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Building AI products

Your AI product strategy is built on a false premise. Here's why getting things 'wrong' is actually the feature.

Reporting by Benedict Evans 1 min brief
Illustration of a transparent lens revealing connected networks across layers of paper.
Exploring the next frontier of AI research.AI illustration by KeyNews
The KeyNews take

Why it matters

A foundational analysis of how to build consumer-scale AI products when the underlying technology is inherently probabilistic and imperfect. This challenges the prevailing assumption that AI must be perfect to be useful—a critical mindset shift for founders and product leaders.

The key facts

4 to know

  1. Published by Benedict Evans (prominent tech analyst)

  2. Focuses on mass-market product viability despite AI limitations

  3. Reframes 'wrongness' as a design problem, not a technical failure

  4. Questions fundamental assumptions about AI product-market fit

Go to the source

Benedict Evansben-evans.com

Publisher excerpt: How do we build mass-market products that change the world around a technology that gets things ‘wrong’? What does wrong mean, and how is that useful?
Read original report
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