I think my "cautious optimism" mostly came from the fact that Gemini found subtle problems with the counterfeit packages that I (someone who looks at fakes as part of my job) didn't notice - like mismatched info between the tube and box, and a malformed Irish postal code. If I didn't notice those, then I seriously doubt that a consumer glancing at a package would notice them. In that case, Gemini's insights would be valuable info for a consumer.

Gemini wrongly called an authentic product a fake, but that was mainly because there really were typographical errors in the authentic product's ingredient list. That's more of an indictment of the manufacturer than it is Gemini.

Finally, a lot of the things that Gemini got wrong seemed to me like they could reasonably be attributed to things like optical artifacts in the photos (glare, shadows, stuff like that). Better/more photos might improve that.

All that being said, this is obviously a tiny study of a single AI tool with a single cosmetic product, so I should probably withhold my "cautious optimism" until we have more data.

That was my thought too. It failed amazingly at the task yet they were still thinking it was helpful. I had to go back and reread bits because I thought I’d missed a bit where Gemini hadn’t cocked up.

Very odd conclusion to an otherwise interesting experiment.

Perhaps a softer critique would be that the author seems to assume that (A) feature-detection will map to (B) accurate and useful conclusions, and that isn't necessarily true.