Wu Zhao Is Out at DingTalk. The Essay Didn't Beat Him. Busywork Did.
It took seven days to bring Wu Zhao down.
On June 4, Teng Yaxin, a core product manager on DingTalk ONE, published “Trapped Inside DingTalk,” a 75,000-word resignation essay. On June 8, a former vice president followed with a sequel, “Trapped Outside DingTalk.” On June 10, Alibaba’s partnership committee did something it hadn’t done in 27 years: publicly called out the management style of a single business line, saying it was “not what Alibaba culture should look like.” On June 11, Chen Hang stepped down as DingTalk CEO. His successor is Chen Yusen, born in 1992, the youngest business unit CEO in Alibaba’s history.
Exactly 437 days after he was invited back.
First, the fair part: he was no slacker
Writing Wu Zhao off as a tyrant who only knew how to squeeze employees is the laziest and least accurate version of this story.
The man is a true believer. The first time around, he built DingTalk out of the wreckage of Alibaba’s failed messenger Laiwang and turned it into a national app. When he was brought back in March 2025, DingTalk had 700 million users but had been overtaken by Feishu on monetization. It was a hot potato. He took it. He launched a “go to the fields” campaign, visited customers himself, and dug up a number nobody had dared report: real customer satisfaction sat at 30%. He rebuilt the support team, pushed satisfaction to 80%, and cut its costs by 90%. He required every product manager to visit three companies a week.
Every one of those moves would pass review in any product management textbook. Staying close to customers, facing real data, staying hungry for results: these were the best habits of the previous era, and Wu Zhao had all of them.
The problem is that he poured all of it into a war with no clear direction, then managed that war with camp beds and “what time do the lights go out in the Feishu building across the street.”
The report card: production maxed out, consumption at zero
Look at the product record of those 437 days and you see a new species of failure: every part spinning at full speed, the whole thing going nowhere.
DingTalk ONE, billed as “the new entry point for the AI era,” went from kickoff to launch in four months. Daily actives hit 3 million, then retention fell off a cliff, and within ten months it was dismantled and folded into the next project, Wukong. Wukong rewrote the foundation and went all in on agents; it shipped less than three months ago and nobody knows how it ends. The platform claims 1.41 million AI applications, but nobody can say how many of them are actually, continuously used.
Building a platform in four months proves the productivity of the AI era. Tearing it down in ten proves the consumption scenario never existed. Put those two numbers side by side and you have the precise definition of busywork.
This is the first trap the AI era has dug for product managers: speed on the production side now hides the vacuum on the demand side. A “new entry point” used to take two years, so you had to weigh it carefully before kickoff. With AI behind you, four months gets it live, and “build it first and see” becomes the default. The faster you build, the easier “we built it” gets mistaken for “someone needs it.” You can manufacture 3 million daily actives with an entry point and a traffic firehose. Retention only comes from a real consumption scenario, and there AI can’t help you. It can only help you expose its absence faster.
The era’s limit: nobody has found the human-AI path
Pinning the whole bill on Wu Zhao is just as distorted. The wall he hit is the wall the entire industry is hitting.
In workplace collaboration, no one has answered the basic question yet: in the AI era, what do humans do at work, and what do machines do? DingTalk turning AI into a “new entry point” was muscle memory from the mobile internet. The winning formula of that era was entry points, daily actives, fast iteration, and stacked headcount, and Wu Zhao is precisely the man who won once with that formula. In the AI era the whole formula breaks down at every joint: entry points are no longer scarce, because every AI is an entry point; daily actives no longer prove value, retention does; fast iteration is no longer an edge, because everyone is fast; and stacked headcount has flipped into a straight liability.
When direction can’t be found, managers instinctively grab the one variable they can still control: effort. Nine o’clock check-ins, late-night spot checks, nobody leaves before midnight. High-pressure management is anxiety at its core rather than malice: when direction is uncertain, hard work is the only certain thing left, so you squeeze hard work for all it’s worth. The partnership committee’s line that “innovation in the AI era is never about high pressure and mechanical execution” got it half right. The unsaid half: when an organization doesn’t know what to innovate, high pressure and mechanical execution are the only things it knows how to do.
Here is the bitterest irony. A company that wants to use AI to free every other company from meaningless work ran itself on camp beds, lights-out contests, and lines-of-code reviews. It failed to find the human-AI division of labor in the product, and failed to find it in the organization, and those look like two problems but they are one. How you treat your employees is how you will end up understanding your users. An organization that treats people as execution machines will build AI products that merely accelerate execution — and accelerated execution is the cheapest commodity of this era.
Our answer: move the effort from building faster to validating faster
If this saga is useful to product managers at all, it’s because it forces the cure for busywork into focus. Beating busywork doesn’t mean working less. It means spending the same effort in a different place.
First, ask the three consumption questions before you touch anything. Whose problem is this? How are they coping today? Why do you believe they’d switch? In DingTalk ONE’s four-month sprint, those three questions almost certainly never got serious answers. The 3 million daily actives came from the entry point and answered none of them. These questions matter more in the AI era, because “we can build it” no longer filters anything out.
Second, replace platform-scale bets with high-fidelity prototypes. AI has driven the cost of validation through the floor. Instead of four months and hundreds of people on a “new entry point,” spend four days on a high-fidelity prototype and run it inside ten real companies. Wu Zhao making PMs visit three companies a week pointed in the right direction, but a visit that only demos and persuades is still production logic. The right way to visit is to bring a working prototype and watch: do they use it, where do they get stuck, what do they fall back on when they don’t. He had the nerve to dig up the 30% satisfaction number, which means he knew what truth is worth. ONE shipping in four months means the organization never turned truth into its operating rhythm.
Third, settle the division of labor: humans supply judgment, AI supplies execution. The micro-mechanism of busywork is humans rushing in to supply execution. Overtime, output, sprints: all execution, with judgment missing from the room. The right split runs the other way. What to build, what counts as good, what to refuse: that’s human work, and it can’t be skipped or outsourced. Building it, running it, revising it: that’s AI work, and less and less worth filling with human hours. An organization that still measures contribution by when the lights go out is seating humans where AI belongs, and that is the greatest waste of people there is.
The verdict
Wu Zhao’s diligence was never the mistake. The mistake is that the formula his diligence served has expired. He was the finest executor of the previous era, dropped into an era where execution is no longer scarce. That’s his personal tragedy, and it deserves better than being filed as his personal failure.
The real exam Chen Yusen inherits has little to do with repairing team morale. It’s answering the question Wu Zhao never got to answer: in AI-era work, what exactly are humans for? Longer hours won’t answer it. Only more honest validation will.
Nobody knows who owns the next era. It’s safe to say it won’t be the building whose lights stay on longest.
Discussion