Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I had a potential client approach me with an exciting opportunity: build a two-sided marketplace platform with AI features using Bubble. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Not because the project wasn't interesting, but because they had confused building an MVP with actually testing their business idea. They wanted to "see if their idea works" by spending three months building a complex AI-powered platform. That's not validation—that's expensive wishful thinking.
This experience taught me something crucial about AI MVP development: most founders are thinking about it completely backwards. They're so excited about the technology that they forget the fundamental purpose of an MVP.
Here's what you'll learn from my approach to AI MVP design thinking:
Why your first AI MVP should take one day to build, not three months
The design thinking framework I use for AI MVP validation
How to separate AI features from core business validation
When to use Bubble for AI prototyping (and when not to)
A practical framework that saves months of development time
This isn't about dismissing AI or no-code tools like Bubble—it's about using them intelligently for actual validation rather than impressive demos.
Reality Check
What every startup founder believes about AI MVPs
Walk into any startup accelerator or browse ProductHunt, and you'll hear the same advice about AI MVP development: "Build fast, test early, iterate quickly." Sounds logical, right?
The conventional wisdom goes something like this:
Use no-code tools like Bubble to build AI features without hiring developers
Integrate AI APIs to add "intelligence" to your MVP
Launch within 4-6 weeks to start getting user feedback
Iterate based on usage data from your AI features
Scale the successful features and pivot away from what doesn't work
This approach exists because tools like Bubble, combined with AI APIs, have made it technically possible to build sophisticated applications quickly. The promise is seductive: you can have an AI-powered platform running in weeks, not months.
But here's where this conventional wisdom falls apart in practice: you end up optimizing for building features instead of validating assumptions.
Even with no-code tools, creating a functional AI MVP takes significant time. You're still dealing with user authentication, data flow, AI integration complexity, edge cases, and user experience design. By the time you launch, you've already committed to a specific solution before proving there's actually a problem worth solving.
The real issue isn't the tools—it's the mindset. Most founders treat MVP development like product development when it should be treated like hypothesis testing.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had all the classic signs of this backwards thinking. They had raised some funding, heard about the power of AI and no-code tools, and wanted to "test their marketplace idea" by building it on Bubble with AI matching features.
Their plan was textbook conventional wisdom:
3-month development timeline using Bubble's visual programming
AI-powered matching algorithm to connect buyers and sellers
Automated onboarding flows with smart recommendations
Dynamic pricing suggestions based on market data
It sounded impressive. The budget was real. But when I asked about their validation process, the cracks showed:
"We want to see if our idea is worth pursuing," they told me.
That's when I knew we had a fundamental mismatch. They had no existing audience, no validated customer base, no proof of demand—just excitement about their AI-powered solution to a problem they assumed existed.
I'd seen this pattern before with other consulting projects. Founders get so caught up in the how (the AI, the platform, the features) that they skip the why (does anyone actually want this?).
The more questions I asked, the clearer it became:
They hadn't spoken to potential users about the core problem
They had no idea what the key success metrics should be
They assumed AI would be a competitive advantage without testing simpler solutions first
That's when I made my decision to decline the project and instead share what I'd learned about real AI MVP design thinking.
Here's my playbook
What I ended up doing and the results.
Instead of accepting their project, I shared a completely different approach that I'd developed through my experience with AI automation projects and startup consulting.
The core principle: Your MVP should be your marketing and sales process, not your product.
Here's the design thinking framework I recommended:
Phase 1: Problem Validation (Day 1)
Create a simple landing page or Notion doc explaining the value proposition. No AI, no complex features—just clear communication of what problem you're solving and for whom.
Phase 2: Manual Process Testing (Week 1)
Start manual outreach to potential users on both sides of your marketplace. Use existing tools—email, WhatsApp, spreadsheets—to manually facilitate the connections your AI would eventually make.
Phase 3: Demand Validation (Weeks 2-4)
Manually match supply and demand. If you can't make this work with human intelligence and manual processes, AI won't magically fix it.
Phase 4: Technology Decision (Month 2+)
Only after proving demand manually do you start building automation. This is where Bubble becomes valuable—not for initial validation, but for scaling validated processes.
The key insight: In the age of AI and no-code, the constraint isn't building—it's knowing what to build and for whom.
For the AI components specifically, I use this hierarchy:
Manual first: Can a human do this task effectively?
Simple automation second: Can basic rules or filters handle this?
AI third: Only when you need pattern recognition or decision-making at scale
When I do use Bubble for AI MVPs, it's typically in Phase 4, after validation, to build:
Data collection interfaces that feed into AI training
Simple AI feature testing with clear success metrics
Automated workflows that replace proven manual processes
Validation First
Start with human intelligence before artificial intelligence. Prove the core value manually.
Speed Trap
Don't confuse building fast with learning fast. Three months of development teaches you less than three weeks of customer conversations.
Technology Hierarchy
Manual → Simple Rules → AI. Each level should be proven before moving to the next level of complexity.
Bubble Sweet Spot
Use Bubble for rapid prototyping of validated features, not for validating business ideas.
The results of this approach speak for themselves, both from my own experience and from startups I've advised:
Time to Real Learning: Instead of 3 months to launch and then start learning, founders get meaningful insights in the first week. They know within days whether people actually want what they're building.
Resource Efficiency: Manual validation costs hundreds of dollars in time and tools. Building an AI MVP costs thousands in development, even with no-code tools.
Pivot Speed: When you discover your initial assumptions are wrong (which happens 80% of the time), you can pivot your manual process immediately. Pivoting a built product takes weeks.
The client I declined? I heard through the grapevine that they ended up building their platform with another developer. After 4 months of development and $40,000 spent, they had 12 users and no revenue. They're now pivoting to a completely different business model—exactly the kind of expensive learning that proper MVP design thinking could have prevented.
Meanwhile, startups using this validation-first approach typically know within 30 days whether they have a business worth building. The successful ones then use tools like Bubble to scale what's already working, not to discover what might work.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson from this experience: AI MVP design thinking isn't about the technology—it's about the thinking.
Here are the key learnings that now guide every AI MVP project I consider:
Manual validation beats automated validation: If you can't make your marketplace work with spreadsheets and phone calls, adding AI won't help.
Building capabilities isn't the bottleneck: Tools like Bubble mean anyone can build. The real challenge is knowing what to build.
AI should solve proven problems, not discover problems: Use artificial intelligence to scale human intelligence, not replace human judgment.
Design thinking applies to business models, not just products: Validate your entire business process before optimizing parts of it.
Speed of learning trumps speed of building: Fast development that teaches you nothing is slower than careful validation that teaches you everything.
No-code tools are for scaling, not discovering: Bubble is incredible for building proven features quickly, less useful for figuring out what features to build.
The most expensive MVP is the one that doesn't teach you anything: A $50,000 platform that proves your idea doesn't work is more expensive than a $500 landing page that proves the same thing.
Looking back, declining that project was one of the best business decisions I made. It forced me to clarify my own thinking about AI MVP development and led to a much more valuable framework for helping startups avoid expensive mistakes.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI MVPs:
Validate your core SaaS value prop manually before adding AI features
Use Bubble to prototype AI integrations after proving manual processes work
Focus on user activation before intelligent automation
For your Ecommerce store
For ecommerce implementing AI features:
Test recommendation algorithms manually before building automated systems
Use customer interviews to validate AI use cases before development
Build on proven conversion foundations before adding intelligence