Amazon cuts 16k jobs

AI as Cause vs Scapegoat

• Many see “AI” in the announcement as PR cover, replacing older excuses like “economic headwinds” and “pandemic over‑hiring.”
• Several commenters describe real AI-driven layoffs, but typically as leadership cutting staff before evidence of productivity gains, then rationalizing failures as “this year’s models will be better.”
• Others argue AI is mainly an opex‑cutting and marketing story: saying “we’re restructuring for AI” signals innovation to investors while justifying headcount reduction.

Overhiring, ZIRP, and Investor Pressure

• A common narrative: easy money 2020–2022 led to massive overhiring into marginal projects; higher rates and weaker growth now force corrections.
• Some think executives are doing herd‑behavior cost cuts because layoffs reliably boost stock; Amazon’s relative underperformance vs other “AI winners” is cited as extra pressure to show profit.

Offshoring, H1B, and India Expansion

• Strong thread linking US cuts to expansion in India and recent $100k H1B fee hikes; many predict accelerated offshoring and “reverse brain drain.”
• Others counter that cuts are global (including India), that Amazon has long had a huge India presence, and that offshoring would happen regardless.
• Big disagreement on H1B: some see it as wage suppression and quasi‑indentured labor; others note H1B workers often cost more once legal and risk costs are included, and argue the real problem is system abuse by middlemen.

Amazon Culture, Management, and Who’s Cut

• Official messaging emphasizes “excessive bureaucracy” and middle‑management layers, but multiple reports say many IC engineers (including senior L6/L7) were laid off.
• Debate over whether large firms are full of “deadweight” vs. employees stuck idle due to managerial paralysis, endless meetings, and reorg churn.
• Some describe Amazon’s culture as PIP‑heavy, with intentional churn and hiring practices that assume it’s easy to fire later.

Productivity, AI Tools, and Remaining Staff

• Some engineers report large personal speedups using LLMs, especially seniors who can correct AI “slop”; others say current models have regressed and are net time‑wasters.
• Commenters note no clear macro‑level productivity spike yet, and cite economic data and outages/security issues as counter‑evidence to “AI is already 10x” marketing.
• Several warn of the classic post‑layoff pattern: short‑term overperformance by “survivors,” followed by burnout, knowledge loss, and expensive rehiring.

Macro Economy and Labor Market

• Split views: one side says the broader economy is relatively strong (GDP, unemployment), the other points to high household debt, inflation in essentials, gig work, and persistent layoffs as signs of a “low‑key recession.”
• Some frame the tech downturn as closer to 2001 than 2008, driven by unwinding pandemic overexpansion and AI capex redirection.

Policy, Ethics, and Future of Work

• Multiple commenters argue this is a structural “rules problem”: firms are rewarded for cutting jobs regardless of profitability, while workers bear all risk.
• Proposed responses range from stronger layoff penalties and offshoring tariffs to UBI and better safety nets; opponents worry about inefficiency and capital flight.
• There’s visible anxiety that experience and high salaries are becoming liabilities, and that AI plus globalization will steadily erode white‑collar work without a coherent social plan.