Vibe Coding? Great! Now you can start learning Software Engineering

For decades, the dream of programming has been consistent: to ascend the ladder of abstraction. We've journeyed from punch cards to binary, then to assembly, high-level languages like C, frameworks, and eventually to low-code/no-code tools. Each progression diminished the importance of syntax, elevating the significance of intent. AI, as I see it, is simply the next, yet monumental, stride in this evolution.

This era of 'vibe coding' perfectly addresses what most of us truly need: momentum. You have an idea, a vision you want to bring to life. You yearn to see something tangible, to grasp its form, to find proof that it isn't merely an illusion. And honestly, if AI can help you transform a mere thought into a working 'thing' in a single day, that is an invaluable gift.

Because I've come to realize that most brilliant ideas, tragically, don't fail because they're 'bad.' They fail because they never escape the confines of our minds. They perish in that foggy realm where you vaguely understand your own meaning, but cannot test it, cannot showcase it, cannot gather feedback, and cannot discern if your idea holds real potential or if you're just daydreaming in high definition. The chasm between imagination and execution is where momentum vanishes. And once momentum fades, the internal negotiations begin: 'more research,' 'more planning,' 'I'll start next week.'

Vibe coding breaks that spell.

It transforms a concept into something you can click, probe, and stress-test. It shifts the conversation from theoretical 'would this be cool?' to practical 'does this help?' No longer 'users will probably do X,' but 'let's observe what they actually do.'

And here's the profound twist. The moment you can generate working software with such ease, code ceases to be the bottleneck. When AI can produce a dozen implementations in an afternoon, the scarce resource isn't typing. It's choosing. It’s knowing what to build, what to gracefully ignore, what to simplify, what to rigorously verify, and ultimately, what to ship.

Once code generation becomes demand-driven, your advantage is no longer about syntax mastery. What remains, and what becomes paramount, is the part we once sidestepped because it's harder to measure:

Judgment.

Judgment is the art of knowing which decisions to make, what to build, and, crucially, what not to build. And true judgment, I've observed, begins with something most people profoundly underestimate:

Clarity.

Because the most arduous aspect of building software has never been about penning instructions for a computer. The real challenge has always been understanding a complex, messy real-world situation with enough depth to describe it unequivocally.

That, in its essence, is software engineering. It's taking a vague aspiration like 'we need an AI feature' or 'we need a better dashboard' and honing it into something sharp and precise:

What specific problem are we solving, and for whom?

Who is experiencing this problem most acutely?

What does unqualified success look like in plain language?

What would define this as a failure?

What constraints are non-negotiable: time, cost, safety, privacy, reliability?

What is the absolute smallest thing we can build that definitively proves the value?

If you bypass this crucial step, you risk constructing a beautiful solution to the wrong problem. You can ship rapidly and still fail. You can generate an entire codebase and still achieve no meaningful impact.

AI doesn’t diminish this reality; it amplifies it. Because when implementation becomes easy, it becomes dangerously simple to conflate output with progress. You can produce a vast quantity without moving the needle. You can ship features that appear impressive but fail to reduce work, save time, increase revenue, foster trust, or genuinely enhance the user's life.

So, the new job isn't merely 'can we build it?' The job is: are we building the right thing, and can we unequivocally prove it?

The second unavoidable question that emerges is: can you build software that truly survives reality?

Because reality, as I've seen countless times, is where prototypes go to die. Not because the prototype was useless, or the idea inherently flawed, but because the moment real users interact with it, the rules fundamentally change. The moment money exchanges hands, expectations escalate. The moment your system must operate consistently, day in and day out, beyond just your demo, the true cost of fragility becomes apparent.

This, my friends, is the product wall. It’s the point where every 'quick hack' transforms into interest-bearing debt. Where every ambiguous decision evolves into a support ticket. Where every overlooked edge case results in a broken flow. Where every 'we’ll clean it up later' becomes 'we can’t touch this without catastrophic failure.'

On the far side of the product wall, your primary objective is no longer simply to make something work. Your job is to ensure it continues working, as requirements evolve, as the team expands, as the customer base proliferates, and as the system is utilized in ways you never initially envisioned. This is precisely why software engineering exists.

When people mention 'software engineering,' they often conjure images of heavy processes, endless meetings, and stacks of paperwork. But that's a misunderstanding of its essence. Engineering is not bureaucracy; engineering is the disciplined pursuit of predictable outcomes.

It encompasses the critical facets of software development that may not be apparent in a dazzling demo, but which ultimately determine a product's longevity:

Requirements and problem definition: Not vague prompts or fleeting 'vibes,' but crystal-clear articulation of what we’re building, for whom, and what 'success' truly signifies.

Architecture and design: The intelligent structuring of a system to enable growth without collapse. Defining boundaries, interfaces, data flow, dependencies, and making forward-looking decisions to preempt future pain points.

Quality and testing: Not merely to avoid the shame of bugs, but because trust is painstakingly earned and easily lost. Quality ensures the product remains usable even when the ideal 'happy path' is disrupted.

Delivery and project control: The disciplined practice of estimation, planning, prioritization, risk management, sequencing work, and tracking progress. It’s the honesty of not deceiving ourselves about timelines.

Operations and maintenance: The continuous effort of monitoring, logging, incident response, performance optimization, security hardening, and regular upgrades. Recognizing that software is a living entity, not a one-time build.

People and teamwork: Establishing standards, clear documentation, fostering ownership, and transparent communication. Because once software becomes a shared asset, the team itself is an integral part of the system.

The software development lifecycle does not vanish in the age of AI. The cycle remains the cycle. What changes is its focus, shifting from 'type everything' to 'steer everything.' The challenge is how we enhance each stage with AI to achieve superior judgment and unwavering survivability.

In the past, most teams were constrained by their typing speed and ability to stitch code together. Now, AI dramatically compresses that build phase, relocating the constraint. The constraint now lies in clearly defining the work, selecting the optimal design, rigorously verifying correctness, and diligently maintaining the system's health over time.

That is what I mean by “steer everything.” You explore by rapidly generating options. You constrain by translating goals into concrete requirements and acceptance criteria. You design by establishing boundaries that make future changes economical. You build with AI, but you keep humans accountable for critical decisions. You verify with comprehensive tests, checks, and real-world validation. You operate by continuously monitoring behavior and measuring tangible impact. You evolve by refactoring and strategically addressing accumulated complexity.

AI accelerates implementation. Engineering governs direction.

That’s the profound “steer > type” shift. Your value is no longer measured by how quickly you can produce code. Your true value is in how effectively you can navigate a product through uncertainty without sacrificing quality, clarity, or trust.

It’s about building a system, and an accompanying workflow, that can truly endure the test of time.

If this resonates, I'd appreciate your thoughts. Please like, share, or comment.

What's your take on this shift? Out of curiosity, if you’re building anything right now, drop a comment: what are you building, and what’s the hardest part - clarity, quality, or shipping?