AI-Native Challenger Strategy: Beating Incumbents

What's inside

1. Level 1: Christensen's Disruption Theory RefresherFree
   Learn the three innovation types from Clayton Christensen's work (low-end disruption, new-market disruption and sustaining innovation) and the tests that tell them apart.
2. Level 2: What AI Changes About DisruptionFree
   Find where AI changes the disruption pattern: lower cost to serve, faster product improvement and work that software could not do before.
3. Level 3: Incumbent Advantages and Blind Spots
   Map an incumbent's main advantages (distribution, data, trust, integration) against its blind spots (business model conflict, seat-based pricing, legacy systems).
4. Level 4: Services-as-Software: Selling the Work
   Learn how AI-native companies sell a finished result instead of a tool, which opens up services budgets that are much larger than software budgets.
5. Level 5: Systems of Record vs Systems of Action
   Separate the systems that store a company's main data (CRM, ERP, HRIS) from the systems that act on that data, and see where AI-native challengers can take value.
6. Level 6: Building Agents on Top of Incumbents
   Weigh the strategy of running AI agents on top of an incumbent's platform against the risk of depending on that platform's APIs, data access and terms.
7. Level 7: Choosing a Wedge and Expanding From It
   Pick a narrow starting use case the incumbent serves poorly, and plan the path from that wedge into adjacent workflows and budgets.
8. Level 8: Why Model Access Is Not a Moat
   Learn why access to frontier models does not protect a business when competitors and incumbents can use the same models through the same APIs.
9. Level 9: Moats That Hold Up in an AI World
   Assess proprietary data, workflow lock-in, network effects, and brand and trust as defences, and learn how a challenger builds each one over time.
10. Level 10: Distribution Channels for AI Challengers
    Compare product-led growth, partnerships, marketplaces and AI-assistant channels as ways to reach customers the incumbent already owns.
11. Level 11: Pricing by Seat, Usage or Outcome
    Compare seat-based, usage-based and outcome-based pricing, and use pricing to exploit an incumbent's dependence on seat revenue.
12. Level 12: Platform and Ecosystem Strategy: APIs and MCP
    Learn when to open a product as a platform through APIs and the Model Context Protocol (MCP), and how ecosystems can create network effects or give away value.
13. Level 13: Regulation as Barrier and Opportunity
    Assess how regulation, compliance and liability protect incumbents in some markets and give AI-native challengers an opening in others.
14. Level 14: How Incumbents Respond and How to Survive
    Learn the three common incumbent responses (bundle, acquire, copy) and the counter-moves a challenger can prepare for each.
15. Level 15: Building a Disruption Thesis Canvas
    Combine disruption type, incumbent blind spot, attack vector, wedge, moat, distribution, pricing and expected response into one structured thesis canvas.
16. Level 16: Red Flags in AI Startup Strategies
    Learn the common warning signs in AI-native strategies, such as thin wrappers, no wedge, borrowed distribution, mismatched pricing and ignoring how incumbents will respond.
17. Level 17: Final Challenge: Full Disruption Analysis Exam
    Apply every idea in the course to a full analysis of a market of your choice, from disruption type to incumbent response and red flags.

Access

The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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