Estimation question

Estimate total storage capacity for all videos on Youtube.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical estimation reasoning about storage at very large scale, with clear stated assumptions.

Estimate total video hours uploaded, using a known public reference point, roughly 500 hours uploaded per minute as a starting anchor.
Convert to a yearly figure: 500 hours per minute times 60 times 24 times 365 gives roughly 260 million hours uploaded per year.
Estimate YouTube's operating history, roughly 19 years, though upload rate has grown over time, so apply a lower average rate for early years.
Apply a rough weighted average upload rate across the platform's history, landing at a cumulative total in the low billions of hours.
Estimate storage per hour of video, assuming multiple resolutions stored (a common real practice), roughly 1GB effective per hour including all resolutions.
Multiply cumulative hours by storage per hour to reach a total in the hundreds of exabytes, presented as a rounded range.

Anchors on a real, known reference point, hours uploaded per minute, rather than guessing from nothing.
Accounts for growth over time by applying a weighted average rather than assuming a flat upload rate across 19 years.
States the multi resolution storage assumption explicitly, since it materially changes the per hour storage figure.
Presents the final answer as a rounded range in exabytes, an appropriately large and clearly labeled unit for this scale.

Assuming a flat upload rate across YouTube's entire history, ignoring that growth was much lower in early years.
Ignoring multi resolution storage, understating the true per hour storage requirement significantly.
Giving a final number with the wrong unit or no unit at all, losing the scale of the answer.

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