A tweet in February coined a phrase that rippled through the developer world: "vibe coding." Andrej Karpathy used it to describe a new habit where people talk to powerful language models, hand over intent, and watch software appear. Fast forward to year-end reflections and conference stages, and "vibe coding" is no longer a novelty — it’s a lens for a much bigger debate about what software should be and who should make it.
Two competing instincts
There are two honest, conflicting takes on this shift. On one side you have evangelists and everyday makers who celebrate the speed and creative possibility. Business Insider reported Karpathy saying vibe coding makes programming feel "free, ephemeral, malleable, discardable after single use," and that it will "terraform software and alter job descriptions." The implication is liberating: more people can build things, iterate fast, and ship ideas that previously would have languished in backlog.
On the other side are engineers and toolmakers sounding caution. Michael Truell, CEO of Cursor, told an audience at Fortune's Brainstorm AI conference that a purely vibe-driven workflow — where you never look under the hood — risks building "shaky foundations." His house-under-construction metaphor captures the anxiety: mockups and prototypes are fine, but when the wiring and plumbing matter, blind trust in an assistant can lead to fragile systems.
Both views are true, and both matter.
What changes — and what doesn’t — when AI writes code
Vibe coding shifts the balance of work. The early phases of idea-to-prototype now compress dramatically: teams and individuals can produce functioning interfaces, prototypes, or scripts in minutes. This echoes coverage from outlets tracking tools that let you generate full features inside browser-based dev environments. But generating code is only part of the lifecycle. As Indian coverage of developer-platforms argued, "only 30% of software engineering happens on the laptop; the real 70% starts after you commit the code." Things like testing, observability, integration, and maintenance remain human-heavy and often brittle.
Empirical studies muddy the triumphalist narrative further. A July METR study found that experienced developers sometimes saw productivity drop with coding assistants, and became overconfident in what the models produced. That suggests vibe coding can speed the first steps while creating debt that slows teams later.
Who benefits — and who bears the cost?
Vibe coding democratizes. Non-technical makers can prototype and, in some cases, ship products — Jack Dorsey vibe-coded a messaging app this year, Business Insider noted. Companies save on time-to-market and can explore more ideas with the same headcount.
But the cost is uneven. Enterprise codebases, safety-critical systems, and long-lived products are more sensitive to the "shaky foundations" Truell warned about. Organizations will need new practices: better model-grounding, stricter code review for AI-generated contributions, comprehensive testing pipelines, and clearer ownership of AI-produced artifacts.
The debate also surfaces a cultural shift. As Korea JoongAng Daily argued in an opinion piece, the key question moves from technical skill to judgment: where do we deploy these tools, and what kinds of human reasoning still matter? In a landscape where intention and strategic meaning often trump syntax, leaders who can frame valuable problems will be at an advantage.
Toolmakers are responding — and the platform stakes are rising
Companies building dev tools are trying to thread the needle. Cursor integrates generative models into the editor so suggestions are contextual and inspectable; its CEO emphasizes the ability to both "dive into the nitty gritty" and step back when appropriate. Other players are pushing agentic workflows and deeper integrations: Google’s broader AI agenda now includes features that let models act across apps, not just autocomplete code. If you’re curious how those agentic capabilities are showing up in productivity apps, recent reporting on Google’s AI Mode and agentic booking features is a useful window into the direction platforms are taking. And Google’s attempts to ground search and workspace features under the Gemini umbrella — for example, Gemini Deep Research that reaches into Gmail and Drive — illustrate how foundation models are migrating from single panes to full workflows.
Expect more integration: code assistants will link to CI, pull-request checks, observability dashboards, and compliance tooling. The era where an AI spits out a function and nobody asks questions will pass, replaced by AI-powered checks that surface risk and provenance.
Practical guardrails for teams
A few pragmatic rules are emerging in companies that wrestle with both speed and safety:
- Treat AI suggestions like drafts, not shipping code — insist on human review and test coverage.
- Track provenance: which model version produced a snippet and what prompt led to it?
- Build observability around newly generated logic so runtime errors and edge cases are visible quickly.
- Institute periodic refactors: throw away throwaway code once it has served its purpose, and harden long-lived paths.
These are straightforward, but require discipline. They also reframe roles: product designers, QA engineers, and system owners take on more responsibility for shaping and policing the output of models.
Vibe coding is a real technical and cultural event. It lowers the barrier to try, but it also raises the bar for stewardship. If you treat AI output like a magic trick, your house might look nice from the street — until you flip the lights and the wiring fails. If you treat it like a draft that needs craftsmanship, you can iterate faster without inviting collapse. The question for organizations now is which habit they’ll choose to form.