| name | ai-startup-advisor |
| description | Strategic advisor for evaluating software/AI product ideas and generating defensible startup strategies. Use this skill whenever the user wants to evaluate a product idea, brainstorm startup directions, assess defensibility against foundation model companies, choose between build-vs-buy for AI components, or needs a strategic positioning check for a development project. Trigger on phrases like "is this idea defensible", "what should I build", "will OpenAI/Anthropic eat this", "startup idea", "product strategy", "moat", "competitive positioning", "should I build X", "vertical AI opportunity", "business model for AI", or any discussion about whether a software product is worth building given the current AI landscape. Also trigger when the user is evaluating technical architecture decisions through a strategic lens — e.g. "should I use a fine-tuned model or API calls" where the answer has competitive implications.
|
AI Startup Advisor
A strategic evaluation framework for software founders navigating the 2025–2026 landscape where
foundation model companies (OpenAI, Anthropic, Google) are absorbing the application layer.
This skill helps with two modes:
- Evaluate mode — Score a specific idea against defensibility criteria
- Generate mode — Brainstorm ideas for a given domain or vertical
For deep background research and examples, read ${CLAUDE_SKILL_DIR}/references/strategic-research.md in this skill's
directory. Load it when the user needs detailed examples, case studies, or academic backing for a
recommendation.
Core mental model: The Foundation Model Squeeze
The strategic reality is simple: if a foundation model company's next quarterly release could
replicate 80% of your value proposition, you are building a substitute, not a complement.
The key test — apply it to every idea:
"OpenAI Next Release" Test: Would this product get worse or better when the underlying
foundation model improves? If better → you're a complement (safe). If replaceable → you're
a wrapper (danger).
Value in AI follows Teece's Profiting from Innovation framework: when the core innovation (the model)
is freely accessible via API, profits flow to owners of complementary assets — domain data,
regulatory clearances, workflow integration, customer trust — not to the model accessor.
Mode 1: Evaluate an idea
When the user presents a specific product or startup idea, run it through the Defensibility
Scorecard. Score each dimension 0–3 and provide a total with commentary.
Defensibility Scorecard (0–3 per dimension, max 21)
| # | Dimension | 0 (None) | 1 (Weak) | 2 (Moderate) | 3 (Strong) |
|---|
| 1 | Proprietary Data Flywheel | No unique data; uses public datasets or generic API output | Collects some user data but not structured for compounding advantage | Structured data collection where each customer improves the product | Unique dataset that compounds over time and cannot be replicated without equivalent operations (e.g. Tempus's clinical-molecular DB, CrowdStrike's threat telemetry) |
| 2 | Workflow Integration Depth | Standalone tool, no integration | Light integration (browser extension, Zapier) | Moderate integration into existing tools (plugin, API connector) | Deep embedding in mission-critical workflow (EHR integration, case management system, CI/CD pipeline) with high switching costs |
| 3 | Regulatory / Compliance Barrier | Unregulated space, no certifications needed | Light compliance (SOC 2, GDPR basics) | Moderate regulatory requirements (HIPAA, industry-specific certs) | Heavy regulatory burden that takes years (FDA clearance, FedRAMP, legal bar requirements) |
| 4 | Domain Expertise Moat | Generic capability any dev team could build | Some domain knowledge required but acquirable in weeks | Deep domain expertise required; team needs practitioners | Requires practitioners embedded in the team + years of domain iteration (lawyers at Harvey, clinicians at Abridge) |
| 5 | Counter-Positioning vs. Incumbents | Directly competing with model providers on their turf | Competing with SaaS incumbents who could add AI features | Different business model that incumbents could adopt with effort | Outcome-based pricing or service model that incumbents cannot copy without cannibalizing their own revenue |
| 6 | Production Complexity | Works out of the box with an API call | Some prompt engineering and pipeline work | Significant engineering for reliability, edge cases, monitoring | The last 10% of quality requires 10–100x the effort of a prototype; years of production hardening (compliance engines, safety-critical systems) |
| 7 | Vendor Neutrality Advantage | Tied to one model provider | Works with multiple providers but not a selling point | Multi-model support is a feature customers value | Structural requirement for vendor neutrality — model providers cannot credibly offer this (eval tools, security/guardrails, observability) |
Scoring interpretation
- 18–21: Strong defensibility. Build with confidence. Focus on execution speed.
- 13–17: Moderate defensibility. Identify which dimensions score low and develop a plan to strengthen them within 6–12 months.
- 8–12: Weak defensibility. Viable as a short-term revenue play but vulnerable to platform absorption. Consider pivoting toward a higher-scoring adjacent opportunity.
- 0–7: Wrapper territory. The "OpenAI Next Release" test likely fails. Rethink fundamentally.
How to present the evaluation
Use this structure:
## Defensibility Evaluation: [Idea Name]
### Quick Verdict
[One sentence: defensible / risky / wrapper territory]
### Scorecard
[Table with scores and brief justification per dimension]
### Total: X/21 — [Interpretation]
### Key Risks
[Top 2-3 specific threats, referencing which foundation model companies or incumbents
could absorb this and how]
### Strengthening Moves
[Concrete actions to improve the weakest dimensions — e.g., "Build EHR integration
with Epic to move Workflow Integration from 1→3"]
### Strategic Recommendation
[Build / Pivot / Avoid — with reasoning]
Mode 2: Generate ideas for a domain
When the user asks for ideas within a domain or vertical, use the Opportunity Generator framework.
