Too Many Developers in CompaniesWhy Small Teams Can Develop Faster

Too Many Developers in Companies - Why Small Teams Can Develop Faster

When you hear announcements like "We're deploying 600 developers," how do you feel? Do you think "What an impressive scale!" or do you question "Do we really need that many?"

In reality, adding more developers often doesn't increase development speed - in fact, it often slows things down. Let's explore the mechanisms that world-leading companies use for "small team, high-speed development" and the scientific reasons behind them.

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Chapter 1: World Companies' "Small Team, High-Speed" Mechanisms - Real Examples

Spotify: Building "Autonomous Squad" Culture

Spotify divides its product organization into "Squads (small team units)" and operates with high autonomy.

• A single Squad typically consists of 6-12 people including design, development, testing, and product owners.
• Each Squad can freely choose their methodology (Scrum/Kanban, etc.) and coordinates with other Squads through loose "Tribe" and "Guild" mechanisms.
• However, the Spotify model has its critics. There are voices saying "Expanding squads alone still leaves coordination costs" and "Excessive autonomy leads to loss of consistency."

Squad Characteristics:

The interesting aspect of this structure is that it's not a "large army formation" but a "federation of small autonomous units," emphasizing the ability of each unit to move quickly.

💡 Key Point: Small teams moving autonomously dramatically improves decision-making speed.

Netflix: Developer Productivity and the Philosophy of "Those Who Build Should Operate"

Netflix has a "Developer Productivity Engineering" department in its engineering organization, fostering a culture of tooling, automation, and development experience.

• They embrace the "Full Cycle Developer" concept, where developers are responsible for their code from development to operations. This reduces the "ambiguity of responsibility" at the boundaries of specialization.
• They have strong infrastructure, CI/CD, test automation, and monitoring foundations, aiming for teams to operate as autonomously as possible.
• Netflix also incorporates "failure assumption" as a design philosophy, publicly sharing Chaos Monkey (a system that randomly shuts down servers) as an innovation. This naturally instills fault tolerance into the organization.

Netflix's Development Philosophy:

This culture of "ensuring operability and reliability from initial design" supports organizations that can safely make even small changes.

🎯 Important Point: When developers take responsibility for operations, they write code that considers operability from the design stage.

Small Example: Netflix Data Department - 8 People Supporting a 400-Person Organization

According to one article, Netflix's Data & Insights department has 8 software engineers providing technical support to a 400-person organization.

This is an excellent example of designing "personnel to support the entire department" with minimal configuration.

✅ Remarkable Efficiency: 8 people supporting 400 means one engineer provides value equivalent to 50 people.

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Chapter 2: Why Adding More People Actually Slows Things Down - The Destructive Power of Communication

It's natural to think that adding more developers will speed up development, but the opposite phenomenon actually occurs. Let's examine the scientific reasons behind this.

1. Code Review / Information Diffusion Network Effects

Research analyzing Spotify's code review practices shows that reviews create "information diffusion networks," and the more reviews fly between multiple teams, the more propagation delays and duplicate discussions occur.

In other words, the deeper the reviews become, the more branch discussions increase, creating a structure where unifying overall thinking becomes costly.

❌ Problem: The more reviewers there are, the more time it takes to align opinions, and actual development work stagnates.

2. Cognitive Load and Specification Consistency Explosion

When 10 people work on the same codebase, the following endless loop occurs:

• No one knows who is touching what
• Discrepancies arise between recent changes and old specifications
• Coordination meetings become frequent
• Re-reviews become necessary

This is a typical pattern that increases "waiting time."

⚠️ Warning: The more people there are, the more difficult it becomes to grasp the overall picture of the codebase.

3. Onboarding / Documentation Costs

New members need to understand past specifications, intentions, and decision backgrounds. The larger the project becomes, the more time spent on explanation and coordination becomes the main focus.

📚 Reality: It's not uncommon for new member education costs to exceed actual development time.

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Chapter 3: The Era Where AI and Automation Enable "One Person to Handle 2-3 People's Work"

In modern development environments, AI and automation technologies enable one developer to create 2-3 times the value of traditional development.

1. Code Generation, Completion, and Test Automation

Using generative AI and completion engines, simple CRUD and routine logic can be written instantly. This allows time previously spent on "straightforward implementation" to be redirected to design, verification, and response.

