I absolutely loved this essay. There are a lot of cautionary points about a post-commodity, relational economy would look like, and I agree with all of them. But there is also well-argued optimism too. The closing paragraph in particular: “What we can say is that the relational economy does not have to be the “Nosedive” dystopia. A free and plural post-commodity future of work is possible. This world wouldn’t be a utopia but it might be one where a teenager at the back of a classroom could sense, correctly, that there is more than one way to exist and be fulfilled.”

There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a *growth cycle* and the latter leads to a *dependence spiral*. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.

Honestly, this is scarier than the "reading for pleasure" graphs. graphsaboutreligion.com/p/ge…

Mason Miller has struck out 56 of the 110 batters he has faced this season (50.9 K%)

Paul Skenes is an awesome human in case anyone was wondering. He was driving by the Ingomar Franklin Park Little League baseball fields last night and saw the lights on and just pulled on in. He was there over 2 hours signing, taking pictures and playing catch with the kids.

The principal parts of φέρω, obvi.

A very good book -- there is undoubtedly a future for intellectual work, but perhaps not in the form we have grown used to. Congratulations @cwellmon!