I Don't Build MVPs Anymore. Here's What I Build Instead.
MVPs create technical debt from day one. There's a better way—and it's faster and cheaper than you think.
I don't build MVPs anymore.
Not because I'm being difficult. Not because I don't understand lean startup methodology. I spent 20 years building software the traditional way. I founded and sold two companies. I was CTO of a fintech startup that grew to $30M ARR.
I understand MVPs. I just don't build them anymore.
Because MVPs are fundamentally broken.
The Problem With MVPs
The "minimum viable product" made sense when it was invented. Building software was expensive and time-consuming. You couldn't afford to build your complete vision, so you built the minimum, learned from it, then iteratively added features.
It was the right solution for the constraint at the time.
But this approach creates massive problems:
1. Technical debt from day one
You're building a foundation that wasn't designed for where you're going. Every feature you add has to work around the limitations of your MVP architecture. By version 10, you have an archaeological dig of compromises.
I've seen this firsthand. As CTO, we launched an MVP payment processing system. Six months later, we needed to support international transactions. The MVP architecture couldn't handle it. We spent three months and $200K rebuilding what should have been designed right from the start.
2. Compromised user experience
Users don't experience your vision—they experience a half-baked product with "coming soon" features. You're asking them to imagine what it could be, rather than showing them what it is.
The reading comprehension app I built for my son? If I'd shipped an MVP, he would have gotten basic flashcards. Instead, he got adaptive difficulty, progress tracking, and a teacher dashboard. The complete experience is what makes it valuable.
3. The rebuild trap
Six months in, you realize the MVP architecture can't support where you need to go. Now you face the painful choice: rebuild from scratch (and waste all that time) or keep patching (and accumulate more debt).
Most teams choose to keep patching. That's how you end up with systems nobody understands and technical debt that takes years to pay down.
4. Feature addiction
Teams get addicted to shipping features. Each release adds another service, another integration, another patch. Nobody can navigate the labyrinth you've created. There's no coherent vision—just accumulated features.
The real problem: We kept building this way even after the constraint disappeared.
What Changed
Building software used to be expensive. It's not anymore.
I spent $500K and two years proving you can fundamentally change how software gets built. Not "using AI to code faster"—understanding how to architect complete systems that can be generated reliably at production quality.
Then Claude Sonnet 4.5 came out in October 2024, and everything clicked.
A client demanded I completely rewrite a 20,000-line document classification system in three days. Different language, different architecture, different database. I thought they were being unreasonable. Traditionally, this would take 6-8 weeks minimum.
I delivered in three days. Complete, production-ready system. Actually deployed. Actually processing documents.
That was my "holy fuck" moment.
If I can build a complete system in days, why would I build an MVP?
What I Build Instead
Here's the fundamental shift: I iterate on concepts, not code.
The old way: Build MVP → Launch → Add features → Patch architecture → Accumulate debt → Eventually rebuild
The new way: Plan complete product → Build in days → Test → Learn → Rebuild complete product → Repeat until right → Launch the real thing
Here's how it works:
1. Plan the complete product
Not an MVP. Not "phase 1." The entire system with all the features you actually want. This takes days of thinking—understanding the complete problem space, all the edge cases, the full user journey.
2. Build it in days
Generate the complete implementation using AI. Full-stack application with database, API, frontend, deployment. This takes days, not months. Cost: a few hundred dollars of compute.
3. Test it with real users
Deploy the complete product. Get actual usage data. Learn what's wrong. Not "would you use this?" feedback—real usage patterns, actual failure modes, concrete insights about what works and what doesn't.
4. Learn and replan
Here's where the magic happens. You're not patching the MVP—you're updating your understanding of what needs to be built. Maybe the workflow is wrong. Maybe users need different features. Maybe the entire approach should change.
5. Rebuild the complete product
Generate a new version from scratch. Incorporate all your learnings. Start with a clean architecture designed for where you're actually going, not where you started.
6. Repeat until you're right
Each cycle takes days, not months. You can do 3-4 complete build-test-learn cycles in the time traditional development does one MVP launch.
The code is disposable. It's just a build artifact. The learning is what's permanent.
When you finally launch, you launch the complete, coherent product. Not a compromised MVP you'll need to rebuild in six months.
What This Looks Like
Reading comprehension app for my autistic son:
I didn't build an MVP with basic flashcards and promise to "add features later." I built the complete system: question generation from any text, adaptive difficulty that adjusts to his level, progress tracking so his teacher can monitor growth, and a dashboard showing which concepts he's mastering.
If I'd been wrong about the approach, I could have rebuilt in days. But I wasn't wrong. And my son got a complete tool that actually helps him, not a prototype he'd outgrow.
Marketing analytics suite for a national paint company:
They needed to understand their marketing spend across channels. I didn't build an MVP that tracked one channel and promise to add others later. I built the complete data pipeline: all channels, custom dashboards, automated reporting, forecasting models, budget allocation recommendations.
The complete system is what creates value. An MVP would have been a toy.
AI prompt versioning platform:
Version control, testing framework, deployment pipeline, analytics, collaboration tools. All of it. From day one. Not "we'll add that in v2."
These aren't MVPs I'll need to rebuild. They're complete products being used by real customers right now.
The Economics
Let's talk about what this actually costs.
