‘A Rotting System’: Former Amazon manager says AI is a smokescreen for the real reason behind all the tech layoffs

Becky Global, a tech and career YouTuber who says she spent seven years at Amazon – five of them as a senior L7 manager – argues that the wave of tech layoffs is being sold with a neat AI story that doesn’t match what she saw from the inside. In her view, AI is not the real cause. It’s the excuse that sounds respectable in front of leaders and investors, while the deeper problem is a company trying to stop “the bleeding” from what she calls a rotting system.

She opens her video with a personal moment that sets the tone. Last month, she says, three former coworkers at Amazon got laid off, all with mortgages, and two on H-1B visas. One texted her asking a painful question: when she quit last year, did she know this was coming?

Becky says she didn’t have a date, but she could see it “on the wall.” And that’s where her main claim begins: the warning signs were there long before ChatGPT or today’s AI hype cycle.

“AI Is Just The Cover Story,” She Says

Becky tells viewers she isn’t making a rage video or trying to dunk on anyone. She frames it as an insider view of how a big company can change slowly until it feels unrecognizable.

She says that before she left, she was in charge of an annual planning process Amazon calls “OP1” for her entire org, and she noticed the tone from the top getting harsher each year after the pandemic. The message, as she experienced it, was that “the math wasn’t checking out.”

Then she explains what she means by “math.”

Becky says Amazon had around 800,000 employees in 2019, and by 2021 it doubled to 1.6 million in just two years. She stresses this wasn’t only fulfillment centers; it was across the board.

She remembers 2020 as a period when everyone was asking for headcount, sometimes for reasons she felt made no sense, like needing six product managers to find UX issues on one checkout page. In her telling, those requests should have been turned down at higher levels, but “almost everything got approved.”

Her theory is that somewhere along the way, Amazon shifted from an “innovation engine” into what felt like a turf-grabbing war machine, where more headcount meant more power, and more power meant more importance.

But, she says, revenue and profit don’t care about org charts.

Becky points to revenue-per-employee numbers to show the squeeze she felt building. She says that in 2020 Amazon’s revenue per employee was around $484K, then in 2021 it dropped to $362K, and in 2022 it dropped again to about $320K. She stresses she’s not leaking confidential data, and says the internal view could look even worse depending on the business unit.

In her mind, this is where the “AI layoffs” storyline starts to feel like a smokescreen. It sounds better to say, “We’re investing heavily in AI for the future,” than to admit the company overhired and let internal politics hollow out productivity.

The Slow Build: Headcount Denials, Hiring Freezes, RTO Pressure

Becky describes the corporate pattern she says came next, and she frames it as predictable if you were paying attention.

First, she says, headcount requests got “hotter and hotter” until it became nearly impossible to get new headcount approved.

Then she says hiring freezes arrived – even for already-approved open roles, meaning teams were told not to hire for positions that technically existed on paper.

Then came what she calls “return to office gimmicks and policies” that, in her view, were used to quietly push people out without paying severance packages.

So when layoffs finally arrived, Becky says she wasn’t shocked. She calls it something “written on the wall” long before the AI boom became the convenient headline.

She does concede AI is “part of it” and can accelerate change, but she repeats her main point: it’s a convenient excuse, not the root cause.

The Meetings, The Docs, And The Feeling Of Being “Dead Inside”

Becky says the question that really bothered her wasn’t whether layoffs would come. It was why a company full of smart people couldn’t translate talent into results.

She says Amazon hires smart engineers, and she worked with genuinely smart ones. So why didn’t the output match the input?

Her answer is the reason she says she quit.

She claims she didn’t leave because she hated her boss – they got along. She didn’t leave because of burnout alone. She left because she felt “dead inside.”

To prove how her days looked, Becky says she filmed a vlog of her workday to see where her time went. On that day, she had 13 meetings, from early morning into late at night.

“What did I accomplish? Nothing,” she says.

And she insists it wasn’t just her. She describes L7 and L8 calendars buried not in building, but in “alignment meetings,” reading docs, blame-shifting, and “framing the stories.”

She offers a specific example: a global checkout page broke so customers worldwide couldn’t check out. She describes it as a severe issue, but says what followed wasn’t focused on fixing the long-term problem.

Instead, she says it became months of documents, meetings, arguments – 23 teams, six VPs, and endless “blame games.” Her claim is that long-term fixes take time and don’t help people win the next political skirmish, so teams did quick patches to claim credit.

Multiply that by dozens of incidents a year, she says, and you end up swamped in work that has “nothing to do with the end customer.”

Then she drops one of her harshest observations: across her seven years, she says she saw zero headquarters engineering projects launched on time. The one project she says did launch on time was built by a Japan team—and she claims that team was the first to be cut during layoffs.

To her, that pattern signals a deeper sickness. The people who quietly deliver don’t get protected. The people who win politics survive.

A Company That Changed From “Customer Obsession” To “Politics Obsession”

Becky says she joined Amazon because she believed in the company’s customer focus. She references the famous “customer obsession” culture as something she once took seriously.

But in recent years, she says, she felt that obsession deteriorated into “politics obsession,” and she describes it as something that slowly killed her motivation.

She makes a point that sounds small but is brutal if you’ve ever worked inside a big org: she says engineers in her org spent about one-third of their time coding, and two-thirds in meetings, writing documents, and explaining things to leaders who “proudly remain non-technical” because they call themselves GMs.

She says an average bug took 200 days to fix, and many bugs were never fixed at all.

If that’s even close to true in pockets of a large company, it helps explain why employees can feel gaslit when layoffs arrive. They’re told the company is cutting “for efficiency,” but they’ve spent years watching the system reward inefficiency if it serves internal status games.

Her Advice: Don’t Wait For The Axe

Becky says she genuinely empathizes with anyone who gets laid off, because losing income is painful and destabilizing.

But she also suggests layoffs can act like a strange reset for some people, forcing them to step out of a system that was already breaking them down. In her view, big tech looks invincible until it doesn’t, because systems “rot quietly before they collapse.”

She argues growth is happening elsewhere, especially in traditional industries where AI is changing how work gets done. She names logistics, energy, manufacturing, healthcare, and education as places that need builders.

Her closing message is personal and blunt. She says she texted her coworker back and admitted she didn’t know when layoffs would come, but she felt she wasn’t worth the money Amazon was paying her, and the company wasn’t worth the life she was giving it – so she left.

“When times change,” Becky says, “we can either wait and then be forced to react, or jump early and try to catch the wave.”

She chose to jump.

And whether someone agrees with her or not, her story lands because it describes layoffs as the final symptom, not the first cause – a corporate body that grew too fast, then started bleeding internally, and finally used AI as the clean, boardroom-friendly way to explain the mess.