The Death of the Minimum Viable Product (MVP)

For years, the startup world has revolved around the concept of the Minimum Viable Product (MVP)—the smallest, most functional version of an idea that can enter the market, be tested, and iterated upon. But AI has changed everything. Execution is no longer the bottleneck. Founders, businesses, and developers can now generate and deploy full-scale applications, products, and solutions in record time with minimal resources.

This leaves us with an unprecedented problem: the supply of solutions is outpacing the supply of meaningful problems.

The market is becoming saturated with AI-generated products that don’t actually solve anything worthwhile. The race to build the next big thing is increasingly leading to an ecosystem of redundant, misaligned, or unnecessary solutions. If AI can build anything, what should we actually be building?

The answer is clear: the future of innovation lies in defining the right problems before rushing to build solutions. We need to shift from a Minimum Viable Product mindset to a Minimum Viable Problem (MVP) approach.

What Is a Minimum Viable Problem?

A Minimum Viable Problem (MVP) is the smallest, most critical, and most actionable problem that still exists in a given space. Unlike a product, which assumes a predefined solution, a Minimum Viable Problem forces us to ask: “Are we even solving the right thing?”

Why It Matters More Than Ever

1. AI Can Build Anything, So Problem Definition Is the Only Competitive Edge

   • The value chain is shifting from building products to discovering real problems.
   • The best companies won’t just develop solutions—they’ll own the problem space before anyone else even sees the need.

2. A Tsunami of Useless Solutions Is Coming

   • Without a problem-first approach, AI will flood the market with solutions to problems that don’t exist.
   • Companies that define problems correctly will be the only ones creating meaningful impact.

3. The Research Lab Model Is Making a Comeback

   • Startups were once about rapid iteration; now, the advantage will belong to those who refine and validate problems before launching anything.
   • AI and human intelligence must work together to refine problems before solutions are even considered.

The Birth of an Open-Source Problem-First Incubator

We are launching a collaborative, open-source community dedicated to defining problems before building solutions. Instead of an accelerator focused on product development, this will be an incubator dedicated to problem discovery, validation, and refinement.

What Will This Look Like?

• A Shared Repository of Open Problems

  • A global, crowdsourced database of real, pressing, and underexplored problems.
  • Developers, thinkers, and AI systems contribute, refine, and debate problem statements before solutions are explored.

• AI-Assisted Problem Refinement

  • A structured system for AI to help categorize, break down, and map the impact of problems.
  • AI doesn’t just build products—it assists in discovering what problems actually need solving.

• A New Kind of Incubation Program

  • Startups won’t apply with ideas—they’ll apply with well-researched, validated problems.
  • Instead of MVPs (products), they’ll submit MVPs (problems) to be refined collectively.

• A Marketplace for Problem-Solvers

  • Developers, researchers, and entrepreneurs can form teams around problems they find compelling.
  • Instead of rushing into solution mode, they’ll get paid for problem definition.

A Framework for Defining Minimum Viable Problems

1. Observation & Research

   • Identify inefficiencies, gaps, or pain points in existing systems.
   • Gather data from real users, industries, or communities.

2. Distillation & Validation

   • Break down broad challenges into specific, solvable problems.
   • Validate the urgency and necessity of the problem before solutioning.

3. Problem Benchmarking

   • Measure impact potential: How widespread is the problem? How costly? How solvable?
   • Ensure it isn’t just a niche frustration but a meaningful, scalable issue.

4. Community & AI Refinement

   • Iterate on the problem statement using collective intelligence.
   • Let AI-assisted analysis detect blind spots and counterpoints.

5. Problem-Market Fit

   • Determine if the problem is actually worth solving before wasting resources.

Join the Movement: Help Build the Future of Problem-First Innovation

The startup world has been obsessed with solutions for too long. The next generation of entrepreneurs, developers, and visionaries will be problem definers, not just problem solvers.

This is an open call for developers, AI engineers, researchers, designers, and entrepreneurs who are ready to rethink how we approach innovation. If you believe in solving the right things, not just building fast and breaking things, join us.

• Contribute to the first open-source Minimum Viable Problem database
• Use AI to refine and validate high-impact problems
• Be part of a new kind of incubator that prioritizes real-world impact

The age of the Minimum Viable Problem is here.

Are you ready to build the future by defining it first?

Sign up to contribute, collaborate, and shape the next wave of meaningful innovation.

Let’s define the future together.