Also, to preempt any pedantic response, yes, I'm aware that Cray computers are still very much a thing. But, using the phrase "Cray computers" as the epitome of computing power is very much a thing of the 1990s and before.

Hah, now see, I left some keywords in my last post you would have picked up on if had done national security work (at least connected to one of the bigger players).

That Wikipedia page is severely out of date. For instance, it quotes Hayden as NSA director talking about "Cray" computers. Hayden hasn't held a position in maybe 20 years, and his last position was as DCIA! US Intel is very, very aware of the computational and hacking abilities of China, North Korea, and others.

While I can see how you arrived at that, I don't think they're all the amoral actors you describe.

Zuck has shown some changes in the last couple years, though I agree he's largely amoral.

Musk has a very peculiar set of morals which might be described as pragmatism or amorality, but he's always been very focal in both action and word about both ethics and responsibility.

You could probably accurately describe them all as transhumanists with varying degree of consistency.

If there's a crisis of trust - which there is - it's almost certainly largely provoked by Amodei. The guy has been a regular prophet of doom regarding AI: it's going to take all the jobs, it's dangerous and going to cause all sorts of disasterous side effects socially, economically, and politically. No wonder people are untrusty.

Pair that with the obscene levels of what appears to be state-level anti-AI and anti-datacenter propaganda, and you've got the current situation. The amount of absurd claims I've seen about datacenters and AI, from people who should know better - everything from completely false narratives about water use, land grabs, power use, and so on. The truth is, an AI DC doesn't uses less water than a Cosco and about as much electricity as a comparable industrial facility, or a similarly sized restaurant.

For anyone who thinks it's ridiculous that the US can't reproduce the Apollo missions, consider this: we can't (due to environmental factors) produce wrought iron anymore (because there are no longer any facilities setup to do so), as a species, and therefore, could not build the Bay Bridge in San Francisco (and I'm sure, any number of others, like the Brooklyn bridge).

The same limitations apply here. They have to redesign the vehicle on first principles because the foundational engineering (not scientific understanding) being used no longer exists. Most people simply do not realize how precariously our technological world hangs in the balance. It's why highly advanced ancient cultures collapsed, in large degree: they couldn't just start at the lower level again before working their way back to 'advanced civilization', they had to start all the way at zero again.

Here's a fun one I like playing with my kids, kind of the nerdy science version of the polysci "go back in time to kill Baby Hitler: which one scientific understanding would you bring back in time to Ancient Rome/the American Revolution/the 1950s/etc. and why? Dollars to donuts they'll say something like "the lightbulb" and not consider that it's at least 3 distinct discoveries and inventions, nevermind the engineering.

You can get this kind of basic image recognition on $20 ARM boards from China. I've got a rock3a (older, barely capable compared to whats available now) board cluster that does this kind of image recognition for my cameras. rpi3 level stuff. That it was nvidia just speaks to what is available.

Sure, let's discuss that. You've got a handful of methods historically:

- GPS/GLONASS/Starlink - can be jammed.
- Wire - inaccurate, not suitable for OTH guided missiles
- Pigeon - inaccurate and expensive
- Radar - can be jammed
- IR/laser - line of sight/requires IR signatures
- inertia - inaccurate, expensive.

There's nothing which really compares to "AI" in capability or accuracy. It's the only option which can't be jammed directly short of active EMP and provides the required/desired accuracy.

That's kind of been the bread and butter for cloud providers - "data velocity". They say: keep all your stuff on our cloud, it'll be safe forever, you don't need to worry about it.

A single copy is zero copies and 2 is 1. This is overlooked on all clouds, on the basis that 3 copies on a cloud provider is still 1 copy.

This kind of oversight failure has ended many companies when they've had DR scenarios. I know of 2 that had to close shop because the egress cost of all their data from AWS was larger than insurance would pay out, and the cash difference was significant.

In most cases, it makes more sense to keep 2 local copies (different storage) than 2 cloud copies, for this reason alone.

Even if frontier models only continue to get 'marginally better' in terms of evaluations, as they have been for the past year, it's quite apparent that the newer models tend to be significantly better in aggregate and practice.

