I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.
The issue wasn't sharing a database, it was not being clear about who owns what.
Having multiple teams with one code base that has one database is fine. Every every line of code, table and column needs to be owned by exactly ONE team.
Ownership is the most important part of making an organization effective.
The vast majority of people who write don't have a voice worth preserving. The rest can build out a voice document to make sure the AI doesn't strip it out.
With the gap between 1 and 2 being driven by the underlying quality of the writer and how well they use AI. A really good writer sees marginal improvements and a really poor one can see vast improvements.
If he was making an argument from data it would be cheap. But he's making an argument from lived experience against both data and someone who lives here.
Are you saying that the data is wrong and the only way to know what it's truly like to make it in America is to not live there? That's sounds insane.
The American media writes articles about what gets clicks not what is true.
If you don't believe the enormous amount of freely available data on the internet. I am American, I had grandparents who were American. Poverty was a whole different beast in the 1930's compared to today.
>But at least they could afford a house, right? I think a lot of people would accept living in a house without AC and more likely to catch fire. Is a house like that cheap today? No, right? It's crazy expensive as well.
I don't know many people who would rather live in a house without climate control than an apartment. A house from 1936 with no improvements is worth very little. When purchasing a house like that you're mostly buying the land.
> Car technology in the past was worse, we know that. Cars were more affordable though.
Car ownership in 1936 was far below what it is today.
> Like today then.
No, groceries were far more expensive. You can buy far more gallons of milks, eggs, lbs of ground beef, or potatoes at today's prices with todays median wage than you could in 1936 on the 1936 median wage. We have records of how much people made, and the cost of basic staples. This isn't something you need to guess about you can just google it.
> Young people are rotting at home unable to go ahead with their lives because wages nowadays are not enough to pay for a house and a family. Why do people try to deny this obvious reality? Productivity didn't benefit everyone equally and people in the past had more opportunities to build a life inside a standard that was socially acceptable.
Because 100 years of data says that this is a difference in expectations vs people being poorer. Yeah housing is more expensive than it should be due to regulation but despite that people are still much better off.
> You're ignoring the gorilla in the room. Why can't one live in a comparable manner today and bank the difference?
For two reasons.
1. They're illegal. You're not allowed to build a house to 1936 climate, safety, and fire codes with un-licensed labor. And boarding houses were effectively banned.
2. Market. Most people would rather live in a smaller apartment than 1936 style un-climate controlled death trap.
And the reasons are the same for cars. You legally can't sell a new 1936 car, and even if you could most people would rather drive an 10 year old civic.
The most impactful censure is not the government coming in and trying to burn copies of studies. It's the the subtle social and professional pressures of an academia that has very strong priors. It's a bunch of studies that were never attempted, never funded, analysis that wasn't included, conclusions that were dropped, and studies sitting in file drawers.
See Roland G. Fryer Jr's, the youngest black professor to receive tenure, experience at Harvard.
Basically when his analysis found no evidence of racial bias in officer-involved shootings he went to his colleagues and he describe the advice they gave him as "Do not publish this if you care about your career or social life". I imagine it would have been worse if he wasn't black.
See "The Impact of Early Medical Treatment in Transgender Youth" where the lead investigator was not releasing the results for a long time because she didn't like the conclusions her study found.
And for every study where there is someone as brave or naive as Roland who publishes something like this, there are 10 where the professor or doctor decided not to study something, dropped an analysis, or just never published a problematic conclusion.
Compared to like a phase 3 clinical trial, sure. Compared to your average paper, and especially your average business paper I don't think that's the case.
This work would suggest that the WFH movement would see a rise in sr. engineer salaries and a reduction in jr. engineers salaries, which we haven't seen.
I get the idea of publicly disclosing security issues to large well funded companies that need to be incentivized to fix them. But I think open source has a good argument that in terms of risk reward tradeoff, publicly disclosing these for small resource constrained open source project probably creates a lot more risk than reward.
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.