From Spreadsheet Chaos to AI-Powered Insights: A 30-Day Vibe Coding Reality Check

Why founders can’t live without Excel—and why they should

Reading time: 7 minutes
Best for: Builders experimenting with AI development, founders tired of manual financial modeling

The Problem Nobody Talks About

Walk into any pitch meeting and you’ll witness the same scene: A VC asks “What happens if churn drops 2%?” The founder’s face goes blank. The answer lives somewhere in a 47-tab Excel nightmare. Three hours of hunting through formulas. One small data entry error. One circular reference that crashes everything.

This isn’t a quirk—it’s the norm. Most early-stage companies still rely on spreadsheets for financial forecasting, and founders universally despise the experience. The math is simple but painful: complex models take days to build, hours to update, and seconds to break.

The problem deserves better. This is what inspired one developer to spend 30 days attempting the impossible—building a financial advisor tool using vibe coding and AI, while documenting every mistake, insight, and lesson learned.

The 30-Day Experiment: By The Numbers

The Setup:

• Duration: 30 days of continuous vibe coding
• Platform: Cloud-based development environment
• Total investment: $127 in platform credits
• Lines of code generated: ~3,500 (mostly AI-assisted)
• Iterations and rollbacks: 73 times

The Results:

• Initial interest expressed: 23 founders
• Actual signups: 2
• Completed onboarding: 3
• Willing to pay: 1
• Revenue generated: $0 (validation: $50/month commitment)

The Scope:

• Target users: Pre-seed to Series A founders
• Core problem tackled: Financial model updates taking hours
• Solution attempted: AI-powered financial advisor
• Key metric tracked: Calculation accuracy

Week 1: The Honeymoon Meets Reality

The initial vision was ambitious: real-time financial dashboards, seamless data sync with accounting software, scenario planning on demand, investor-ready exports in seconds. The timeline seemed reasonable: 2-3 weeks to launch.

It wasn’t.

The first week exposed three critical oversights:

Oversight #1: Parallel Processing Doesn’t Work Submitting multiple instructions simultaneously to an AI agent creates confusion. Asking for dark mode, bug fixes, and performance improvements in one prompt resulted in a Frankenstein product that delivered none of them well. The fix: one instruction at a time, wait for completion, then assess results.

Cost: 6 rollbacks, $23 in credits, 3 hours lost

Oversight #2: UI Complexity Isn’t Trivial A simple request for “night mode” triggered 47 unintended changes. White text on white backgrounds. Invisible buttons. Font mismatches that required manual pixel-level adjustments. UI implementation consumed 3 additional weeks than anticipated.

Oversight #3: Vague Instructions Generate Expensive Mistakes Saying “make it more intuitive” without specifics led to complete layout restructures. Precision became the difference between $2 iterations and $50 iterations. A detailed prompt describing exact colors, dimensions, and positioning eliminated ambiguity.

The breakthrough moment came from discovering a single instruction that transformed the entire workflow: “Don’t make any changes without confirming your understanding with me first.”

This phrase alone could have prevented $50+ in wasted credits through unnecessary iterations.

The Middle Stretch: When Things Break

Mid-project complications emerged during week two. Traveling with unreliable WiFi made debugging TypeScript errors nearly impossible on mobile devices. The rollback feature became indispensable—sometimes reverting 12 times in a single day when experimental features cascaded into multiple system failures.

By day 15, credit spending had accelerated dramatically. Week 1 consumed $34; Week 2 reached $93. Each iteration cost between $2-5 depending on complexity. This led to establishing a weekly budget ceiling: exceed it, and pause for strategic reflection.

The Calculation Crisis

The turning point came when testers discovered a critical flaw: financial calculations were wrong by approximately 20%. A customer acquisition cost showed $47 when the correct answer was $58.75—a variance that could derail funding rounds.

The culprit: AI made unstated assumptions about terminology. “Monthly churn” sometimes meant annual rates. “Customer lifetime value” calculations used invented formulas instead of standard methods.

This led to one essential principle: Always validate AI outputs manually. A parallel spreadsheet for verification became standard practice. Vague prompts like “calculate LTV” were replaced with surgically precise instructions:

Calculate LTV as: (Average Revenue Per User × Gross Margin) / Monthly Churn Rate

Where:

• Average Revenue Per User = Total MRR / Active Customers
• Gross Margin = (Revenue - COGS) / Revenue
• Monthly Churn Rate = Churned Customers This Month / Active Customers Start of Month

Show calculations step-by-step.

With precision, accuracy improved dramatically.

User Feedback Changes Everything

After two weeks of building, the first group of beta testers provided brutal but illuminating feedback:

• Calculations were inaccurate by significant margins
• Export features crashed with datasets over 50 rows
• Core features were buried under navigation layers
• Users completed only 0% onboarding rate despite initial interest

One feedback comment proved transformational: “I don’t want another financial model tool. I want someone to tell me if my numbers make sense.”

This single insight reframed the entire product direction. The tool wasn’t a better spreadsheet—it was an advisor. Not another financial modeling app, but an AI consultant that validates assumptions, flags unrealistic projections, benchmarks against industry standards, and answers “what if” scenarios.

