Software Engineering Has Been Commoditized and Automated. What’s Next?

2026-03-23 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

Software engineering is undergoing a transformation from a specialized craft to a commoditized infrastructure, mirroring historical patterns seen in skilled trades like sewing and machining. This shift is characterized by four stages: discovery, professionalization, commoditization, and automation. Early programming, like hand-sewing, was a guild-era skill, but the introduction of high-level languages such as FORTRAN and later coding bootcamps abstracted away complexity, increasing accessibility and productivity while eroding exclusivity. Today, AI code assistants are automating tasks previously handled by junior engineers, contributing to a 35% drop in U.S. entry-level job postings between 2023 and 2025. This trend is exemplified by Block's February 2026 decision to cut over 4,000 employees, with its CFO citing AI's role in enabling smaller, highly talented teams to achieve more.

Key takeaway

For CTOs and VPs of Engineering assessing workforce strategy, recognize that AI is accelerating the commoditization of routine coding tasks, threatening the "middle" of the engineering profession. You should prioritize hiring and developing talent in systems architecture, cross-disciplinary integration, and AI ethics, as these roles will retain scarcity and value. Failure to adapt risks increased burnout among senior staff and a less competitive, less resilient engineering organization.

Key insights

Software engineering is transitioning from a specialized craft to a commoditized infrastructure due to AI automation.

Principles

• Technological breakthroughs follow a craft-to-commodity arc.
• Abstraction increases productivity but erodes exclusivity.
• Demand for output does not guarantee wage premiums for labor.

In practice

• Focus on systems architecture and cross-disciplinary integration.
• Develop expertise in accountability and ethical AI deployment.
• Become a "tool builder" to create next-gen abstractions.

Topics

• AI Code Assistants
• Software Engineering Automation
• Labor Market Transformation
• Skill Commoditization
• Workforce Reduction

Best for: CTO, VP of Engineering/Data, Executive, Software Engineer, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.