I’ve stopped mentoring juniors. Not because I don’t care—but because training AI is just… faster.

And I don’t think I’m alone.

The Part of the AI Story No One Talks About

This is the part of the AI story no one talks about. Everyone debates whether AI will “replace jobs.” However, the quieter shift is already happening: AI is replacing the mentorship that used to create those jobs in the first place.

We hired a junior employee a few months ago. Smart, eager, willing to learn. The kind of person who, five years ago, I would have spent hours mentoring—sitting in meetings together, walking through decks, reviewing their work line by line.

But I’ve noticed something.

The Time That Disappeared

I’m not spending that time anymore.

Every hour I spend giving feedback to a new hire, I find myself thinking: I could build this into a skill file for Claude and never explain it again.

The math keeps running in my head, even when I don’t want it to.

Training a person: repeat the same feedback weekly. Watch them make the same mistakes. Wait months for them to internalize patterns.

Training an AI: one feedback session. One skill file. Immediate execution. No drift. No emotional days.

Honestly, I don’t like this math. But I can’t unsee it.

The Silent Choice Every Manager Is Making

Every manager I talk to is quietly facing the same tension. They won’t say it out loud—it sounds cold. Nevertheless, the time they used to spend mentoring juniors is slowly migrating toward building AI workflows.

And this creates a problem no one is talking about.

Junior talent isn’t struggling because skills are harder to learn. If anything, AI made learning easier. You can teach yourself almost anything with YouTube and ChatGPT.

Instead, the struggle is different.

It’s that no one is investing in them anymore.

Why the Apprenticeship Model Is Breaking

The apprenticeship model—seniors spending years passing down intuition, correcting mistakes in real-time, building someone up through repetition—is quietly breaking. Not because seniors are cruel. Rather, it’s because the ROI equation changed.

When I spend 10 hours this week mentoring a new hire, I’m betting on a payoff 12 months from now. Maybe longer. Maybe never, if they leave.

In contrast, when I spend 2 hours building an AI workflow, I get the payoff today.

Most managers are making the same choice. They just won’t admit it.

Programmer: A Preview of What’s Coming

I keep watching what’s happening to software engineers. It feels like a preview.

For instance, junior developers are being squeezed out. Not because coding is harder—AI actually made it easier to write code. Instead, the squeeze is coming from a different direction.

Companies are realizing: Why hire someone who can learn to code, when I can hire an agent that already codes?

As a result, the entry-level pipeline is collapsing. Internships are disappearing. Meanwhile, the people still getting hired are architects—engineers who can design systems, not just write functions.

The same pattern is starting in sales. In product management. In marketing.

Specifically, the jobs that require “learning by doing under supervision” are exactly the jobs most vulnerable to this shift. Because the supervision is going away.

How the Career Ladder Broke

The old career ladder looked like this:

Junior (learn from seniors) → Mid-level (execute independently) → Senior (mentor others)

Each rung depended on the one above it. Seniors invested in juniors because they remembered being juniors. The system was self-reinforcing.

But AI broke the first rung.

If seniors stop investing in juniors—because AI is more efficient—then juniors never become mid-level. They get stuck. Or they leave. Or they’re never hired in the first place.

The ladder doesn’t disappear. Instead, the bottom rungs do.

What’s left is a jump: from “outsider” directly to “architect-level thinking.” No gradual climb. No hand-holding.

The New Hiring Reality

Companies aren’t hiring people who can be trained. Instead, they’re hiring people who already think like architects.

And if you’re a new professional, no one is coming to bridge that gap for you.

What Do You Do If You’re Early in Your Career?

I don’t have a neat answer. But I’ve been thinking about what I would tell myself if I were starting over today.

1) Stop waiting to be trained

The old playbook was: join a company, find a mentor, learn by watching and learning from others. That playbook assumed someone would invest in you.

Unfortunately, that assumption is increasingly wrong.

If you’re waiting for a senior to pull you aside and teach you the ropes, you might be waiting forever. After all, the people who used to do that are now building AI workflows instead.

The new default is self-driven. You teach yourself—using AI as your tutor, not your replacement.

