Why I Told a $50K Client to Skip Bubble and Build Their 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 AI integrations, and wanted to leverage machine learning features for matching. The technical challenge was interesting, and it would have been one of my biggest Bubble projects to date.

I said no.

Not because I couldn't deliver—Bubble's AI features could absolutely handle their requirements. But because their core statement revealed a 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 the latest AI tools.

This experience taught me something crucial about AI MVP development that most startups get wrong in 2025. The constraint isn't building—it's knowing what to build and for whom. Here's what you'll learn:

• Why Bubble (even with AI features) can be the wrong first move for startups
• The real purpose of AI MVPs in the age of no-code tools
• My manual validation framework that proves AI demand before you code
• When to choose Bubble vs manual processes for AI testing
• How to structure AI startup experiments that actually matter

Let me share what I recommended instead—and how this approach has changed my entire philosophy about AI product development.

Walk into any startup accelerator in 2025 and you'll hear the same advice about AI MVPs:

• "Build fast and iterate" - Use platforms like Bubble, Lovable, or custom no-code solutions
• "AI tools make prototyping instant" - Leverage ChatGPT integration, machine learning APIs, and pre-built components
• "Test in production" - Launch quickly and let user feedback guide your development
• "The technology is democratized" - Anyone can build AI products without deep technical expertise
• "Speed to market wins" - First mover advantage matters more than perfect execution

This conventional wisdom exists because it's partially true. Bubble does make AI integration easier. No-code tools do lower the barrier to building functional prototypes. AI APIs are more accessible than ever.

But here's where it falls short: easier building doesn't equal faster validation.

I've watched startups spend months perfecting their Bubble AI workflows, only to discover that nobody wants what they've built. They treat building as validation, when building is actually just expensive assumption-testing.

The real constraint in AI startup success isn't technical complexity—it's market understanding. And platforms like Bubble can actually slow down the learning process by making it too easy to build the wrong thing beautifully.

When this client pitched their two-sided AI marketplace, they had everything except the most important thing: evidence that their specific AI solution solved a real problem people would pay for.

Their plan was textbook 2025 startup thinking:

1. Build a sophisticated matching algorithm using Bubble's AI integrations
2. Create beautiful user interfaces for both sides of the marketplace
3. Launch to see if people use it
4. Iterate based on user behavior data

The budget was there. The technical skills were available. The timeline seemed reasonable. But I recognized a pattern I'd seen before—and it never ends well.

So I asked them a simple question: "If you're truly testing market demand, shouldn't your MVP take one day to build, not three months?"

That question changed everything. Because here's what I've learned: if you're validating whether people want your AI solution, your first MVP shouldn't be a product at all. It should be your marketing and sales process.

I told them exactly that. Build the distribution and validation first. Prove demand manually. Then automate what works.

Their response? "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 where 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 landing page describing your AI solution
• Add a form where users can 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 scores
• Document the "decision rules" you use to deliver valuable 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 team members can replicate your results using your documented patterns
• Validate that users still get value from "templated intelligence"
• Introduce pricing to test willingness to pay

Phase 3: Simple Automation (Month 2)

• Use basic 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
• Track metrics: response quality, processing time, customer satisfaction

Phase 4: Platform Decision (Month 3+)

• Only if you have paying customers and documented patterns, evaluate development platforms
• Bubble works great for complex user interfaces with proven workflows
• Custom development makes sense when platform limitations would hurt user experience
• Choose technology based on validated user needs, not available features

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

For my marketplace client, this meant manually matching suppliers and buyers via email and WhatsApp, charging a small fee for successful connections. No Bubble. No AI APIs. Just human intelligence applied systematically.

The client who initially wanted the $50K Bubble marketplace? They followed my manual validation approach instead.

Here's what happened:

• Week 1: Created a simple landing page and started manually matching requests
• Week 3: Had 12 successful matches and their first $500 in revenue
• Month 2: Documented patterns in successful matches and trained a VA to handle simple cases
• Month 4: Built a simple Airtable system to track matches and automate notifications
• Month 6: Finally built a proper platform—but only after proving the core business model

Total cost to validate: $2,000 (mostly time and a VA).

Revenue before building anything complex: $8,000.

Compare that to the original plan: $50K spent before seeing a single dollar of revenue.

The manual approach didn't just save money—it revealed insights no amount of Bubble development could have uncovered. They learned that successful matches required industry expertise, not algorithmic sophistication. Their "AI" was actually human pattern recognition applied to relationship building.

When they finally did build their platform, it reflected these learnings. The result: a successful business built on validated demand, not technical capability.

This experience fundamentally changed how I approach AI startup development:

1. Technology follows validation, never leads it. The best AI MVPs start with manual processes that prove value before automating.
2. Platforms like Bubble are tools for scaling proven concepts, not for testing unproven assumptions.
3. "AI" often means "applied intelligence," which can be human expertise systematically applied before it's machine learning.
4. Speed to market means speed to learning, not speed to building complex software.
5. Manual processes reveal the decision rules that make automation valuable—skip the manual phase and you'll automate the wrong things.
6. Real market validation requires payment, not just usage. Free tools don't validate willingness to pay.
7. The constraint isn't building capability—it's understanding what capability matters. Focus on the "what" before the "how."

When you're building AI startups, remember: the goal isn't to prove you can build intelligent software. It's to prove people will pay for intelligent assistance.

Everything else—including your choice of platform—should follow from that proof.