The Restructuring of Work in the AI Era: From Disappearance to Reconstruction

As a PM and developer in the software industry, I observe every day how AI tools reshape workflows. The shift is not just about large-scale layoffs reported in the news — there is also a quieter, ongoing restructuring. The very shape of work is changing.

Invisible Shrinkage

Imagine sitting at your familiar desk, but colleagues around you vanish one by one. Departures are not replaced. With processes simplified, tasks once requiring ten people can now be completed by three, and the remaining roles quietly dissolve.

In practice, I have seen AI cut knowledge work time in half or more, but demand on the client side has not expanded accordingly. Instead, the surplus workforce is naturally eliminated.

The recent waves of layoffs in North American tech highlight this trend. Big software firms (Meta, Google, Amazon) made repeated cuts from 2023–2025. Beyond efficiency gains from AI, overexpansion during the pandemic, economic downturn, and shareholder pressure all played a part. Other industries follow the same pattern: Intel’s recent mass layoffs also reflect broader organizational pressures.

The Disappearance of Middle Roles

This tension between efficiency gains and stagnant demand directly compresses the middle layers of work:

• Building a website shrinks from a week to half a day — but customer demand does not grow 14-fold.
• Market analysis shrinks from three people over a month to one person in a week — but companies don’t request ten times more reports.
• AI-driven education content allows one person to produce curriculum, quizzes, and interactivity — but schools don’t need three times more courses.

As a result, workflows that used to require multiple roles—such as draft writers, editors, designers, and testers—can now often be completed by just one or two people working with AI tools. The middle roles are quietly losing their place. From my own experience in the software industry, many projects used to require at least a designer, an engineer, and a PM to collaborate in order to deliver results. But today, if the goal is just to build a POC, often a single person with the help of AI is enough to cover most of the essential needs.

Freelance Crowding and Skill Pressure

Many shift to freelancing, side hustles, or content creation. But according to the MBO Partners 2024 Report, nearly 72.7 million Americans now work independently — making the market more crowded than ever. Income stability and security are falling.

Freelance is no longer about “having skills.” Independent workers must also handle marketing, client acquisition, project management, contract negotiation, and financial planning. For most developers, analysts, or designers, mastering all these domains is difficult. Platform fees and biased algorithms only worsen the imbalance. The market is not expanding fast enough to sustain the old freelance model, creating long-term challenges.

One Direction: Modular Collaboration

Against this backdrop, I believe that freelancing in the future may evolve into a more structured form of modular collaboration.

The key shift here is moving from billing based on hours worked to billing based on value and outcomes. Traditionally, project compensation has been tied to estimated working hours—less time meant less income. In modular collaboration, however, professionals no longer sell “time”; they sell clearly defined, high-value modules of deliverables.

For example, the true value of a website project does not lie in the number of hours spent, but in the benefits it brings to the client. A skilled expert, leveraging AI tools, might finish what used to take a week—say, a UI/UX optimization module—in just half a day. Their compensation should not be discounted just because they were more efficient; in fact, their ability to deliver faster amplifies the value for the client.

This is similar to how top surgeons charge for operations: not based on how many hours they spent in the operating room, but on the successful completion of a complex surgery. They focus on the high-skill, high-value “surgery module,” while delegating pre-op assessments or post-op care to other specialists. This maximizes their unique value and allows them to handle more cases in less time.

Example Scenario – Software Project

A project that once required five people working for a month could, under modular collaboration, be broken down into:

1. Requirements Analysis Module: AI summarizes user interviews into needs and pain points.
2. System Architecture Module: AI generates architecture diagrams and deployment scripts.
3. Core Development Module: AI scaffolds the codebase and optimizes algorithms, with humans reviewing critical logic.
4. UI/UX Module: AI produces design drafts and runs A/B tests.
5. Testing & QA Module: AI generates and executes test cases, compiling defect reports.
6. Coordinator Role: Oversees cross-module collaboration, tracks progress via Jira/Notion, runs weekly check-ins, and ensures consistent delivery.

Through this decomposition and efficiency boost, specialists can deliver higher-priced modules and complete more projects within the same time frame. Ultimately, this aligns compensation directly with expertise and value delivered, rather than with the number of hours spent.

Unresolved Challenges

But modular collaboration is no cure-all. It raises key issues:

1. Market Size: If demand doesn’t grow, are we just slicing the same pie thinner?
2. Fair Value Distribution: How do we price modules fairly across difficulty and contribution?
3. Coordination Costs: Who handles cross-module communication and management? The coordinator role is essential.
4. Quality Consistency: Finer division increases risks of mismatched style and quality. Standards and review processes are crucial.
5. Trust and Reputation: Transparent evaluation and fulfillment records are key to scaling collaboration.

Continuing Exploration

AI blurs the boundaries of traditional roles. Some will continue freelancing, some will move toward modular squads, others may invent new models entirely. Staying open and experimental may help us find fairer and more efficient ways to create value.

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Further Reading

• MBO Partners State of Independence Report 2024
• Tech Layoffs Tracker by layoffs.fyi