoted his own public statements. Despite his expertise, he experienced doubt about confronting the candidate and later confirmed through third-party analysis that the applicant was a deepfake.
How widespread is the deepfake job applicant problem?
Nearly every major technology company has encountered fake IT workers applying for positions. North Korean operatives have been identified using synthetic identities to obtain remote positions at American companies. Experian’s 2026 fraud forecast identifies deepfake job candidates as a top threat. The problem extends beyond tech: any organization using remote video interviews is potentially vulnerable.
How can companies protect against deepfake job candidates?
Emerging solutions include deepfake detection services that analyze video feeds for artifacts, biometric identity verification through trusted third parties, multi-stage verification processes combining video screening with live exercises and reference checks through established networks, and returning to in-person interviews for sensitive positions. No solution is costless; all involve tradeoffs between security and hiring flexibility.
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.
By the time 2025 ended, the illusion of stability was gone. Not because of one single collapse, but because nearly every system we interact with began behaving more honestly, if not more recklessly. Technology, finance, culture, and governance stopped pretending they were aligned with the public interest and started acting in ways that exposed their real incentives.
This was the year when the gap between how things are marketed and how they actually function became impossible to ignore.
Across every category Laterstack covers, the same pattern repeated. Speed over safety. Growth over trust. Automation over accountability. And a public that is slowly realizing it has been participating in systems it no longer understands or controls.
What follows is not a highlight reel. It is a map.
Technology Stopped Feeling Neutral
In 2025, technology finally lost its last claim to neutrality. AI tools moved from novelty to infrastructure. They quietly embedded themselves into hiring systems, creative pipelines, customer service, surveillance tools, and financial decision making.
The problem was not that AI existed. It was that it became untraceable. Companies stopped clearly disclosing when it was used. Awards bodies struggled to define what counted as acceptable use. Developers admitted that AI tools were already baked into workflows long before public conversations caught up.
The result was confusion and mistrust. Not because people rejected technology, but because they were no longer sure who was making decisions. When a system fails and no human is clearly responsible, accountability evaporates.
This year showed that convenience scales faster than ethics.
Cybersecurity Became Personal
Data breaches in 2025 were no longer abstract. They were intimate. Search histories. Viewing habits. Location data. Internal employee communications. Entire lives reduced to databases and then passed around as leverage.
What stood out was not just the volume of breaches, but the normalization of them. Companies issued statements. Regulators promised reviews. Users were advised to reset passwords and move on.
At the same time, governments expanded surveillance quietly. Employee monitoring increased. Border technologies became permanent. Drones, analytics platforms, and internal tracking tools moved from pilot programs into standard operations.
The line between protection and observation blurred. Many people did not notice it happening. That was the point.
Startups Learned Capital Has a Shorter Memory Than Hype
2025 was brutal for startups that required massive infrastructure, long timelines, or regulatory patience. Battery swapping. Autonomous logistics. Climate hardware. Ambitious platforms that once raised hundreds of millions quietly filed for bankruptcy.
The lesson was not that innovation failed. It was that venture capital rewards narrative far longer than viability. Once market conditions tightened and incentives shifted, many companies were left without a path forward.
Meanwhile, smaller and less visible startups thrived. Tools that solved narrow problems. Services that operated in legal gray areas. Platforms that scaled first and dealt with consequences later.
It became clear that the future belongs less to vision and more to adaptability.
Finance and Gambling Drifted Into the Same Space
Prediction markets, crypto casinos, and financialized gaming expanded rapidly in 2025. Often faster than regulators could respond. Often faster than users understood the risks.
These platforms did not advertise themselves as gambling. They framed participation as insight, forecasting, or strategy. But the mechanics were familiar. Risk was abstracted. Losses were individualized. Profits were centralized.
What made this year different was the confidence. Companies no longer acted like they were pushing boundaries. They acted like boundaries no longer mattered.
This was not deregulation. It was enforcement lag. And it created a new digital frontier where speed determined legitimacy.
Culture Fragmented, Then Hardened
Online culture in 2025 did not just fracture. It calcified. Algorithms rewarded outrage, certainty, and repetition. Nuance became expensive. Long form thinking felt foreign.
At the same time, distrust of institutions deepened. Media. Tech companies. Governments. Even creators. Every entity was assumed to have an agenda, usually financial.
Yet people still searched for meaning. That tension defined the year. A desire for clarity paired with systems designed to obscure it.
