Much of what you wrote is wrong or misleading or more of a rant than anything with content. You didn't respond at all to the point that the environmental damage here is tiny. But I do want to address two specific bits which are the most contentful parts:

This is confused at multiple levels, but does almost touch on a valid point. It is true that a lot of people were awful maskers, and lifted up their masks all the time, or had terrible seals, making their masks not function. And then you had people doing things like wearing masks in indoor restaurants and then taking them off when they sat down to eat as if their dining table was somehow protected. And if you go back to Slashdot, you'll see me explicitly saying that all of this was awful behavior. People failing to mask properly isn't a problem with masks though any more than people dying in car accidents due to not wearing their seatbelts is a sign that seatbelts don't work. And the point about car windows out non-airtight cars misses something we figured out pretty early in the pandemic, namely that outdoor environments in general were pretty safe.

People didn't need a hundred vaccines, but we're still getting new covid variants, and vaccines are still helping prevent people from getting seriously sick. Vaccines worked, and they drastically saved lives. Here is a really good essay about that https://www.natesilver.net/p/the-2-key-facts-about-us-covid-policy which shows how prior to the vaccines, death rates in the US among Democrats and Republicans looked nearly identical and only after vaccines showed up they started diverging. There's one easy explanation for that. And it is worth noting that the author there Nate Silver, generally takes the position (and argues explicitly in that piece) that mask mandates were not substantially effective, even as vaccines worked really well. It may be worth realizing that while you somehow see vaccines and masks as interconnected issues, they aren't, and not everyone falls into the vaccines-bad-masks-bad and vaccines-good-masks-good categories. To use two fun anecdotes: One of the most strict maskers I know was a couple who were highly anti-vax and was also taking HCQ as a preventative. One of the most vocally pro-vax people I know is a Rabbi who despised not getting to see his congregant's faces and was pushing for the vaccines so people would feel comfortable unmasking. Maybe don't treat all matters as some giant soccer game with two large sizes?

Completely true and also true that this is a problem. But note that these are tiny levels of pollution which if not for the controversial nature of masks would likely be getting little attention. For example, they estimate that this lead to 214kg of bisphenol B into the environment as their upper bound. But in 2010 around 400,000 kg of bisphenol A were released into the environment with slightly smaller numbers for bisphenol B https://linkinghub.elsevier.com/retrieve/pii/S0882596309001407. Since 2010, there's been a concerted effort to reduce bisphenol release, but even if that's been by an order of magnitude, this is a a less than 0.1% increase in worldwide production. Similar remarks apply to the microplastic production where the ratio is even more extreme.

Yeah, this is a very good analogy for what the concern is.

No, the concern is that as these systems get better, they will be substitutes for the sort of exploration and connection making that a mathematician needs to learn as a fundamental foundation for doing research. Using an LLM to suggest approaches and connections can be useful, but it doesn't give the same underlying basic skill set to do deeply original work.

The emphasis here is interesting. The users were impressed by it, and comparing it to a junior researcher is wildly better than earlier systems. And the warning at the end is precisely relevant because the systems are starting to have some potential usefulness in research. I'm a mathematician and this sort of very mixed experience with ChatGPT in the GPT5 form is close to my own experience. Relevant recent anecdote:

Relevant math background: the Gaussian integers are the complex numbers of the form a+bi where a and b are good, old-fashioned integers. For example, 2+3i or -1 +2i are Gaussian integers. Any integer n is a Gaussian integer since you can write it as n+0i. But say or 3- 0.5 i would not be Gaussian integers. Also notation: We write x|y to mean y is a multiple of x. We can use this notation in our regular integers (so for example 2|8 but it is not true that 3|8 ) or in the Gaussian integers where we are then allowed to multiple by another Gaussian integer. For example (2+i)| (2+i)(3-i). A good exercise if you have not seen the Gaussian integers before: Convince yourself that 1+i | 1+3i.

It also turns out that the Gaussian integers have an analog of unique prime factorization just as that in the usual integers. The Gaussian integers also have a notion of size called the norm. For a given Gaussian integer a+bi, the norm is a^2 +b^2 .