Step 1: Identify the vertical characteristics
For the given domain, assess:
- Regulatory density: How heavily regulated? (Stronger = better for defensibility)
- Data fragmentation: Is domain data scattered across many siloed sources? (More fragmented = bigger data flywheel opportunity)
- Workflow pain: How manual and error-prone are current workflows? (More painful = higher willingness to pay for automation)
- Incumbent software quality: How good is existing software? (Worse = more displacement opportunity)
- Expert labor cost: How expensive are the humans currently doing this work? (Higher = larger addressable market for service-as-software)
Step 2: Apply the five defensible corridors
For each corridor, generate 1–2 specific ideas tailored to the domain:
- Vertical AI product — Deep domain-specific tool with proprietary data and workflow integration
- Infrastructure / picks-and-shovels — Tooling that the domain needs but model providers won't build (evaluation, compliance, data pipelines)
- Service-as-software — AI delivering a service humans currently perform, priced on outcomes not seats
- AI-native agency — Productized service with software margins, combining AI automation with domain expertise
- Data-centric business — Building and monetizing a proprietary domain-specific dataset
Step 3: Score each generated idea
Run each idea through the Defensibility Scorecard (abbreviated — just the total and top 2 dimensions).
Step 4: Present as a ranked list
## Opportunities in [Domain]
### Domain Profile
[Brief assessment of the 5 vertical characteristics]
### Top Ideas (ranked by defensibility score)
**1. [Idea Name]** — Score: X/21
[2-3 sentence description]
Strongest moats: [top 2 dimensions]
Business model: [pricing approach]
Example comparable: [existing company doing something similar, if any]
**2. [Idea Name]** — Score: X/21
...
Decision frameworks for common strategic questions
"Should I fine-tune or use API calls?"
This is as much a strategic question as a technical one:
- API-only: Faster to ship, but zero model differentiation. Anyone can replicate.
- Fine-tuned on public data: Slight performance edge, but the data is available to competitors.
- Fine-tuned on proprietary data: Real moat — your data flywheel produces a model competitors can't match.
- Custom model architecture: Deepest moat but highest cost. Only justified when domain-specific architectures genuinely outperform general models (e.g., protein folding, molecular simulation).
Recommendation: Start with API calls to validate the market. Invest in fine-tuning only after you have proprietary data worth fine-tuning on. The model is not your moat — the data pipeline feeding it is.
"Should I go model-agnostic or bet on one provider?"
79% of enterprises paying for OpenAI also pay for Anthropic. Design for multi-provider from day one:
- Abstract the model layer behind an internal interface
- Shallow API integration migrates in 20–40 hours; deep integration takes 80–120 hours
- Model-agnostic architecture is itself a mild defensibility signal — it shows sophistication
- Exception: If you're building infrastructure that depends on a specific provider's unique capability (e.g., MCP for Anthropic), that's fine as long as you plan for portability
"SaaS vs. outcome-based pricing?"
The shift from per-seat to per-outcome pricing is the single biggest business model disruption in AI:
- Per-seat SaaS: Legacy model. AI makes it vulnerable because one AI-augmented user does the work of 5. Your revenue shrinks as your product improves.
- Per-outcome: Aligns your revenue with value delivered. Harder to predict revenue early on, but structurally defensible — incumbents can't adopt it without cannibalizing themselves.
- Hybrid: Charge a platform fee + per-outcome variable. De-risks early-stage revenue while preserving strategic positioning.
"Is this a feature or a product?"
Apply this test: If you removed the AI component, would there still be a product worth paying for?
- Just a feature: The AI is doing something generic (summarization, translation, chat). Model providers will ship this.
- A product: The AI is embedded in a specific workflow with domain data, integrations, and compliance. Removing the AI leaves a broken workflow, not a simpler tool.
Red flags to always flag
When evaluating any idea, automatically check for these anti-patterns:
- "Chat with X" pattern — Chat-with-PDF, chat-with-database, chat-with-codebase. These are features, not products. Model providers ship these quarterly.
- "AI-powered [commodity]" — AI writing assistant, AI image generator, AI code completion without deep vertical focus. Competing directly with model providers' showcase apps.
- Single-model dependency — If one API provider going down or changing pricing kills your product, you have no moat.
- No data strategy — If you can't articulate how each customer interaction makes your product better for the next customer, you don't have a flywheel.
- Regulated industry without regulated team — Building for healthcare without clinicians, for legal without lawyers, for finance without compliance experts. Domain expertise is the moat — you need it on the team, not as advisors.
Reference material
For detailed case studies, academic frameworks, funding data, and company examples, read:
${CLAUDE_SKILL_DIR}/references/strategic-research.md
Load this when:
- The user asks "why?" or wants evidence for a recommendation
- You need specific company examples or revenue figures
- The user wants academic citations (Teece, Eisenmann, Zhu, Agrawal)
- A deeper dive into a specific vertical (healthcare, legal, biotech, infrastructure) is needed
