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⏱️ Estimated Reading Time: 18 minutes

Introduction: The Decisive Moment for the Developer Profession

In August 2025, GitHub CEO Thomas Dohmke’s “Developers, Reinvented” is a revolutionary manifesto that goes beyond simple technology trend analysis. The reality revealed through in-depth interviews with 22 active developers is shocking: “Accept AI, or you’d better quit this profession.”

This isn’t a hypothetical scenario. It’s the change happening right now, at this moment. Dohmke’s research shows that the developer profession isn’t disappearing but is being completely reborn in a new form. A fundamental transformation from code writers to strategists who orchestrate AI agents is becoming reality.

In this post, we’ll analyze in detail the 4 stages of developer evolution revealed by CEO Dohmke, the identity changes they experience, and the ripple effects this has on education and society as a whole.

1. The Frontlines of Change: The Reality Shown by 22 Developers

Journey from Skepticism to Innovation

The 22 developers who participated in CEO Dohmke’s research are all pioneers who use AI tools as the core of their workflow. However, their beginning was far from smooth. Most of their reactions when first encountering AI tools were “Pretty cool, but just a gimmick” - healthy skepticism.

This initial skepticism arose from the collision between high expectations and AI’s unpredictable capabilities. Many developers give up at this stage, judging AI tools as “unhelpful,” but those who continued experimenting experienced a decisive “aha! moment”.

The Power of Continuous Experimentation

The common trait of successful developers was strong motivation to anticipate and prepare for comprehensive reorganization of the future work environment. They continuously experimented with various AI tools even when the tools weren’t consistently helpful. As one developer said, the urgency that “you either accept AI or leave this profession” was the driving force behind their actions.

This experimental spirit was a survival strategy beyond mere curiosity. Even when AI wasn’t perfect, they found ways to understand and utilize its limitations. As a result, they were able to systematically develop AI collaboration skills through daily trial and error.

2. The 4 Stages of Developer Evolution: From Skeptic to Strategist

Stage 1: AI Skeptic

First-stage developers lightly use AI for small tasks and questions. They mainly use code autocompletion features and have low tolerance for repetition and errors. The key at this stage is abandoning expectations for one-time success.

Many developers get frustrated and give up on AI tools at this stage, but those who persist begin to understand AI’s characteristics. By accepting the reality that perfect results can’t be obtained immediately, they lay the foundation to move to the next stage.

Stage 2: AI Explorer

In the second stage, they begin using AI for debugging, boilerplate code, and code snippet writing. They use both autocompletion and chat features, employing copy-paste methods from browser-based LLMs.

The key to this stage is beginning to understand AI’s limitations. Developers use AI for brainstorming more complex tasks, accept repetitive prompting, and realize that when results aren’t good, it’s more efficient to start over from the beginning rather than pushing forward.

Stage 3: AI Collaborator

The third stage involves actively co-creating with AI and developing context engineering intuition. Using AI-supported IDEs to perform multi-step tasks and multi-file changes, they develop habits such as:

  • Asking for plans first: Requesting AI to establish an overall plan before starting work
  • Curating agent rules: Adjusting AI’s behavior patterns to match the developer’s work style
  • Strategic tool and model switching: Learning AI’s “thought process” and selecting optimal tools for situations

Developers at this stage also begin participating in internal discussions or demos sharing effective prompts, use cases, and learned lessons. This is the process of creating team-level AI utilization culture beyond individual learning.

Stage 4: AI Strategist

Final-stage developers treat AI as a powerful partner for feature development, complex tasks, and large-scale refactoring. They build sophisticated multi-agent workflows using planning and coding models, gradually expanding autonomy and parallel processing.

Developers who reach this stage unanimously declare that their role has fundamentally changed. Now their focus is on Delegation and Verification.

3. New Developer Identity: Delegator and Verifier

The Art of Delegation

The delegation that stage 4 developers speak of isn’t simply assigning tasks to AI. It includes the following sophisticated processes:

Setting up agents with rich context and instructions: Providing sufficient background information and clear guidelines for AI to succeed

Designing and refining prompts: Developing effective questions and instructions to achieve desired results

Reviewing AI’s plans and trade-offs: Analyzing AI’s proposed solutions and their pros and cons

Pre-execution adjustments: Final fine-tuning for optimization before execution

The Importance of Verification

Verification is equally important as delegation. Stage 4 developers have developed sophisticated abilities to:

Evaluate code quality: Quickly assessing whether AI-generated code meets standards

Identify potential issues: Spotting bugs, security vulnerabilities, or performance problems

Ensure architectural consistency: Verifying that new code aligns with existing system architecture

Validate business logic: Confirming that implementation correctly reflects requirements

4. Fundamental Changes in Work Patterns

From Sequential to Parallel Work

Traditional development was largely sequential: design → code → test → debug → deploy. AI-enabled developers work in parallel streams:

  • Multiple AI agents working on different parts of the system simultaneously
  • Continuous testing and validation while development proceeds
  • Real-time refactoring and optimization
  • Parallel exploration of alternative approaches

New Metrics of Productivity

The definition of developer productivity is changing:

Traditional Metrics:

  • Lines of code written
  • Features completed
  • Bugs fixed

New Metrics:

  • Problems solved
  • Value delivered
  • System improvements orchestrated
  • AI agents effectively managed

