Strategy question
How should Cursor respond when its model providers (OpenAI, Anthropic) launch competing IDEs and CLIs?
- Cursor
- Strategy
- Hard
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What this question tests
Competitive strategy against a supplier turned competitor, a common dynamic for AI-native products built on third-party model APIs.
How to approach it
- State the core tension: Cursor depends on OpenAI and Anthropic for models, and both now ship their own coding tools (like Claude Code).
- Identify Cursor's actual moat versus the model providers: the IDE experience, codebase-wide context, and multi-model flexibility.
- Propose doubling down on being model-agnostic, letting users switch between providers, which a single-vendor tool cannot offer.
- Invest in workflow depth that is hard to replicate quickly, such as deep repo indexing, review tools, and team collaboration features.
- Consider deepening relationships with alternative or open-weight model providers to reduce dependence on any single supplier.
- Define what to monitor, such as churn to Claude Code or Codex CLI among power users, as an early warning signal.
What a strong answer includes
- Correctly identifies the supplier-becomes-competitor dynamic as the central strategic risk, not just a minor competitive threat.
- Names model-agnosticism as a genuine differentiator that single-model tools structurally cannot match.
- Proposes concrete workflow depth (team review, codebase context) rather than vague 'better UX'.
- Suggests hedging supply risk via multiple model partners or open-weight models.
- Acknowledges the tension honestly rather than assuming Cursor easily wins.
Common mistakes
- Assuming Cursor can out-build OpenAI or Anthropic on raw model quality.
- Ignoring the model-agnostic differentiator, which is Cursor's clearest structural advantage.
Likely follow-up questions
- What would you do if a model provider degraded API access to Cursor?
- How would you measure whether model-agnosticism is actually valued by users?
- Would you pursue an exclusive versus open model strategy?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop