We treat code like it’s precious.

We version it. We review it. We refactor it. We optimize it. We document it. We build entire careers around maintaining it.

But what if we’ve been wrong about what matters?

The Sacred Cow

For decades, code has been the source of truth. The specification might say one thing, but the code is what actually runs. Over time, the code diverges from the spec. The spec becomes outdated. Eventually, nobody trusts the documentation—they read the code to understand what the system does.

This makes sense when building is expensive. If it takes months to write code, you better take care of it. You can’t afford to throw it away and start over.

So we built an entire industry around code preservation:

• Version control systems to track every change
• Code review processes to maintain quality
• Refactoring techniques to improve existing code
• Technical debt management to prioritize fixes
• Legacy system maintenance as a specialized skill

But the fundamental constraint just disappeared.

The Three-Day Rewrite

A client demanded I completely rewrite a 20,000-line document classification system. Different language. Different architecture. Different database. Everything.

They wanted it in three days.

I thought they were being unreasonable. Traditionally, this would take 6-8 weeks minimum. But I had been experimenting with Claude Sonnet 4.5, so I tried it.

I delivered in three days.

Not a prototype. Not an MVP. A complete, production-ready system that replaced the original. Actually deployed. Actually processing documents.

That was my "holy fuck" moment. If I can rebuild a complete system in days, why am I treating the code as sacred?

The Paradigm Shift

Here’s what I realized: Code is just a build artifact.

Think about it. When you compile a program, you don’t carefully preserve the compiled binary and refactor it when requirements change. You change the source code and recompile.

What if code itself is the "compiled binary" and the specification is the actual source?

When you can rebuild in days instead of months, everything changes:

• Technical debt disappears - Why refactor when you can rebuild from scratch?
• Architecture decisions aren’t permanent - Wrong choice? Rebuild with the right one.
• The codebase stays clean - Every version starts fresh with current best practices.
• Learning compounds - Each rebuild incorporates everything you learned from the previous version.

This isn’t theoretical. This is how I build software now. And it only became possible in the last few months.

What This Looks Like In Practice

I’ve built four production systems in recent weeks:

• Reading comprehension app for my autistic son (now used in his classroom)
• AI prompt versioning platform
• Marketing analytics suite for a national paint company
• Multiple other applications being used by real customers

None of these are MVPs I’ll need to rebuild. They’re complete products. But here’s the key: I’m not precious about the code.

If requirements change significantly, I don’t refactor. I respecify and rebuild. It takes days, not months.

The specification is my source of truth. It’s written in precise, unambiguous language that looks more like an ISO standard than traditional requirements docs. It includes:

• Complete data models with all edge cases
• Business logic specifications with explicit validation rules
• User interface specifications with exact behavior definitions
• API contracts that both sides reference
• Component dependencies clearly mapped
• Integration requirements with error handling

Here’s the difference. Traditional requirements doc:

"Users should be able to upload documents and classify them"

My specification:

"Document Upload Component accepts files up to 50MB in PDF, DOCX, or TXT format. On upload, the system generates a SHA-256 hash for deduplication, extracts text content via Apache Tika, and queues the document for classification. If extraction fails, the system logs the error with document ID and presents the user with a retry option. Classification results are stored in the classifications table with a foreign key to documents.id and include confidence_score (float 0-1), classified_category (enum from categories table), and classification_timestamp (UTC)."

The specification is what I maintain, version, and refine. The code? That’s just the current build artifact.

The Process

Here’s the actual workflow I use:

1. Write a detailed specification I start with discovery—asking questions until I understand the complete system. Then I write a spec that’s precise enough that AI can generate working code from it. This takes days of thinking, not hours.

2. Generate the code Using AI, I generate a complete implementation from the spec. Full-stack application with database, API, frontend, deployment. This takes hours or days, not weeks or months.

3. Test with real users Deploy it. Get actual usage data. Learn what’s wrong. Not "would you use this?" feedback—actual usage patterns and failure modes.

4. Update the specification Incorporate learnings. Fix misunderstandings. Clarify ambiguities. Add missing requirements. This is where the real work happens—refining your understanding of what needs to be built.

