AIGC Development Trends and Their Impact on Programmers

Recently a viral story on a social platform compared two programmers: Alex, a 19‑year veteran who insists on writing 100% of code manually, and Hamid, a 4‑year programmer who leverages Copilot and GPT‑4. In an interview Alex was rejected while Hamid received a full‑time offer, illustrating that in the AIGC era the ability to use AI‑assisted coding tools can outweigh years of experience.

The article then presents a cost comparison. Hamid completed the first version of a project in one week, achieving 100 % test coverage and completing 95 % of the work. He used Bubble for UI and workflow, Copilot for code completion, and GPT‑4 for test case generation. The tool costs were $211 for GPT‑4, $20 for Copilot, $5 for Cloudflare, and $124 for Bubble, plus a monthly hosting fee of $139.

工具成本

GPT4

Copilot

Cloudflare

Bubble

花费（美元）

211

20

5

124

Alex, despite 19 years of experience, finished only about 7 % of the same task. His expenses included $20 for a hosting service (Vecl), a salary of $3 500, a total development cost of $45 000, an additional $11 000 for testing, and a low monthly hosting cost of $20.

These figures show that while Hamid’s approach was faster, it incurred higher operational costs; Alex’s method was slower and far more expensive in development effort, but resulted in lower ongoing costs.

The article then analyzes AIGC development trends, noting the evolution from early AI concepts to modern large‑scale models such as GPT‑4, and predicts three stages: assistant, collaboration, and originality.

Impact on programmers:

Programmers will not be eliminated; the demand for software development remains strong.

Those who master AI tools can increase productivity dramatically.

Junior developers who rely solely on code generation may face reduced demand.

Senior developers can add value through architecture design, communication, and leadership.

To thrive in the AIGC era, the article suggests:

Learn to use AI tools correctly and understand their limitations.

Upgrade skills toward higher‑level roles such as system architect, project manager, or technical consultant.

Pay attention to ethical and legal issues (IP, security) when using AI‑generated code.

Continuously study the underlying principles of the tools to avoid pitfalls.

Adopt a full‑stack mindset, using AI to handle UI, backend, testing, documentation, and data retrieval.

Specific AI‑assisted capabilities highlighted include:

Code generation and refactoring (e.g., GitHub Copilot, GPT‑4).

Automated code review and bug detection (e.g., Codiga).

Unit‑test generation (e.g., Refraction AI).

Script generation from natural‑language prompts.

Documentation creation and code annotation.

Knowledge retrieval via ChatGPT, ChatPDF, etc.

In summary, the case of Alex versus Hamid exemplifies a broader truth: professionals who embrace new AI tools and continuously upskill will remain competitive, while those who cling to outdated practices risk obsolescence.