Seems like there are a number of issues with this.

1. Not defining "reliability challenge" in a meaningful way. (How many of these are problems that are expensive or time-consuming to repair? How expensive and how time-consuming? Are these problems that prevent the car from driving safely, or are they inconveniences that can be put off?)

2. Not controlling for manufacturer. (Toyota has long-been regarded as a reliable manufacturer, but they make 2 plug-in hybrids and 1 EV, all of which are new this year. Meanwhile, they offer about a dozen different traditional hybrids. I can believe that the Tesla Model 3 is less reliable than the Toyota Camry, but is a full-electric Hyundai Ioniq less reliable than a Hyundai Sonata?)

3. Including plug-in hybrids and full electric vehicles as one category. (Plug-in hybrids combine the old breakable parts such as transmissions with the new breakable parts such as lithium batteries. This is the trade-off that buyers make to get the efficiency of an electric vehicle at short ranges and the convenience of an ICE at long ranges.)

Important to note that this is a workaround. Solidarity strikes (which normally include general strikes) are illegal, but there's no law that prevents every union from happening to strike on their own behalf at the same time.

American unions are kneecapped by the government. The 1947 Taft-Hartley Act made solidarity strikes (and several other forms of labor protest) illegal. It also opened the door for states to enact "right-to-work" laws.

This law is still standing in part because US courts have been anti-labor for their entire existence, aside from a brief period during FDR's administration.

“Your hands don’t look right!”

• AI models in 2023

They stopped publishing youth unemployment because it was useless data, the job of the youth is to become educated, not to work in the economy. Having a low youth unemployment means your youth are either not getting educated, or are being forced to work during their education.

At least in the US, unemployment is almost always defined defined as people who want to work but can't find work. Students are generally excluded.

I don’t think the drive actually failed. The article said that the files disappeared from the drive one-by-one, which sounds like a firmware bug to me.

You could theoretically have the same problem due to a buggy RAID controller or driver.

I bet with current knowledge and technologies, humanity could afford to lose 99.999% individuals, and the remaining million would still be better off than those primordial 10 thousand. Society is not likely to collapse.

There's a line of thinking that if we backslide far enough (i.e. lose the Internet, lose electronics, and lose electricity generation), there's no coming back to this point. The industrial revolution wouldn't have happened without easy-to-extract coal and oil. Today's reserves require a fairly high level of technological advancement to access.

For what it's worth, I don't think that humanity is going to hit that point of no return.

For now, we're special.

LLMs are far more training data-intensive, hardware-intensive, and energy-intensive than a human brain. They're still very much a brute-force method of getting computers to work with language.

AIs are trained for the equivalent of thousands of human lifetimes (if not more). There's no precedent for anything like this.

There are a few reasons why music models haven't exploded the way that large-language models and generative image models have. Maybe the strength of the copyright-holders is part of it, but I think that the technical issues are a bigger obstacle right now.

• Generative models are extremely data-inefficient. The Internet is loaded with text and images, but there isn't as much music.

• Language and vision are the two problems that machine learning researchers have been obsessed with for decades. They built up "good" datasets for these problems and "good" benchmarks for models. They also did a lot of work on figuring out how to encode these types of data to make them easier for machine learning models. (I'm particularly thinking of all of the research done on word embeddings, which are still pivotal to large language models.)

Even still, there are fairly impressive models for generative music.