5. Rebuild from scratch Generate a new version from the updated spec. Start fresh. No technical debt from the previous version. No compromises to work around.

6. Repeat until it’s right Each cycle takes days. You can do 3-4 complete build-test-learn cycles in the time traditional development does one feature release.

The code is disposable. The learning is permanent.

The Economics

Traditional approach to build a complete product:

• $200K-$500K in development costs
• 6-12 months timeline
• Accumulating technical debt from day one
• Each change fights previous compromises

My approach:

• $50K-$100K total investment
• 3-6 weeks to production
• Clean architecture with each version
• Changes are rebuilds, not patches

The difference isn’t just cost—it’s what becomes possible. You can afford to be wrong. You can test complete concepts, not compromised MVPs. You can iterate on your understanding of the problem, not just the implementation.

The Objections

"But 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’s about understanding system architecture at a deep level and knowing how to write specifications that can be generated reliably.

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 documents, managing real data, serving real users.

But it requires expertise. You need to know how to architect systems, how to specify them completely, and how to work with AI at this level. That’s not something you pick up from a tutorial.

"What about maintenance?"

The specification is what you maintain. When bugs appear, you fix them in the spec and rebuild. When requirements change, you update the spec and rebuild. The rebuild takes days, not months.

And here’s what’s interesting: because you’re rebuilding from a clean spec each time, bugs often disappear. They were artifacts of the previous implementation, not fundamental to the requirements.

"Isn’t this just throwing away all your work?"

No. The work is in the specification. That’s what you’re building and refining. The code is just the current expression of that specification.

When you rewrite a paragraph in an article, you’re not throwing away work—you’re improving the expression of your idea. Same principle.

The Transformation

When code becomes disposable, you make different decisions.

Architecture decisions become reversible. I built a system with a document-based database. After testing, I realized a relational model made more sense. In the old world, that’s a 6-week migration project. In this world, I updated the spec and rebuilt in 3 days.

You can be more ambitious. I built the complete reading comprehension app—not an MVP with "coming soon" features. Full question generation, adaptive difficulty, progress tracking, teacher dashboard. If I’d been wrong about the approach, rebuilding would have been cheap. But I wasn’t wrong, and my son got a complete tool that actually helps him.

Technical debt doesn’t accumulate. Each version starts clean. No archaeological layers of compromises. No "we’ll fix it later" promises. The current version is always built with current best practices.

Your codebase stays modern. Every rebuild uses the latest framework versions, current security practices, and improved patterns. You’re not maintaining code from 2019—you’re generating fresh code in 2025.

You learn faster. Test complete implementations, get real feedback, incorporate learnings into the next version. The iteration cycle is measured in days, not quarters.

What This Requires

I’m not going to pretend this is easy or that anyone can do it:

Deep architectural expertise - You need to understand systems well enough to specify them completely upfront. This comes from years of experience building production software.

Specification discipline - Writing precise, unambiguous specs is a skill. Most developers are trained to think in code, not specifications. You need to learn a different way of thinking.

AI tooling expertise - Knowing how to architect systems that AI can generate reliably isn’t trivial. It requires understanding both the capabilities and limitations of current AI.

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 quickly.

This is expertise I spent $500K developing. It’s not something you can replicate by watching a YouTube video.

The Future Is Here

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. Thinking is expensive. Strategy is expensive. Understanding what to build is expensive.

The bottleneck has shifted from implementation to specification. From coding to clarity. From building to understanding.

So spend your time on specifications. Make them precise. Make them complete. Make them your source of truth.

And treat the code as what it actually is: a disposable build artifact that expresses your current understanding of what needs to be built.

When you need to change that understanding, don’t refactor the artifact. Update the specification and rebuild.

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I spent $500K and two years proving this works. I’ve built multiple production systems this way. Real applications being used by real customers.

The code is disposable. The specification is permanent.

Once you internalize that shift, everything changes.

Still treating your code as sacred? Still refactoring instead of rebuilding? You’re optimizing for a constraint that no longer exists.