From an engineering and product perspective, I use generative AI every damn day, so this is not a critique of the technology or any particular application of it. But as someone who's been knee-deep in building AI-native products for 4+ years, when I look at the messaging today in the product and dev ecosystem, it almost seems like a pre-requisite that products have some sort of generative AI capability to be relevant.
The obvious statement is that not every product benefits from having generative AI. But even for the ones that do, it's far from being all upside; there are considerable costs, risks, and even stark product-level trade-offs, which I don't see as widely discussed, but are worth serious consideration.
Oftentimes, as users, we seek products to help us navigate ambiguity within a problem space. We're often looking for systems that help make sense of something that's difficult, complex, or tedious for us to accomplish, understand, or maintain, rather than something that delivers a clear-cut solution to a clear-cut problem (that we may not be able to even articulate).
While extremely powerful when leveraged correctly, generative AI doesn't replace a strong conceptual model, nor does it provide an intuitive experience that delivers a clear, differentiated value proposition. That's the job of a good product, and while generative AI can fit into that as part of a larger strategy, it should always be considered a tool that strategically helps deliver on a core value prop.
