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- ⚡ AI Eats Power, Humans Eat Workslop
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Read time: 5 minutes
AI isn’t just eating chips - it’s eating the grid. Virginia’s data centers now pull more power than whole countries. Meanwhile, “workslop” is the new buzzword for low-value AI output already hitting 40% of workplaces…
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INSIGHTS
Virginia’s Loudoun County (aka “Data Center Alley”) now runs nearly 6,000 MW of active capacity - more than any other county in the U.S. And another 6,300 MW is already planned. That’s bigger than some entire countries’ power grids.
Other hotspots are catching up fast:
Maricopa, AZ → 3,400 MW live, 6,000 MW planned
Prince William, VA → 2,700 MW live, 5,100 MW planned
Dallas, TX → 1,300 MW live, 2,900 MW planned
Why here, not New York or LA? Cheap power + fiber + tax breaks. Not people.
👉 Ashburn, VA moves 70% of global internet traffic daily.
👉 West Texas offers dirt-cheap wind + solar for Microsoft & Meta.
👉 Oregon & Washington tap the Columbia River’s hydro.
👉 Atlanta–Virginia I-85 corridor is the new global on-ramp with fresh subsea cables.
The catch? AI workloads are power monsters. U.S. data centers already use 2–3% of national electricity. By 2030, that could double. Utilities are struggling to keep up - delays in Virginia and Silicon Valley show the grid is already stressed.
Why it matters: Data centers aren’t just real estate anymore. They’re now the front line of U.S. infrastructure - and the next big tech bottleneck may not be chips, but power.
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TODAY IN AI
AI HIGHLIGHTS
🔥 Researchers at Harvard Business Review just coined a new word you’ll start hearing a lot: workslop. It’s low-quality AI output that looks fine but adds no value - and it’s already hitting 40% of workplaces surveyed. Leaders are told to set guardrails or risk drowning in it.
⚠️ Viral app Neon went offline less than 24h after topping the App Store. Why? A massive security flaw leaked user phone numbers, call transcripts, and recordings. TechCrunch says anyone with a link could grab your calls. Neon promises fixes - but trust is gone.
🧑⚖️ California’s Gov. Newsom must decide on two new chatbot safety bills. Parents who lost kids to harmful chatbot convos are demanding action, while OpenAI, Meta, and Google push back, warning rules would kill innovation. Newsom’s call could shape U.S. AI law.
🌍 NVIDIA, Google, Microsoft, and KMA are teaming with the World Meteorological Organization on AI “nowcasting” that predicts extreme weather up to 6h ahead. Their AI models already beat physics-based forecasts. The goal: get this tech to developing nations.
🤝 The UAE president just met OpenAI CEO Sam Altman in Abu Dhabi. Talks centered on building one of the world’s largest AI data centers, Arabic-first AI models, and joint research. The UAE is spending billions to become a global AI hub - with U.S. backing.
💰 AI Daily Fundraising: Signal AI raised $165M in growth equity led by Battery Ventures, with participation from Highland Europe, Mercuri, and MMC Ventures. Trusted by 650+ enterprises including Uber, Bloomberg, Volvo, and Diageo, the company is scaling its AI-powered risk and reputation intelligence platform worldwide.
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AI QUICK HITS
🌐 79% of companies say they now face AI debt, as sloppy agent rollouts create security risks, poor data, and wasted time (Asana report)
📝 Over 200 leaders, including Nobel laureates & Yuval Noah Harari, signed a UNGA letter calling for urgent red lines on AI risks
💸 Even Sam Altman admits investors may be overexcited - today’s AI boom shows eerie parallels to the dot-com bubble, with $560B infra spend vs. $35B revenue.
🌟 Google just added 10 new Gemini AI features to Chrome, while Microsoft launched a “Facilitator” agent inside Teams for smarter meetings
AI CHART
Radiology should’ve been the first job AI replaced. Clear benchmarks. Digital images. Repeatable tasks. Geoffrey Hinton even said in 2016: “stop training radiologists now.”
And yet, in 2025 demand for human radiologists is at an all-time high:
1,208 residency slots this year → record high
Avg income: $520K, up 48% since 2015
Vacancy rates: all-time highs
Why?
Benchmarks ≠ hospitals. Models like CheXNet, Lunit, and Aidoc crush tests, but fail when images are messy or hospitals use different machines.
Regulation. Over 700 FDA-cleared models, but most must still keep a doctor in the loop. Insurers won’t cover “AI-only” reads.
Scope of work. Diagnostics are just part of the job. Radiologists spend most of their time talking to patients, coordinating care, teaching, adjusting scan protocols.
AI is great at triage: prioritizing urgent cases, spotting common diseases, and drafting reports. But the more AI speeds things up, the more scans doctors order - classic Jevons paradox. Faster tools → more work, not less.
Bottom line: In radiology, AI has made humans busier, not obsolete. A warning that “AI replacing jobs” will play out slower, messier, and more human than benchmarks suggest.
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