Why learning to program is still worth it in the age of AI and vibe coding
You hear it more and more often: “Why learn to program when AI will write it for me?” Tools like Cursor, Claude Code or Copilot can assemble a working application from a prompt. In February 2025 Andrej Karpathy called this vibe coding — you say, you run, you iterate and “forget that the code even exists” (MIT Technology Review).
AI has not abolished the need to know how to program — it has raised the value of knowing how. The more you vibe code, the more your programming fundamentals pay off.
1. What the data says about AI in development
First let’s separate hype from measurement. The numbers show a paradox: almost everyone uses AI — but trust and reliability lag behind.
of developers use or plan to use AI (76 % last year)
Stack Overflow 2025
trust the accuracy of AI — a record low; 46 % distrust the outputs
Stack Overflow 2025
do not use vibe coding as part of professional work
Stack Overflow 2025
of AI code in Veracode tests contains an OWASP Top 10 vulnerability
Veracode 2025
The most common frustration according to developers? Code that is “almost right” — and fine-tuning it often takes more time than writing it from scratch (SO AI report, press release).
Productivity depends on context
| Context | Result | Source |
|---|---|---|
| Simple new task | +55.8 % faster with Copilot | Peng et al., 2023 |
| Experienced dev, large familiar repo | −19 % speed with AI — and the developers thought the opposite | METR, 2025 |
We break down how to read such numbers in the article Benchmarks for coding assistants.
AI can speed up the start and the routine. It does not guarantee correctness, security or real productivity — those are held by the person who can judge the output.
2. Why fundamentals improve vibe coding
Vibe coding is not “programming without programming”. It is programming through natural language — and the people who do well at it are those who can think like programmers.
The strongest evidence so far comes from a preregistered study with 100 students (arXiv, 2026). Participants built an application purely by vibe coding: they wrote prompts, evaluated the result and iterated without editing the code directly. The authors then examined what actually predicted their success — the ability to formulate text well, or computer-science fundamentals.
The result speaks clearly. Prior academic achievement in computer science correlated with vibe-coding performance at r = 0.39, while written communication skills only at r = 0.29. And when the authors put both factors into a joint model, computer-science knowledge explained roughly twice the unique variance compared with writing. In other words: writing good prompts helps, but being able to think like a programmer helps even more — even when you “don’t see” the code.
Qualitative research into what vibe coding looks like in practice points the same way (arXiv, 2025). Successful vibe coders are not people who “just throw prompts” — they combine traditional programming skills with AI literacy and product thinking. They understand what they want from the model, can recognise when the output makes no sense, and can run the conversation like a technical brief, not like crystal-ball gazing. We break down how such systems work under the hood in the article What is an AI agent and how does it work.
A prompt is not magic. It is a technical specification written in human language — and without fundamentals you don’t know what to ask for.
3. When vibe coding is enough — and when it isn’t
So that we are not “anti-AI”: vibe coding is a great tool. Just not for everything.
| Vibe coding is usually enough | You need fundamentals + careful engineering |
|---|---|
| Personal script, one-off data transformation | Login, payments, personal data |
| Internal tool for a few people | Public app with users |
| UI prototype / demo for a workshop | Production with SLA, audit, compliance |
| Learning a new library “live” | Security, authorisation, multi-tenant data |
| Throwaway weekend experiment | Long-term maintenance, team, handover |
The higher the cost of a mistake, the less “forget that the code exists.” AI generates — you vouch for it.
4. Six skills that vibe coding accelerates
Not “AI is bad” — but why vibe coding quickly hits a wall without fundamentals.
Better briefs
Without knowledge: “Make a login.” With knowledge: JWT, rate limit, password hashing, tests, trade-offs. You know what to ask for.
Spotting “almost right”
The biggest tax of AI is code that looks finished and fails on an edge case. Without reading code you have nothing to verify the output with — see also LLM hallucinations.
A feel for security
Veracode 2025: in 45 % of cases the model chooses the insecure variant (XSS, SQL injection…). A programmer knows what to look for.