Recently I had to prove a specific Lemma where I needed to find all Gaussian integers and where both are Gaussian primes, and b|a^2 + a +1 and a|b+1. I had as a template a very similar Lemma in the integers which was a Lemma which said exactly which integers and b such that b|a^2 + a +1 and a|b+1. I worked out the proof, essentially modifying the version in the integers. Then, I did something I've often been doing after I've completed a small Lemma, namely giving the task to ChatGPT or another system and seen how they've done. For prior iterations (GPT3, ChatGPT , GPT4, 4o) this has almost universally been a disaster. But this time I gave the task to GPT5, and gave it the integer version to start with. It tried to do the same basic task and produced a result pretty close to mine, but it had multiple small errors in the process, to the point where I'm unsure if using it would have sped things up. But at the same time, the errors were genuinely small. For example, in proving in one subcase the system claimed that a specific number's norm needed to be at most 9, when it needed to be at most 10. These are not the sort of large jumps in reasoning that one saw with GPT4 or 4o. It might have been the case that if I had given this to GPT5 before proving it myself and then had corrected its errors I would have saved time. I generally doubt this is the case, but the fact that it is close to the point where that's now plausible is striking.

The world we've built is built for our shape -- bipedal, upright. While specialist shapes can be useful in limited circumstances, a bipedal human-shaped robot would be adaptable to the world that already exists.

The internet bubble burst in the late 1990s. We still have an internet and the internet is if anything more useful and ubiquitous than most people predicted in the late 1990s. There likely is an AI bubble, but if/when it bursts we'll likely see the same thing. A technology can have a lot of potential and staying power and also be part of a bubble.

Investment right now is hard since Trump is making everything unstable. People do not want to invest when there's instability. Trump's administration has also taken an extremely anti-EV and anti-solar/anti-wind position, which means for the next few years, all the obvious use cases for these systems will be at best precarious.

That shrinkage has been almost exclusively in the United States, while the standard of living around the world has gone up with poverty rates going down almost everywhere and with drastic improvements by other metrics also. See e.g. https://ourworldindata.org/poverty . And even in the US there's evidence that the rise in inequality slowed down and even reversed in the last few years. See discussion at https://www.noahpinion.blog/p/inequality-might-be-going-down-now. So if there's a problem, it is US policy specific, not about computers or any other tech, and even in the US the trend isn't clear at this point.

The vast majority of innovations don't happen from the Einsteins or Terry Taos but from regular slightly bright people who are working hard. So yes, in that sense, population matters. It is true that there are a lot of people spending time on junk like making Facebook more addictive, but there's no obvious move to reduce that at all.

At this point, you are just copying and pasting Google's AI summary claims, while not actually addressing any of the substantive points about how many chargers would be needed to replace gas stations or any of the other major aspects. Have a good day.

I prefer discussion to debate thank you. And it isn't a good sign of a productive conversation when you feel a need to call people you disagree with disingenuous. And if you do want to consider this a "debate" then you may want to avoid implied strawmans and red herrings. A response that argues that an EV uses a lot of copper to an ICE is utterly irrelevant to any statement about the amount of copper used as a fraction of all copper.

I certainly would not mind sources, but your math seems off at multiple points. For example, you say "Then add the transmission lines to get that power TO the charging station from the power grid" but charging stations frequently use a lot of pre-existing lines to that since they are using other locations. But let's take your maximum number of 100,000 lb per a station (which is almost certainly an overestimate). There are around 150,000 gas stations in the US https://www.api.org/oil-and-natural-gas/consumer-information/consumer-resources/service-station-faqs. If every gas station was replaced then with a set of 10 superchargers, that would get you around 15 billion pounds. And this is an extreme overestimate. A typical gas station has 4 to 6 pumps, so the idea one needs 10 superchargers per is likely an overestimate. (Since car charging is slower than pumping gas there will need to be a slight increase in charges compared to number of pumps possibly 8 supercharges is plausible). But given home charges (no equivalent home gas pumps), one will likely need a lot fewer supercharging stations than one needs gas pumps. And now, you don't get to also add the copper from cars and trucks to get your 24 billion number, since the claim was 24 billion from a charging network.

Charging stations are a lot of copper but they are one and done. I'm also curious where you are getting 24 billion pounds from. I've seen estimates as low as 5000 tons which would be about 10 million pounds, and has high as about 25,000 tons which would be about 50 million pounds. A claim of 24 billion pounds seems wildly too high. Where is that number coming from?

I think you may want to reread what I wrote. The statement was not that an EV doesn't use the same amount of copper as an ICE. The statement was that the amount of copper used by EVs is a small fraction of total copper usage. Do you see how those are different statements?