Founders do not struggle with a shortage of things to do. They struggle with focusing on the right things to do, at the right time. That single insight changed everything about how we designed LEANSpark.

The Story

Throughout October and November, I conducted problem discovery interviews using the Concierge MVP reviews as my entry point. Every founder who signed up for a canvas assessment got a personalized walkthrough — and at the end, I asked if I could interview them separately to understand how they were currently validating their idea.

I used the Customer Forces framework, which views every customer journey through four lenses: Push (what drives them to seek a new solution), Pull (what attracts them toward a specific solution), Inertia (what keeps them stuck with their current approach), and Friction (what creates anxiety about switching).

The framing was deliberately broad. I did not ask “Tell me how you use AI.” Instead, I asked “Tell me how you are currently validating your idea” — what occupies their attention, what tools they use, and how they use them. This broad-match approach prevented me from falling into the local maxima trap of only talking to AI enthusiasts and missing the bigger market.

Three findings shaped everything that followed. First, every founder I spoke with had already tried some AI tools in their startup process. This was relevant for positioning — if everyone was already using AI, then AI tools were the true competition to anchor against. Second, the use cases were scattered: research with Perplexity, brainstorming with Claude Projects, interview analysis, coding, marketing campaigns. No single dominant use case.

But the third finding was the breakthrough: the Context Switching Tax. Founders context-switch constantly between building, talking to customers, fundraising, managing teams, and learning new frameworks. And on top of that, they have to manage the context window of their AI tools. Every conversation starts from scratch. They are constantly re-explaining who they are, what their startup does, what they have already tried, and what they are trying to do now.

I started calling this the Memento problem, after the movie where Guy Pearce wakes up every day with no short-term memory, relying on notes and tattoos to remember who he is. That is what using most AI tools feels like for founders — a constant re-orientation with no memory of past sessions.

The counterpoint was the Matrix. In the movie, characters download programs just-in-time and instantly learn new skills. That is what good AI should feel like. Load massive context in seconds, remember everything, and learn new skills on the fly.

This insight shifted LEANSpark from being a collection of discrete workflow tools — run a stress test here, generate a pitch there — to being an AI co-founder that remembers who you are, where you are in your journey, and what your next right action should be. Every session starts with a summary of where you left off, not a blank prompt.

Key Frameworks

  • Customer Forces: Innovation is about causing a switch from an old way to a new way. Map the four forces — Push, Pull, Inertia, and Friction — to understand why customers switch (or do not).
  • Broad-Match vs. Narrow-Match Interviews: Start broad to map the opportunity space without selection bias, then narrow to validate specific use cases and go deeper on struggles, pet peeves, and workarounds.
  • The Context Switching Tax: The compounding cost of constantly re-establishing context — both at the founder level (switching between roles) and at the AI tool level (re-explaining everything each session).

Key Takeaways

  • Good problem discovery is not about validating a list of problems — it is about naturally uncovering struggles by focusing on specific workflows customers are already running.
  • Start broad with interviews to avoid selection bias, then narrow once you identify underserved problems your product can address.
  • The Memento problem — every AI session starting from scratch with no memory — was the single biggest pain point founders experienced across all tools.
  • The shift from “AI coaching tool” to “AI co-founder” came directly from understanding that founders want continuous context, not discrete interactions.
  • Not all discovered use cases need to fit your product — explore adjacent workflows (what happens before and after) to find where your unique value lies.