The bridge to wealth is being pulled up with AI

Leaving aside the sloppiness of the article, I think a lot of the behaviour in recent memory around crypto and meme stocks, and to an extent the whole rotating bubbles mode that markets seem to be in, can be attributed to this general trend.

It's harder and harder to see the traditional path from school to work to some acceptable level of family wealth as being effective/worthwhile, and so we see different flavours of roulette-with-more-steps capturing more of the population's attention.

There are lots of subtle LLM-ism sentence structures that you can perceive, sort of subconsciously. Spend any amount of time with Claude and you'll unconsciously start to copy them.

This essay has several: Sentence structures, heading phrasing, and yes, dashes. I expect people know to start steering LLMs away from em dashes at this point though.

The sims are really well done, the dynasty simulator especially. You can actually stress-test the argument instead of just nodding along. Appreciate the craft.

I have issues with the economics though. The income model is calibrated from three separate literatures that were never estimated together. Different samples, different decades, different identification strategies. Then the big move, βIQ drops to 0.10, βW jumps to 0.65, gets asserted as a scenario and fed into the simulator like it’s an empirical result. The interactivity makes it feel rigorous but you’re mostly just exploring the author’s priors.

The skill premium has survived every automation wave we’ve thrown at it, including ones that felt just as terminal. ATMs didn’t kill bank tellers. US teller count went from ~300k to ~500k between 1970 and 2010 (see Bessen paper), because cheaper branches meant more branches.

The essay waves off Jevons with “human attention is fixed” but US legal spend is ~$400B/yr against ~$100B in estimated unmet need (LSC data). That’s 25% latent demand just sitting there at current prices. I would see that as saturated.

The “27.5% programmer decline” is doing a lot of work. BLS SOC 15-1251 (“computer programmers”) is a narrow legacy bucket that excludes software devs, DevOps, ML engineers, all of which grew. Total software dev employment (15-1252) was up in 2024 vs 2022. Classification artifact, not a labor market signal. And the historical base rate on “this time the bridge closes for good” is… zero. Power loom, ag mechanization, manufacturing to services, analog to digital,etc. each killed the old skill-to-capital channel and built a new one within a generation. You can’t just assert AI is different from all prior GPTs, you have to show the mechanism that prevents a new channel from forming. The essay doesn’t really do that for me.

The assortative mating argument cuts against itself imo. If credentials lose signal value, the institutions where sorting happens (elite unis, professional firms) lose sorting power too. The essay predicts mating shifts to “wealth directly” but… how exactly? Credentials were legible because institutions verified them. Strip the institution and you’d expect noisier matching, not tighter. The Fagereng et al. paper it cites is Norwegian data, which has among the lowest wealth inequality in the OECD. Not obvious that translates.

Again I generally like the writeup, and I think the essay is right that capital returns are pulling away from labor income and AI accelerates it. But “the bridge narrows and the crossing gets harder” is the defensible version. “Closes permanently within a decade” requires believing something unprecedented will happen on a specific timeline..

If you denominate your income in silly money such as USD, then it won't go down, probably will go up!

However if you start asking questions on how much housing medical and materials it buys, then I think it will squeeze people even more than now.