I have long associated with smart nerdy folks with broad interests, especially re tech/future. Groups like “extropians”, “rationalists” and “effective altruists”. While there are many smart nerdy amateur groups who focus on rather concrete topics, like old cars or poker, the folks I’ve like have had a “taste for abstraction”. They like more to reason abstractly, and so over time have collected many abstractions to help them reason. This seems to me a key common element across the diverse topics they like.

When such people are nearer to academia, they tend more to learn established abstractions from academic disciplines. Others tend more to collect abstractions from online thinkers, who more often invent their own new abstractions, instead of using established ones. Such novel abstractions are generative, adding to our innovation in abstractions. But they also tend to be less reliable, leading such thinkers more often astray. Academics, in contrast, are slower to adopt new abstractions, as they hold new proposals to higher standards.

This is my main criticism of the communities collected around these online thinkers. I like them personally, but think they too often go wrong by inventing new abstractions, and then overly trusting these due to their trusting folks inside their community much more than outsiders. In particular, I think such folks have been led astray by new abstractions re AI risk; they’d do better with vetted abstractions from biology, culture, or economics.

I’m now an academic, though I was once an amateur. Over my lifetime, I have been tempted into many diverse topic areas, due to their immediate interest to me. This induced me to learn many new-to-me-but-standard abstractions. As a result I’ve stumbled into a polymath lifetime strategy: the more fields I learn, the more intersections I find where I can apply the tools of one field to the problems of another.

As a result my productivity has increased over time, even though I’m getting old; knowing N fields empowers me to look for N(N-1)/2 intersections between fields. Most of my contributions have been applying stuff we know in some areas to other areas. And note how this approach allows you to be a pretty reliable contrarian. Contrary approaches within a discipline tend to be wrong more often than just applying established abstractions from other disciplines to this one. As folks inside each discipline tend to resist accepting corrections from other disciplines, that will make you a contrarian, at least for a time.

Oddly, few people plan when young to adopt such a polymath life strategy. I think this is in part because we find it hard to believe that other fields besides where we started actually know a lot. When we feel that our intuitions seem adequate to guide practical action in an area of life like romance or physics, we find it hard to see that there could be that much to learn about it. I have been surprised by just how powerful are the abstractions that I’ve learned from areas outside my early life focus areas, and how much more productive I’ve become by learning them.

Academia neglects interdisciplinary work that combines insights from multiple areas. Each field has expert versions which experts use among themselves, and public versions seen by outsiders, and people in field B won’t accept your using the expert version of A if that differs from the non-expert version of A that B folks have in mind. Also, if you hold an academic event on the topic of A intersect B, you’ll usually invite the most prestigious people you can get in A, and in B, but you won’t usually invite people who have specialized in A intersect B, as they will tend to be less prestigious.

Thus humanity’s beliefs on many important topics have long been just inconsistent and incoherent across disparate fields of inquiry. Creating a huge opportunity to learn lots of big stuff fast: search for more contradictions between fields, and resolve them. And as humans have long neglected this opportunity, this may now be a promising option for LLMs, who seem to know quite a lot on a very wide range of topics.

Thus we might get a huge burst of progress soon if only we could get LLMs to look carefully at pairs of distant areas, ask if what they know about those two areas are in conflict, and if so substitute new more consistent views. Use the new better consensus views to lather, rinse, and repeat. Of course I’m sure there will be many obstacles to making this work in practice. Maybe LLMs just aren’t able to reason well enough yet in such cases. But maybe we should try?