This paper, authored by Terry Cho on May 21, 2025, discusses the evolving landscape for software developers in the age of Big Data, Machine Learning, and Generative AI (like ChatGPT). The central argument is that AI technologies are profoundly integrating into the development sector, necessitating a significant shift in the skillset and role of software engineers to remain relevant.

The paper begins by observing the deep penetration of AI tools such as Vibe Coding and Cursor AI into the development process. It cites statistics from Anthropic showing that 37.2% of LLM (Large Language Model) usage is computer-related (development), with another 10.3% in Art & Media. While AI is widely discussed, its practical application in business is not yet universally high, with IT professionals showing a higher adoption rate.

A key consequence highlighted is the increased productivity in software development due to AI. This has a direct impact on the job market; a chart from the Financial Times is referenced, indicating a sharp decline in software engineer hiring since 2024. The author posits that as per-person productivity increases, the need to hire more developers decreases. Furthermore, the paper suggests that application development may transform into a labor-intensive business, potentially shifting to developing countries with the aid of AI. Supporting this, Microsoft's recent layoffs are cited, where software engineers constituted an overwhelmingly large proportion of those let go, unlike roles requiring direct human interaction (e.g., Business Development, Sales).

The paper derives several key insights from these observations:

1. Decreased Software Developer Hiring and Company Downsizing: Analogous to the App Store era, which enabled one-person or small teams to establish software businesses, AI is accelerating this trend. High-revenue AI startups like Midjourney, with only 11 employees generating 270 billion KRW in revenue, exemplify a future where high value is created with significantly fewer personnel. The focus shifts from simple application development to high-value-added areas like AI algorithm development.
2. Polarization of Software Engineers: The creation of websites, REST APIs, and standard applications will transition from a "technology domain" to a "labor domain" leveraging AI. This will lead to a divergence among engineers:
   • Those who use existing frameworks (e.g., Spring).
   • Those who create these frameworks.

Consequently, engineers will be bifurcated into:

• High-level engineers: Possessing deep algorithmic knowledge, creative thinking, and strong business acumen.
• Implementation-focused engineers: Primarily executing tasks based on specifications. The paper warns that the latter group will increasingly be replaced by AI, leading to job reduction.

Given these transformations, the paper outlines the necessary path forward for engineers. Traditional roles like backend, frontend, or operations might become less distinct. With AI's assistance, anyone can potentially become a full-stack engineer, making such specialized labels less meaningful. While AI coding technology is not yet fully mature, its rapid advancement suggests substantial automation within 2-3 years.

The paper introduces the concept of a "General Software Engineer" prevalent in Silicon Valley. This type of engineer is defined as someone who solves business problems using technology. Their responsibilities extend beyond simply implementing requirements; they must think critically, understand the business impact of their work, and maximize business value through effective communication within their organization. These engineers are adaptable, willing to learn frontend, backend, or new technologies/frameworks as needed, and even create them if necessary. They are less dependent on specific technologies, leveraging AI for technical expertise (while maintaining their core algorithmic and coding abilities).

The paper identifies three crucial capabilities for engineers in this new era:

3. Critical Thinking-based Problem-Solving Ability: This is paramount. It involves defining problems in given situations, gathering necessary information, formulating hypotheses, proposing solutions, managing risks, and quantitatively measuring outcomes.
4. Leadership: As tasks shift from being assigned to being self-initiated, engineers must work collaboratively, making leadership essential for problem-solving.
5. Communication Skills: Effective collaboration is vital to gain recognition for one's work, persuade others of ideas, and achieve results.

The paper concludes by referencing insights from a Korean engineer's experience working in Silicon Valley, highlighting differences in expectations between Korean and American engineering cultures. These insights are presented as guidance for the future direction of engineers.