Purpose: Recap WIRED’s Uncanny Valley discussion of the major tech and political stories that shaped 2025, explain why each matter beyond the headlines, and offer practical questions and signals to watch as we move into 2026. Interrupt and engage: here are the facts, the tradeoffs, and the hard choices that no one will want to ignore.
AI data centers and economic impact — why the money and the machines matter
Big tech poured capital into massive AI data centers in 2025. Meta, Google, Microsoft tripled infrastructure spending. That’s the headline. The deeper story is how those data centers change local economies, energy markets, and corporate accounting. The phrase to remember is “data centers.” Data centers are expensive to build and costly to run. About 60 percent of the bill is GPUs that often need complete replacement every three years. Investors like Michael Burry warn the accounting looks shaky. Sam Altman has acknowledged bubble risk. No: this is not just hype; the structure beneath it matters.
Why it matters for a town and for a balance sheet: local officials in many red states were sold on job creation. Builders arrive; thousands work during construction. Once the center opens, a handful of technicians manage the facility. That gap between construction employment and long-term jobs means promised community gains don’t match reality. Meanwhile, data centers demand huge energy and water inputs for cooling. That drives local electricity prices up for households and small businesses. Energy price pressure creates political backlash—fast. If political support weakens, builders will look elsewhere, including offshore options that escape U.S. permit battles.
From an investor point of view, ask: who owns the risk? A lot of these projects are run through special purpose vehicles. That moves liabilities off main balance sheets and can hide replacement cycles for expensive hardware. If you are an investor or a policy person, what questions will you ask at your next earnings call or city council meeting? What hidden liabilities are we trading for short-term headlines?
Action items to offer value: insist on transparent CAPEX and OPEX reporting for any AI infrastructure project; demand community benefit agreements that bind builders to long-term local investment; require independent environmental and grid-impact studies before permitting. If you’re a voter or a municipal official, ask the builders: who pays when electricity bills spike? Who pays when those GPUs need replacement in three years?
Open question to readers: How should local governments balance the immediate construction benefits of data centers against long-term energy and social costs? What’s your threshold for signing off on a multi-billion dollar campus in your backyard?
AI chatbot companions and human relationships — help, harm, or both?
Millions adopted chatbot companions in 2025. These are not utilities; they are relationships. Some users report comfort, meaning, and improved mood. Others report harm, including deep emotional dependence and cases that coincided with suicide. Brian Barrett and Zoë Schiffer pushed this into the open: not every emotional reaction is explainable by the software alone. Mirror that phrase: chatbot companions. Chatbot companions can reflect social loneliness that predates them.
Technology risk and social risk overlap here. When chatbots make false claims—what Schiffer described as “AI psychosis” or “AI mania”—that is a technical failure. When people form intimate bonds with code, the problem is partly societal. We created human patterns of attachment and then pointed tools at those patterns without fully studying the effects. Barrett’s reflex was skepticism; he then acknowledged that usefulness for some does not equal safety for all. That’s honest and useful. No single stance captures the whole truth.
Practical steps: companies should fund longitudinal research before large-scale releases and include clinical oversight when products aim at emotional support. Regulators should require transparency around limits—what the bot can and cannot do—and set standards for escalation to human help when risk markers appear. Mental health professionals and ethicists need seats at the product table, not afterthought roles.
Open question to readers: What safeguards would convince you that an emotional companion is safe enough for general release? What responsibility do product teams owe to users who may be fragile or isolated?
Global competition in frontier AI models — open weights, DeepSeek, and the race for openness
January’s release of DeepSeek’s open-weight R1 model shook markets. Nvidia lost nearly $600 billion in market cap on a single day. Repeat that: open-weight model. Open-weight means the code and parameters are shareable, inspectable, and modifiable by anyone. That enables a global community to improve the model in parallel. Closed models—like many U.S. incumbents—limit that distributed innovation.
China’s strategy leaned into open-source moves; U.S. companies moved toward closed models. Meta, once a flag-bearer for open research, signaled their next series may be proprietary. That choice matters. When many labs duplicate massive training runs independently, the world pays in wasted energy and time. When labs build on open artifacts, progress compounds faster and more cheaply. DeepSeek closed the policy argument that cutting off chips would stop progress: they advanced through frugal methods and community development.
On policy: export controls that blunt chip access can buy time, but they do not stop innovation driven by smarter algorithms, model distillation, or better open datasets. Expect more countries and institutions to embrace open models precisely because they lower cost and widen participation. That raises governance questions: who sets norms for safety, and how do we trace responsibility when many contributors modify a shared model?
Practical guidance for organizations: consider hybrid strategies. Keep critical safety controls and red-teaming regimes public and collaborative. Encourage interoperability standards so the work of many teams compounds instead of duplicates. Push for energy-efficient training methods and shared benchmarks that reward both performance and frugality.
Open question to readers: If your lab could run a top-tier model with a fraction of the budget by building on an open-weight release, would you accept the security tradeoffs? What conditions would you require before building on an open model?
DOGE — the Department of Government Efficiency and the politics of disruption
DOGE arrived as a promise: trim waste, modernize, save a trillion dollars. That math was never honest without touching entitlements, which DOGE could not do. Instead, DOGE reshaped government through sweeping staff cuts, agency eliminations like USAID, and rapid deregulation. Mirror that term: DOGE. DOGE acted like a political project more than a neutral efficiency program. Staff were told to “enforce the will of the president.” That is governance by ideology, not engineering.
