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Coding Skills That Matter in the Age of AI - mklabgr - 09-03-2026
Coding Skills That Matter in the Age of AI — Brilliant
Brilliant argues that AI is making the mechanical act of writing code increasingly cheap, so the most valuable programming skills are shifting away from simply knowing syntax. The developer’s role increasingly becomes deciding what should be built, how it should be designed, how AI should be directed, and how to determine whether the resulting software is actually correct. Brilliant organizes these abilities around seven areas: Taste, Incrementality, Specification & Design, Build, Verify, Security, and Abstraction.
A central idea is that developers should work in small, understandable, verifiable increments. AI can generate code much faster than humans can review or understand it, creating what Brilliant calls comprehension debt: software may appear to work while nobody fully understands its assumptions, dependencies, or failure modes. The solution is to build thin end-to-end pieces, test risky assumptions early, use version control, keep changes reversible, and make sure each increment can be explained before moving on.
The biggest change comes in the traditional development loop of specify → build → verify. As AI performs more of the build stage, Brilliant expects human expertise to concentrate increasingly on specification and verification. Developers need to define observable success criteria, design good data models and interfaces, decompose systems into independent components, understand dependencies and constraints, and then rigorously test AI-generated work. Brilliant therefore portrays the future programmer less as someone who types thousands of lines of code and more as a software architect, problem solver, reviewer, and director of AI systems.
Key takeaways
- Writing code is becoming less scarce; good judgment is becoming more valuable.
- Knowing the problem domain remains essential because AI cannot reliably tell you when its own plausible-looking answer is wrong.
- Specification may become as important as programming: clearly define behavior, constraints, architecture, and tests before asking AI to implement something.
- Verification becomes the bottleneck. Generating software is easy; proving that it is correct, secure and maintainable is much harder.
- Developers should avoid blindly accepting large AI-generated changes and instead work in small, testable increments.
- Traditional CS foundations—algorithms, data structures, functions, abstraction, efficiency and reasoning about correctness—still matter because they give developers the knowledge needed to evaluate AI-generated solutions.
Overall: Brilliant's thesis is compelling: AI probably does not eliminate the need to understand programming. Instead, it moves expertise one level higher—from “Can you write this code?” toward “Can you design the right system, instruct AI to build it, understand what it produced, and prove that it works?”
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