🐯虎嗅•Stalecollected in 11m
Data Design: True AI Product Moat
💡Data design > algos: Build AI moats via product choices, not PhDs
⚡ 30-Second TL;DR
What Changed
A product tracked resume edits against interview invites for causal training data.
Why It Matters
Shifts AI focus from models to product decisions, enabling startups to compete via data flywheels without superior algorithms.
What To Do Next
Map your AI product's user flow to identify and implement one closed-loop data signal this week.
Who should care:Founders & Product Leaders
Key Points
- •A product tracked resume edits against interview invites for causal training data.
- •Data design layers: entrances from user behaviors, structured labels, closed-loop flows.
- •'Let users do' embeds implicit labels vs noisy surveys.
- •Sequence feedback captures context over single-point signals.
- •Accumulable data creates time-based moats vs disposable logs.
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Original source: 虎嗅 ↗