Why Code Is Getting Cheap and the Three Roles That Are Gaining Value

Recent posts show many developers fearing that AI can write code ten times faster, rendering a decade of CRUD experience worthless. The author, with over ten years of experience, reports that AI‑assisted development has tripled his team’s efficiency: a feature that once took three days now has a usable draft in half a day.

Code Is Becoming Cheap

AI can generate functional code for generic tasks within minutes. For example, the author described a markdown‑to‑HTML conversion tool that previously required half a day of work; after describing the requirements to an AI, a complete, runnable solution was produced in five minutes, with only minor tweaks before deployment. AI can work 24/7, producing code of comparable quality to an average programmer at almost no cost.

Consequently, hiring processes no longer test trivial coding questions like quick‑sort or Vue two‑way binding, because AI can solve them faster and more accurately.

Three Profiles That Are Gaining Value

1. The “AI Corrector” – Deep Technical Knowledge

AI often writes code that looks correct but contains hidden flaws. The author recounts a colleague who used AI to implement a bulk‑user‑import feature; it worked in tests but crashed in production due to loading all records into memory, causing OOM on large datasets. Only someone familiar with memory management could spot the issue. An interviewee who relied entirely on AI and could not explain JVM memory models was rejected. The author defines the AI Corrector as someone who can detect missing indexes, absent locks, or inefficient algorithms in AI‑generated code—skills that AI is unlikely to acquire even after a decade of development.

2. The “Requirement Translator” – User Understanding

AI lacks insight into what users truly need. When a client asked for a simple ticket‑system, AI produced a functional prototype, but it missed essential details such as required fields, workflow logic, permission rules, and reporting needs. After three rounds of detailed user interviews, the author produced a system that the client claimed was ten times better than a previous outsourced solution. The author emphasizes that AI can answer “how to build” but not “what to build.” Programmers who can converse with users, clarify ambiguous requirements, and translate them into clear implementation plans will remain valuable.

3. The “Value Creator” – Business Acumen

Understanding the business impact of code distinguishes valuable engineers. The author cites a senior engineer at an e‑commerce firm who spent a week reducing page‑load time by 200 ms; this modest improvement lifted conversion rates by 0.8%, generating tens of millions of revenue annually. AI cannot recognize that a 200 ms gain translates into significant business value, nor can it proactively seek such optimizations. Engineers who know how their work affects revenue, cost savings, or user satisfaction create value that AI cannot replicate.

Three Practical Recommendations

Shift time from writing code to understanding problems. Spend 80 % of effort clarifying who the feature serves, the pain points it solves, potential technical pitfalls, and the expected business impact before letting AI generate code.

Deepen low‑level technical and business knowledge. Focus on operating systems, networking, JVM, database fundamentals, architecture, as well as industry‑specific business logic and soft skills like communication and logical reasoning—areas AI cannot master.

Treat AI as a partner, not a competitor. Use AI as a powerful tool akin to an IDE or Git; the programmer remains the decision‑maker who validates, optimizes, and integrates AI‑generated output.

In conclusion, while AI makes routine code cheap, programmers who can correct AI’s output, translate user needs, and align code with business goals will see their value rise, not fall.