Data-Driven Leadership and Careers

Why multi-purpose systems are hard to design safely

A non-AI analogy to explain a difference between generative and traditional enterprise AI systems

In the previous installment of this series, I told you that solving trust and safety for traditional enterprise-scale AI systems is a walk in the park compared with trust and safety for generative AI. Let me summarize the two key insights from that article:

• Most enterprise-grade AI systems of the past decade were designed to do one very specific thing measurably well at scale.
• It’s a lot easier to protect a varied group of users from a single-purpose system than to protect the same group from a multi-purpose system.

I’ve written this article for those who felt that those ideas zoomed by too fast and need a bit of digestion. (If that’s not you, head straight to Part 4.)

So, to understand these ideas in a non-AI setting, imagine you’ve been tasked with designing a set of bread-cutting stations for shoppers to use in every grocery store in a major national chain.

Image: Wikipedia

That’s a lot of stores and a lot of bread cutting stations, which might be serving millions of shoppers. That’s plenty of…