AI writes it, it runs, you can’t explain a line of it. That’s vibe coding - and it’s the most seductive trap in modern education, because it worksright up until it matters. This essay is our full argument for the unfashionable position we’ve built two schools on: fundamentals first, then AI - hard. With the reasoning, the receipts, and the honest case for when vibing is actually fine.
Let’s start by respecting the temptation, because dismissing it convinces no one. Vibe coding is intoxicating. You describe an app, and minutes later it exists - running, clickable, real-looking. For someone who always wanted to build software, this feels like the locked door finally opening. And partly, it is: we’re genuinely glad the barrier fell. Prototypes that took weeks take an afternoon. Non-technical founders can sketch their idea in working code. Weekend projects ship on the weekend. For disposable software - prototypes, experiments, personal scripts - vibe coding isn’t a trap. It’s the correct tool, and we teach our own students to use it for exactly that.
The trap is a category error: mistaking what works for disposable software as a way to build non-disposable things - a product with users’ data in it, or the most non-disposable thing you own, a career. That’s where the afternoon of magic quietly converts into what we’ve watched happen to real learners, repeatedly: three months of “productive” vibing, a portfolio of apps they can’t discuss, and a first technical interview that takes eleven minutes to expose the difference between having built and being able to build. Employers developed the detection instinct fast - because they got burned fast.
The economics: why fundamentals appreciated
Here’s the argument that settles it, and it’s economic, not nostalgic: everyone gets the same models at the same price.Claude, GPT, Gemini - the world’s best AI is a subscription anyone can buy. When every builder on earth holds the same tool, the tool differentiates no one. What differentiates is everything the tool cannot supply: knowing whatto build, specifying it precisely, recognising when generated code is subtly wrong (it compiles; it also leaks your users’ data), debugging the failure the AI circles helplessly, and signing your name to what ships. Strip those away and “AI-powered developer” means “person who forwards requests to an API anyone can call.”
Every one of those differentiators is a fundamentals skill. Which produces the era’s great irony: AI made fundamentals more valuable, not less - it turned them from the price of entry into the scarce half of a partnership. And simultaneously (this is the part that should make you optimistic) it made them faster to acquire: with an infinitely patient tutor to interrogate, the old years of grind compress into months. As we put it on our about page: AI compresses the path, not the person walking it. This is the same pattern we trace through history in AI is the new Excel - the spreadsheet didn’t make accounting judgement obsolete; it made the judgement the whole job.
How we enforce it (the receipts)
Positions are cheap. Here’s the actual machinery, built into Sigmo and the programme, that makes vibing-through impossible.
Defense interviews - explain it or no graduation
Every mission submission on Sigmo requires a video where the student explains their code - what it does, why it’s built that way, what breaks if you change it. Real instructors verify understanding before the next mission unlocks. No Loom walkthrough, no graduation. Period.
A Socratic AI coach, not an answer machine
Sigmo AI - the built-in tutor - is deliberately configured to give progressively warmer hints rather than copy-paste solutions. A chatbot can give you an answer, but an answer is not an education. The struggle before the click is the education.
Missions with AI switched off
Some early missions disable AI assistance entirely - because you can’t develop the judgement to review AI’s code until you’ve carried the weight of writing your own. You learn to lift before you learn to use the forklift.
Then AI - hard
Once fundamentals hold, the same curriculum swings fully AI-native: building with Claude and Cursor daily, and by Phase 3, directing an AI coding agent through a full spec → plan → review → deploy cycle. Fundamentals aren’t instead of AI. They’re what makes AI usable at full power.
Students feel the difference and say so in public reviews: “students are not spoon-fed” (MJ Chung, Google review) - and one graduate’s line we’d frame: “if ChatGPT can’t crack your code, rest assured, the Sigma team can guide you to your answer.” The friction is the feature.
