Vibe coding — or describing what you want to an AI and then letting it scaffold the code for you — has been quietly rewriting senior engineers’ job descriptions. Today, veterans spend more time herding models than they do typing — wrangling know‑it-alls who ship workable drafts with head‑scratching bugs. The consensus among leaders: It’s a tax worth paying.
It’s a bit like managing a clever intern who moves at superhuman speed, then leaves you with a pull request that needs handrails. Teams receive prototypes within hours, not days. But eventually senior devs serve as AI babysitters: validating packages, disentangling logic, hardening security and making sure the code runs in sync with the system architecture.
Nearly 95% of the almost 800 developers surveyed in a 2020 study by Fastly spend extra time to fix AI-generated code, and senior engineers bear most of the verification burden.
The failure modes, when they exist for whatever system was supposed to help you out with this approach, are goofily predictable: hallucinated imports, duplicate features implemented five different ways with some gear stuck between the teeth, missing tests and subtle security regressions that would never get through a 30-second code review from a grizzled veteran developer.
Why vibe coding requires adult supervision
LLMs are trained for plausibility, not correctness. In code, that means they’re great at writing boilerplate and “obviously” doing the right thing; but intransigent when requirements collide, context isn’t fully known or to get it right involves cross cutting design. They have demonstrated that developers who use autocomplete provided by an AI assistant are more likely to produce code with security vulnerabilities, and also feel more confident about the code they write — a toxic combination without being tested thoroughly before shipping.
Security leaders caution that assistants tend to take shortcuts: loose CORS rulings, broad IAM edicts, unsafe string construction for SQL and un-pinned dependencies. Unreviewed, those shortcuts turn into production liabilities. Practitioners also observe nondeterminism—similar prompts producing different diffs—which makes reproducibility and auditing difficult.
The net is a new role coming to fruition inside teams—call it the vibe code custodian—whose job it is to intercept silent failures: configuration lines quietly removed, error handling shaved away, migrations spawned with edge cases unvalidated.
It’s not as sexy as greenfield coding, but it’s where reliability is built for the long run.
The new workflow: prompt, police, patch
Senior engineers sketch out a rebalanced day: more time experimenting with limitations and crisp requirements, an explosion of A.I.-driven generation followed by months in review-and-repair mode. A typical (in my experience) ratio is “40-60% spec-and-design, 10-20% letting agent write ‘first draft’, rest cleanup-tests-refactor-so-it-does-one-things.”
Here’s what the public stack of modern guardrails looks like: tests first alerts; ephemerality sandboxes and strict CI with coverage gates, coupled with static analysis and secret scanning and SBOM file generation or dependency pinning; human approval gating on every AI written change.
Some IT vendors, including NinjaOne, promote “safe vibe coding” policies — access controls on AI tools, peer review requirements and automated security checks — so experimental speed doesn’t get ahead of governance.
Teams are also instituting process hygiene targeted to AI work: logging prompts and diffs for audit ability, disallowing multi-file refactors from a single prompt, and tracking an “AI babysitting” metric that measures the time spent on verification and fixes. When that metric goes up, that’s a signal to shore up prompts, add architectural docs or restrict who the model gets to talk to.
Productivity gains are real — but uneven
On the bright side, you can definitely feel that pregnancy glow. GitHub research has shown that developers can complete some tasks up to 55% faster with the help of AI, and there are multiple industry studies available showing material time saving for relevant operations such as boilerplate / scaffolding generation and test writing. According to the most recent Developer Survey, AI assistance is now popular even among junior ICs (in fact, especially daily use) and fully mainstream across experience levels.
Where AI makes sense: spinning up CRUD backends, generating API clients, transcoding languages and frameworks, iterating interface polish. Where it struggles: systems thinking. If not held in check, an assistant will solve local problems that turns another crank for a particular feature and end up with the same thing being done multiple different ways – a maintenance nightmare that USB devs have to try and rationalize.
And if there is one practical takeaway from the engineering leader perspective, vibe coding can speed up “first draft” velocity and eliminate so much toil — but that last mile — making it robust, secure, intentional under the hood? – Still humans all the way down.
Security and governance catch-up
Security teams are adjusting in kind. New best practices include secret redaction and policy filtering prior to forwarding context to an assistant, fine-grained permissions for code-writing agents, and blocking of commits that include unapproved dependency changes. Organizations are mapping their AI development controls to the NIST AI Risk Management Framework and integrating AI-assisted code reviews into existing SOC 2 and ISO cadence.
Increasingly, teams keep an internal “vulnerable by default” checklist for AI code quality before pushing it to production: boundaries of authentication verification, input validation checks, error handling checks, logging levels and least-privilege configurations. That checklist is enforced mercilessly against every AI-authored pull request, however confident the assistant may sound.
How senior devs make vibe coding pay
Write the spec as if it were a contract: specify acceptance criteria, performance budgets, and failure modes that the assistant must respect. Break work into small PRs and disallow cross-cutting refactors in a single generation pass.
Lock down the sandbox: pin versions, freeze dependency updates, and restrict network calls. Demand tests (and fuzz cases) before merging; employ coverage and mutation to catch brittly logic.
Instrument everything: measure the regression rate on AI-authored code, log prompts for reproducibility and monitor “mean time to fix” for AI-induced bugs. As those signals waver, it will tweak the nudges that you receive, add in-repo design docs or decrease an assistant’s scope.
The upshot: older developers may spend more time monitoring code than writing it but the deal is worthwhile. A force multiplier vibe coding is when you have the right guardrails in place — seasoned engineers asking fewer questions, and writing more code that matters (architecture, performance, product decisions moving the needle), not boilerplate.