Ship First Think Later
The vibe coding vs traditional MVP debate is basically Drake vs Kendrick for founders. One spends six months and a chunk of cash building the perfect product before launch. The other ships in a week, learns from real users, and iterates based on what actually works. Drake perfected the album. Kendrick won the summer.
Drake's approach (traditional software development) is highly produced, brand-first, tightly controlled. Big budget rollout. Hundreds of thousands spent on studio time, features, marketing machines. Perfect polish. Careful positioning. Launch when the algorithm says launch.
Kendrick's approach (vibe coding) is different: drop the record. Let the people decide. Iterate in real time. Let reality, streams, crowds, culture settle the argument. "Not Like Us" didn't need a three-month runway. It just needed to hit.
Building a startup used to be pure Drake energy. Raise capital. Hire a team. Spend six months building the "right" MVP. Polish the pitch deck. Coordinate the launch. Hope it lands. By the time you know if people actually want it, you've burned a lot of your runway.
Vibe coding is Kendrick energy. You're not polishing the album for months. You're dropping the track, watching the reaction, iterating live, and letting users tell you if it hits. Real software beats mockups. Real streams beat marketing budgets. Real feedback beats assumptions.
The product-market-fit race has fundamentally changed. Founders can now reach validated learning and paying customers before they've hired a single engineer or built a formal pitch deck. You shift the heaviest validation gate, talking to real users with real software, from the end of your runway to the beginning.
"Building something nobody wants kills more startups than technical debt."
Bryn Sherwood, Fractional Product Manager @ BS Consulting
Why vibe coding works for startups
Real software beats clickable prototypes. The quality of feedback you get is orders of magnitude better when users interact with something that actually works versus looking at a static design. Most people don't know what they want until they live it, i.e. Henry Ford's famous quote “if I had asked people what they wanted, they would have said faster horses.”
Eric Ries captured this perfectly in The Lean Startup and this is even more true with AI today:
"The big question of our time is not can it be built? but should it be built? This places us in an unusual historical moment: our future prosperity depends on the quality of our collective imaginations."
"True startup productivity is not just making more stuff, but systematically figuring out the right things to build."
Vibe coding accelerates that process. You frame the problem, validate the job-to-be-done , and show up to investor conversations with a waitlist or twenty paying customers instead of a concept on paper or better yet not even needing funding at all. That's a fundamentally different conversation.
Design still matters
This doesn't mean design is dead. If anything, quality UX and intentional design can be a main differentiator when everyone can ship quickly.
But here's the thing: invest in design after you've validated the core problem and confirmed you're building in the right direction for product-market fit. Once you know you're solving a real problem people care about, that's when you call in that Drake hotline bling precision, polish, intentionality.
Why? Because if anyone can vibe code a web app branding and technical excellence are now your only competitive moats. You need both speed and craft. Just not at the same time.
The hard truth on tech debt
Every developer will tell you vibe coding creates suboptimal tech stacks and debugging nightmares. They're not wrong. But here's the harder question: what's the opportunity cost of building the wrong thing?
Building something nobody wants kills more startups than technical debt I would argue. A messy codebase is fixable. A product nobody wants is not.
Early-stage startups should prioritise validated learning, then pay down technical debt once you've proven the business model. Vibe coding lets you make that trade explicitly and fast.
Case Study: Chat Genie: From car ride idea to shipped product
Here's how vibe coding actually works in practice for me. But as I wrote this article Lovable just dropped Missions structured learning paths that teach vibe coding systematically so you don't need to listen to me.
The problem
I'm obsessed with the podcast Off Menu. Long drives, I'm always listening. But weeks after an episode, I'd find myself wondering: what was that restaurant? That dish? That comedian's story? Trawling through Google or reading transcripts on the podcast website is painful. So I built Chat Genie, a conversational AI search tool for Off Menu fans.
What I built
One afternoon. One Lovable subscription. Some OpenAI API keys. I had an AI-powered search tool that lets you ask natural questions across 197 podcast episodes and roughly 48,000 transcript chunks.
Ask it "What is the best Gin Martini in London according to Ed Gamble?" and you get back a witty, context-aware answer styled after the show's hosts.
Why this matters
Ask any engineer if this is an optimal tech stack to search 48k transcript chunks and they'll sigh. But I solved the problem in an afternoon without writing a single line of code myself. More importantly, I could see if it solved anyone else's problem within days.
That's the real power. You validate the core problem today. You worry about brand, design and perfect architecture later. Instead of debating tech stacks for weeks or polishing pixels for months, you get something real into people's hands and see if they care.
Want to know more about Chat Genie. Read here.
Final thought
Vibe coding isn't a fit for every problem but it can help founders systematically figure out the right things to build with less wasted time and capital. That's better for founders, product managers, investors, and ultimately customers.
Need fractional support scaling your startup or scale up? Let’s vibe. brynsherwood.xyz
ABOUT THE AUTHOR
Bryn is a data-driven product manager with 9 years of experience building and scaling products and operations across fast-growth tech companies.
With a background spanning operations, UX, program delivery, and technical project management, he brings a rare ability to navigate both the detail of how things work and the broader picture of how they come together.
He has delivered 0 - 1 AI products, accelerated financial operations at Block through large-scale automation and tooling migrations, and built the digital foundations that helped Dineamic, a ready meal provider in Australia, scale nationally. Earlier in his career, he supported Deliveroo’s hypergrowth by scaling rider onboarding and compliance across the UK & Ireland.
Bryn thrives in fast-paced environments where clarity, alignment, and thoughtful execution are needed to turn complex problems into scalable solutions.
