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Google·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
Jun 2026

Summary

Google PM interview with a single case question about Waymo insurance pricing. Pretty open-ended, no obvious right answer, which was either freeing or terrifying depending on how you look at it.

Questions Asked (1)

Q1

Google opens Waymo's driverless fleet to the general public. How would you set insurance pricing for the program?

Pricing & MonetizationProduct StrategyAdaptability & Ambiguity
Author's notes

I went straight to risk segmentation and kind of forgot to question the business model first, which I think hurt me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: is this pricing for Waymo's own insurance product, or for underwriting by third-party insurers? Then structure your answer around risk assessment, data-driven pricing, and regulatory considerations, emphasizing the unique aspects of autonomous vehicles. Conclude with a phased approach to pricing as data accumulates and public trust grows.

Pro tip: Acknowledge that traditional auto insurance pricing models rely on human driver behavior, which is irrelevant for autonomous vehicles; instead, focus on vehicle system reliability, operational design domain, and cybersecurity risks. Also, mention the importance of partnering with reinsurers and regulators to build a sustainable insurance framework.

1. Clarify the Scope and Objectives

Determine whether Waymo is offering its own insurance or facilitating third-party insurance, and what the primary goals are (e.g., profitability, adoption, risk mitigation).

2. Identify and Quantify Risks

Assess risks unique to autonomous vehicles: software/hardware failures, sensor limitations, cyber threats, and liability in mixed autonomy environments. Use simulation and real-world data to quantify.

3. Design a Data-Driven Pricing Model

Leverage telemetry, disengagement reports, and incident data to create actuarial models. Consider usage-based or subscription models, and factor in geographic and temporal variations.

4. Address Regulatory and Liability Frameworks

Engage with regulators to establish liability rules and insurance requirements. Consider pooling risk or creating a captive insurance entity to manage exposure.

5. Iterate and Scale with Feedback Loops

Start with pilot pricing, gather data, and adjust dynamically. Communicate transparently with users and regulators to build trust and refine the model.

Key Points to Mention

  • Risk factors shift from human error to system reliability, cybersecurity, and operational design domain.
  • Data from sensors, disengagements, and simulations can inform actuarial models.
  • Regulatory landscape is evolving; proactive engagement is key.
  • Liability allocation between manufacturer, operator, and third parties must be defined.
  • Pricing models could include usage-based, subscription, or pooled risk approaches.
  • Public perception and trust impact willingness to pay and adoption.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.