2) Use AI to compress the learning curve, not to skip it

Here’s the trap I see junior people falling into: they use AI to do the work, instead of using AI to learn the work.

If you let Claude write your code, your emails, your analysis—you’ll ship faster. But you won’t build intuition.

The better move: use AI to explain why. Ask it to critique your thinking. Have it simulate a senior giving you feedback. Extract the mentorship you’re not getting from humans.

In short, AI can be your tutor. But only if you treat it as a learning partner, not an outsourcing machine.

3) Chase architecture problems, not execution tasks

The roles that are disappearing are the ones that say: “Do this task, then do the next one.”

In contrast, the roles that are growing are the ones that say: “Design the system. Decide what tasks should exist.”

If you’re early in your career, actively seek out architecture-level work—even if it’s not in your job description. Volunteer for projects where you define scope, not just deliver output. Push yourself into the uncomfortable zone where you’re making judgment calls, not following instructions.

Ultimately, the goal is to become someone who designs workflows—including workflows that involve AI—rather than someone who executes them.

4) Build in public. Create your own proof

The old career path gave you credentials through association. “I learned from so-and-so.” “I was trained at such-and-such company.”

If that path is closing, you need a new way to signal competence.

The answer is proof of work.

Ship side projects. Write about what you’re learning. Build a portfolio that shows your thinking, not just your resume.

When no one is recommending you, your work has to vouch for itself.

I want to be honest about something.

I’m not sure this is a better world.

The old apprenticeship model had inefficiencies. It was slow. It depended on the generosity of senior people. Not everyone had access to good mentors.

But it also worked. It built human capital. It created continuity. It gave people a way in.

What’s replacing it is faster and more efficient—but also colder. The burden of growth is shifting entirely onto the individual. Sink or swim. Figure it out yourself.

That’s not a moral judgment. It’s just what’s happening.

– Felix

The post The End of Learn from Your Senior – Why Mentorship Is Migrating to Machines appeared first on Felix Kang.

• Product is the problem you solve.
• Team is the talent you assemble to solve it.
• Resources are the fuel—capital, brand, and distribution.
• Culture is the operating system that governs how everything runs.

Recently, I’ve gained a deeper appreciation for the role of management systems and culture.

I’ve come to see its evolution in three distinct stages, best measured by how the team experiences its leader.

Stage 1: The System — The CEO is Feared (or at least, closely watched)

In the beginning, the founder’s will is everything. You have a unique insight, and you drive progress through sheer force and tight control. The product is the only thing that matters.

But once you find product-market fit, the company has to grow beyond you. This is the awkward stage of scaling. You introduce formal management: departments, levels, OKRs, KPIs. It’s a heavy, clunky scaffolding, but it’s necessary to stop things from breaking.

In this phase, the CEO’s job is to be the system’s enforcer. You’re in the trenches, driving execution, and making unpopular decisions to instill discipline. The team might not love you for it, but they follow the rules because you’re watching. Leadership is direct, visible, and top-down. It works, but it doesn’t scale.

Stage 2: The Habits — The CEO is Respected

The management system doesn’t change much between Stage 1 and 2. What changes is the team’s behavior. The rules on paper start to become rituals in practice.

Culture emerges in the small, consistent actions that need no top-down command:

• Every meeting ends with clear action items and owners.
• Product mocks are debated based on customer delight, not just short-term metrics.

But here’s the trap: many assume culture comes simply from hiring for perfect alignment. In reality, very few candidates are born fully compatible with company values. More often, people bring different strengths and weaknesses:

• Someone executes with precision but lack first-principles thinking.
• Someone thinks deeply but dislike tedious execution.
• Human nature is hard to change. No amount of lectures, rules, or KPIs can rewrite someone’s core traits.

Your job isn’t to change their nature. It’s to shape the environment. You create a feedback loop—rewarding aligned behaviors, course-correcting misaligned ones—until the desired habits become muscle memory. The team follows the norms not because the CEO is watching, but because the team is watching.

