I’m a systems thinker. It started with software. Old enough to have had an TRS-80. Built a small-town startup out of my first programming job at 15 (1993). Later my interest in systems escaped the computer world and has reached economics, philosophy and philanthropy. So, that’s how I got here.
These days, my first outlet is my Substack. Perhaps unsurprisingly, AI has become a central topic. My years in software took me into architect roles, with security, reliability and development processes being core there, though you’d probably call me a generalist as I can’t resist learning about everything.
I see policy as very important, but I see a big gap between the public and the internals of the IT industry, much less the AI industry. I first became interested in EA when I heard about it from some local Chicago groups (2010′s). Climate change would have been top of my mind at that time.
In the background, I’m very interested in global development. I didn’t get concerned about climate change because I’m an environmentalist. The typical environmentalist has a much deeper prioritization of nature than I do. I respect that view, but my path was always that humanity depends upon our environment, and it’s inescapable that we have to take care of it, so it can take care of us. Mostly those views end up at the same place in terms of actions, but they do involve different ways about talking about the why of things.
It’s a brave project. I agree that the narrow views are deceiving. I wonder how helpful economics can be in understanding a post-AGI world. It can extrapolate from some initial conditions, or at least make a good effort at that. But the initial conditions are uncertain enough that you have to maintain many models that have lost correlation with each other. When those scenarios have lost their correlation, you can’t average them, or smooth them, you simply hold them apart. I’d say you’re gaining knowledge in the pursuit, but will it ever be useful knowledge?
If you pick one scenario, you can start to act as you believe it. But that’s not very useful if it’s one of many and the pick was arbitrary. You probably cannot find any compromise when correlation is absent. It’s the same problem you have with all the narrow views. They vary one condition, and hold others constant, when it’s unlikely anything holds constant. Would you average all of their conclusions? You really can’t. Moving from 1 dimensional scenarios to multi-dimensional scenarios doesn’t reduce the intractableness of the problem.