Cognitive Load Redistribution

AI handles routine cognitive tasks, allowing developers to focus on:

  • Strategic thinking: Architecture and system design decisions
  • Creative problem-solving: Novel approaches to complex challenges
  • Quality assurance: Ensuring overall system integrity
  • Stakeholder communication: Translating business needs into technical solutions

5. Impact on Software Development Teams

Team Structure Evolution

Development teams are restructuring around AI capabilities:

Traditional Structure:

  • Senior developers
  • Junior developers
  • QA engineers
  • DevOps engineers

AI-Enhanced Structure:

  • AI orchestrators (former senior developers)
  • AI collaborators (former mid-level developers)
  • Verification specialists
  • AI tool specialists

New Collaboration Patterns

Teams are developing new ways to work together:

  • Shared AI workflows: Standardized approaches to common tasks
  • AI pair programming: Human-AI-human collaboration patterns
  • Collective prompt libraries: Shared repositories of effective AI interactions
  • Cross-functional AI integration: AI tools spanning traditional role boundaries

Knowledge Management Transformation

How teams capture and share knowledge is changing:

  • AI interaction patterns: Documenting effective ways to work with AI
  • Context templates: Reusable frameworks for AI delegation
  • Verification checklists: Systematic approaches to validating AI output
  • Evolution tracking: Monitoring how AI capabilities change over time

6. Educational Implications

Computer Science Curriculum Revolution

Educational institutions must fundamentally rethink computer science education:

Traditional Focus:

  • Programming language syntax
  • Algorithm implementation
  • Data structure manipulation
  • Software engineering principles

AI-Era Focus:

  • AI collaboration techniques
  • Prompt engineering and context design
  • System architecture and integration
  • Verification and validation methods
  • Human-AI interaction design

New Learning Pathways

Students need different preparation:

  • AI literacy: Understanding capabilities and limitations of various AI tools
  • Delegation skills: Learning to effectively direct AI agents
  • Quality assessment: Developing judgment for AI-generated outputs
  • Continuous adaptation: Staying current with rapidly evolving AI capabilities

Practical Training Requirements

Education must include hands-on experience with:

  • Real AI development workflows: Not just theoretical knowledge
  • Multi-agent orchestration: Managing complex AI interactions
  • Production-scale verification: Validating AI work in realistic scenarios
  • Cross-functional collaboration: Working with AI across different domains

7. Industry-Wide Transformation

Software Company Reorganization

Companies are restructuring around AI-enhanced development:

Hiring Priorities:

  • AI collaboration experience over traditional coding skills
  • Strategic thinking and system design capabilities
  • Quality assurance and verification expertise
  • Adaptability and continuous learning mindset

Team Composition:

  • Fewer traditional programmers
  • More AI orchestrators and strategists
  • Specialized verification roles
  • AI tool and workflow specialists

Economic Implications

The transformation has significant economic effects:

Productivity Gains:

  • Faster development cycles
  • Higher quality outputs
  • Reduced routine maintenance
  • More innovative solutions

Workforce Transition:

  • Retraining existing developers
  • New skill requirements for entry-level positions
  • Potential displacement of those who don’t adapt
  • Creation of new specialized roles

Competitive Advantages

Organizations that successfully navigate this transition gain:

  • Speed to market: Faster development and deployment
  • Innovation capacity: Ability to explore more possibilities
  • Quality improvements: Better verification and testing
  • Cost efficiency: Reduced development overhead

8. Future Predictions: The 90% AI Coding Era

Timeline and Milestones

CEO Dohmke predicts that within 2-5 years, 90% of code will be AI-generated. This transformation will happen in phases:

Year 1-2: AI handles routine tasks and boilerplate code Year 2-3: AI manages complex feature development Year 3-4: AI orchestrates entire system architectures Year 4-5: AI handles most coding tasks with human oversight

What Remains Human

Even in the 90% AI coding era, humans will be essential for:

  • Strategic decision-making: Choosing what to build and why
  • Quality assurance: Ensuring AI outputs meet standards
  • Creative problem-solving: Addressing novel challenges
  • Stakeholder communication: Bridging technical and business domains
  • Ethical oversight: Ensuring responsible AI use

Preparing for the Transition

Individuals and organizations should:

  • Start experimenting now: Don’t wait for perfect tools
  • Develop verification skills: Learn to evaluate AI outputs
  • Build AI collaboration experience: Practice working with AI tools
  • Stay adaptable: Be ready for rapid changes in capabilities
  • Focus on unique human value: Develop skills AI can’t replicate

Conclusion: Embracing the Developer Renaissance

The transformation described by GitHub CEO Thomas Dohmke isn’t just about new tools or techniques—it’s about a fundamental redefinition of what it means to be a developer. The profession isn’t disappearing; it’s evolving into something more strategic, more creative, and potentially more fulfilling.

The developers who thrive in this new era will be those who embrace AI as a powerful collaborator rather than a threat. They’ll be the orchestrators, the strategists, the quality guardians who ensure that AI’s capabilities are harnessed effectively and responsibly.

This transformation challenges us all—developers, educators, managers, and society as a whole—to rethink our assumptions about work, learning, and human-AI collaboration. The future belongs to those who can adapt, learn, and find new ways to create value in an AI-enhanced world.

The developer renaissance is here. The question isn’t whether it will happen, but whether we’ll be ready to participate in it.

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