The one-question self-test
If you’re learning right now and wondering which side of this line you’re on, the test takes one question: open the last thing you built and explain it, out loud, to nobody.What does this function do? Why this structure and not another? What breaks if this line changes? If the explanations come - even clumsily - you’re building competence, and AI is your accelerator. If you hear yourself narrating what you asked for rather than what it does, you’ve been vibing, and every further week of it deepens a hole you’ll eventually have to climb out of - in an interview, or worse, in production. The full recovery regimen is in don’t become dependent on AI, and the day-one habits that prevent the hole are in how to learn coding with AI.
And if you’re choosing where to learn - us or anyone else - make this the screening question: “How do you verify I understand what I build?”A school with a real answer (defenses, reviews, humans checking) is teaching you to be AI-native. A school without one is charging you for a supervised vibe. The difference won’t show on day 30. It decides everything at year three - which is exactly the horizon a career is priced on. You can feel our version of the answer free: the free trial includes real Sigmo missions and a live Buildroom session - one signup, no card, and the Socratic coach will decline to spoon-feed you from minute one.
FAQ
What is vibe coding?
Vibe coding is building software by prompting AI and accepting what runs - without understanding the code produced. Describe what you want, the AI writes it, it works (apparently), you move on. The term went mainstream in 2025 as AI tools got good enough to make this genuinely possible. Our one-line definition, which doubles as the warning: AI writes it, it runs, you can’t explain a line of it. Feels like progress. Isn’t - at least, not when what you’re building is a career or a product real people depend on.
Is vibe coding bad?
It’s a tool with a domain, and honesty requires drawing it precisely. Vibe coding is genuinely fine - even optimal - for throwaway software: prototypes, personal scripts, weekend experiments, testing whether an idea deserves real investment. It becomes a trap in exactly two situations: when you’re LEARNING (because accepting code you can’t evaluate builds the appearance of skill, which collapses in your first technical interview or production incident), and when real users, real money, or real data depend on the code (because you cannot secure, debug, or maintain what you don’t understand). The problem isn’t the vibing - it’s mistaking it for competence.
Can I get a job by vibe coding?
The market has already answered this. Employers moved quickly from "can you use AI?" to "can you defend what the AI produced?" - technical interviews increasingly have candidates work WITH AI while explaining their decisions, and every real codebase eventually presents the 2am bug that no amount of re-prompting fixes. What employers pay the documented AI-fluency premium for (RM 6,000-9,000/month for juniors in Malaysia, versus RM 3,500-6,500 standard) is the combination: AI speed WITH the judgement to review, catch, and own the output. Vibe coding alone gets you a portfolio you can’t discuss - which interviewers detect within minutes.
Why do fundamentals matter MORE in the AI era, not less?
Because of a simple economic fact: everyone gets the same models at the same price. When every developer on earth has the same AI, the AI itself differentiates no one - the differentiator is everything the model can’t supply: knowing what to build, specifying it precisely, recognising when generated code is subtly wrong, debugging what the AI can’t, and owning the result. Every one of those is a fundamentals skill. AI didn’t make understanding obsolete; it made understanding the scarce half of the partnership - and it compressed the time needed to build that understanding from years to months.
How do I learn fundamentals without giving up AI’s speed?
Sequence, don’t choose. The failure modes are symmetric: learning pre-AI style (grinding syntax alone, ignoring the tools you’ll be hired to use) and learning AI-first (vibing from day one, building dependence instead of judgement). The sequence that works: fundamentals first with AI as a Socratic tutor you interrogate - explain this error, why this approach - while writing your early code yourself; then, as judgement forms, graduate deliberately to AI-native building where you direct and review. That’s the exact architecture of our programme: roughly three months of real fundamentals, then AI - hard. AI compresses the path, not the person walking it.
Hard is what makes it real. Fundamentals first. Then AI - hard.
The free trial puts you inside the real pedagogy this week: Sigmo missions with a Socratic AI coach, a live Buildroom session with an instructor, no card needed. Feel the difference between an answer and an education.
