"Boss culture", 996, and KPI-driven management in China's tech sector
A viral Alibaba resignation letter gives an inside look at labor relations in China's tech sector.
Last month, a veteran employee at Alibaba marked his departure with a 10,000-word internal letter that delivered a pointed critique of the company’s cultural and organizational decline. The letter quickly went viral in China, resonating with tech workers well beyond Alibaba, and even drawing a public response from Alibaba founder Jack Ma, who seemed to agree with many of its observations.
The author highlighted a series of internal/organizational issues such as the erosion of collaboration in favor of cutthroat internal competition; the dominance of short-term KPI-driven metrics over longer-term innovation; the rise of “boss culture”; and the normalization of long work schedules like 996 (9am to 9pm, six days a week). The result, he argues, is mediocrity, short-term thinking, and growing dysfunction at Alibaba.
For close observers of China’s tech sector, the criticisms in the letter aren't new. Many who have closely followed labor developments in the sector (myself included) have seen these problems emerge across the industry. In 2022, I wrote about tech workers “lying flat” and how Chinese tech companies, facing growing backlash, attempted to reform their management practices in response. Some companies even pledged to end 996 in hopes of fostering greater efficiency and innovation—albeit with limited success. What is apparent, as highlighted in this letter, is that the organizational problems within China's tech giants persists to this day.
Although the letter is focused on Alibaba, the issues it raised are far from unique; they are also severe in other Chinese tech companies ranging from ByteDance to Pinduoduo to JD.com. Even non-Chinese firms aren't immune. I've written about a similar organizational decline among tech giants in the U.S. with the rise of “bullshit tech work,” which you can find here (but I should emphasized that the organizational issues among big tech firms in the U.S. are very different from those in China). The point, nevertheless, is that these issues aren't just about firm culture but are structural—rooted in the very business model of the platform firm.
This post is structured in two parts. First I’ll summarize some of the key themes from the viral resignation letter. Second, I’ll explore how these organizational problems stem from the platform business model itself, which systematically prioritizes short-term commercialization—and how the organizational logic that once fueled Alibaba’s commercial dominance may now be the very thing holding it back from more innovative domains like AI.
Key themes from the resignation letter
The resignation letter opens with a nostalgic fondness for what Alibaba used to be, with the author proudly talking about the immense impact the company use to have in the economy. But over his 15-year tenure, he witnessed a steady decline in the company's position and a shift away from the values and vision that made the company great. The bulk of the letter is a detailed account of how Alibaba's internal culture is at the root of the problem. Rather than walk through it point by point (which an AI translator could surely do better than I), I'll highlight a few key themes from the letter.
First is the company’s obsession with short-term metrics and KPIs. Because promotions are tightly bound to these metrics, employees compromise on the integrity of their work in order to hit their numbers. They resort to all kinds of “operational” hacks: running marketing promo campaigns for their product, massaging the metrics, moving the baselines, etc. They even fake the entire product life cycle—hyping it up to get resources, manipulating data to simulate success, and shifting blame once it inevitably collapses. In this environment, workers who cut corners, pursue vanity metrics, and even sabotage their colleagues are the ones that get ahead.
Second is the rise of “boss culture”—a hierarchical system in which upper management holds all the power and can determine the fate of lower ranked employees. In this system, promotions depend less on actual contributions or innovation and more on loyalty to one's manager. This makes going against the decisions of leadership unthinkable—sometimes at the cost of doing what's best for the customer or the product.
Third, and relatedly, is that the company has become captive to market trends. When a new business line seems promising, teams rush in and reorgs cascade through the company as managers scramble to stake out more territory. The letter describes a cycle of endless reshuffling—frequent leadership changes, shifting priorities, and KPIs that are constantly being updated. Strategic direction grows increasingly vague, and long-term product vision becomes untenable. With this pattern of trend-chasing, employees know that today’s projects may be scrapped tomorrow—so instead of building for durability, they build to fast-track their promotion.
Fourth is a culture dominated by internal competition rather than collaboration. Where individual KPIs are more important than shared goals, employees stop working together and start working against each other. In the letter, one passage describes how “wild dogs”—the kind of employee who plays dirty and sabotages teammates to further their own careers—will advance their careers far faster than the “gold cows,” the dependable and high-performing employees who once formed Alibaba’s backbone. As internal competition becomes the driving motivation for work, cross-team cooperation breaks down, and with it, the ability to harness bottom up innovation from the company's talent base.
Finally, the letter paints a stark picture of how forcing workers to work overtime undermines innovation. Reflecting on Alibaba’s 996 culture, the letter points out that tech work is not manual labor and longer hours do not translate to better outputs. In fact, it often breeds a culture of performative busyness, where workers compete over who can work the longest to appear the most committed. The result is a system optimized not for real product development, but for the appearance of productivity.
An organizational culture oriented towards short-term commercialization
The labor relations described in the letter—the focus on KPIs, top-down decision making and managerial control, “boss culture”, and cutthroat internal competition—aren’t accidental nor are they unique to Alibaba. They’re the result of the business model that Alibaba and other platform firms operate under.
