This salmon had become more distracting to babysit than if I’d just cooked it on my own. This salmon had become a metaphor for Silicon Valley itself. Automated yet distracting. Boastful yet mediocre. Confident yet wrong. Most of all, the June is a product built less for you, the user, and more for its own ever-impending perfection as a platform. When you cook salmon wrong, you learn about cooking it right. When the June cooks salmon wrong, its findings are uploaded, aggregated, and averaged into a June database that you hope will allow all June ovens to get it right the next time.
And yet, June is taking something important away from the cooking process: the home cook’s ability to observe and learn. The sizzle of a steak on a pan will tell you if it’s hot enough. The smell will tell you when it starts to brown. These are soft skills that we gain through practice over time. June eliminates this self-education. Instead of teaching ourselves to cook, we’re teaching a machine to cook. And while that might make a product more valuable in the long term for a greater number of users, it’s inherently less valuable to us as individuals, if for no other reason than that even in the best-case scenarios of machine learning, we all have individual tastes. And what averages out across millions of people may end up tasting pretty … average.Mark Wilson
Interesting idea! I’m sure many people would love to have an intelligent oven that can quickly cook meals without much intervention while they’re doing other things – the closest thing today to a Star Trek food replicator. But the current implementation looks all kinds of bad.
I would say the quotes selected from the article highlight not problems with Silicon Valley design directly, but rather the limitations and downsides of applying algorithms and machine learning as an universal solution to every problem under the sun: how AI could slowly replace humans in the most trivial, but still enjoyable tasks; the opaque nature of complex algorithms, asking for faith in proprietary software (which is clearly failing miserably in this example); and average results when people really want something exceptional – or at least matching their tastes.