Bryan Caplan

Volume is incredibly important for any truly serious endeavor. Bryan Caplan's excellent post Do Ten Times As Much makes the point succinctly. [1] I've come to believe the real reason people shouldn't pursue these kinds of sports or games professionally unless they're born with a deep thirst for winning them is twofold. First, if you love it from day one, the chances that you're actually better than average are higher than they would be for a randomly selected person in the population (e.g. Nike CEO Phil Knight really was able to run a 4-minute mile in college). But second, deeply enjoying the game makes the requisite 10 (20, 50, 100) thousand hours you need to become a true pro go much faster than for someone who's just putting in the reps, and it even gives you drive to do related things in the likely case that that doesn't pan out (e.g., Nike CEO Phil Knight did not become an Olympian after college, he instead sold Japanese shoes out of his car for half a decade or so as a side hustle to track and field meets across the country while working a day job). [1]: https://www.betonit.ai/p/do-ten-times-as-much

Interesting fact. Bryan Caplan is a economist who became famous for making bets on the future. He would take the "nothing ever happens" side of the bet, and he would almost always win. In 2023 he took the "nothing ever happens" side of an AI bet. Just 6 months later, he wrote: "When the answers change, I change my mind [...] Base rates have clearly failed me. I’m not conceding the bet, because I still think there’s a 10-15% chance I win via luck... make no mistake, this software truly is the exception that proves the rule." https://www.betonit.ai/p/gpt-retakes-my-midterm-and-gets-an At this point, straightforward trend extrapolation suggests that AI continues to get better and better. Arguably, continued rapid AI progress is the "nothing ever happens" bet at this point. If AI progress stops, or slows dramatically, something will have happened.

It's generally a myth that government employees are somehow receiving low total compensation and they could do better in the real economy. Here's one government employees providing stats and experiences about what it is like to work for the government: https://www.betonit.ai/p/the-joy-of-government-employment

I like to drive. Edit: see also this pro-car, pro-toll note by Bryan Caplan. https://www.betonit.ai/p/cars-could-be-even-more-convenient

In addition to passing bar exam[0], improved performance on medical questions[1], economics questions that experts thought it was years away from[2], all the other things marked in green on page 6 were just the changes from 3.5 to 4: https://arxiv.org/pdf/2303.08774 4o added image analysis. The o-series starting at o1 improves on 4o as per the margins in these charts: https://openai.com/index/learning-to-reason-with-llms/ I'll have to wait and see about o3, because only the mini model is out yet. [0] https://law.stanford.edu/2023/04/19/gpt-4-passes-the-bar-exam-what-tha… [1] https://ai.nejm.org/doi/full/10.1056/AIdbp2300192 [2] https://www.betonit.ai/p/gpt-4-takes-a-new-midterm-and-gets