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AI tools such as deepseek are the double-edged swords of programmers' skills development

Popularity：578 ℃/2025-02-11 19:49:38

In 2025, 73% of programmers around the world use AI encoding tools on a daily basis (Gartner 2025Q1 data). When we marvel at the fact that GitHub Copilot only takes 10 seconds to generate a full functional module, we also need to be wary of a phenomenon: the frequency of the new generation of programmers typing "/" in the IDE is far more than the number of times they manually tap the keyboard. This skill revolution triggered by AI is reshaping the professional genes of programmers.

1. Efficiency Revolution: Skill Accelerator brought by AI Tools

1.1 Paradigm transfer of code production

  • Basic code automation: AI tools have implemented the second generation of modules such as sorting algorithm (91% accuracy), API interface (87% completeness) and other modules.[6]
  • Knowledge search upgrade: In the past, it took 2 hours of search, but now it takes 5 minutes to get the solution through natural language conversation[2]
  • Code quality monitoring: AI's ability to detect memory leaks in real time exceeds that of human engineers, with a false alarm rate of only 3.2%.[5]

1.2 Reconstruction of development process

graph TD A[Requirement Analysis] --> B{AI Pre-Evaluation} B -->|Simple Requirements|C[AI Automatically Generate] B -->|Complex Requirements|D[Human-machine Collaborative Development] C --> E[Manual review] D --> F[AI Real-time Recommendations] E --> G[Deployment online] F --> G

2. Skill Transformation: Programmer's New Ability Map

2.1 Core capability evolution

Traditional abilities New compound capability Typical tool scenarios
Grammatical memory Semantic understanding Natural Language Code Transfer[1]
Manual debugging Exception pattern recognition AI Error Analysis[4]
Single-soldier combat AI Team Management Intelligent task assignment[7]

2.2 Innovation in the education system

  • MIT new curriculum system: Include AI collaborative development into the compulsory course, students need to complete the practical project of "AI Reconstruct Legacy Systems"
  • Capability certification changes: AWS certification has added a special project "AI Code Auditor" to examine the optimization capabilities of generating code[3]

3. Undercurrents: The risk of degradation that cannot be ignored

3.1 Empirical evidence of capacity degradation

# Traditional debugging process
 def manual_debug(error):
     print(log_analysis(error))
     step_through_code()
     identify_root_cause()

 #AI era debugging
 def ai_debug(error):
     return (error) # Return directly to the solution

- (Code examples illustrate simplification of thinking path)[6]*

3.2 Cognitive attenuation

  • Deep thinking time: From an average of 47 minutes/problem to 11 minutes[4]
  • The proportion of innovative solutions: Using AI tool group vs control group decreased by 28%[7]
  • Understanding of the underlying principle: Only 39% of developers can explain the memory management mechanism of AI-generated code[1]

4. The way to break the deadlock: Best practices of human-computer collaboration

4.1 Personal development strategy

  1. Principles for using AI grading

    graph LR Core business code --> Manual development Unit Test Cases --> Manual Review of AI Generation Document generation --> Fully automatic processing

  2. Capability preservation plan

    • 8 hours a week "no AI encoding time"
    • Monthly in-depth analysis of the underlying logic of AI-generated code[2]

4.2 Team management innovation

Microsoft Research Institute’s Rule 3-3-3：

  • 30% of the basic code is completed by AI
  • 30% manual optimization of AI output
  • 30% focused on innovative development
  • 10% used for fine-tuning of AI models[5]

5. Future Outlook: Programmer Portraits in 2040

According to IDC 2025 forecast, programmers will be divided into three major directions in the future:

  1. AI Trainer: Focus on optimizing code generation model (salary premium + 45%)
  2. Technical Architect: Design AI-friendly system architecture (demand growth of 300%)
  3. Human-computer interaction expert: Build a natural language programming interface

Conclusion: Finding a balance between tools and abilities

When we see "Co-authored-by: AI-Assistant" in the GitHub submission record, we need to be aware that AI tools should become "mechanical exoskeletons" of extended capabilities, rather than "electron opium" that replaces thinking. As Linus Torvalds, the father of Linux, said: "Real programmers are always creating tools, not shaped by tools."

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