Vibe Coding Is Here. Value Is the Moat.

TL;DR: AI has made building fast, cheap, and good enough. The advantage is no longer how you build. It’s whether what you build actually solves something people care about. In other words: understand the customer, or get left behind.

What’s “vibe coding” anyway?

Vibe coding is building by intent. You describe the outcome and constraints in plain language, and AI generates the first version—screens, data models, workflows, copy, even code. You adjust the prompt, test with a user, and iterate. Minutes, not months.

It’s not magic. It’s a faster path from “idea in your head” to “something a customer can click.” The craft shifts from writing perfect code to writing clear intent and getting real feedback.

The shift: from How we build → What value we deliver

For decades, teams won by mastering the how: hiring great engineers, picking the right stack, building scalable systems. That still matters—but it’s no longer a moat. Today:

• Time-to-first-prototype is near-zero.

• Switching costs are falling because alternatives are one click away.

• Users judge on outcomes, not architecture.

If anyone can ship a decent v1, the differentiator becomes being right about the problem and the outcome. Put simply: value beats velocity only when velocity points at value.

Why “understanding the customer” is now oxygen

AI makes output abundant. Attention is scarce. Your product only earns attention if it removes a real pain or unlocks a real gain. That requires:

• Clear jobs-to-be-done (JTBD): What’s the moment of struggle? What are they trying to get done?

• Concrete success metrics: How will the user know it worked? Time saved? Fewer errors? More sales?

• Continuous discovery: Short, frequent conversations beat long, rare research projects.

Without this, AI just helps you build the wrong thing faster.

A simple loop: Vibe → Value → Verify

Use this loop to keep AI speed pointed at real outcomes.

1. Vibe (Intent): Write a one-paragraph product brief in plain English. Include user, job, constraints, and a “done when…” statement.

2. Value (Prototype): Ask AI to produce the smallest artifact that lets a user feel the outcome: a clickable mock, form, small script, or two-screen app.

3. Verify (Signal): Put it in front of 5 target users. Watch them use it. Measure time-to-value and ask, “What did you try to do first?” and “What felt clunky?”

4. Repeat weekly. Keep scope thin and cycles short.

Guardrail: Never do two “build” cycles in a row without a “verify” cycle. Shipping ≠ learning.

What to measure (because opinions don’t scale)

Pick simple, behavior-based metrics:

• TTV (Time to Value): Time from signup → first meaningful outcome. Aim to reduce this every sprint.

• Activation Rate: % of users who hit that first outcome.

• Task Success Rate: % of sessions where the core job is completed.

• Retention on the job: Do people come back to do the same job next week/month?

• Manual work removed: Minutes/hours saved compared to the old way.

When metrics move, you’re building value. When they don’t, you’re polishing.

The 5–5–5 customer cadence

Make learning a habit, not a phase.

• 5 interviews/week: 15–20 minutes each. Focus on recent behavior, not hypotheticals.

• 5 wins logged/week: Short notes where the product clearly saved time, money, or stress.

• 5 obstacles removed/week: Copy tweaks, defaults, shortcuts, or small automations that cut friction.

This cadence compounds. In a quarter, you’ll know your user better than competitors do in a year.

From features to outcomes: a message map

Translate intent into user-facing value before you build.

• User: “Sales manager at a 20-person startup.”

• Job: “Send weekly follow-ups without dropping balls.”

• Obstacle: “Manual tracking across 5 tools.”

• Outcome: “Everyone gets a relevant follow-up within 48 hours.”

• Metric: “Follow-up coverage ≥95%, prep time <15 min.”

• Smallest artifact: “One-page UI to import leads and auto-draft next-step messages.”

Build that first. Not an org chart, not a design system.

Patterns that win in the AI era

• Thin slices over big bangs. Ship one job end-to-end. Expand after it works.

• Defaults that make people feel smart. Pre-fill, pre-sort, pre-draft.

• Explain the magic. When AI acts, show why, with an easy “undo.” Trust is UX now.

• Human handoff by design. Let people edit drafts, approve actions, and set guardrails.

• Make it personal. Personal data + context → higher perceived value.

Anti-patterns to avoid

• Feature ladders without ladders of value. More toggles ≠ more outcomes.

• Endless refactors “just in case.” Build for today’s job. Earn tomorrow’s complexity.

• Vanity demos. If a user can’t reach the outcome, the demo is theater.

• Over-automation. If errors are costly, keep the human in the loop.

When the how still matters

Yes, there are cases where architecture is the product:

• Regulated workflows (finance, health, safety)

• Hard real-time systems (latency is the value)

• Massive scale/edge costs (pennies matter at billions of ops)

Even then, the bar for why remains the same: can the user get the promised outcome faster, safer, cheaper?

A one-week playbook to prove you’re building value

Day 1: Problem safari. Talk to 5 target users. Capture exact quotes about recent struggles. Pick one job.

Day 2: Write the vibe. One-page brief with user, job, constraints, “done when,” metric.

Day 3: Prototype with AI. Generate a thin slice. Keep it ugly but usable.

Day 4: Test with 3 users. Measure TTV. Note where they hesitate. Fix copy and defaults.

Day 5: Ship a real slice. Put it in production for a tiny cohort. Add basic logging.

Day 6: Follow the value. Watch usage. Remove 3 friction points.

Day 7: Share the story. Publish the outcome and what changed. Invite 5 more users.

Repeat. Velocity + direction beats velocity alone.

The mindset shift

• From roadmaps to evidence maps

• From requirements to jobs

• From ship dates to activation dates

• From features shipped to minutes saved

This is how small teams beat big ones in the AI era.

Close: Build with vibes. Win with value.

AI has lowered the cost of making things. It has not lowered the bar for making things that matter. The winners will be the teams who treat customer understanding like a core competency, not a quarterly task.

Your stack won’t save you. Your customer will.

Try this today

Pick one job your best users do every week. Write a 5-sentence brief. Use AI to create the smallest artifact that helps them do it faster. Put it in front of 3 users by Friday. Measure time-to-value.