Why generative AI is a raw material, not a finished product
The new rhetoric around AI regulation
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My previous blog posts on AI safety and effectiveness have tended to take the perspective of those who build AI systems, since until recently users traditionally held very little responsibility in the AI space. I wasn’t writing only for these folks, but also for a general audience of people keen to take their places as informed citizens in an increasingly data-fueled world.
In the context of the last decade, both groups needed to understand the same thing: what AI is useful for and how smart organizations make decisions about the AI systems they build.
I’d intended for business leaders and AI professionals to use my musings to sanity check their own thought processes, while members of the general public could use them to learn enough about the decision-making that goes into these systems to insist on accountability and better technology leadership.
Seeing generative AI as a raw material might be the perspective shift that’ll make everything click.
In a previous blog post, I explained that the key difference between today’s most hyped AI products and the traditional enterprise-grade AI of the last decade boils down to UX:
Last decade’s AI user experience (UX) emphasized seamlessness, so users didn’t need to know about the AI components they were interacting with. That’s changing with a new philosophy that encourages a user’s direct interaction with AI outputs as useful raw materials rather than finished products.
In other words, let’s give the users an easy interface for tinkering with AI and let them use it in whatever creative ways they like. Today’s AI buzz is the result of all those suddenly-empowered voices. The revolution is a more of a user experience (UX) revolution, much more so than an AI revolution per se. And as a UX revolution, it deserves its hype: what an exciting new way to interact with computers!