• GitHub Copilot - Code completion and suggestions
• Amazon CodeWhisperer - Security-conscious code generation
• ChatGPT - Complex logic design support

Specific Tool Examples:

These tools have reported 10-30% reduction in development time through type input completion and code suggestions (though case-dependent).

🚀 Effect: Automating routine tasks allows focus on creative work.

2. Test Design, Mock Generation, and CI Integration

Tools and AI-assisted tools that can automatically generate tests, mocks, and documentation that were previously written manually are increasing. Standardizing these can create designs that reduce the probability of review/rollback occurrences.

• Automatic test case generation
• Mock generation from API specifications
• Automatic documentation updates

Automation Examples:

3. Operations Support and Monitoring Alert Automation

Using AI for production monitoring, anomaly detection, and automatic alert analysis enables faster incident response with fewer people.

• Log analysis AI for anomaly pattern detection
• Anomaly detection for predictive monitoring
• Automatic application of self-healing scripts

Specific Use Cases:

💡 Important Perspective: AI should not replace developers but be used as a tool to extend developers' capabilities.

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Chapter 4: Thought Experiments - "How Many People Are Really Needed in These Situations?"

Theory alone might not provide a real sense of understanding. Let's think about "how many people are really needed" in actual projects.

Thought Experiment A: 10-Person Scale, 3-Feature Business Application

Let's say we're building a medium-scale application combining "employee management," "attendance," and "application workflow."

• UI/API/authentication/authorization/logging/notifications/batch processing... and many other feature elements

Feature Elements:

• If everyone handles different features, coordination between features becomes difficult
• Duplication occurs with common processes (logging, authentication, exception handling)
• Common design agreement/correction propagation becomes hell

Problems with 10-Person Structure:

• Squad-complete design + API contracts for inter-department coordination
• Discussion and coordination volume can be dramatically reduced

Benefits of 5-7 Person Structure:

In this way, "defining feature boundaries" clearly makes it possible to ensure consistency even with fewer people.

🎯 Key Point: Clearly defining feature boundaries enables efficient development even with small teams.

Thought Experiment B: Applying to Government IT Projects

Let's consider a local government's resident information verification system as an example.

• CRUD, search, integration (data import/export with other systems), logging, auditing
• Specifications are often not completely fixed in the early stages

Typical Requirements:

• "10 people for 3 years" development

Traditional Approach (Problematic):

• "Initial 5 people for 6-month launch → verification → specification finalization → expansion phase" method

Recommended Approach:

For "government business IT" like this, where there's significant room for specification changes and operational responses, assigning large numbers from the start often results in many wasteful areas.

💡 Learning: Phased development enables efficient project advancement while maintaining flexibility.

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Chapter 5: Practical Guidelines for Optimal Team Size Design

Based on the analysis so far, let's summarize guidelines for actually designing small, elite teams.

Design Guidelines: Optimal Team Size Principles

• Keep teams to 6-12 people
• Each team has independent decision-making authority
• Inter-team coordination through clear API contracts

1. Squad Division Principles

• Clearly define functional areas for each team
• Centralize common processes (authentication, logging, monitoring)
• Development teams also take operational responsibility (Full Cycle Developer)

2. Clear Responsibility Boundaries

• Automate routine tasks as much as possible
• Standardize code generation tools
• Thoroughly automate testing, deployment, and monitoring

3. AI and Automation Utilization

Challenges and Considerations

• Avoid situations where only one person holds important knowledge
• Build documentation and knowledge sharing mechanisms
• Utilize pair programming and code reviews

Bus Factor Countermeasures

• For strict security requirements, coordinate with specialized teams
• Design processes to meet compliance requirements
• Design logging to ensure traceability

Compliance Requirements Response

• Use AI tools in a supporting role
• Maintain clear human judgment and responsibility
• Regular quality checks and review processes

AI Over-reliance Risks

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Conclusion: The Era of Small Elite Teams

Mass team philosophy might be an old "illusion of security." However, in this digital age, we have powerful weapons: AI and automation.

What matters is not the number of people, but value creation.

• Spotify's Squad model
• Netflix's Full Cycle Developer
• AI and automation technology utilization

By combining these elements, we can realize organizations that "create maximum value with minimum people."

🚀 Action Guideline: Why not start by reducing your current team to 5-7 people and implementing a development system that utilizes AI tools?

Rather than "stacking disposable human resources," building an organizational culture where "each person creates maximum value" is what's required in this era.

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