Strategy question
Should Facebook invest in a separate experience for news?
- Meta
- Strategy
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Strategic trade-off reasoning involving reputational, regulatory, and business considerations, grounded in real history.
How to approach it
- Frame the real context: Facebook actually launched and later scaled back Facebook News, paying publishers, a useful grounding fact.
- Weigh the upside: could rebuild trust and engagement around quality information and create a licensing relationship with publishers.
- Weigh the downside: news is a low-margin, politically fraught category inviting regulatory scrutiny disproportionate to its engagement value.
- Compare against the core: the feed already surfaces news organically, so a dedicated surface may not add proportional value versus the risk and cost.
- Recommend based on the real outcome: consistent with what Meta actually did, deprioritize dedicated News investment and let news live in the core feed and Groups.
- Note the risk of the recommendation: ceding ground on quality journalism distribution could hurt public trust longer-term.
What a strong answer includes
- Grounds the answer in the real, known history, Facebook News launched then scaled back, rather than speculating blind.
- Weighs concrete costs, publisher payments and regulatory attention, against a real but modest engagement upside.
- Reaches a clear recommendation with an honestly stated risk, not a wishy-washy 'it depends.'
Common mistakes
- Not knowing or acknowledging that this is a real product Meta already tried and pulled back from.
- Giving a one-sided answer with no counter-risk to the recommendation.
Likely follow-up questions
- What would change your recommendation?
- How would you measure whether a news surface builds trust versus just adding cost?
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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