Our experience shapes our decisions, and vice-versa.
No AI can experience something for us. When learning something, we have to understand it for ourselves. Work can be important to experience, in turn coloring and shaping our thinking. Should that work be done by AI, in place of us?
Maybe we decide we don’t care. Learning necessarily assumes a boundary around how much we need to know—it’s impossible to develop a curriculum that exhaustively explains everything. We outsource many tasks to others as part of modern life. We delegate grunt work.
Or maybe we decide we do care. When we take the shortcut, we have to also accept that we forgo the journey. Is that experience something we care about?
“One analogy I’ve given in the past is that science is a little bit like going on a hike. You’ve heard there’s some interesting, beautiful waterfall out there. So, you decide to to to go hike with some friends to to find it, but you need to make a map. You get lost a little bit, but maybe while getting lost, you discover something else which is interesting, and you make a note of it. On the way to to this waterfall, you find an even more spectacular waterfall in the distance. You can’t get there yet, but maybe some future hiker will figure out a way to get there, too. And so there’s there’s a whole process to get to your goal, which is also very valuable. But these AI tools, they can be like helicopters that will just fly you directly to this waterfall and you can see it and then you fly back, but you learn nothing about how to get there. You may not see any other interesting phenomena than the specific thing that you asked for. And so even though technically you achieve your goal much more efficiently, there may be something that that is lost.”
—Terence Tao, in “The Paradox at the Heart of AI and Science”
On the other hand, AI is increasingly capable of deciding for us. When intelligence is no longer a bottleneck, what is?
When technology let us cook food by touching a button on the induction instead of starting a fire from twigs and logs, chefs still needed to know how heat affects the ingredients being prepared, and why they wanted to use that particular cooking technique.
When technology let us compute calculations with a calculator, we still needed to know which formulas to apply, how these mathematical theories or concepts work, when to use them, and why we decided to use those ones in particular.
When this technology is an increasingly able intelligence, we’ll let it decide and do things we care less about. It’s relative. So we have to always know—what do we really care about? Subject expertise and personal purpose are key to knowing what we want.
We bring our own direction and motivation to how we use technology to make things easier for us. When we’re unclear about what we really want, we do busywork and get slop.
At the end of the day, you decide: What do you want?