Build · founder · 8 min read

Vibe Coding Is Maturing: What Smart Founders Should Do Differently Now

The naive era of pure vibe coding is over. Here's what the community is learning, and what it means for how you build.

Published April 9, 2026 ·

vibe codingbest practicesAI toolsfounder advice

In early 2025, Andrej Karpathy described vibe coding as “fully giving into the vibes” — prompting an AI, accepting what it returns, and not worrying too much about what’s under the hood. For a year or so, this was a reasonable approach for founders who just needed something to work. If you didn’t know how to code, you had nothing to lose by not reviewing the code.

That era is wrapping up. Not because the tools got worse — they got much better. But because the gap between “demos that work” and “products that hold up” is now the thing founders are running into, and pure vibing is not getting you across it.

The good news: the shift is manageable if you know what it looks like.

What “vibe coding is over” actually means

There’s a wave of April 2026 commentary declaring vibe coding dead. Multiple Medium posts, a Harvard Gazette piece, even an academic paper (literally titled “Vibe Coding Kills Open Source”) are framing the methodology as something that ran its course.

Don’t take the hyperbole literally. What’s actually ending is a specific flavor of vibe coding: the version where you accept whatever the AI produces, move on, and figure out problems later. That approach worked fine when you were building a prototype to show investors or validate an idea in a weekend. It breaks down the moment you have real users and real data and real edge cases.

What’s replacing it isn’t “go back to writing code yourself.” It’s a more structured version of the same workflow — one that adds a few deliberate checkpoints that non-technical founders can actually execute without engineering skills.

The shift in how teams are building

Talk to founders who’ve been shipping with AI tools for more than six months and you’ll hear a consistent pattern. The ones who built something sustainable changed how they work, usually in one of four ways.

Planning before prompting

The worst vibe coding mistake is opening a builder and typing “build me a SaaS.” The AI will generate something. You’ll iterate on it. Three weeks later you’ll have a codebase you don’t understand, with a data model that doesn’t match how your users actually behave, and changing anything will feel like defusing a bomb.

Teams shipping reliably in 2026 are writing one page of intent before they prompt anything. Not a full product spec — just enough to answer: what does this do, who is it for, what are the three most important flows, and what does the data look like? When you have that, the AI’s output is oriented by something real instead of generating its best guess at what you might mean.

This isn’t a coding skill. It’s a PM skill. You already have it.

Reviewing AI output as a product person, not a developer

You don’t need to understand every line of code the AI produces. But you do need to be able to test every user-facing behavior it claims to have implemented.

The new discipline is treating AI-generated code the same way you’d treat a contractor’s deliverable: you don’t review the wiring, but you do test all the switches. Every time you accept a chunk of AI output, your next step is clicking through the thing it just built and verifying it behaves the way you expected. If it doesn’t, the AI has been lying to itself about what it built — which happens more than the marketing materials admit.

This takes about ten minutes per major change. The founders who skip this step are the ones who end up with features that work in demos and fail in production.

Building incrementally rather than in one shot

One of the most persistent Hacker News threads this month is titled “Vibe Coding: One Prompt to Build, One Day to Fix.” The title is the lesson.

The temptation with modern AI builders is to describe your whole application in one long prompt and let the AI generate everything at once. This produces something impressive to look at and fragile to maintain. When things go wrong — and they will — you have no way to isolate where the problem is.

The discipline that works: build one component, test it, understand what you have, then describe the next component. This feels slower. It isn’t. The debugging time you avoid more than compensates.

Treating security as a first-class concern

AI-generated code is not secure by default. This isn’t a criticism of any particular tool — it’s how AI-generated code works. The model optimizes for “does this seem correct” rather than “is this resistant to attack.”

The practical implication for non-technical founders: anything involving user authentication, payment processing, or storage of personal data needs human review before it goes live. Most good no-code tools now have built-in security reviews as part of their deployment flow — use them. If you’re using a lower-level builder that doesn’t, pay a developer to spend a few hours looking at it before you launch. It’s cheaper than the alternative.

What the community is arguing about

The most interesting debate in the AI coding community right now is whether vibe coding is becoming a liability rather than a shortcut. A few different camps have formed.

The skeptics point to the open source ecosystem: maintainers of major projects are closing external PRs because they’re flooded with AI-generated contributions that pass a quick read but introduce subtle bugs. Daniel Stenberg (cURL) shut down his bug bounty program because 20% of submissions were AI-generated. The argument is that vibe coding is creating a debt that the engineering community will spend years paying back.

The pragmatists — mostly the founders and PMs reading this site — point out that they’re not maintaining open source infrastructure. They’re building products. The trade-off calculus is different: a small amount of technical debt is acceptable if it means you shipped and validated instead of spent six months building perfectly. The problems of vibe coding at scale don’t apply to founders building their first product.

Both positions are correct, just for different audiences.

The tools are catching up

Part of why the methodology is maturing is that the tools themselves are adding the guardrails that pure vibing was missing.

Lovable added security audits and Supabase Row Level Security guides directly into the build flow after a high-profile security incident earlier this year. Bolt added a staging environment so you can test before you push to production. Cursor 3 introduced an Agents Window that explicitly shows you what the AI is doing across multiple tasks — making the “black box” problem of AI coding more transparent.

The tools are converging on a shared assumption: you, the builder, need to stay in the loop. The role of the AI is shifting from “write code for you” to “write code with you watching.”

For non-technical founders, this is actually an easier relationship than pure vibe coding. You’re not expected to understand the code. You’re expected to understand what it should do, verify that it does it, and flag when it doesn’t.

What you should do starting now

If you’re in the middle of a build, here’s the shortest version of what changes:

Before your next session, write down what you’re about to build in one paragraph. Include the behavior you expect, the data it will touch, and the edge cases you can think of. Prompt from that document, not from your head.

After each session, test every behavior the AI changed. Not just the thing you asked for — all the adjacent things it might have touched. AI systems frequently “fix” one problem by creating another.

Once a month, look at your authentication and payment flows with fresh eyes. If something looks complicated in a way you don’t understand, ask a developer to take a look. This is cheap insurance.

That’s it. You don’t need to learn to code. You need to stop treating AI output as automatically correct and start treating it as a first draft from a very fast, occasionally overconfident junior developer.

The ceiling of what you can build with AI tools is higher than ever. Getting there requires just enough structure to keep the vibe from drifting.