Where Vibe Coding Sits

Vibe coding — or creating clickable, coded UI prototypes through natural language prompting using an AI coding assistant — is a genuinely impressive capability. The tools and speed are real, and they've made the UX community ask: does every designer need to learn how to vibe code?

It's a good question. But in a typical pre-AI UX Design workflow, prototyping would happen as part of a "Develop" phase, after a team has discovered, defined, validated, and prioritized a problem that, if solved, would deliver value to customers. Said differently, prototyping in a pre-AI UX Design workflow wasn't seen as a sufficient starting point.

Most vibe coding tutorials skip the difficult part — determining if the problem you're solving is real, and whether solving it would actually deliver value to customers.

Vibe coding can absolutely accelerate the concept-to-code timeline. The question is whether the team has aligned on what they should be building before they start.

What Comes Before — And Why It's Critical

Discovery and problem framing are arguably the two most important things a cross-functional team can do together. Not because it's a ritual, but because it's where you learn you have the right to build fast with confidence.

Working backwards from the customer — understanding what they actually need, not what we assume they need — is how teams using AI avoid the most expensive mistake in product development: building the wrong thing at scale.

Without discovery, a prototype is one person's vision of a solution to a problem that may or may not exist. A validated problem shared by the whole team is a completely different starting point. One gets you a demo. The other gets you a product.

Further and faster together, not faster alone. The goal isn't to slow the team down — it's to make sure that when we go fast, we're all headed in the same direction.

The Collaboration Gap

Vibe coding is often a single-player activity — one person, one AI, one vision of what the solution should be. That's a different thing than collaborative product design.

The real gap is team alignment on a shared problem. When a team vibe codes without it, you don't get a portfolio of good options — you get a collection of disconnected experiments, each potentially solving a slightly different version of a problem no one has validated.

UX Design & Research, Product Management, and Engineering. Each brings expertise and skills that complement the other.

While vibe coding didn't break that model, it emerged in isolation from it. If we treat it as a standalone workflow instead of the single piece it is, we'll miss out on what a full Product Team makes possible.

The Full Lifecycle — A Bigger Opportunity

Early AI focus has naturally been prototyping, and designers leaned in. But what if we take a step back to think about how AI can be used in work that's unique to design?

Vibe coding is what you get when AI meets engineering-side prototyping. AI-Augmented UX Design is what you get when AI is applied across the entire design side of the lifecycle. The engineering half of the story has been written. We're still at the beginning of the design half.

That's a much bigger opportunity. And it's one that requires us to decide where AI can be of the highest and best use at each stage of the work — from discovery through delivery and beyond.

AI Where It Matters Most

A few high-leverage moments immediately stand out when thinking about AI augmentation in a design workflow.

Research synthesis: qualitative data that used to require significant time to analyze can be pattern-matched at a fraction of the cost. Personas that once required multiple research rounds can be sketched quickly and sharpened with each new insight. The skills shift from processing data to asking better questions of it.

Problem framing: AI can help challenge assumptions, surface edge cases, and stress-test a problem definition before the team commits to a direction. That doesn't replace the designer's judgment, it gives them a sharper tool for exercising it.

The goal is an AI assistant that shows up at the right moments in the workflow, not one that replaces the workflow entirely. Measure twice, cut once — at the speed the SDLC demands.

What Stays Human

None of this replaces a stakeholder judgment call when two competing values are in tension. It doesn't replace the empathy required to understand that a user's real problem is different from what they reported. It doesn't replace the sharp eye that distinguishes a technically correct interface from one that will make people feel good when they use it.

AI-Augmented UX Design compresses everything around that human core — the research overhead, the documentation tax, the iteration cost — so designers and researchers can spend more time doing the work only they can do.

The practitioners who will define the next decade of UX are the ones who build a working relationship with AI at every stage of their workflow — while keeping their hands on the parts that matter most.

That's what AI-Augmented UX Design looks like. An AI practitioner with a broader design toolkit — helping the whole team go further and faster together, while shipping value to customers.