Long-Range Google Product Bet: Making Face-to-Face Meetings Happen
Company: Google
Role: Product Manager
Category: Product Design & Strategy
Difficulty: medium
Interview Round: Other
Google's CEO asks you to create a product that makes in-person meetings possible, looking out over the next five to ten years. What would you build?
```hint Decide what "in person" means
The ask can be read more than one way. Commit to a reading, say why it is the bigger opportunity for Google, then work out where getting people together breaks down today.
```
### Clarifying Questions
- Does "meetings in person" mean helping people physically get together, or making remote meetings feel as if everyone shares a room?
- Are we focused on work meetings, such as hybrid teams and client visits, or also on friends and family?
- Should the product extend Google products like Calendar, Maps and Meet, or can it be a new stand-alone product or device?
- What does the CEO want from it over that horizon: Workspace growth, new revenue, or a new platform?
### What a Strong Answer Covers
- A stated interpretation of the ask, a mission, and the long-term trend that makes it worth a five-to-ten-year bet
- A chosen user segment with reasons, and the concrete points where meeting in person fails for that segment
- A phased roadmap from a near-term product to the long-term vision, built on what Google already owns
- Prioritization with explicit criteria, plus a success metric and guardrails
- Risks, especially location privacy and workplace surveillance concerns, and how they shape the design
### Follow-up Questions
- How would your answer change if the CEO meant technology that makes a remote meeting feel in person?
- What would you ship in the first year to learn whether the long-term bet is right?
- How would you keep managers from using the product to monitor attendance?
Overview: A product strategy interview prompt in which Google's CEO asks for a product that makes in-person meetings possible over the next five to ten years. It tests how a candidate interprets an ambiguous ask, picks a user segment, finds pain points, and lays out a phased roadmap with metrics and privacy risks.