Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I had a potential client approach me with what seemed like a dream project. They wanted to build a sophisticated two-sided marketplace platform, had a substantial budget, and were excited about leveraging AI tools like Bubble.io and no-code solutions. The technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
This decision forced me to completely rethink how we approach lean startup methodology in the AI era. While everyone's rushing to build complex AI-powered MVPs, most founders are making the same critical mistake: they're treating AI tools as a solution to validation problems, not development problems.
Here's what you'll learn from this experience:
Why AI tools don't solve the fundamental validation challenge
The real purpose of MVPs in 2025 (hint: it's not what you think)
My framework for product-market fit testing before building anything
When to actually use AI in your lean startup process
The distribution-first validation method that saves months of development
Conventional wisdom
What every startup founder has been told about MVPs
The startup world has been preaching the same gospel for years: build fast, fail fast, iterate quickly. With the rise of AI and no-code tools, this message has only gotten louder. Every founder is told they can now build their MVP in weeks instead of months.
The conventional lean startup wisdom goes like this:
Come up with an idea
Build a minimum viable product
Launch it to users
Gather feedback and iterate
Repeat until you find product-market fit
This methodology made sense when building software required significant time and resources. The MVP was truly "minimum" because development was expensive and slow. But here's the problem: in the age of AI and no-code, the constraint isn't building anymore—it's knowing what to build and for whom.
Most founders are now using AI tools to build increasingly sophisticated "MVPs" that take months to develop. They're essentially building full products and calling them MVPs. This completely defeats the purpose of lean methodology.
The real issue? Everyone's optimizing for the wrong bottleneck. The bottleneck isn't development speed—it's validation speed.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this potential client came to me, they had all the classic symptoms of premature solution syndrome. They'd spent months researching AI tools, mapping out complex user flows, and designing sophisticated marketplace mechanics. They wanted to "test if their idea was worth pursuing" by building a complete platform.
Here's what they brought to the table:
No existing audience
No validated customer base
No proof of demand
Just enthusiasm and a solid budget
Their approach reminded me of countless other clients I'd worked with who treated technology as the solution to business problems. They were excited about what they could build, not what they should build.
I'd seen this pattern before with B2B SaaS clients who spent months perfecting their product before realizing they had no distribution strategy. The technology was impressive, but the business fundamentals were missing.
This client was particularly interesting because they explicitly said: "We want to see if our idea is worth pursuing." Yet their proposed solution was to spend three months building a complex platform to test market demand. That's not testing—that's gambling.
What struck me most was how AI tools had actually made this problem worse. Because building was now "easier," they felt justified in creating increasingly complex solutions before validating basic assumptions.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money and building what they asked for, I shared 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 the framework I recommended instead:
Week 1: Manual Validation
Create a simple landing page or Notion doc explaining the value proposition
Start manual outreach to potential users on both sides of the marketplace
Test messaging and positioning through direct conversations
Week 2-4: Distribution Testing
Manually match supply and demand via email/WhatsApp
Document every friction point in the process
Validate pricing and business model assumptions
Month 2: Automation Decision
Only after proving demand, consider building automation
Use AI tools to automate proven processes, not to test unvalidated assumptions
The key insight I shared: Your MVP should be your marketing and sales process, not your product. Distribution and validation come before development.
This approach leverages what I call "AI-enhanced manual validation"—using AI tools for research, outreach, and analysis while keeping the core validation process human and direct. Instead of building an AI-powered marketplace, we'd use AI to:
Research potential customers and competitors
Generate personalized outreach messages
Analyze feedback and identify patterns
Create rapid prototypes for user testing
Reality Check
Don't confuse building capability with market validation
Manual First
Test demand through direct human interaction before automating
AI as Research
Use AI tools for market research and analysis, not product development
Constraint Focus
Optimize for validation speed, not development speed
Here's what happened when I shared this approach: the client initially pushed back. They wanted to build something impressive, not "just" test demand manually. But after walking through the numbers, reality set in.
Manual validation would cost them $2,000 in time and research tools. Building the platform would cost $30,000+ and three months. If the manual approach proved demand didn't exist, they'd save $28,000 and two months. If it proved demand did exist, they'd have real customers waiting for the product and actual requirements based on user behavior.
More importantly, this approach revealed the real constraint: finding and engaging both sides of their marketplace. No amount of AI-powered features would solve a fundamental distribution problem.
The client ultimately decided to follow this path. Within two weeks, they discovered their initial assumptions about user behavior were completely wrong. Rather than the complex matching algorithm they'd planned to build, users actually wanted something much simpler. This insight saved them months of building the wrong solution.
This experience reinforced a principle I now share with every potential AI-focused client: technology amplifies existing processes, it doesn't create them from scratch.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Through this experience and similar situations with other clients, I've learned that the most successful lean startup approaches in the AI era flip the conventional wisdom:
Validate distribution before building features - Most AI tools solve development problems, not market problems
Use AI for research and analysis, not product development - AI excels at pattern recognition and data processing
Manual processes reveal user behavior better than assumptions - Automation hides the nuances you need to understand
Time-to-validation trumps time-to-product - Getting to "no" quickly is more valuable than building slowly
AI enables faster iteration on proven concepts - Once validated, AI tools become incredibly powerful
Distribution is your real MVP - Your ability to reach customers matters more than your features
Human feedback beats algorithmic assumptions - AI can process feedback, but humans must gather it first
The biggest lesson: in the age of AI and no-code, the constraint isn't building—it's knowing what to build and for whom. The most successful startups I work with now spend 80% of their time on validation and distribution, and only 20% on development.
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 features
Use AI tools for market research and customer outreach
Focus on distribution strategy before product development
Test pricing and business model assumptions manually first
For your Ecommerce store
Validate demand through direct customer conversations before building
Use AI to analyze customer behavior patterns and feedback
Test conversion assumptions through manual processes
Build automation only after proving manual processes work