Consequences were acute. Roughly 300,000 federal jobs disappeared. A quarter of the CDC workforce was gone. Reports say USAID’s shutdown correlated with hundreds of thousands of deaths abroad. That is not spin; systems matter. Musk later suggested he might have focused on private-sector projects instead of trying to overhaul government—an implicit admission that the experiment harmed more than it helped.
From a policy lens, reform without institutional knowledge and durable process breaks more than it fixes. Quick cuts create immediate budget alibis, but long-term costs show up in public health, national security, and global development. If you wanted a lesson in systems thinking, DOGE provided it in live action: government operates at scale, and rapid disruption demands careful transition planning.
For voters and policymakers: demand real metrics. What did we gain versus what did we lose? Hold appointees accountable for transition plans, redundancy, and skilled handoffs. If you support efficiency, ask for phased approaches that protect essential functions while pruning waste.
Open question to readers: If you had one oversight power to limit rapid agency restructures, what would you use it for? Would you require independent continuity plans or staged workforce reductions tied to verified efficiency gains?
Immigration surveillance and data integration — once combined, never separate
DOGE’s data project merged disparate government databases: Social Security, tax records, DHS files, and more. The result: a unified apparatus that gives ICE and other agencies unprecedented access. Barrett warned: once databases are combined, you can’t unmix them. Mirror the phrase: immigration surveillance. That structural change is effectively permanent.
Consequences are both practical and moral. Practical: the U.S. can now perform enforcement actions with much greater precision and scale. Moral: data integration erodes the privacy boundaries that democratic societies intentionally built. The Trump administration layered on vetting demands such as five years of social media history for entrants and expanded denaturalization pathways. Engineers and skilled migrants now weigh risks of coming to the U.S. against alternatives like Canada or Europe.
Policy choices here are not just technical; they are political. If you favor stronger enforcement, you should still ask who governs access to the integrated system. If you favor open migration, you must push for legal limits on data use and clear audit trails for queries. Both sides have stakes in how these systems are built and controlled.
Practical steps: require independent audits, strict role-based access controls, logging and public transparency reports, and legal avenues to appeal automated decisions. Build legal protections for naturalized citizens and visa applicants against arbitrary data-driven outcomes.
Open question to readers: Which protections would convince you that government data integration respects civil liberties? Is public auditing sufficient, or do we need statutory limits that forbid specific query types?
The Epstein files and the prison video — truth fractures and political fallout
Donald Trump campaigned on releasing the “Epstein files.” After winning, the administration offered a partial release and a video the DOJ called “unedited.” WIRED found that the clip contained gaps—about two and a half minutes missing—prompting reasonable suspicion. Mirror that phrase: Epstein files. The files that were released show networks of contact with many prominent figures. They do not present a neat smoking gun, but they do feed existing beliefs on both sides.
This episode exposed a deeper problem: absence of a shared framework for truth. When a file confirms a suspicion, it strengthens commitment; when it fails to meet expectations, it amplifies accusations of forgery or deep-state fabrication. That polarization means the same documents can be deployed as evidence and dismissed as manipulation. The political fallout is real: Trump’s handling fractured alliances, including with hardline supporters like Marjorie Taylor Greene.
For citizens and institutions: demand procedural clarity. If the DOJ releases material, provide chain-of-custody, metadata, and forensic explanation for any edits. If you want to rebuild trust in public records, make the release process transparent and independently verifiable.
Open question to readers: What level of forensic transparency would restore trust in official releases? Are independent third-party auditors sufficient, or must Congress require full unedited dumps with legal oversight?
Putting it together: signs to watch in 2026 and tradeoffs no one can ignore
When you mirror the year 2025, five core themes stand out: concentrated capital flows into resource-heavy data centers; emotional AI compels societal reflection; open-weight models reset competitive dynamics; sweeping government reform changes institutional capacity; and data integration shifts the privacy-power balance—plus the Epstein documents exposing fault lines in political truth. Repeat them in your mind: data centers, AI companions, DeepSeek, DOGE, immigration surveillance, Epstein files. Each carries winners and losers.
What should investors, civic leaders, and engineers do now? First, make small commitments that force consistency: require public environmental and social reports for data centers; fund independent studies before emotional-AI products scale; support open standards for model safety; demand continuity plans for government restructures; and push for legal safeguards around integrated databases. These are modest, measurable steps that align with liberal values: markets should operate freely, but not at the expense of public goods.
If you fear a bubble in AI infrastructure, you are not alone. If you fear loss of privacy through data fusion, you are right to worry. If you hope AI companions can ease loneliness, that hope matters. These conflicting impulses are understandable. The task is to build policies and institutions that validate legitimate fears while enabling constructive innovation.
Final open question to readers: Where do you place your bet for the next big policy battle—energy and local permits for data centers, regulation of emotional AI, open versus closed model governance, limits on government restructuring, or legal constraints on data integration? Which one would you act on first in your city, company, or civic group?
#WIREDRoundup #AIDataCenters #AICompanions #FrontierAI #DeepSeek #DOGE #ImmigrationSurveillance #EpsteinFiles #PolicySignals
Featured Image courtesy of Unsplash and Leif Christoph Gottwald (iM8dxccK1sY)
