AI ImpactWhat is AI actually doing to people?
You don't need to understand the technology to understand what's happening. Five numbers, in plain words, each with its receipt. This page is written for the person who just walked in — everything on it links one level deeper when you're ready.
people have lost their jobs in 2026 where AI was named as a reason. That's more people than live in Dayton, Ohio. Each one is on our board with a date and a source — no estimates, no rounding up.
is what the companies doing that cutting plan to spend on AI buildings and chips in 2026 alone. That's about $100 for every person on Earth — spent by twenty companies, in one year.
of electricity will feed AI data centers this year — more than the entire United Kingdom uses, homes and factories included. And the plan is to triple it by 2030. Somebody's grid pays for that.
layoffs at the companies that actually make AI. The model labs are hiring. If AI itself forced the cuts, wouldn't the AI companies be cutting too? The cuts cluster where the money leaves — not where it lands.
of people under 30 now use AI — the fastest adoption of any technology in the record — while trust in AI at work stays flat. People like the tool. It's what's being done in its name they don't trust. The numbers say they're reading it right.
None of this was decided by a machine. It was decided by people — in the name of AI, in the name of compute, in the name of money. The machine is the constant. The people are the variable — and the cause.
The Infrastructure Subsidy: Your Power Bill Is an AI Investor Now
Utilities asked for $29 billion in rate increases in six months. Harvard's electricity-law scholars traced where the money goes: infrastructure built for data centers, paid for by everyone else.
The Invisible Tax: The Company Town, Metered Monthly
Virginia regulators just raised the average household's power bill toward $165 a month — and then wrote a new rule forcing data centers to finally pay their own way. That second half only exists because someone proved the first half was subsidizing the machines.
The Private Grid: When the Data Center Builds Its Own Power Plant
Rather than wait five years for a grid connection, AI labs are trucking in gas turbines and running them as private power plants — classified as "non-road engines" to skip the permits. The grid, and the neighborhood, keep the bill and the smog.
Thirsty Machines: What the Cloud Drinks in the Desert
A single Google campus in Mesa is permitted for up to four million gallons of water a day — in a county the government rates in extreme drought. The cloud has a plumbing bill, and it is being paid in an aquifer that was already over-drawn.
The Synthetic Dependency: The Perfect Companion Never Argues Back
MIT and OpenAI studied forty million conversations and found the heaviest users of a companionable machine were lonelier, more dependent, and spent less time with people. The machine didn't choose that design. We did.
The ROI Fallacy: The Layoffs Paid for the Machines. The Machines Haven't Paid Anyone Back.
Roughly 80 percent of large organizations cut staff in the name of AI. Gartner went looking for the return on those cuts and found none. So who was the layoff actually for?
The Divergence Signal: Semiconductors +91%, the Workforce Cut 123,653
In the same six months, the market bid semiconductors up 91 percent and companies cut over 123,000 AI-and-tech jobs. The boom and the cut are two prices on one trade — and they point in opposite directions.
The Compute-Capex Mirage: $700 Billion Goes Into the Ground. What Comes Back Out?
The four biggest buyers will spend three-quarters of a trillion dollars on infrastructure this year. The revenue that is supposed to justify it is the quietest number in tech.
The Data-Center Deal: The Savings Were a Rounding Error on the Spend
Amazon cut 30,000 corporate jobs and, in the same fiscal breath, guided to as much as $200 billion in capital spending. Trace the money and you find the same trade everywhere: capital walking out of the payroll and into the hyperscaler's invoice.
The Efficiency Illusion: The Polite Word for Feeding the Furnace
"Efficiency" is the word 2026 uses for cutting payroll to fund compute. But the filings show the savings and the spend are not in the same league — they are not even the same sport. This is the anatomy of a transfer, dressed as a strategy.
The Executive's Signature: We Don't Blame the Algorithm
The machine did not fire the sales team or sign the GPU contract. People did — the CEO, the CFO who booked compute as "efficiency," the board that demanded a pivot. This desk traces the trade back to the signature, because that is where accountability actually lives.
The Corporate Capture Mapping: Who Benefits When the Builders Preach Doom?
Researchers catalogued 249 cases of "Big AI" steering its own regulation — the same playbook regulators once saw from tobacco and oil. The existential-risk sermon turns out to have a business model.
The Homogenization Feedback Loop: A Culture Trained on Its Own Average
Nature published the mathematics: models trained on model output collapse toward the mean and the rare disappears first. The same loop is now running on the culture the models feed.
The Systemic Bias Audit: The Machine Is Doing Exactly What We Built It to Do
OpenAI's own researchers showed models guess confidently because our benchmarks punish "I don't know." Bloomberg showed GPT ranking résumés by the race coded in a name. Neither is a glitch. Both are the design.
Plain-face figures above are AI-analysed restatements of the desk's sourced records; every tile links to its receipt. Public-opinion data: Stanford University · Institute for Human-Centered AI · AI Index Report 2026 — Public Data · CC BY-ND 4.0.