I have several friends who used to lament the loss of manufacturing jobs as a ticket to the middle class, but now say they're going to protest a proposed data center, which feels a bit ironic. None really link it to AI's social impact like Gizmodo does here, the argument always starts with "I don't understand what they need a data center for" (often genuinely wanting me to explain it since I work with computers) and then goes into noise, water use, or loss of farmland. I'd probably not want to live near the noise pollution of a data center or any other kind of noisy industry either, so their views aren't incomprehensible or anything (though the farmland one makes zero sense to me), but it does seem like an instance of the revealed preference that many Americans are just deeply skeptical of anything more intensive than an Amazon warehouse going on in their area, even if they enjoy a fantasy version of the country where (usually other) people have a nice union job in a widget factory. It's good to remember when political extremists try to claim there's some easy fix that will make America an industrial powerhouse again; in reality, most of us don't want anything close to that.

I think locals (unlike economists apparently) are also thinking what is produced in particular in those facilities.

Yes, a chip fab might in fact be more of an environmental hazard, but at least the benefits of the products are clear.

I think this even used to be true with data centers before AI: It's sort of easy to see the need for one if you're hosting your own website in one or at least understand a bit more how the internet works.

The problem with AI is that both the product and its production now have a negative reputation.

Why should people tolerate the downsides of a factory if its only product is actively causing job losses, mental health problems and large-scale cognitive decline?

Datacenters don't create jobs and drain local resources.

It takes a couple dozen people to fully staff a datacenter. That's literally a rounding error in employment statistics.

Framing it as an argument against American manufacturing or jobs is complete nonsense.

> Datacenters don't create jobs and drain local resources.

Recap

    DATACENTERS DON'T CREATE JOBS

    DATACENTERS DRAIN LOCAL RESOURCES

    DATACENTERS DRIVE UP PRICES Of CRITICAL COMPONENTS 5-FOLD

    [there are countless more lines but you get the idea]

Datacenters are the greatest epic tragedy since car culture/trespassing culture mass-murdered childhood.

>Framing it as an argument against American manufacturing or jobs is complete nonsense.

It is a strong argument against those things in at least three ways: (1) if you want to mandate that high tech manufacturing come back to America (e.g. "just make iPhones here" which seems to be a common sentiment in my circles), it would be foolish to suppress an industry creating insatiable demand for high tech components and ensuring that it goes offshore at a time when other countries are also trying to build more local manufacturing. To say nothing of components and construction materials that are already manufactured here. (2) there are very few types of industry that would create less local environmental impact than a data center, no chance if you think data centers use too much water you'd be okay with the toxic chemicals that chip fabs work with. (3) since America is a high wage country with a lot of R&D strength, any factories we build are naturally going to be much more automated than they are in low wage countries. Being against entire industries because individual facilities don't create enough jobs would probably be quite limiting in the types of manufacturing you'd approve of as well.

How are data centers creating manufacturing jobs?

I'm generally defensive of Reuters coverage of Tesla (most of the things Musk has called lies turned out to be true) but this one does seem overly sensationalized. About half of the article focuses on the fact that Tesla does precision mapping and annotation of routes...which yes, all self-driving cars rely on this, it's a major safety advantage they have over human drivers that don't have access to a 3D map and near real time feed of road conditions everywhere. They also have teams scrutinizing collisions and near misses to improve the system. I'm not sure why Reuters frames all this as a deceptive "behind the curtain" thing or a strike against the safety of the system.

The rest is about the flaws of Tesla's safety statistics, which Tesla themselves appeared to acknowledge on their last big earnings call where they said wider launch of Robotaxi would have to wait for software improvements to improve safety. This is why getting safety information from a neutral party is so important.

I guess that first piece was important to me. I actually assumed, based on statements Musk has made in the past, that they are purely working off cameras and AI. Isn't that his whole pitch as to why Tesla FSD will scale out faster than waymo?

I believe there is some level of deception there that needs to be stated.

> Tesla does precision mapping and annotation of routes...which yes, all self-driving cars rely on this

Musk has long promised that Tesla's don't need this. Tesla's approach is to be able to drive anywhere, as opposed to only in precisely mapped regions. So this is a major fail.

