Estimation question
If you want to build the world's most popular mobile messaging product, how would you estimate how much network bandwidth would be used in a year?
- Estimation
- Hard
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What this question tests
Advanced estimation combining user scale, usage patterns, and technical data unit conversion.
How to approach it
- Clarify scope: define what counts, text messages, voice notes, images, and video shared through the app over a year.
- Estimate the user base for a world-leading messaging app, roughly 2 billion monthly active users (assumption).
- Break usage into message types and estimate daily volume per type: mostly small text messages, with a smaller share of images and voice notes which are far larger in size.
- Estimate average data size per type: text near-negligible, images roughly 200 KB to 1 MB, voice notes roughly 500 KB to a few MB.
- Estimate daily messages per user by type, then multiply by user count and average size per type to get daily bandwidth, then multiply by 365 for the year.
- Sum across types and sanity check the order of magnitude, landing in the range of tens of exabytes per year given billions of users sharing rich media daily.
What a strong answer includes
- Breaks bandwidth by message type instead of treating all messages as equal size, since images and voice notes dominate total bytes despite being a smaller share of message count.
- States clear per-type size and volume assumptions before combining them (assumption).
- Sanity checks the final magnitude against known internet traffic scale, landing in the exabyte range for a billion-plus user app.
Common mistakes
- Treating all messages as equal size, wildly understating bandwidth from media-heavy content.
- No unit conversion shown between KB, MB, and eventual exabyte-scale totals.
- Skipping the media message types entirely and only estimating text volume.
Likely follow-up questions
- Which message type dominates total bandwidth and why?
- How would video calling change this estimate significantly?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop