能力的分拆与重组：AI 时代角色变化的九种运动

Early in my product career, I wore many hats. I conducted research, analyzed data, sketched interfaces and wireframes, wrote requirements, coordinated releases, answered support questions, and even, shudder, wrote some code. Some of that was empowering. Some of it was amateur hour. But it was all bundled into the loose idea of “product management.”

Over time, those capabilities separated into dedicated roles and disciplines.

Designers, analysts, product marketers, program managers, and product-operations teams developed deeper expertise than any one generalist could reasonably maintain. Some joined cross-functional teams and became part of the daily work. Others became centralized experts that teams accessed through requests, reviews, and shared services. Repeatable parts of their expertise were then encoded into analytics platforms, design systems, research repositories, templates, standards, and workflows.

A role is only a temporary bundle inside the system. Capabilities themselves are distributed across people, teams, routines, tools, platforms, institutions, and external partners. When technology changes work, it rarely swaps cleanly for one person or role. Instead, capabilities move through several different motions:

• Specialization: broad work splits into deeper expertise.
• Diffusion: expertise spreads to more people.
• Centralization: expertise concentrates somewhere else.
• Integration: separate capabilities are coordinated.
• Embedding: expertise moves into tools and routines.
• Rebundling: separated capabilities come back together.
• Externalization: capability moves outside the organization.
• Elimination: work genuinely disappears.
• Loss: work disappears before its underlying capability has been reproduced elsewhere.

The Headlines

Scan the recent headlines and work appears to be changing in every direction at once.

Product managers are becoming “AI builders.”

Designers are becoming “designer engineers.”

Software engineers are becoming managers of AI-generated code.

Data platforms are absorbing parts of data engineering, analysis, and model development.

Middle managers are becoming “player-coaches” and “org leads.”

Product titles are collapsing into a single category: “builder.”

Roles are simultaneously broadening, disappearing, becoming more specialized, and merging with adjacent roles. There are several different motions happening at once. You have all the patterns I described above.

• Technical and analytical skills are diffusing across role boundaries.
• Product, design, engineering, and management tasks are being rebundled into broader roles.
• Knowledge is being embedded in AI tools and externalized to platforms.
• New specialties are appearing around architecture, evaluation, orchestration, reliability, and governance.
• Some repetitive work is genuinely going away. And some organizations (though they may not realize it yet) are losing the apprenticeship, coordination, context, and judgment that the old work helped sustain.

The headlines are observing different capabilities moving in different directions and collapsing that story into catchy headlines.

Capability Moves Through Loops, Not Stages

It is easy to imagine the motions in the design example as a linear journey, or a single, repeating loop. In practice, there are many loops occurring simultaneously, and each motion is creating pressure and tension for other motions.

Each motion changes the conditions around a capability and creates pressure for another motion. Specialization produces depth but ALSO handoffs. Diffusion creates access can ALSO eventually produce inconsistency. Centralization produces consistency but pulls decision-making away from the people closest to the work (or context away from centralized capability). Moving expertise into tools so people don’t have to perform the work directly (embedding) can weaken the learning pathways through which they develop judgment.

These loops do not occur in a fixed sequence. Several may operate simultaneously, at different levels and over different periods of time. A capability might be:

• Diffusing across teams while control over its infrastructure centralizes;
• Embedded in technology while new specialist work emerges at its frontier;
• Externally supplied while internal capability is rebuilt around direction and assessment;
• Rebundled into broader roles while its underlying knowledge becomes increasingly specialized;
• Made more productive while the resulting increase in demand creates more work overall.

Each loop describes a recurring tension that may produce useful adaptation, harmful instability, or both.

What Actually Changes With AI?

I often ask myself, “Does AI change the physics of this pattern?” That is difficult to answer because a system can continue to obey the same underlying principles while the variables change so dramatically that those principles appear in entirely new forms.

There is already evidence that AI is affecting specialization, diffusion, centralization, integration, embedding, externalization, convergence, and loss. The real questions are to what extent, under what conditions, and with what second- and third-order effects. For example:

Diffusion

To what extent will AI allow experts to distribute their expertise more easily, without being constrained by the rigid interfaces we rely on today?

AI can make codified expertise available to more people, but we do not yet know how well it transfers tacit, contextual, or embodied expertise. Polanyi (1966) put it simply: we know more than we can tell. If that remains true, diffusion through AI will have a ceiling that is difficult to see from outside the expertise.

Integration

Can it actually lower the cost of specialists working with generalists and with each other?

The translation, handoff, and queueing burden created by specialization is real, and AI can friction-bust. But it hasn’t eliminated the “social” work of coordination, negotiation, and shared judgment.

Convergence and Rebundling

There’s a version of the future where individuals and teams recover capabilities that previously required several dedicated roles. A new kind of higher-level generalist.

AI does let people perform tasks across more role boundaries. But producing more kinds of artifacts is not the same as possessing the underlying expertise.

Specialization

And where will the opposite happen? AI is already creating new areas of specialization, new forms of technical complexity, and new specialists required to build, evaluate, govern, and adapt these systems. It may reduce demand for some specialist tasks while creating entirely new specialties around context, evaluation, orchestration, governance, and exception handling.

Embedding and Extension

Expertise is increasingly being encoded directly in tools and workflows, allowing people to act competently without possessing the underlying skill themselves. This is not hypothetical. It’s happening now. The question is what happens to the role that remains: it shifts toward direction, assessment, and oversight, which are genuinely different skills.

Centralization

AI may broaden local access to a capability while concentrating control over models, infrastructure, standards, data, and defaults in a small number of firms. More people become locally capable. Organizations become collectively dependent on increasingly centralized systems. Both things are true at once.

Loss