We're also starting to see a stratification across frontier models:

- Anthropic seems focused on "one shot" capabilities (latest generations have suffered significantly in other departments)
- GLM seems to be increasing their focus on security and coding robustness
- Google seems to be leaning in on 'fast and cheap' and 'good enough'
- Agnes (while not quite frontier) seems to be going for "broad scope of capabilities"
- LiquidAI is focusing on specialized, small local models for agentic pipeline work
- OpenAI seems stratified on do-everything frontier models with functional stratification based on task complexity
- Qwen - local capability focus.
- Grok - code centric + fast.
- Deepseek - also local-capability focus while trying to optimize the full stack

Then you've got the mess of models like Laguna (last generation capabilities but local-focus) and Agnes (more like google's approach, with a wide and cheap portfolio).

As for Anthropic (and , it is quite apparent that they're architecturally behind in many ways to other models. The release of dflash papers and the subsequent speed increase on the competitor models made that plainly obvious.

> Indeed. The Chinese also know that publishing a story about a found backdoor in their software would be a great exploit for the US and hence they know that the US is looking. Strangely, nothing has been found so far. Or rather very obviously, nothing has been found and the whole thing is nothing but lies.

Or maybe, just maybe, when the Chinese AND the Americans IC agencies find exploits and backdoors in the other's countries, they keep them quiet and monitor, save for later, watch other people exploit, etc.

Maybe, when 3-letter agencies in McClean and elsewhere catch incursions, they monitor them closely, analyze their tactics and techniques, and contain. It's a pretty Byzantine system, but that's modern spycraft.

The problem is that AI has made it very obvious that teachers have not been doing their jobs.

For instance: one history test, one report. Two different responses: "Contrast the political sentiments at the time of the American Revolution with modern day American sentiments and political events." 1 page report.

You could just paste that into chat and get a useable if not undetectable result. The result is obvious. Instead, a curious student could use the tool to pose a more complete question to chat: take into mind more emotional similarities between different political factions, as well as the contrast amongst social classes. It'll help them stand out a bit and it'll be different, and they might learn something.

But, I do note: this would require a curious student. Teachers have all but punished curiosity and a love for education for decades, catering to the dumb ones to keep them engaged instead of punishing them for wanting to be dumb.

That's the dumbest thing I've ever heard.

AI has only accelerated my desire for learning. I can learn so much more (though perhaps not as thoroughly or as deeply, because you don't have to go below the architectural surface as much anymore) now than I could before: much less switching between tabs, copy/pasta'ing, digging for documentation (and reading a lot of things that I don't remember 3 seconds later because I'm looking for something specific).

The same goes for reading about history, or anything else. Curiosity is rewarded rapidly with AI.

No, the biggest problem with education in the schools is the schools and how they teach. They've made it un-interesting and curiosity is generally punished. They have completely destroyed education long before AI was even a question - I'd argue shortly after the Internet became available and approachable for your typical 100-IQ public school teacher, in the late 90s.

"3 dimensional thinking and problem solving is something you can learn to be good at"

It really isn't, not to a meaningful degree. You can sharpen cast iron, too, but that doesn't mean you'll have a sharp knife.

Spatial thinking (which is heavily linked to problem solving) is fairly tightly coupled to inborn intellect and early education capability attainment.

Family member who lives there is a Environmental Studies professor. He uses his own trash/garbage pickup as a case study for students to calculate vehicle wear+tear, ROAD wear+tear, impact on traffic through the area, transportation-related CO2 emissions, and so forth.

Not that it's a shocker, but yeah, tl;dr, privatizing garbage is a ridiculously bad idea.

Hah--funny! No, needless to say, they do not. I have had a sticker put on my rolling recycle bin before saying that it was picked for a random inspection to make sure we're only putting recyclables in there, and threatening fines and penalties, but that's it.

I also read up on my city's solid waste quarterly reports, and the vast majority of what goes in recycle bins ends up going to a landful. It's a pitiful fraction, especially for plastics. So, I just through those away. I do dispose of some kinds of batteries and other ewaste specially.

Single stream recycling is about 90% performative theater. Other kids of recycling are like 80% performative.