This is why subtlety matters now more than ever. People resist being told what to think. But they are still capable of noticing patterns when space is created for them to connect the dots themselves.
What This Meant for Everyday People
For most people, 2025 felt exhausting rather than explosive. Systems did not collapse overnight. They eroded quietly.
Jobs became more automated but less secure. Privacy became conditional. Entertainment blurred with monetization. Participation increasingly meant exposure.
The common thread was that choice remained, but clarity did not. And without clarity, consent becomes performative.
Recognizing that is the first step toward reclaiming agency.
Where Laterstack Fits Into 2026
Laterstack exists to slow the scroll. To connect stories that are usually siloed. To treat readers like adults who can hold competing ideas without needing a conclusion handed to them.
If 2025 showed us anything, it is that understanding the world now requires synthesis, not speed.
Related Laterstack Stories
Graduating from Stanford used to guarantee a career in tech. Today, many computer science graduates are discovering their degrees no longer provide the same opportunities. AI coding tools have advanced to the point where one experienced engineer paired with an AI agent can replace ten junior developers.
For students entering the workforce in 2025, the landscape is markedly different. Companies are hiring fewer entry-level engineers, prioritizing those with experience and the ability to work alongside AI. Recent graduates report difficulty securing jobs at top tech firms, prompting many to turn to master’s programs, less prestigious employers, or their own startups.
“Stanford computer science graduates are struggling to find entry-level positions at the biggest tech companies,” said Jan Liphardt, associate professor of bioengineering at Stanford. AI has increased productivity for seasoned engineers but reduced opportunities for newcomers.
The shift is not isolated to Stanford. Universities across California, including UC Berkeley and USC, report similar challenges. For many students, the reality is a split in the market: a small fraction of highly capable engineers still secure top roles, while others face a shrinking pool of opportunities.
Entry-level software jobs are particularly exposed. AI agents can code continuously, handle basic programming tasks faster, and make fewer errors. As a result, even graduates from prestigious institutions find themselves competing in a market where their traditional advantage has been eroded. Studies suggest that hiring for AI-exposed entry-level roles has dropped nearly 20% since 2022. Roles in customer service, accounting, and other fields are also affected, with 40% of tasks potentially automated.
While AI is still limited in consistency and often requires human oversight, the demand for junior developers is declining. Students are adapting by seeking additional skills, learning to manage AI tools, or extending their education with fifth-year master’s programs. Others are lowering their expectations and joining smaller companies or startups.
Some recent graduates who struggled for months eventually found positions where they now manage AI-assisted workflows, effectively performing the work of multiple developers. Universities are being challenged to rethink curricula to prepare students for a world where AI is a constant collaborator.
For everyday readers, this trend illustrates a broader cultural shift: traditional career pathways are being disrupted by automation, and skills that once guaranteed success may need continuous updating to remain relevant.
For questions, tips, or inquiries, email us at hello@laterstack.com.
Related Laterstack Tech Stories
Microsoft’s Copilot Holiday Ad Shows Everything That Doesn’t Work
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Microsoft has released another Copilot ad, this time holiday-themed, featuring users asking the AI to assist with lighting, cooking, decorations, and more. The ad is festive and cinematic, showing smart lights pulsing to music and toy production “delays” blamed on elves drinking too much cocoa. But testing the prompts from the ad reveals a different story: most of the Copilot actions do not work as advertised.
In the spot, a homeowner asks Copilot to sync holiday lights to music using a website called Relecloud. On screen, lights pulse to a song. The issue is Relecloud is not a real company, but a fictional example Microsoft has used in past case studies. When tested in real applications like Philips Hue, Copilot can identify some buttons correctly but often hallucinates elements that do not exist and misguides users.
Other ad scenarios include scaling a recipe, following IKEA assembly instructions, and checking HOA rules for decorations. Copilot often gives incomplete calculations, mislabels steps or ingredients, and defers judgment to the user rather than providing actionable guidance. In some cases it claims to highlight buttons or text on screen when nothing is actually there.
Even when shown real apps, Copilot struggles to reliably complete tasks. Recipe scaling only partially works, assembly instructions are misread, and lighting automation frequently fails to perform as intended. The ad’s holiday cheer masks the reality that these AI features are far from ready for everyday tasks.
