TrueUp's tech layoff tracker shows 148,092 jobs displaced across 354 events between January 1 and June 1, 2026 — a daily rate of 981, running 46% above 2025's average pace of 674 per day. Profitable companies including Meta (8,000 cuts on May 20, roughly 10% of its headcount), Intuit (3,000, 17%), Amazon, and Oracle have all announced reductions explicitly tied to funding a combined ~$700 billion of 2026 AI capex. AI was the stated cause for about 25–26% of tech layoffs in March and April — the leading single cause for two consecutive months.
The age curve is the part that is new. According to data cited by CBS News and Tom's Hardware, employment for software developers aged 22 to 25 has fallen nearly 20% since 2024 — the precise window in which production-grade AI coding tools (Copilot, Cursor, Claude Code, Codex) became standard. Developers aged 30 and older at the same companies saw employment grow between 6% and 12% over the same period. The jobs AI is most efficiently replacing are exactly the tasks that used to onboard junior engineers: boilerplate code, scripted tests, routine bug fixes, the first eighteen months of a career.
The structural piece is worse than the cyclical piece. Layoffs in past tech downturns were absorbed by lateral moves — a customer support specialist cut at Salesforce got hired at HubSpot. When the same capability, say ambient summarization, becomes available to every company in an industry on the same day, every company restructures the same role on the same day, and the lateral hire stops existing. Mercer's 2026 Global Talent Trends report says 99% of surveyed CEOs expect AI-driven headcount cuts within two years, while only 32% believe their organizations can integrate human and machine capabilities well. That gap is the predictable middle of the curve.
Takeaway for learners: if you are early-career or about to graduate, the working assumption to plan around is that the traditional junior software role is structurally smaller, not temporarily smaller. The roles that are growing — ML engineer openings are up 59% year on year — reward people who can wire models into production systems, not people who write the same CRUD code an LLM can generate in a second. Pick a problem domain (healthcare, climate, security, hardware) where the data and the regulatory friction are real, then become the person who knows both the model side and the domain side. Generalist junior developer is not the safe path it was in 2022. Specialist junior developer still is.