It’s a derivation of the Y combinator from ruby lambdas

To be clear, there is real, underlying value & revenue. But there’s a lot of froth right now

The greats on youtube are also worth watching: 3B1B, numberphile, etc.

[0] https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9Gv... [1] https://youtu.be/qx7hirqgfuU?si=8zmrbazuvnz379gk

The short answer, as far as I’m aware, is that no one really knows. The longer answer is that we have a lot of partial answers that, in my mind, basically boil down to: model architectures draw a walk through the high dimensional vector space of concepts, and we’ve tuned them to land on the right answer. The fact that they do so consistently says something about how we encode logic in language and the effectiveness of these embedding/latent spaces.

Or another thought; why is it that a stochastic parrot can solve logic puzzles consistently and accurately? It might not be 100%, but it’s still much better than what you might expect from a markov model of ngrams.

Openclaw is only sort of interesting. How to vibe code your first product is uninteresting. Claims about productivity increase from model usage are speculative and uninteresting. Endless think pieces on the effects of AI slop are uninteresting. There’s a lot of hype and grift and bullshit that is downstream of this very interesting technology, and basically none of that is interesting. The cool parts are when you actually open the models up and try to figure out what’s going on.

So no, I’m not bored of talking about AI. I’m not sure I ever will be. My suspicion is that those who are bored of it aren’t digging deep enough. With that said, that will likely only be interesting to people who think math is fun and cool. On the whole, AI is unlikely to affect our lives in proportion to the ink spilled by influencers.

You could also lie and claim your address as a US address, and then just live in another country. This is obviously illegal, but I’ve met a few people who made it work for a while. But I’m also speaking abstractly on the internet, so maybe I’m just making all this up.

The first is that very few people (especially rich people) are anonymous. A motivated person who has had a psychotic break can be very dangerous, and if you’re even a little bit famous, the probability of that happening to you goes up substantially.

The second issue is the one that everyone is getting riled up about - wealth inequality.

These are distinct issues, and I think it does harm - in the form of polarization - to not explicitly call them out.

With all that said, I haven’t mentioned anything about the economics, and like much of the AI industry, those might be overstated. But running a local language model on my macbook that helps me with messaging productivity is a compelling idea.

How do you measure developer productivity? Code quality? Developer happiness? As far as I know, no one in the industry can put concrete numbers to these things. This makes it basically impossible to answer the question you pose.

The book basically argues that a significant amount of the world is headed for destabilization, and a destabilized world involves a lot less trust.

Side note: personally, I find the writing style and general tone to be hyperbolic, but some of the analysis is interesting.

Eliminating jobs has absolutely happened. How many jobs exist today for newspaper printing? Photograph development? Film development? Call switchboard operation? Technology absolutely eats jobs. There have been more jobs created over time, but the current economic situation makes large scale jobs adjustment work less well.