Microsoft insists all Copilot responses in the ad are real responses generated by the AI at the time, shortened for brevity. Still, the disconnect between ad depiction and actual functionality points to a broader pattern: the promise of seamless AI assistance often outpaces what is technically achievable.
For consumers, this serves as a reminder that technology marketing can exaggerate capabilities, and even widely used AI assistants may not deliver on advertised promises. Understanding these gaps helps set realistic expectations for home automation, AI tools, and digital assistants.
For questions, tips, or inquiries, email us at hello@laterstack.com.
Related Laterstack Tech Stories
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Google has filed a lawsuit against SerpApi, a company offering tools to scrape web content, including Google search results. The complaint alleges that SerpApi used automated methods to bypass protections, access copyrighted data at scale, and sell it to customers. Google says these actions violate federal copyright law and threaten the integrity of its search ecosystem.
This legal conflict is part of a wider pattern. Reddit also sued SerpApi and other data scrapers for taking content from its platform to feed AI tools. While Google’s complaint references Reddit’s case, it does not name any AI companies using the scraped data.
At the center of the dispute is SearchGuard, a technology Google introduced earlier in 2025 to block automated scraping. Google claims SerpApi quickly discovered ways to bypass the system, sending hundreds of millions of queries daily while masking them to appear as human-generated. Each circumvention, Google argues, constitutes a violation of federal law.
SearchGuard was designed to protect Google’s search results and the copyrighted content of its partners. After the tool went live in January 2025, Google alleges SerpApi immediately worked to evade it, continuing large-scale data extraction. Google frames this as a major breach of both technical safeguards and intellectual property rights.
The case highlights broader questions about the evolving digital economy. Tools for scraping, AI data collection, and automated analysis are increasingly central to technology, but they also raise legal and ethical concerns. The tension between innovation and copyright protection is becoming a defining issue for the tech industry.
For everyday users, the story shows that the technology we rely on is underpinned by complex legal and technical frameworks. What seems like a simple search or AI query involves layers of agreements, protections, and limitations that most people never see. Recognizing these layers can change how we understand digital services and the unseen mechanics behind them.
If you have tips, insights, or want to contact Laterstack, email us at hello@laterstack.com
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After months of relentless speculation, the pulse of the crypto market has slowed. Bitcoin’s latest slide toward 100,000 dollars and sharp ETF outflows are signaling a wider pullback from the AI and digital asset frenzy that defined 2025.
Markets did not crash this week, but the tone has shifted. Big tech stocks like Palantir and Oracle took heavy hits, and their losses echoed across the leveraged trades that powered the latest rally. Bitcoin and other major coins fell sharply as retail investors and institutions began scaling back their risk.
Peter Atwater, a behavioral economics professor at the College of William and Mary, called it a confidence break. “AI and crypto live in the same neighborhood of belief,” he said. “When the mood shifts, it hits everything tied to that optimism.”
Retail energy drains from crypto and AI
The retreat is visible in the data. More than 700 million dollars left digital asset ETFs this week, including 600 million from BlackRock’s Bitcoin fund and 370 million from its Ether fund. Solana and Dogecoin products are also down double digits since their launch.
Meanwhile, the Roundhill Meme ETF, marketed as a retail sentiment tracker, is down more than 20 percent just a month after debuting. Indexes that follow speculative tech names and new IPOs also fell hard, with losses not seen since the summer.
Stephen Kolano, chief investment officer at Integrated Partners, said the selloff is not panic but a reset. “The profit taking is coming from trades that ran the most since spring,” he said. “That’s AI, that’s crypto, that’s anything fueled by momentum.”
Bitcoin as a signal
Bitcoin’s 15 percent drop this month has some analysts watching closely. Bloomberg Intelligence’s Eric Balchunas said Bitcoin often acts as an early indicator for shifts in broader market sentiment. “It trades around the clock. It reacts before most other assets do,” he said.
A Citi report noted that large holders, often called whales, have been quietly exiting. That is unusual, since this group tends to ride through downturns. Their selling adds weight to the idea that liquidity and conviction are thinning.
What it means beyond crypto
This is not a collapse, but it is a cooling of risk appetite. Retail traders who flooded into meme stocks and tokenized assets are pulling back. As capital leaves the edges of the market, liquidity tightens and timing begins to matter again.
The total crypto market cap, which peaked at 4.4 trillion dollars in October, has fallen nearly 20 percent. For now, the thrill ride that defined 2025 looks to be slowing.