Spend a few minutes on developer Twitter and you’ll run into it: “vibe coding.” With a name like that, it might sound like a passing internet trend, but it’s become a real, visible part of software culture. It’s shorthand for letting AI generate code from simple language prompts instead of writing it manually.
In many ways, it’s great. AI has lowered the barrier to entry for coding, and that’s pulled in a wave of hobbyists, designers, and side-project tinkerers who might never have touched a codebase before. Tools like Warp, Cursor, and Claude Code uplevel even professional developers, making it possible to ship something working in hours instead of weeks.
But here’s the flip side: when AI can move faster than you can think, it’s easy to run straight past the guardrails. We’ve already seen how that can go wrong, like with the recent Tea app breach, which shows even polished, fully tested code can hide critical vulnerabilities if humans don’t review it thoroughly. Optimizing for speed over clarity lets AI produce something that works in the moment, but without understanding it, you can’t know what might break later. This isn’t just technical debt anymore; it’s a risk to customer trust.
The instinctive reaction to solve this trade-off is to throw more tech at the problem: add automated scans, add a “secure by default” setting. Those things matter. But I’d argue that failure in vibe coding doesn’t start with tooling, it starts with leadership. If you don’t lead your team through this new way of working, they’ll either move too slow to benefit from AI or move so fast they start breaking things in ways a security checklist can’t save you from.
The real job is steering, not slowing down
When we built agentic coding agent Warp 2.0, we put a simple mandate in place: “Use Warp to build Warp.” That means every coding task started with prompting an AI agent. Sometimes it nailed it in one shot; sometimes we had to drop back to manual coding. But the point wasn’t dogma, it was to force us to learn, as a team, how to work in an agent-driven world.
We learned quickly that “more AI” doesn’t automatically mean “better.” AI can write a thousand lines of plausible-looking code before you’ve finished your coffee. Without structure, that’s a recipe for brittle, unmaintainable systems. The real challenge was getting people to treat AI-generated code with the same discipline as code they wrote themselves.
That’s a leadership problem. It’s about setting cultural norms and making sure they stick.
Three things leaders need to get right
1. Hold developers accountable
The biggest mental trap is treating the AI as a second engineer who “owns” what it wrote. It doesn’t. If someone contributes code to a project, they own that code. They need to understand it as deeply as if they typed it out line by line. “AI wrote it” should never be an excuse for a bug.
Leaders can’t just say this once; they have to model it. When you review code, ask questions that make it clear you expect comprehension, not just functionality: “Why does this query take so long to run?” “What happens if the input is null?” That’s how you set the standard that understanding is part of shipping.
2. Guide AI with specifics
Using large, one-shot prompts is like cooking without tasting as you go: sometimes it works, but usually it’s a mess. AI is far more effective when you request small, testable changes and review them step by step. It’s not just about quality, it also builds a feedback loop that helps your team get better at prompting over time.
In practice, this means teaching your team to guide the AI like they’d mentor a junior engineer: explain the architecture, specify where tests should live, and review work in progress. You can even have the AI write tests as it goes as one way to force smaller, verifiable units of work.
3. Build the review culture now
In AI workflows, teams move fastest when AI and humans work side by side, generating and reviewing in small steps. The first draft of a feature is the most important one to get eyes on. Have someone review AI-generated work early and focus on the big-picture questions first, like whether it’s secure, reliable, and solves the right problem.
The leadership challenge is making reviews a priority without slowing anyone down. Have teams aim to give feedback in hours, not days, and encourage finding ways for work to keep moving while reviews happen. This builds momentum while creating a culture that values careful, early oversight over rushing to get something done.
Guardrails only work if people use them
Safety tools and checks can help catch mistakes, but they don’t replace good habits. If a team prioritizes speed over care, AI guardrails just get in the way, and people will find ways around them.
That’s why the core of leading in the AI era is cultural: you have to teach people how to integrate AI into their workflow without losing sight of the fundamentals. The teams that get this right will be able to take advantage of the speed AI enables without bleeding quality or trust. The ones that don’t will move fast for a while, until they ship something that takes them down.
Vibe coding isn’t going away, and I think that’s a good thing. So long as teams lead with people, not just technology, they will come out ahead and create better experiences for users along the way.