Traditional MVP approach:
- $100K-$200K to build MVP (3-6 months)
- Launch, get feedback, realize architecture is wrong
- Another $100K-$300K rebuilding (6-12 months)
- Total: $200K-$500K, 9-18 months
- Plus opportunity cost of delayed launch with real product
My approach:
- $50K-$100K total investment (3-6 weeks)
- Multiple complete build-test-learn cycles in that timeframe
- Launch with the real thing, not a prototype
- Total: $50K-$100K, 3-6 weeks
But here's what really matters: time to market with the actual product.
Traditional approach: 9-18 months before you have the real thing
My approach: 3-6 weeks before you have the real thing
That's 6-15 months of runway saved. For a startup, that's the difference between success and running out of money.
The Objections
"But you need it in users' hands to know what they want!"
Exactly. So put the complete product in their hands.
You'll get better feedback from a coherent system than from a half-baked MVP. Users can actually use the complete product and tell you what's wrong. With an MVP, they're imagining what it could be—that's not real feedback.
The difference: I'm testing complete implementations, not prototypes. The insights are real because the product is real.
"This sounds like waterfall. Waterfall doesn't work."
This isn't waterfall. Waterfall was: plan for months, build for months, launch once, hope you got it right.
This is: plan for days, build for days, test with real users, learn what's actually wrong, replan based on reality, rebuild in days. Rapid iteration on complete systems.
I can do three complete build-test-learn cycles in the time traditional development does one MVP launch. That's not waterfall—that's faster iteration than agile.
"AI can't write production-quality code!"
You're right to be skeptical. This isn't about prompting ChatGPT and hoping for the best.
It requires deep architectural expertise—understanding systems well enough to specify them completely. It requires knowing how to write specifications that AI can generate reliably. It requires understanding both the capabilities and limitations of current AI tools.
I spent $500K and thousands of hours figuring out how to do this at production quality. The systems I'm building aren't toys—they're processing real data, serving real users, running real businesses.
But yes, it requires expertise. Not everyone can replicate this. That's the honest truth.
"What if you build the wrong thing?"
Then you rebuild. That's the entire point.
The cost of being wrong is days, not months. You can afford to test bold ideas because rebuilding is cheap. You can be ambitious because the risk is manageable.
Traditional development can't afford to be wrong—so you compromise, build safe MVPs, and never test your real vision. You're optimizing for risk avoidance instead of learning speed.
I'm optimizing for learning speed. Build fast, learn what's wrong, rebuild with those insights. The cost of being wrong once is less than the cost of building on a compromised foundation for years.
What This Requires
I'm not going to pretend anyone can do this:
Deep architectural expertise - You need to understand systems well enough to specify them completely upfront. This comes from years of building production software, understanding how pieces fit together, knowing where the complexity hides.
Specification discipline - You need to write precise, unambiguous specs that cover the complete system with all edge cases. Most developers are trained to think in code, not specifications. This is a different skill.
AI tooling expertise - Knowing how to architect systems that AI can generate reliably isn't trivial. It requires understanding what AI is good at, what it struggles with, and how to structure specifications for reliable generation.
Strategic thinking - You need to understand what to build, not just how to build it. Business context matters more than code. You're making product decisions, not just technical decisions.
Willingness to rebuild - You have to overcome the psychological attachment to code. It feels wasteful to throw away working code—but it's not if you can rebuild it in days.
This is expertise I spent $500K and thousands of hours developing. It's not something you pick up from a tutorial.
The Transformation
When you stop building MVPs, everything changes:
You can be ambitious. Build the complete vision. If you're wrong, rebuilding is cheap. Stop compromising before you start. The reading comprehension app has features I would have cut from an MVP—but those features are what make it valuable.
You launch with pride. Show users the real product, not a prototype. No "we're working on that" or "coming soon." It's all there. Users get the complete experience, not a promise of what it could become.
You learn faster. Test complete concepts, get meaningful feedback, iterate on your understanding. The cycle is measured in days, not quarters. By the time traditional development launches their MVP, I've done three complete iterations.
You avoid the rebuild trap. You're not locked into an MVP architecture that can't scale. Each version starts clean, designed for where you're actually going. No technical debt accumulation. No "we'll fix it later" promises.
You stay coherent. Users get a unified experience, not a cobbled-together maze of features. There's a vision, not just accumulated functionality. The product makes sense as a whole, not as a collection of patches.
The Future
This isn't how most people build software today. But it's how I build software. And it's how more people will build software once they realize the constraint has changed.
Building isn't expensive anymore. Planning is expensive. Strategy is expensive. Understanding what to build is expensive.
The bottleneck shifted from implementation to understanding. From coding to clarity. From building to thinking.
So stop wasting time on MVPs that you'll need to rebuild. Stop compromising your vision before you start. Stop accumulating technical debt from day one.
Plan the complete product. Build it in days. Test it with real users. Learn what's actually wrong. Rebuild it with those insights. Repeat until you're right.
Then launch the real thing.
I spent $500K proving this works. I've built multiple production systems this way. Real applications being used by real customers.
MVPs made sense when building was expensive. Building isn't expensive anymore.
The choice is yours. Keep building MVPs and rebuilding later. Or build complete products from the start and iterate on concepts, not code.
I know which one I'm choosing. The question is: which one are you choosing?
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