Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was approached by a potential client with an exciting opportunity: build a complex two-sided marketplace platform. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why that decision taught me everything about the real purpose of validation in 2025. Most founders today are obsessed with building—especially with AI and no-code tools making development faster than ever. But here's what I've learned after years of working with startups: if you're truly testing market demand, your MVP should take one day to build, not three months.
The problem isn't technology anymore. Tools like Bubble, AI assistants, and no-code platforms can build almost anything you imagine. The real challenge? Knowing what to build and for whom. That's where AI-powered validation comes in—not as a replacement for building, but as a way to validate before you waste months on the wrong solution.
In this playbook, you'll discover:
Why most MVP approaches fail in the age of AI and no-code
How to use AI to validate concepts without building anything
The validation framework that saved me from a $XX,XXX mistake
When to actually start building (spoiler: it's later than you think)
How AI can simulate user feedback before you have real users
If you're considering building something with Bubble, AI tools, or any no-code platform, this approach could save you months of wasted effort. Let me show you how validation-first thinking changes everything about SaaS development in the AI era.
Industry Reality
What every startup founder believes about MVPs
The startup world is drunk on building. Every accelerator, every guru, every "build in public" influencer preaches the same gospel: "Just ship it and iterate based on feedback." The logic seems sound—get something out there, let users tell you what's wrong, then fix it.
Here's what the industry typically recommends for MVP development:
Build a basic version fast — Strip features to core functionality
Launch to a small group — Get it in front of early adopters
Collect user feedback — Surveys, analytics, user interviews
Iterate quickly — Fix problems and add requested features
Scale gradually — Expand to larger audiences once validated
This conventional wisdom exists because it worked in an era when building was expensive and time-consuming. When development required months of coding, it made sense to build first and validate later. The "fail fast" mentality was revolutionary when failing meant expensive custom development.
But here's where it falls short in 2025: Building isn't the constraint anymore—knowing what to build is. With AI and no-code tools, you can build almost anything in days or weeks. The real challenge isn't technical execution; it's market validation.
I've watched countless founders spend months building "MVPs" that could have been validated in days using AI-powered research, simulation, and testing. They're optimizing for the wrong bottleneck. In the age of AI-powered development, the question isn't "Can we build it?" but "Should we build it?"
That's why I now approach validation completely differently.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had everything that should have excited me: a clear vision for a two-sided marketplace, sufficient budget, and enthusiasm about the no-code revolution. They'd heard about tools like Bubble and AI assistants making development faster and cheaper, and they wanted to test their idea.
But their core statement revealed the fundamental flaw: "We want to see if our idea is worth pursuing."
Here's what they brought to the table:
No existing audience or customer base
No validated proof of demand
Just an idea and enthusiasm (sound familiar?)
A budget that could fund months of development
My first instinct was to say yes. This was exactly the type of project I'd been building with no-code tools. Technically, I absolutely could have delivered what they wanted. But something felt wrong about spending three months building something to "test" an idea.
That's when I realized I'd been thinking about validation backwards. They weren't looking for a technical solution—they were looking for market validation. And if you're truly testing whether people want something, your validation method should be fast, cheap, and focused on learning, not building.
I told them something that initially shocked them: "If you're testing market demand, your validation should take one day to build, not three months."
Instead of immediately jumping into Bubble development, I suggested we start with what I now call "AI-powered demand validation"—using artificial intelligence to simulate, research, and test concepts before writing a single line of code or creating any workflows.
This wasn't just about saving time or money. It was about asking the right questions in the right order.
Here's my playbook
What I ended up doing and the results.
