A former Amazon manager shares an insider view on recent tech layoffs, arguing that the layoffs are not mainly due to AI but stem from systemic issues like overhiring, internal politics, and declining productivity. The article explores the decline in revenue per employee, the shift from innovation to turf wars, and the emotional toll on employees, concluding with a call to embrace change proactively.
Last month, three of my former co-workers at Amazon were laid off, all of whom have mortgages, and two are on H-1B visas. One of them asked me, "When you quit last year, did you know this was coming?" To be honest, I did not have a specific date, but I could see it coming. In my humble opinion, as a former Amazonian, I don't believe this is truly about AI, despite the company's claims.
Before I quit last year, I had been at Amazon for seven years, five of which I served as an L7 manager. I witnessed the situation from the inside. AI is just a cover story. What I think is really happening is that the company is trying to stop the bleeding from what felt like a rotting system.
I want to share what I truly felt while working at Amazon and why I decided to leave.
For years, I was in charge of the annual plan at Amazon, known as OP1, for my entire organization. Since the end of the pandemic, the tone from the top became harsher. The message was clear: the math wasn't adding up.
In 2019, Amazon had about 800,000 employees. By 2021, that number doubled to 1.6 million. This growth was not limited to fulfillment centers but was across the board.
In 2020, everyone in the organization was asking for headcount, sometimes for questionable reasons. For example, needing six product managers to find UX issues on a single checkout page. You would expect such requests to be denied at the VP level, but they were almost always approved. It seemed Amazon had shifted from an innovation engine to a turf-grabbing war machine. More headcount meant more power, and more power meant more importance.
However, revenue and profit do not care about organizational charts. Looking at the financials revealed the problem:
Internal data showed it was even worse depending on the business units.
Anyone who has owned an OP1 probably felt this in recent years. Headcount requests became harder to get approved, hiring freezes were implemented, and return-to-office policies were used to quietly push people out without severance packages.
If you were paying attention, you would have noticed the layoffs coming long before AI became a buzzword.
Is AI part of the reason? Sure, it accelerates the process. But more than anything, it's a convenient excuse. It's easier to tell the board that the company is investing heavily in AI for the future than to admit that overhiring and internal politics hollowed out the organization.
Amazon hires smart people. I worked with genuinely talented engineers. So why couldn't the company turn this talent into results? This question is why I quit—not because I hated my boss or was burned out, but because I felt dead inside.
I once filmed a vlog of my workday to see where my time went. On that day, I had 13 meetings from early morning to late at night. That wasn't even a particularly busy day. What did I accomplish? Nothing.
This was not unique to me. If you're an L7 or L8 manager, your calendar is likely filled not with innovation but with alignment meetings, reading documents, blame-shifting, and framing stories.
For example, when the global checkout page broke—meaning customers worldwide couldn't check out—the response was not to fix the problem immediately. Instead, it was months of documents, meetings, arguments involving 23 teams and six VPs, and endless political games. While this was happening, nobody fixed the long-term problem because long-term fixes take time and don't score political points. Everyone did quick patches to claim credit.
Multiply this by dozens of incidents a year, reorganizations, annual planning, quarterly business reviews, and roadmap resets, and you'll find yourself swamped with tasks unrelated to the end customer.
Over seven years, I saw zero headquarter engineering projects launched on time. The only project launched on time was built by a Japan team, which was ironically the first team to be laid off.
At Amazon, technical skills were not what got you rewarded. Engineers in my organization spent about one-third of their time coding; the rest was spent in meetings, writing documents, and explaining things to leaders who proudly remained non-technical, calling themselves General Managers.
An average bug took 200 days to fix, and many bugs were never fixed.
I joined Amazon because I believed in its customer focus. About 10 years ago, that was true. In recent years, however, customer obsession deteriorated into politics obsession, which slowly killed something in me.
Getting laid off is painful, and I genuinely empathize with anyone who suddenly lost their income. But for some, this might be a strange kind of reset.
Big tech might look invincible today, but systems rot quietly before they collapse—even empires do.
Meanwhile, growth is happening elsewhere, especially in traditional industries where AI is changing how things are done, such as logistics, energy, manufacturing, healthcare, and education. These sectors need builders.
I texted my coworker back, saying I did not know when the layoff would come. I was just a lowly L7, but I felt I wasn't worth the money the company paid me, and the company wasn't worth the life I was giving it. So I left.
When times change, we can either wait and be forced to react or jump early and try to catch the wave. I chose to jump early.
This insider perspective sheds light on the complex realities behind tech layoffs at Amazon, challenging the narrative that AI is the primary cause and highlighting deeper systemic issues within the company.