The pivot eliminated complexity. Instead of building enterprise integrations, advanced version control, and multi-user collaboration, the minimum viable product focused on:

• Manual financial model input
• AI-powered validation and benchmarking
• Basic scenario planning (3 scenarios maximum)
• Natural language question-answering about financial metrics
• Export to common formats

The Technical Obstacles

Three major technical limitations became apparent:

Language Selection Regret: Starting with TypeScript instead of Python created friction. Type errors consumed hours of debugging time. Future projects needed language selection based on actual developer comfort, not popularity.

Integration Promises vs. Reality: Founders kept asking about QuickBooks synchronization. The reality: OAuth 2.0 flows, webhook validation, data mapping, error handling, token refresh logic, and accounting rules validation. This wasn’t a vibe-coding task.

Precision in Financial Calculations: Complex financial formulas—cohort retention curves, NPV calculations, customer lifetime value—pushed AI assistance to its limits. “Easy” prompts generated confident but incorrect outputs. Only hyperspecific instructions with explicit formulas produced reliable results.

The Pivot Decision

By day 28, scaling back proved necessary. The full vision was simply too complex for rapid prototyping. The core MVP launched with:

✅ Manual financial model builder
✅ AI advisor for benchmarking validation
✅ Basic scenario planning
✅ Export functionality
✅ Natural language Q&A

❌ Real-time integrations (deferred)
❌ Advanced collaboration (deferred)
❌ Enterprise security (deferred)

Sometimes less is more.

What Worked, What Didn’t, What’s Ahead

Key Principles That Stuck

1. Surgical Precision Beats Vague Instruction “Make it better” → Waste. “Change button to #0066CC, increase font to 16px, add 8px padding” → Success.

2. Sequential Updates Over Parallel Changes Give one instruction. Wait. Review. Proceed. Never multitask the AI agent.

3. Manual Validation Is Non-Negotiable Never trust AI calculations without independent verification, especially in financial contexts.

4. Rollback Liberally Without Guilt 73 rollbacks in 30 days meant rapid iteration without fear. Reverting is faster than debugging.

5. Users Know What They Need The winning insight came from listening: “Tell me if my numbers make sense” became the product strategy.

What Would Change Tomorrow

If starting fresh, priorities would shift:

1. 10 user interviews BEFORE building anything—Discover the “advisor not tool” insight on Day 1, not Day 21
2. Choose Python over TypeScript—Language comfort matters more than framework popularity
3. Hard credit budget of $200-300—Forces better prompt engineering and prevents iteration death spiral
4. Manual process first, automation second—Validate demand before building integrations
5. Two-week MVP deadline—Prevents feature creep, forces prioritization

What To Skip Entirely

• Night mode (nobody requested it; consumed 3 days)
• Perfect UI (founders prioritize function over aesthetics)
• Integration promises (validate manual workflows first)
• Advanced features (get 10 paying users before expanding)

The Path Forward

Success doesn’t mean perfection—it means one founder saying they’d pay $50/month for the simplified version. That’s validation.

The realistic roadmap:

Phase 1 (Weeks 5-8): Validate core value proposition with vibe coding. Target: 10 paying customers at $50/month. Success markers: <10% monthly churn, NPS >40.

Phase 2 (After 50-100 customers): Graduate to traditional development. Hire fintech developers. Build integrations. Add enterprise features. Budget: $50K-100K.

When Vibe Coding Reaches Its Ceiling

Where it excels:

• Rapid prototyping (weeks vs. months)
• CRUD operations
• AI API integrations
• Export functionality
• Landing pages
• Fast iteration cycles

Where it hits walls:

• Complex financial formulas (NPV, cohort retention curves)
• Enterprise API integrations (OAuth, webhooks)
• Background data synchronization jobs
• Multi-tenant security architecture
• Performance optimization (<300ms queries)
• Real-time collaboration

The graduation threshold: When 10+ paying customers request features vibe coding fundamentally cannot deliver.

Lessons For Any Builder Experimenting With AI Development

Before starting:

• Choose a language you actually understand
• Set a weekly credit budget and honor it
• Define “done” in writing
• Find 3 real testers (not interested observers)
• Interview 10+ potential users first

While building:

• One prompt per iteration; wait for completion
• Define vague terms (“intuitive,” “clean,” “simple”) explicitly
• Validate all calculations independently
• Track daily spending
• Screenshot working versions before major pivots

When to step back:

• Same error persists after 5 attempts
• You’re explaining more than building
• Test users can’t complete core workflows
• Enterprise feature requests keep surfacing
• Credits spent exceed $200 without paying users

The Bottom Line:

Vibe coding delivered a working MVP in 30 days for $127. It proved the core problem (founders hate Excel) and discovered the core solution (they need an advisor, not another tool). It failed to deliver calculation precision and discovered that AI struggles with financial formula specificity.

Most importantly: One founder willing to pay validated the entire experiment.

The journey continues beyond Day 30. The next phase focuses on converting validation into revenue, scaling from idea to sustainable product, and knowing when to graduate from rapid prototyping to professional development.

Kill the 47-tab Excel model. Every founder deserves real-time financial intelligence, AI explanation, instant scenario planning, and investor-ready exports. The tools exist. The question is whether founders will actually use them.

Day 31 starts tomorrow.