Stage 3: The Values — The CEO is Invisible

The final stage is the hardest to reach. Here, the CEO’s presence is barely felt in day-to-day operations. The company runs on a deeply embedded set of values that function like an immune system, automatically rejecting behaviors that don’t fit and nurturing those that do.

It takes years to reach this stage.

A vivid example comes from Anker, the global leader in charging accessories (even Trump has used their power banks). At one point, Anker had over 80 versions of power banks list on their store. Quantity diluted efficiency, and despite repeated warnings, the pattern persisted.

The CEO,Steven Yang wanted an Apple-like focus: fewer, better products. But commands, OKRs, and KPIs all failed. For an individual product manager, shipping more SKUs was a rational way to de-risk their career and look busy.

To flip the logic, the CEO introduced a penalty mechanism:

• Each category had a products cap. If a team exceeded it, their bonuses were cut. Too many SKUs, and the bonus dropped to zero.

The initial friction was immense. Many PMs quit.

But the ones who stayed believed in the philosophy of “less is more.” The culture shifted almost overnight. The wasteful 80-product problem vanished because the system was now wired to reward focus. The CEO didn’t need to approve every product decision anymore; the culture did it for him.

It is a process from stage 1 to stage 2, and he claimed that Anker yet enter stage 3.

When we look across these three stages, a pattern emerges:

• Stage 1 depends on systems and management.
• Stage 2 depends on habits and shared practices.
• Stage 3 depends on values embedded so deeply that they function as an invisible operating system.

This is the paradox of building: the more invisible the CEO becomes, the more powerful the company becomes. The less you rely on top-down control, the more the organization becomes self-sustaining.

Culture isn’t the soft stuff you deal with after you’ve figured out product and growth. It’s the hardest system you will ever build—and the only one that truly scales. In an era where AI can automate workflows, a dense culture is what automates decisions. One offers efficiency; the other builds an enduring company.

The post How to Manage a Company When the CEO Disappears: Culture Does appeared first on Felix Kang.

Here’s a concise overview of my observations:

1. Centralized and Efficient Organizational Structure: Rapid information transmission, efficient decision-making, and guaranteed execution efficiency characterize their approach.
2. “Amoeba” Team Organization: Teams compete internally, with the winner gaining more resources and business space—a unique strategy fostering competitiveness.
3. Embracing Simplicity and Efficiency: The commitment to simple execution not only streamlines processes but also contributes to employee satisfaction.

While building a successful company involves numerous factors, these insights serve as a solid starting point. Let’s delve deeper:

1. Centralized and Efficient Organizational Structure

Before delving into how PDD achieves this, it’s crucial to note that different industries may favor different organizational structures. In the case of e-commerce, a mature and low-innovation-required industry, PDD strategically positions itself as a later player in the game, both in China and the US. This positioning requires them to rapidly learn from others and implement innovations swiftly.

Centralized VS Decentralized

A centralized organization implies that the majority of decisions are made by the C-team or central management. At PDD, the central managing team, including figures like Colkin Huang, Abu, Jiazhen, proposes and decides on the majority of company decisions—typically numbering between 10 to 20 key individuals.

On the other hand, a decentralized organization distributes decision-making authority throughout the entire company. Take Bytedance, the ‘App Factory,’ as an example. It launched apps like TikTok, Douyin, Xigua Video, Huoshan Video, etc., in a decentralized manner. This approach allowed for redundancy in projects and human resources, attracting numerous talented individuals to drive various innovations. The success of Tiktok is a testament to the effectiveness of this decentralized model.

Implementing Centralized Management at PDD

The primary advantage of a centralized decision-making structure lies in minimizing time spent on meetings.

Meetings, while standard, often consume valuable time and are considered a less efficient means of decision-making. Instead of relying on discussions alone, PDD opts for in-depth research and simulation to make informed decisions. This approach mitigates the need for excessive meetings, freeing up time for teams to focus on execution, ultimately yielding a higher ROI

Many teams resort to meetings when they don’t have a clear understanding of issues. PDD, however, discovered that most of the meetings only bring less than 10% incremental information, which hardly resulting in fundamental changes to decisions. Recognizing this, PDD emphasizes rapid work implementation and iterative feedback is more beneficial, the 10% incremental inforamtion could also be made up during thie process.

