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The Future of Engineering Skills

The Future of Engineering Skills

Simon Obstbaum is a software engineering researcher at Stanford University, specializing in measuring and improving software engineering productivity with modern methods, including AI and large language models

Nov 23, 2025

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5 min read

Key Takeaways

Prioritize architecture over implementation. AI excels at code generation but fails at high-level design. A clean, flexible architecture is the key skill for enabling rapid adaptation to future business needs.
Master AI tools for a competitive edge. Early adoption of advanced AI workflows (pipelining, agents) creates a compounding productivity advantage, making engineers 2-5x more effective over time.
AI research confirms clean code boosts productivity for both humans and AI. This finding highlights a common paradox: companies often clean up codebases for AI agents but not for their human engineers, despite identical benefits.
A new research paper on AI adoption is embargoed until Saturday. The paper, based on 120k engineers, will be presented at the AI Engineer conference in NYC.

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