Why I Told a Client to Skip Bubble and Build an AI MVP With Spreadsheets Instead

Last year, a potential client approached me with what seemed like every no-code developer's dream project: build a two-sided AI marketplace platform using Bubble. They had a substantial budget, were excited about Bubble's AI capabilities, and wanted to integrate machine learning features. The technical challenge was interesting, and it would have been one of my biggest Bubble projects to date.

I said no.

Why? Because they wanted to "test if their AI idea works" by building a full platform first. They had heard about Bubble's AI integrations, Lovable's rapid prototyping, and the no-code revolution. Technically, they could build their vision. But they had no existing audience, no validated customer base, and no proof that anyone wanted their specific AI solution.

This is the trap I see everywhere in 2025: founders think AI tools and platforms like Bubble make validation faster, when they actually make expensive assumptions faster. After working with multiple AI startups and no-code projects, I've learned that the shiniest tools often create the most expensive failures.

Here's what you'll learn from my experience with AI MVP development that actually works:

• Why your first AI "MVP" shouldn't be built in Bubble (or any platform)
• The manual validation framework I use before touching any no-code tools
• How to test AI product-market fit without training any models
• When Bubble becomes the right choice (and the red flags to avoid)
• The "Wizard of Oz" approach that saves months of development

This approach has saved my clients from building beautiful, functional AI platforms that nobody wanted.

The conventional wisdom in 2025 goes like this: AI is the future, no-code platforms like Bubble make AI accessible, therefore you should build your AI MVP on Bubble as fast as possible. The ecosystem reinforces this thinking everywhere.

YouTube tutorials show you how to integrate OpenAI APIs with Bubble in 20 minutes. No-code communities celebrate rapid AI prototypes. Platform documentation promises you can build "production-ready AI apps without coding." The message is clear: speed to market wins.

This approach treats AI MVPs like traditional software MVPs, just with fancier features. Build fast, launch, iterate. Get your AI chatbot, recommendation engine, or automation tool in front of users quickly, then optimize based on feedback.

The problem? AI products have a validation problem that no-code platforms can't solve.

Traditional software solves known problems with predictable solutions. AI products often solve unknown problems with unpredictable solutions. Users don't know if they want AI to automate their workflow until they experience it. They can't evaluate an AI recommendation engine until it learns their preferences.

Building an AI platform before understanding these nuances is like building a restaurant before knowing what food people want to eat. Bubble makes it faster to build the restaurant, but it doesn't help you figure out the menu.

When that client approached me about their two-sided AI marketplace, they were excited about everything Bubble could do. They'd researched AI integrations, studied successful marketplace templates, and planned complex user flows. They wanted to build something that would use machine learning to match supply and demand automatically.

But their core statement revealed the fundamental problem: "We want to see if our AI idea is worth pursuing."

They had no existing audience, no validated customer base, no proof of demand—just an idea and enthusiasm for AI automation. They were ready to invest months in Bubble development to "test" their concept through a fully built platform.

This is when I realized something crucial: if you're truly testing AI market demand, your MVP should take one day to build—not three months in Bubble.

Instead of taking their project, I shared what has become my standard framework for AI validation:

1. Day 1: Create a simple landing page explaining the AI value proposition
2. Week 1: Manually do what the AI would do—match supply and demand via email
3. Week 2-4: Document patterns in successful matches to understand the "intelligence" needed
4. Month 2: Only after proving manual demand, consider automating with simple tools

Their first reaction was resistance. "But that's not scalable! We want to build something with AI!" Exactly. That was the point.

The most successful AI products I've seen started as human-powered services. The "AI" was actually a person making smart decisions. Only after proving people valued those decisions did they automate them.

Here's the manual validation framework I now use with all AI startup clients before they touch Bubble, Lovable, or any development platform:

Phase 1: Human-Powered AI (Week 1-2)

• Create a simple form where users submit requests for your "AI" service
• Manually fulfill these requests using your expertise and existing tools
• Track time spent, patterns in requests, and user satisfaction
• Document the "decision rules" you use to deliver good results

Phase 2: Pattern Recognition (Week 3-4)

• Analyze your manual results to identify what makes responses valuable
• Create templates and workflows for common request types
• Test if junior team members can replicate your results using your templates
• Validate that users still get value from "templated intelligence"

Phase 3: Simple Automation (Month 2)

• Use existing tools (Airtable + Zapier, Google Sheets + Apps Script) to automate simple patterns
• Keep human review for complex cases
• Measure if automation maintains the value users experienced manually
• Only after proving this hybrid model works, consider platforms like Bubble

Phase 4: Platform Decision (Month 3)

• If you have paying customers and documented patterns, evaluate development platforms
• Bubble works great for complex user interfaces with proven workflows
• Lovable excels at rapid iteration when you know exactly what to build
• Custom development makes sense when platform limitations would hurt user experience

The key insight: your AI MVP should test willingness to pay for intelligent assistance, not ability to build intelligent software.

For my marketplace client, this meant manually matching suppliers and buyers via email, charging a small fee for successful connections. If they couldn't make that work manually, no amount of AI automation would fix the fundamental market mismatch.

Following this approach with three different AI startup clients over the past year has produced consistent results:

Time to First Paying Customer: 2-4 weeks vs 4-6 months with platform-first approach

Development Cost Reduction: 80-90% lower initial investment before validation

Product-Market Fit Clarity: Clear understanding of user needs before building complex features

Most importantly, two out of three clients discovered their original AI concept wasn't what users wanted. The manual process revealed different valuable applications of their domain expertise. They built successful products, just not the ones they originally planned.

The third client validated their original concept and built a successful Bubble app—but only after proving manual demand first.

The biggest lesson? No-code platforms are amplifiers, not validators. They make good ideas better and bad ideas fail faster and more expensively.

Here are the key insights from manual AI validation:

1. Manual beats automated for discovery: You learn more about user needs in one week of manual delivery than one month of automated analytics
2. Intelligence is often simple patterns: Most "AI" value comes from applying domain expertise consistently, not complex algorithms
3. Users pay for outcomes, not technology: Nobody wants AI—they want better results with less effort
4. Platform choice matters after product-market fit: Bubble excels when you know exactly what workflows to optimize
5. Speed to market isn't speed to revenue: Fast building without validation creates fast, expensive failures

The goal isn't to avoid Bubble or other no-code platforms. The goal is to find product-market fit before committing to any specific technology approach.