The Truth
About AI

The Jobs Are Already Gone

Challenger, Gray & Christmas — the industry-standard layoff tracking firm — recorded 71,825 U.S. job cuts explicitly attributed to AI since it began tracking in 2023, with 54,836 in 2025 alone. These numbers almost certainly undercount the real displacement. Many companies restructure without naming AI. The primary mechanism isn't firing — it's not hiring.

A landmark August 2025 Stanford study using ADP payroll data found a 13% relative decline in employment for early-career workers (ages 22–25) in AI-exposed occupations. For software developers aged 22–25, employment fell approximately 20%. Experienced workers in the same roles saw stable or growing employment. The researchers concluded companies are "removing the bottom rungs of the career ladder."

The retraining promise is hollow. A Brookings Institution analysis found "at best inconclusive evidence on retraining efficacy." An NBER working paper showed displaced workers who retrain into high-AI-exposure occupations earn 29% lower returns than those targeting low-exposure fields. The WEF projects 92 million jobs displaced and 170 million new roles created by 2030 — but 77% of AI-adjacent new jobs require master's degrees, 18% require doctorates. The workers being displaced are not the workers who will fill those roles.

Your Data Trained Their Models

The foundational dataset for virtually every major language model is Common Crawl — 3.1 billion web pages, 380 terabytes of compressed data, archived since 2008. More than 80% of GPT-3's tokens came from it. If you posted anything on a publicly indexed website in the last 17 years, the probability that your text was ingested is high. You were not asked. You were not paid. You were not told.

Books3, part of the open-source Pile dataset, contained 196,640 copyrighted books scraped from a pirated ebook tracker — without compensation to any author. It trained GPT, LLaMA, and others before a DMCA takedown. Reddit sold API access to Google for $60 million per year and to OpenAI for an estimated $70 million annually. The users who created every question, answer, and thread received nothing. The copyright lawsuits are piling up but moving slowly. OpenAI was ordered to produce 20 million de-identified ChatGPT logs to copyright plaintiffs in January 2026.

The United States has no comprehensive federal AI law and no federal privacy law. The EU AI Act, effective August 2024, bans social scoring, mandates risk assessments, and imposes penalties up to €35 million or 7% of global annual turnover. The U.S. response: 1,080–1,208 state AI bills introduced in 2025 across all 50 states, 118–145 enacted. Meanwhile, under pressure from U.S. tech companies and the Trump administration, the EU Commission began considering a grace period for enforcement of its own law.

Who Actually Owns AI

AI infrastructure is concentrated in a handful of companies to a degree that makes the oil industry look diversified. NVIDIA controls 87% of the AI accelerator market by revenue. Its data center segment generated over $100 billion in FY2025 at 73.6% gross margins. Three cloud providers control 62–66% of all global cloud infrastructure. The model layer is similarly locked: in Q1 2026, four AI companies absorbed $188 billion in venture capital — 65% of all global venture investment.

The cost to train frontier models has grown 287,000x since 2017 — from roughly $670 to over $100 million for GPT-4. This makes frontier AI development functionally inaccessible without massive corporate or sovereign backing. Then DeepSeek arrived. The Chinese lab trained its V3 model for approximately $5.6 million — 200 employees, a fraction of OpenAI's workforce, using restricted chips. NVIDIA lost $589 billion in market cap in a single day. The chip restrictions may have accelerated Chinese algorithmic innovation through necessity.

The Race That's Almost Tied

U.S. private AI investment in 2025 reached $285.9 billion versus China's $12.4 billion — a 23x gap in private capital. But this comparison obscures massive Chinese state investment: between 2000 and 2023, China deployed approximately $912 billion in government guidance funds across strategic industries including AI. The U.S. government spent just over $12 billion on AI-related obligations over the same period.

AI at War — Already Deployed

The Pentagon created its first dedicated AI budget line for FY2026: $13.4 billion. Project Maven — the military's AI targeting system — evolved from a $480 million contract to a $10 billion Army enterprise framework and was designated an official "program of record" in March 2026. It has over 20,000 active users across every unified combatant command. A Pentagon official stated Maven will soon transmit "100 percent machine-generated" intelligence with no human hands participating.

The labor counterattack has started — barely. The 2023 WGA and SAG-AFTRA strikes established the first major contractual AI guardrails. These provisions expire June 30, 2026. The AFL-CIO published "Workers First" AI principles demanding advance notice and prohibiting AI as a union-busting tool. New York became the first state to require employers to disclose AI as a reason for layoffs. But organized labor covers only a fraction of the affected workforce. 71% of Americans are concerned "too many people will lose jobs" to AI. Congress has passed no federal legislation in response.

The Numbers That Tell The Story