Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with an exciting opportunity: build a complex AI-powered marketplace platform using Bubble. The budget was substantial, the technical challenge was interesting, and the client was convinced that no-code AI tools could validate their "revolutionary" idea quickly.
I said no.
Not because Bubble can't handle AI integrations—it absolutely can. Not because their idea was bad—it actually had potential. I declined because they were asking the wrong question entirely. They wanted to know "Can we build this?" when they should have been asking "Should we build this?"
Here's what I've learned after working with dozens of startups: the constraint isn't building anymore—it's knowing what to build and for whom. In the age of AI and no-code, most founders are solving the wrong problem first.
In this playbook, you'll discover:
Why "Can startups use Bubble for AI MVPs?" is the wrong question
The real purpose of MVPs in 2025 (hint: it's not building)
My framework for when to build vs. when to validate manually
Why most no-code AI MVPs fail before they launch
A step-by-step approach that saves founders months of wasted development
Ready to challenge everything you think you know about AI-powered MVPs?
Industry Reality
What every startup founder believes about no-code AI MVPs
Walk into any startup accelerator today, and you'll hear the same narrative repeated like gospel: "With AI and no-code tools like Bubble, you can build anything quickly and cheaply. Just ship an MVP, get user feedback, and iterate."
The conventional wisdom sounds compelling:
Speed to market is everything - Get your AI-powered solution live in weeks, not months
No-code democratizes development - Non-technical founders can build complex platforms
AI makes everything possible - Integrate machine learning without hiring a team
MVPs should be functional products - Users need to experience the real thing to give feedback
Build first, validate second - Launch quickly and let the market tell you if you're right
This advice isn't wrong in every situation. Tools like Bubble have genuinely revolutionized what's possible for non-technical founders. You can absolutely build sophisticated AI-powered applications without writing code.
But here's where this conventional wisdom falls apart: it assumes the hardest part of building a startup is the technical execution. In 2025, that's simply not true anymore.
The real bottleneck isn't "Can I build this?" It's "Who will use this, and why?" Most founders spend 90% of their time on the 10% of the problem that's already been solved by no-code tools, while completely ignoring the 90% that actually matters: distribution and market validation.
When you start with "Let's build an AI MVP on Bubble," you're already three steps ahead of where you should be. You're optimizing for building when you should be optimizing for learning.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had done everything the startup world tells you to do. They'd identified a "problem" in the marketplace space, convinced themselves AI could solve it, and researched the perfect tech stack. Bubble for the front-end, various AI APIs for the intelligence, and a solid plan for a two-sided platform.
"We want to test if our idea works," they explained excitedly. "We've heard these no-code AI tools can build anything quickly. Can you help us validate our concept?"
Red flags immediately went up. Not because of their technical choices—those were actually sound. The red flags were in their language: "test if our idea works" and "validate our concept."
When I dug deeper, the picture became clearer:
No existing audience or customer base
No validated proof of demand
No manual process they were trying to automate
Just an idea and enthusiasm
They wanted to spend three months building a sophisticated platform to "see if people would use it." Classic solution-first thinking.
I've seen this pattern dozens of times. Founders get excited about the possibility of building, confuse building with validating, and end up with beautiful products nobody wants. The tools make it so easy to build that building becomes procrastination disguised as progress.
This reminded me of another project where I helped a SaaS startup realize their "MVP" should be their marketing and sales process, not their product. That insight saved them six months of development and helped them find product-market fit with a completely different solution than they originally imagined.
So I told this marketplace client something that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build—not three months."
Here's my playbook
What I ended up doing and the results.
Instead of building their AI-powered marketplace platform, I walked them through what I call the "Manual-First MVP Framework"—a systematic approach to validation that treats building as the last resort, not the first step.
Step 1: The One-Day MVP Challenge
"If you can't validate your core value proposition manually in one day, don't spend three months automating it," I told them. Here's what we built instead:
Day 1: A simple landing page explaining the value proposition
Week 1: Manual outreach to potential users on both sides of their marketplace
Week 2-4: Manually facilitate transactions via email and WhatsApp
Month 2: Only after proving demand, consider building automation
Step 2: The Distribution-First Validation
Most founders ask "How do I build this?" I taught them to ask "How do I reach these people?" first. We mapped out:
Where their target users currently solve this problem
What communities and platforms they frequent
How they'd find our solution without paid ads
Who would refer others to this service
This exercise revealed something crucial: they didn't actually know how to reach their target market. Building a platform would have been pointless.
Step 3: The Manual Transaction Test
Instead of building AI matching algorithms, we created a manual process:
Collected problems from one side via a simple form
Manually sourced solutions from the other side
Facilitated introductions via email
Tracked completion rates and satisfaction
This manual process taught us more about user behavior in two weeks than a sophisticated platform would have in six months. We discovered the real friction points, learned what users actually valued, and identified the features that mattered most.
Step 4: The Build-or-Buy Decision Matrix
Only after proving the manual process worked did we evaluate technical solutions. The decision matrix considered:
Volume threshold: How many transactions before manual breaks down?
Automation value: What would automation enable that manual doesn't?
Technical complexity: Could we solve this with existing tools?
Competitive advantage: Is custom technology a moat or distraction?
Interestingly, this analysis revealed that their idea needed SaaS-style workflows more than marketplace-style matching—a pivot that would have been impossible to discover through building first.
Validation Speed
Manual validation takes days while building takes months - start with what's fastest to prove or disprove your assumptions
Real User Behavior
You learn how users actually behave when problems are solved manually - this insight is impossible to get from using a prototype
Market Understanding
Direct interaction reveals what users value most - often different from what founders assume matters
Technical Clarity
Manual processes show you exactly what needs automation - preventing over-engineering and feature bloat
The results were dramatically different from what would have happened with a build-first approach:
Timeline Comparison:
Manual validation: 4 weeks to definitive market feedback
Building approach: Would have taken 12+ weeks just to launch
Discovery Impact:
Found the real problem was workflow management, not marketplace matching
Identified distribution channels that actually worked
Learned users needed different features than originally planned
Validated willingness to pay before investing in development
Cost Savings: By validating manually first, they avoided building the wrong product and saved an estimated $50,000+ in development costs and opportunity cost.
Most importantly, this approach gave them conviction about what to build when they did decide to develop technology. They weren't guessing anymore—they had data from real user interactions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from applying the Manual-First MVP Framework across multiple client projects:
Building is procrastination in disguise: When you're not sure what to build, building feels productive but often just delays the hard work of understanding your market.
Distribution beats product every time: The best product in the world is worthless if you don't know how to reach your users.
Manual processes reveal user truth: How users behave when you're manually solving their problem tells you everything about what they actually value.
AI makes the wrong parts easy: No-code AI tools make building easy but don't help with the hard parts—finding customers and understanding needs.
Speed to learning trumps speed to building: Getting market feedback in days beats launching a product in months.
Your first MVP should be your sales and marketing process: Before you automate value delivery, prove you can create and distribute value manually.
Technology should solve proven problems: Build automation for processes you've already proven work, not for problems you think exist.
The biggest mindset shift: stop asking "Can I build this?" and start asking "Should I build this?" In 2025, the constraint isn't technical capability—it's market understanding.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Start with manual validation before building any AI-powered features
Focus on SaaS distribution strategy before product development
Use no-code tools for rapid testing, not just rapid building
Validate willingness to pay through manual processes first
For your Ecommerce store
Test customer acquisition channels manually before automating anything
Understand user behavior through direct interaction, not analytics
Use e-commerce lessons about customer journey mapping
Focus on retention and repeat usage patterns before scaling