Amazon insider

A former Amazon L7 manager with seven years at the company (five in management) argues that recent tech layoffs are fundamentally not about AI, despite what companies claim. Instead, she describes them as the inevitable consequence of systemic organizational rot that began during the pandemic hiring surge.

The Real Cause: Over-hiring and Political Bloat

Between 2019 and 2021, Amazon doubled its workforce from 800,000 to 1.6 million employees across all divisions—not just fulfilment centres. The manager describes this period as when Amazon transformed from "an innovation engine to a turf-grabbing war machine," where headcount became synonymous with power and importance.

The financial consequences were stark: revenue per employee collapsed from $484K in 2020 to $362K in 2021, then to approximately $320K in 2022. The manager notes that internal data across business units showed even worse figures.

Warning signs accumulated for years before any layoffs: increasingly difficult headcount approvals, hiring freezes, and "return to office" policies designed to push employees out without severance packages. The manager states, "This was written on the wall long before ChatGPT."

Why Smart People Fail to Produce Results

The most troubling question for the manager was why Amazon's talented engineers couldn't translate their skills into outcomes. Her answer: the organization had devolved into "politics obsession" rather than "customer obsession."

She documented a typical workday with 13 meetings from morning to night—and accomplished nothing. L7 and L8 managers, she explains, spend their time in "alignment meetings, reading someone's docs, blame shifting and 'framing the stories'" rather than innovation.

A revealing example: when the global checkout page broke (preventing worldwide customers from completing purchases), 23 teams and 6 VPs spent months in "documents, meetings, arguments" and "endless blame games" instead of fixing the underlying problem. Quick patches were prioritized because long-term solutions didn't serve immediate political needs.

Systemic Dysfunction

The manager highlights several shocking operational realities:

• Engineers spent only one-third of their time coding; the rest went to meetings, documentation, and explaining work to proudly non-technical "GMs" (General Managers)
• Average bugs took 200 days to fix, with many never addressed at all
• In seven years, she never saw a headquarters engineering project launch on time—the only on-time delivery came from a Japan team that was "the first to let go" in layoffs

Personal Decision to Leave

The manager quit not due to burnout or a bad boss, but because she "felt dead inside" and believed she "wasn't worth the money that the company was paying me" while "the company wasn't worth the life I was giving it."

Looking Forward

She suggests that laid-off workers may experience "some strange kind of reset." While Big Tech appears invincible, "systems rot quietly before they collapse." She identifies growth opportunities in traditional industries—logistics, energy, manufacturing, healthcare, and education—where AI is genuinely transforming operations and "actually need builders."