Code Is Cheap. Engineering Judgement Is Now the Scarce Resource

2026-06-02 · Source: Towards Data Science · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

The shifting game theory of software engineering, driven by AI, is making code cheaper to implement but elevating the importance of engineering judgment and strategic decision-making. Max Buckley, Head of Knowledge Research at Exa, speaking at AI Engineer Singapore (May 15-17, 2026), highlighted that while AI agents reduce implementation costs from weeks to days or hours, this ease can lead to building technically impressive but strategically irrelevant software. Jimmy Lai, Director of Next.js at Vercel, reinforced this, noting that cheap creation leads to expensive ownership, predicting a future of "building for agents," "building with agents," and the necessity to "learn what not to own." Phil Hedayatnia from Airfoil added that "taste is not a checklist," using the Shinkansen bullet train example to illustrate that understanding the "why" behind effective solutions is more valuable than merely knowing "what" a good output looks like.

Key takeaway

For AI Engineers and product leaders navigating AI-driven development, recognize that while AI agents accelerate prototyping, your strategic judgment is paramount. Focus on intentionally deciding what deserves to exist, as cheap creation leads to expensive ownership and maintenance burdens. Prioritize understanding the "why" behind effective solutions, not just the "what," to build truly valuable products and avoid creating technically impressive but strategically irrelevant software.

Key insights

AI makes code cheap, shifting value to engineering judgment, intentional ownership, and understanding "why" things work.

Principles

• Implementation cost is no longer the main bottleneck.
• Cheap creation increases maintenance burden.
• Focus becomes an engineering asset.

In practice

• Build more, evaluate more, discard what doesn't work.
• Be intentional about what systems to maintain.
• Understand the "why" behind effective designs.

Topics

• AI Engineering
• Software Development
• Engineering Judgment
• AI Agents
• Product Ownership
• Design Principles

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.