Before Temu entered the US market, their business lead conducted in-depth research on the operation experience of existing e-commerce platforms, the backgrounds of their senior teams, as well as user habits in various market segments, and consumer category demands. This comprehensive research informed the deployment of Temu’s factory supply chain. While the research and decision-making process was time-consuming, once the optimal solution was identified, PDD swiftly executed it, emphasizing the importance of timely implementation.

2. “Amoeba” team organization

What is Amoeba?

Amoeba Management begins with dividing an organization into small units called “amoebas.” Each amoeba leader is responsible for drafting plans and goals for the unit. Amoebas achieve their goals through collaboration and the hard efforts of all their amoeba members. In this system, every employee plays a major role and voluntarily participates in managing the unit, achieving what is known as “Management by All.”

Amoeba in PDD

TEMU has a flat organizational structure, which facilitates rapid information flow and decision-making to adapt to the fast-changing market environment.

PDD’s middle management practices amoeba management logic – continuous learning and innovation, because they need to compete for resources. Under the amoeba management model, the winning winners have a strong fighting spirit, and a stable core team.

The organizational design ensures that the winning team can get more opportunities and room. PDD retains these people through great incentives, while ensuring they maintain their fighting capacity.

How does PDD divide each Amoeba unit?

As the organization expands, the conventional method of allocating business units based on manager’s ability and experiences may not be a good approach.

So what does PDD do instead?

A notable example is the internal competition for the second tab in PDD’s Chinese app. Rather than assigning units by a top-down approach, there were two potential proposals—an engaging community farming game and a short video feature —competed internally between teams.

Pinduoduo made the two teams compete (PK). The farming game team could request DAU and certain amount of content exposure from the company. PDD gave those resources to the farming game team to test if it could bring higher GMV to the product compared to the short video team with the same resources. The business that performed better would be placed in the second tab.

However, this challenge model does not provide unlimited chances for teams to take other teams’ resources. They have to pay a “challenging fee” to the company (usually $1.5M/day) to earn the right to challenge. Also, challengers who fail multiple times will lose company’s trust, making it hard to undertake important projects in the future… This encapsulates how PDD enforces Amoeba management, ensuring a fair balance in the allocation of business opportunities among internal teams.

3. Embracing Simplicity and Efficiency

Frontline employees at PDD do not have much understanding of the company’s overall strategy and insights, such as data, conversion rates, industry background. Everyone is doing some simple execution work. In conversations with their employees, it becomes apparent that they typically allocate less than 10% of their working time each week to strategic-level thinking.

Within PDD’s business, employees are not burdened with extensive strategic contemplation. The nature of their responsibilities is direct and uncomplicated. Whether specializing in programming or sales, employees are encouraged to hone specific skills. PDD offers industry-leading salaries for these roles, attracting individuals with robust execution capabilities.

In the retail sector, a landscape of standards and clarity prevails. The first and second-level management tiers meticulously analyze business logic, leaving frontline employees with a focus on simple execution. The emphasis on simplicity and efficiency is not just a preference at PDD; it’s a fundamental aspect of their operational philosophy.

Applicability to Your Startup

PDD, as a late entrant to the e-commerce scene, demonstrates the effectiveness of tailoring management systems to a clear target market and user demands. Their commitment to high-efficiency practices allows them to move three times faster, enabling them to catch up with and surpass competitors.

For startups, especially those in industries where success stories are well-documented, adopting a lean and rapid execution approach is crucial. Take, for instance, X AI learning from OpenAI’s experience, demonstrating the importance of hiring strategically and swiftly developing based on a clear direction.

However, it’s essential to note that PDD’s structure might not seamlessly align with startups in AI or highly innovative industries. Decentralized management, as seen in ByteDance, could be an alternative solution for fostering innovation in such contexts. Recognizing the unique demands of your industry and learning from both successful and diverse approaches can guide your startup towards efficient and effective management practices.

Comments? Any lessons to share?
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