To understand the roots of the problem, we need to look at the changing dynamics of China’s platform economy. Since the late 2010s, China’s digital economy has become hypercompetitive. In earlier years, the rapid rise in internet users was the rising tide that lifted all boats, creating enough demand for competing firms to expand together. But today, that pie has stopped growing. With user growth plateauing, China’s internet giants are locked in zero-sum competition, where growth requires the direct cannibalization of a rival's userbase.
In response to this hypercompetitive landscape, internet companies reshaped their internal cultures to stay ahead—prioritizing speed, adaptability, and execution. Strategic direction appeared to be in constant flux; not for a lack of leadership but because firms had to be able to react quickly to new market pressures or consumer trends. Especially in China's environment where copying competitors is the accepted norm, success depended less on novel ideas, and more on executing new products, scaling up quickly, and responding constantly to what competitors are doing.
In this context, the obsession with KPIs and the use of 996 made some sense as a managerial tactic. KPIs offered a way to discipline workers around commercial goals that were immediately measurable: user growth, engagement, conversion, sales. Similarly, the enforcement of 996 had for years enabled leading platforms (including Alibaba) to outcompete challengers and retain their dominance in several markets. These practices made sense in an era of hypercompetition where rapid execution, maximizing short-term gains, and maintaining market share were critical, as even a slight slip up could give competitors the momentum to activate powerful network effects.
Today, as China’s internet economy slows down, the opportunities for short-term commercialization have narrowed. Most of the easy gains have been squeezed dry. And tech firms are now pivoting towards robotics, enterprise software, biotech, and most of all, AI—all of which demand more thoughtful innovation, sustained experimentation, deep technical collaboration, and empowered teams incentivized to take risks. But as the Alibaba resignation letter makes clear, the current workplace culture isn’t built for that future.
Alternative models of labor relations are beginning to appear in China’s tech sector. DeepSeek is one example. As I’ve written in a previous post, the small AI company's workplace culture stands out for its collaborative nature, bottom-up approach to decision making, and the high degree of autonomy it grants employees. And it also isn’t weighed down by a workplace culture that was shaped during the hypercompetitive internet boom years.
As the Alibaba resignation letter writes: AI is here, embrace this era. But the question is whether China’s internet giants—most of which rose to prominence during those boom years—can change their workplace culture, or whether the inertia of the old labor model will persist.
I'm a product manager at a platform company in Shanghai and I've been working in China tech for over a decade.
I'll comment on the focus on optimizing KPIs for your coming performance review. We have two main flow, pre-purchase and post-purchase. Pre-purchase, we look at optimizing conversion rates. Post-purchase, we look at decreasing customer service contacts, either by providing self-service functionality or by fixing the source of contacts.
We have a domestic product and one for the international markets. For international, the conversion rate is between 3% and 9% depending on market and market maturity, and business line.
What our product managers do is run experiments hoping to increase the 3 to a higher number. Every week we have thousands of A/B test running and some of them end up as "statistically significant" with a 1% relative increase to baseline. From 3% to 3.03% that is.
The whole idea -- and trust me, there are no other ideas here -- is to randomly perform experiments until the 3 turns into a 9.
It never will, for many reasons.
First, these experiments disregard effect size. A 1% relative increase has no practical significance. It is noise.
Science has a reproducibility crisis, and it also has had a crisis to overfixation on NHST as a method of testing and "proving" hypotheses. But as we know, few scientific results are reproducible by other researchers, especially in the social science. The three-time reproducibility rate of many findings is low. The same goes for these A/B tests. They are never tested for reproducibility, and most of them are not reproducible.
So the idea that results stack is clearly false in theory. It is also empirically false, because otherwise conversion rates wouldn't be 5% after a decade of running thousands of experiments per week. Mathematically, it would be approaching 100%. It isn't. Product management isn't math. It isn't a hard science. It is a social science and has the same issues and challenges.
You may have notice that the same product converts at 9% in some markets and 3% in other markets. Nobody can figure out why. That's because it has nothing to do with the user experience - it is equally poor in every market, essentially like taking the Meituan app and translating it to English, then launching it in Europe and wondering why it doesn't work - it has to do with brand awareness and attitude. Our users actually aren't robots who click on buttons at a pre-defined rates whenever presented to them - they actually think for themselves! A terrible problem for a 互联网产品经理。I wrote about this here: https://dilemmaworks.substack.com/p/brand-awareness-is-the-most-overlooked
We do user research and every problem with the user experience has been documented, across several markets.
We still don't get fixed though. Because we're focused on "quick iterations" for 1% relative improvements. And hey, we A/B tested this experience before, so it must be fine. The research is wrong. Listen to the data. Not understanding that we're in a local maxima, not a global maxima.
We make an active decision to not listen to users, to not listen to local market reps. Our product development is a random walk, where HQ comes up with and tests random ideas and selects whichever are "statistically significant". And that's the product.