Implicit in a lot of "AI jobs apocalypse" predictions is the assumption that most tasks are ridiculously easy compared to AI research, so naturally the smart AI researchers can understand any profession well enough to credibly predict that AI will be able to replace it. I'm personally not sure the apocalypse has been truly disproven as opposed to progress just being slower than some of the overexuberant predictions, but there does seem to be a pattern of famous AI researchers predicting a job would be automated and turning out to be wrong because they focused too hard on a single aspect of it that could be automated while handwaving or ignoring the hard parts. This has prominently happened with radiology, then with customer service, and now they are walking back on programming too. Maybe take these guys with a grain of salt going forward? I trust them to be able to tell us frontier AI models will keep getting better, not to predict the impact that will have on specific industries. Some people will insist we should give them half credit for predicting there would be impact at all (as opposed to the "it's a bubble" refrain) but I think it should be possible to ignore two categories of obviously dumb predictions at the same time.

I think we too often treat other people’s jobs like spherical cows out of ignorance. Not just AI researchers.

Long before LLMs, programmers regularly and massively underestimated how hard it is to automate other people’s work. Knowledge workers often think carpenters just bang nails into wood, while blue collar workers think knowledge work as sitting in front of a screen copying values from Excel on the left into a form on the right while sipping a latte.

Only like 2.5 years ago, I thought programming would be one of the last knowledge worker jobs to be significantly affected by LLMs, not one of the first. I think AI models will continue to be very impactful. But for quite a while, they may mostly turn knowledge work into a last mile problem rather than eliminating it.

Programming has been successfully automated though. Programmers used to write programs line-by-line in raw binary code or assembly mnemonics, now they just write high-level formal code in languages like C++ or Rust and the computer spends much of its working time processing those lexer and parser 'tokens' and translating the whole thing into assembly and binary code. It all works quite well.

Before:

- programmers spend time in meetings discussing requirements

- programmers spend time thinking how to improve performance and reliability

- programmers spend time tracking down issues in existing code

- programmers write binary/assembler code

Now:

- programmers spend time in meetings discussing requirements

- programmers spend time thinking how to improve performance and reliability

- programmers spend time tracking down issues in existing code

- programmers write C++/Rust code

Pray tell, where do you see the “programming has been successfully automated” part?

There is a wide chasm between writing code in python vs "write a star craft clone". And that is not where near writing python vs writing binary code.

To put in another way, we have been building abstractions to make things easier for us to code. With coding agents you don't even code in the first place. It almost feels like a logical fallacy to compare the two

I used it for about a week, thought it was an interesting demo of the possibilities of general purpose automation with a local model (even though most OpenClaw users use hosted models). The approach to scheduled jobs still makes more sense than anything else I've seen implemented. But like a lot of self-hosted software with passionate evangelists, it wants to be your new main hobby instead of just getting out of the way, and I lost interest because I didn't want a new hobby. It feels like a more thoughtful community could have made something useful with the concept, but as it is the community around it is too absorbed in marketing and shipping stuff for its own sake.

Good quote from the author's earlier post about iCloud Photos:

> Software and services need a warranty. Until they have one, we completely control how much we value our data. That is the best we can do.

Best to treat these photo sharing apps, commercial or open source, as social media. Would you use Instagram or Flickr to store your most important photos and delete your own copies? I would not, same applies to Apple/Google Photos and similar apps. Besides the risk of the company suddenly shutting down or (more realistically for big tech) changing how their service works in a way that makes it useless to you, even if self hosted it just adds a bunch of things that could go wrong which don't apply to keeping it in a folder somewhere with an offsite backup. Filesystems don't have a warranty either, but at least they're easier to reason about.

As I understand it, "mercenary spyware" is Apple's preferred euphemism for the "(semi)private israeli companies selling their solutions happily to all regimes regardless of consequences"

You’re just reciting your priors, which I think supports GP’s point: no one is getting new information out of the posted link, so it’s probably premature to comment on it.

You are misusing some of those words and I'm not even sure how to interpret them even with a hefty dose of good-faith reading.

The report is not premature and it's not premature to comment on them.

Can you clearly and explicitly state why you feel like the report or the commentary is premature?

I was agreeing with kvuj and rguyorama that the original link is to an announcement that an investigation is happening, and it's too early in the process to productively discuss it. People have very strong and emotional pro or anti stances on the Tesla Vision system in general, and love an excuse to have the debate again, but in the comments here where people are talking about their stance you might notice that they don't reference any specific facts from the linked report to support their arguments. This is because the report is still vague at this stage and doesn't provide any specifics that inform the discussion.

If they earnestly believe in fast ASI timelines then political grudges have to be pretty low on OAI's list of worries about 2029.