Solo AI Product Incubator
AI has collapsed the time and cost to build software. What used to take a team months now takes one person days. This creates two simultaneous opportunities.
The Premise
Build AI-powered tools
Find existing processes that are painful, slow, or error-prone. Build tools that fundamentally change what's possible — not just faster or cheaper, but structurally different.
Develop a repeatable system
The process of building and validating tools becomes a methodology that itself becomes a product. The system improves with every cycle.
The Model
Three layers that feed each other. Each layer stands on its own. None depends on personal brand — the products, the system, and the ecosystem are the value.
The Incubator
~1 MVP per weekA one-person product studio. The thesis for every project is the same: find an existing process that's painful, and build an AI-native tool that fundamentally changes the equation.
Inputs
- • Process pain points from experience & observation
- • Reusable build patterns that improve each cycle
- • Evaluation frameworks that sharpen over time
Outputs
- • Deployed MVPs with minimum viable distribution
- • Kill/continue decisions with real data
- • Documented thesis, build time, and signal
Constraint: Clear pain point, AI changes the game, path to revenue without a sales team, buildable and testable by one person.
The Workflow Engine
The system becomes the productThe incubator runs on a system: idea sourcing, evaluation, build process, distribution approach, signal reading, portfolio decisions. That system improves with every cycle.
Inputs
- • Operational data from Layer 1 — what worked, what failed, why
- • How long things actually took
- • Which distribution channels produced signal
Outputs
- • Proven, documented methodology
- • Tooling and templates for others
- • Track record that speaks for itself
Audience: Product managers, consultants, agency owners, independent operators — anyone trying to figure out how to build with AI effectively.
The Ecosystem
Connected tools create compounding valueAs the tool portfolio grows, natural patterns emerge. The tools share a common philosophy about how AI should augment existing processes. That shared DNA creates interoperability.
Inputs
- • Strongest tools with proven traction
- • Methodology defining how tools relate
- • Market signal about which combinations users want
Outputs
- • Integrated platform with compounding value
- • Data-and-platform agnostic system
- • Distribution leverage for new tools
Shape: The specific ecosystem reveals itself through incubator work rather than being predetermined.
Key Principles
Revenue is a learning signal
Every cycle pushes toward a transaction as fast as possible — not because the money matters at small scale, but because willingness to pay is the highest-fidelity signal for whether a problem is real.
The products are the product
The founder is not the brand. Each tool has to sell itself on the strength of the problem it solves. This makes everything more durable, more sellable, and more scalable.
Volume with intention
One MVP per week is ambitious but sustainable. Some weeks produce multiple quick tests. Some weeks a project earns a second cycle. The rhythm is consistent but not rigid.
Let most things fail
Portfolio value comes from the combination of wins, deliberate kills, and pivots — each with clear reasoning and real data. The methodology is proven by the full distribution of outcomes.
The system improves itself
Every cycle makes the next cycle better. Build kits accumulate patterns. Evaluation sharpens. Distribution channels get tested and ranked. The compounding isn't just in revenue.
What This Is Not
It's a system for continuously generating, testing, and scaling AI-powered tools — where the system itself becomes as valuable as any individual tool it produces.
Open Questions
How should kill/continue decisions be structured to balance learning value against revenue potential?
What's the right distribution strategy when each tool needs to stand on its own without personal brand behind it?
At what point does the Layer 2 methodology have enough proof behind it to productize? What does "enough" look like?
When and how does Layer 3 transition from emergent pattern to deliberate product strategy?
What's the right balance between exploring new ideas and investing deeper in ones showing traction?