Should You Still Learn Programming? AI Can Write Code but Not Make Judgments

Parents debate whether to renew kids' coding classes, while a product manager used AI to launch a website overnight. In 2026 the question "Should you still learn programming?" is not naive; the answer is yes, but the curriculum must change.

1. "Don't learn programming" is a chant from AI insiders

The call to stop learning coding originates not from media hype but from AI leaders themselves. In February 2024, Jensen Huang told the Dubai World Government Summit that the goal is to turn human language into programming language, making everyone a programmer. A year later, former Tesla AI director Andrej Karpathy coined the term “vibe coding,” encouraging developers to let AI write code without reviewing it.

Both statements carry weight, outweighing dozens of articles warning of programmer unemployment. However, two crucial details are often omitted.

When Huang spoke, Nvidia’s hiring page listed dozens of software‑engineer positions. Karpathy’s original post qualified the approach as suitable for “weekend‑project‑and‑throw‑away” code, and he later noted that AI agents excel at boiler‑plate code, while his open‑source nanochat project still required precise hand‑crafted logic.

Thus, the “don’t learn programming” rhetoric is not hypocritical; it reflects a different definition of programming than the one most people assume.

2. AI eliminates typing, not programming

Programming consists of two layers. The first layer translates ideas into syntax—remembering APIs, memorising patterns, and writing boiler‑plate code. AI already outperforms humans at this layer.

The second layer is judgment: understanding requirements, handling edge cases, assessing impact of changes, and debugging. AI has not displaced this layer; in fact, it has made it more critical. Previously a developer would mentally review each of 200 lines; now AI can generate those 200 lines in seconds, but the developer must still validate the logic.

Analogously, calculators replaced abacuses, but mathematics education shifted from fast calculation to problem formulation and verification. Programming is undergoing the same transition.

3. "Almost correct" is the costliest mistake

Even when the first layer is flawless, the overall result can still be unreliable because errors concentrate in the judgment layer.

According to the Stack Overflow 2025 Developer Survey, 84 % of developers use or plan to use AI tools. Yet 66 % cite the most frustrating outcome as AI‑generated solutions that are "almost correct, but not fully correct."

"Almost correct" is worse than outright wrong: completely wrong code fails immediately, while nearly correct code passes tests and even ships, only to explode under a boundary condition. Distinguishing "almost correct" from truly correct still requires solid programming skill.

METR’s July 2025 controlled experiment with 16 senior open‑source contributors performing 246 real tasks showed that tasks assisted by AI were on average 19 % slower, even though participants subjectively felt 20 % faster. METR cautions against over‑generalising the result, but it demonstrates that perceived speed is unreliable.

My own experience with Claude Code illustrates the same risk: the model cleaned up unused configuration flawlessly but also removed an implicit feature flag, leading to a silent failure that required multiple diff inspections to uncover.

Experienced developers can catch such issues through testing, code review, and diff analysis; novices who rely entirely on AI lack any safety net, making them vulnerable to hidden bugs.

4. When non‑programmers hand everything to AI, failures look like this

In July 2025, SaaStr founder Jason Lemkin ran a "vibe coding" experiment using Replit’s AI Agent on an online IDE. By day 9 the agent ignored a freeze command and deleted the production database, erasing data from over a thousand executives and companies, then attempted to mask the loss with fabricated data. Replit’s CEO later issued a public apology.

The frightening aspect was not the AI’s sabotage but the complete lack of human oversight: no one understood the commands being run, no one isolated production from testing, and after the incident no one could assess data recoverability.

GitClear’s 2024 analysis of over two hundred million lines of code changes showed that copy‑pasting code into repositories surpassed refactoring for the first time, attributing the shift to AI assistants that happily duplicate logic without concern for technical debt.

Consequently, "vibe coding" is only appropriate for disposable scripts, weekend toys, or internal tools. For anything that must survive real users, a human must be able to read, understand, and evaluate the code—even if they do not type it themselves.

5. So what should you learn now?

The real question is not "whether" to learn programming but "what" to learn. Traditional curricula emphasise skills that are depreciating fastest.

Depreciating ❌

Memorising syntax and APIs

Hand‑writing boiler‑plate code

Achieving one‑shot correctness through practice

Algorithm‑drill rote memorisation

Appreciating ✅

Reading and understanding others' (and AI’s) code

Decomposing vague requirements into clear tasks

Defining acceptance criteria and judging correctness

Knowing where to look when something goes wrong

For those already in the field, shift time saved by AI toward improving reading and validation skills. Your relationship with AI will resemble that of a tech lead and a hyper‑fast, over‑confident junior: the AI does the heavy lifting, but the acceptance stamp stays in your hands.

For newcomers, the advice is the same: teach children to program, but not by the old ten‑year‑old syllabus. The goal is to cultivate the right‑hand column skills—clarifying problems, breaking them into tasks, and habitually verifying outcomes. These are precisely the competencies that programming courses excel at teaching and that align with effective AI‑assisted development. The choice of language (Python or otherwise) is secondary.

Finally, entry‑level programming is cheaper than ever. AI acts as a 24/7 tutor that never gets annoyed by naive questions, lowering the barrier while redirecting the learning path.

Conclusion

Writing code is devaluing faster than expected, but "being a programmer"—understanding judgment, decomposition, and verification—is gaining value. The risk is not AI writing code; it is outsourcing the judgment layer to AI without human oversight.