The waterfall trap is back
You describe what you want in plain English: “a shared calendar app with Slack notifications.” You press enter, and a few minutes later, the AI hands you a working codebase. It runs. It looks polished. It feels like the future.
This speed really feels transformative. Yet beneath the surface, a familiar old pattern lies. The workflow is linear, brittle, and quite resistant to change. Vibe coding has reintroduced the very problems that drove software engineering away from waterfall development two decades ago (or a little bit more recent based on the company you work for :D).
The technology is new. The trap for product teams is not.
Same old waterfall, shiny new package
Going back a few years, waterfall development followed a tidy, stage‑based sequence: requirements, design, implementation, testing, and release. I still remember the very first product I've built back in the day pretending to know how mySQL and PHP worked while learning it from a book, using waterfall.
On paper it looked efficient. In practice, it collapsed under its own weight. Any fundamental change discovered late in the process was expensive and demoralizing. Plus almost impossible to revert.
Agile methodologies rose in response, favoring incremental delivery and continuous feedback. Product teams learned to ship smaller increments, validate with real users, and adjust quickly before a wrong assumption lead to technical debt. This was luckily my day-to-day in my first full-time role.
But here is what is happening with vibe coding these days and where I see similarities:
- Waterfall had big spec documents → Vibe coding has giant AI prompts (PRDs)
- Waterfall meant months of coding → AI often generates everything at once
The timeline is compressed. The structure is the same. You get great velocity without adaptability (ever tried to easily change stuff without AI after the initial creation?), which honestly feels worse because it tricks you into thinking you are being agile.
Here is why this matters. I read about three different startups in the same week that failed this way. They showed beautiful demos to investors, didn't have a technical co-founder because "everyone can code now" and got funding, then spend months stuck when users wanted something slightly different. One team rewrote their entire app four times because they could not adapt the AI‑generated foundation.
Speed without comprehension
Part of vibe coding’s allure lies in its immediate productivity. We feel a surge of progress as entire applications materialize in minutes. This early success, however, conceals the erosion of understanding that underpins sustainable development.
AI‑generated code often arrives with minimal inline documentation and little architectural context. Decisions about structure, dependencies, and scalability are embedded in the model’s output rather than the team’s collective reasoning. Prompts and commits serve as the only record of intent.
Similar to waterfall. where teams got buried under documentation that quickly became obsolete, vibe coding swings to the opposite extreme: functional code with almost no shared comprehension. In both cases, teams lose the confidence to change what they do not fully understand.
Feedback loops that fail to loop
Agile succeeded by insisting that feedback must not only exist but must shape the work in short, observable cycles. Build, measure, learn, adjust—repeat.
Vibe coding creates the appearance of iteration while often denying its substance. Generating new features is trivial, yet the moment feedback demands a fundamental change—a different data model, a shift in architecture, a new user journey—the linearity of the workflow asserts itself. Rewrites become more appealing than true adaptation.
This is the essence of velocity without learning. Products evolve through accumulation rather than insight, and technical debt accrues invisibly until it becomes unavoidable.
The human role in an AI‑driven workflow
AI does not remove the need for human oversight; it intensifies it. Without guidance, vibe coding drifts toward a fragile, one‑way process that accelerates short‑term output at the expense of long‑term adaptability.
Product managers and technical leads can mitigate this by insisting on a hybrid approach:
- Treat prompts as exploratory tools rather than specifications.
- Deliver in small, testable increments.
- Maintain shared understanding through light documentation and team ownership.
- Prioritize learning velocity over shipping speed -> sustainable pace comes from knowing why a feature works, not just that it does.
A sustainable approach to AI‑assisted development
The most effective teams treat AI as an accelerant, not an autopilot.
They pair the raw speed of generation with deliberate, human‑driven iteration.
They maintain tight feedback loops and protect the collective comprehension that enables true agility.
This hybrid AI development approach preserves the benefits of modern workflows while avoiding the quiet reintroduction of waterfall’s failures. It ensures that products remain flexible, comprehensible, and capable of evolving as user needs shift.
The conclusion: speed is not enough
Waterfall was not abandoned because it was slow. It was abandoned because it could not adapt.
Vibe coding risks repeating that failure at ten times the speed. Teams that allow AI to dictate the workflow will produce code quickly but understanding slowly. They will generate demos that impress, then stumble when the second or third iteration demands structural change.
The organizations that succeed will use AI as leverage, not as a replacement for thinking. They will pair generation with judgment, speed with reflection, and output with ownership.
AI can write the code.
Humans still make it a product.