Steering the system, not just the chat
Production = data model, API, tests, CI, rollback. Fundamentals tell you when to vibe a prototype and when to switch to engineering.
Debugging without magic
You can isolate the cause and tell the AI precisely what to fix — not just “fix it” through another layer of patches.
Reality calibration
METR: people with AI thought they were faster even though they were slower. Measurement (tests, regressions) beats the feeling of flow.
Example: what knowledge changes in a prompt
Udělej mi login do aplikace.
Přidej e-mail/heslo login do naší appky (FastAPI + React, uživatelé v Postgresu přes SQLAlchemy — model viz app/models/user.py). Bezpečnost: - access JWT (15 min) + refresh token s rotací a revokací; oba v HTTP-only Secure cookies (SameSite=Lax) + CSRF ochrana - rate limit na /login: 5 pokusů / 15 min, na IP i na účet; při překročení vrať 429 - hesla jen přes argon2; nikdy je nedávej do logů ani do chybových hlášek - stejná odpověď pro neexistující e-mail i špatné heslo (žádné user enumeration) Kvalita: - validace na serveru (Pydantic) i na klientovi - pytesty: happy path, špatné heslo, expirovaný a ukradený refresh token, brute-force → 429 Postup: nejdřív pošli návrh endpointů a stručný trade-off vs. server-side sessions v Redis. Implementuj až po odsouhlasení, po malých krocích (backend → testy → frontend), ať můžu každý krok zreviewovat.
5. How to learn programming with AI
According to the Stack Overflow Developer Survey 2025, around 44 % of people already use AI to learn code. That’s great — as long as AI isn’t a crutch that takes away your brain’s exercise.
- First 5–15 minutes on your own, only then AI. Without your own attempt you won’t consolidate the pattern.
- Have it explained, not just generated. “Explain line by line and where it fails.”
- Rewrite the key part without AI. If you can’t, you don’t know it yet.
- You write the tests — let the AI implement against them. A test is both a specification and protection against skill atrophy.
- Review it like someone else’s PR. Edge cases, security, readability — not just “whether it runs.”
- Build small end-to-end things (CRUD, auth, upload) and measure whether AI really saves you time.
If you are starting completely from scratch, first go through the practical 30-day plan for starting to program; it helps you choose Python or C# and build your first project in manageable steps.
6. What is worth learning (and what less so)
In the AI era the mix of work changes — it’s not that it disappears.
| Less valuable on its own | More valuable than before |
|---|---|
| Memorising syntax | Reading and reviewing AI code |
| Boilerplate from scratch | API and data-model design |
| “I can write a for loop” | Testing, debugging, security |
| Copying from Stack Overflow | Precise specification of intent for AI |
| Typing speed | Judgement: what to build, discard, not risk |
Learning to program today does not mean becoming a “typist of semicolons.” It means learning to think in structures, states and trade-offs — and then steering AI with that.
7. Key takeaways
- Vibe coding is useful for prototypes and speed-ups — it is not proof that programming is pointless.
- CS fundamentals predict success in vibe coding more strongly than prompt writing itself.
- AI is used massively, but trust and the security of AI code are weak points.
- Human value shifts to the brief, control, architecture and responsibility.
- Learning to program is not resistance to AI — it is the way to steer it.
Read also
Want to learn to program so that you really fly with AI?
In the Python Programming and Vibe Coding course we combine vibe coding with AI tools and classic programming fundamentals — because exactly this combination makes you someone who steers AI rather than just “throws prompts”. And if you already code and want to bring AI into the whole development cycle, take a look at Vibe Coding and AI in Software Development. Other directions — data, AI agents, testing, C# or DevOps — can be found in the overview of AI, data and programming courses.
Sources and further reading
- Karpathy / MIT Technology Review: What is vibe coding, exactly? (2025)
- Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency (2026)
- Vibe coding: programming through conversation with AI (2025)
- Stack Overflow Developer Survey 2025 — AI
- METR: Early-2025 AI productivity study
- Peng et al.: GitHub Copilot productivity evidence (2023)
- Veracode: 2025 GenAI Code Security Report