Instead of building their marketplace platform, I walked them through my new validation approach. This framework combines AI research, simulation, and testing to validate concepts without any development. Here's exactly what we did:
Step 1: AI-Powered Market Research
Rather than assuming we knew the market, I used AI to conduct comprehensive research. I fed ChatGPT and Claude detailed prompts about their marketplace concept, asking for:
Competitive landscape analysis
Potential customer segments and pain points
Market size estimation and barriers to entry
Revenue model viability
Step 2: Create a Simple Landing Page or Notion Doc
Instead of building the platform, we created a one-page explanation of the value proposition. This took literally one day. The page included:
Clear problem statement
Proposed solution benefits
"Join waitlist" call-to-action
Simple email capture
Step 3: Manual Outreach and Validation
Here's where the real validation happened. Instead of building automated systems, we manually reached out to potential users on both sides of the marketplace:
Week 1: Identified and contacted 50 potential supply-side users
Week 2-4: Reached out to 100 potential demand-side users
Month 2: Manually facilitated matches via email and WhatsApp
Step 4: AI-Simulated User Scenarios
This is where it gets interesting. I used AI to simulate different user personas and their journey through the marketplace. I created detailed prompts asking AI to role-play as different user types, identifying potential friction points, objections, and feature requests before building anything.
The AI simulation revealed three critical issues we would never have discovered through traditional MVP development:
Trust barriers — New marketplaces face chicken-and-egg trust problems
Payment complexity — Users wanted payment features we hadn't considered
Mobile-first expectations — Desktop-focused approach would have failed
The lesson became clear: Your MVP should be your marketing and sales process, not your product. Distribution and validation come before development, especially when building tools make development so accessible.
After six weeks of this validation approach, we had definitive answers about market demand, user expectations, and business model viability—insights that would have taken months to gather through traditional "build first" MVP approaches.
Market Research
Use AI to understand your space before building anything
Validation Speed
Manual processes reveal demand faster than automated ones
User Simulation
AI can predict user behavior patterns and friction points
Strategic Clarity
Focus validation on business model viability not technical feasibility
The results of this AI-first validation approach were eye-opening. Within six weeks, we had gathered insights that typically take months of post-launch iteration to discover:
Demand Validation Results:
52% of contacted supply-side users showed genuine interest
31% were willing to commit time for manual facilitation
89% requested features we hadn't originally planned
Time and Cost Savings:
Total validation time: 6 weeks vs 3+ months of development
Cost: Under $500 vs tens of thousands in development
Learning velocity: Immediate feedback vs waiting for launch
Most importantly, the manual facilitation process revealed that the core assumption about automation was wrong. Users actually valued the human touch in early transactions, suggesting the MVP should start manual and gradually automate—the opposite of our original approach.
This validation-first methodology proved that in the age of AI and no-code tools, the constraint isn't building capability—it's knowing what to build and for whom.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience fundamentally changed how I approach product validation. Here are the seven most important lessons learned:
AI amplifies research, doesn't replace human connection — Use AI for analysis and simulation, but validate demand through direct human contact
Manual processes reveal more than automated ones — The friction of manual facilitation taught us what users actually valued
Speed of learning matters more than speed of building — Six weeks of focused validation beats months of building the wrong thing
Distribution should be validated before development — If you can't manually acquire your first 100 users, automation won't save you
AI simulation can predict real user behavior — Well-crafted AI personas identified friction points we never considered
Building tools are commoditized, market insight isn't — Anyone can use Bubble or AI to build; few validate properly
Validation-first saves relationships — Better to discover problems early than disappoint users with a premature launch
The biggest shift in thinking: In 2025, your first MVP should be your marketing and sales process, not your product. Build the audience first, validate demand manually, then automate the parts that work. This approach works especially well for growth-focused strategies where understanding your market is more valuable than technical sophistication.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement this validation approach:
Start with AI market research before any development
Create simple landing pages to capture interest
Manually onboard your first 50 users
Use AI to simulate user personas and journey mapping
Build automation only after proving manual processes work
For your Ecommerce store
For ecommerce businesses testing new products or markets:
Use AI to analyze competitor landscapes and pricing
Create waitlist campaigns before product development
Test demand through pre-orders or manual fulfillment
Validate shipping and logistics manually first
Scale systems only after proving unit economics