I have also been WFH for years before covid. I don't believe my circumstances are the same as everybody's, but I do know I am far from the only person whose duties almost exclusively involve working with people and things that are in another region anyway.

Put another way, employers who gave everyone an effective raise due to exigent circumstances and suffered no harm as a result are now mystified as to why their employees are reluctant to accept an effective pay cut now for no apparent benefit. Given the latter part, they rightly suspect some sort of malfeasance somewhere in management.

Workers want or even need a cost of living adjustment. Employers do not want to and may not be able to afford to increase payroll. For that reason, the COL increases are often several years in arrears as it is. WFH gives the employees an effective raise by eliminating their commute costs and an opportunity to save even more by living somewhere with a lower COL.

As an additional incentive, the employer saves on office space.

On the other hand, WHF was a win-win for employers too. The employees got an effective raise by lowering their cost of living and a reduction in unproductive daily aggravation and wasted time. The employer didn't have to actually pay them any extra to make that happen and could even save on expensive office space. It just wasn't a win for the managers who could no longer hide how bad they were at their jobs.

Perhaps not narcicism so much as short-sighted self interest. Either way, it would be in the company's interest (and the shareholders) to give THEM the heave-ho and leave the workers remote. The workaround for their ineffectiveness costs millions a year.

Adoption is a function of existing adoption and existing availability of resources.

Extremely good OS' and truly amazing computers have failed, not because they were too expensive or had issues, but because they were perceived to be unpopular OR simply lacked enough software that could run on them.

Extremely bad OS' and truly awful computers have succeeded for the opposite reason.

Linux has an extremely good architecture, at least for machines that are mostly operating with CPUs. When most of the work is done by the GPU, it's less obvious that Linux provides the necessary mechanisms to manage resources and abstract away the hardware specifics. That doesn't mean I expect Linux to start fading, but I don't think a centralised OS is necessarily where things will go. However, the reason it hasn't done better so far is simply because potential users and potential software houses had a negative view of it (even though actual users were almost invariably loving the system).

Windows is crashing now not because it is a slow, bug-ridden piece of spyware (which it is, but users have never cared about that in the past), but because it is perceived in increasingly negative light.

They can get vertical samples from space and the ground. What they can't get from space or the ground is how the internals of a system evolve.

The problem with "models" in the abstract is that there are a lot of them and some of the time the thing you want is to know the same point relative to the system and not to the mudball.

Lots of people have gut feelings about AI, positive or negative.

Most of the AI research I can see people doing is focusing on single-element problems (such as finding counter-examples to a mathematics hypothesis) or solving very simple engineering systems (at most a dozen or so components). Most of the benchmarks are even simpler (write a short story at the level generally asked in English classes of primary school kids).

As a result, I think that people have developed either a very cynical outlook or a very optimistic look. Neither of these is entirely realistic, for the simple reason that no actual useful problem fits any of those descriptions.

Digging much deeper, I'm finding that when problems get into the hundreds or thousands of elements, even ChatGPT Sol 5.6 and Claude Fable 5 struggle badly to keep track of accurate relationships, let alone any level of detail.

If you want to solve a relatively simple coupled system (whether to use solar panels or direct solar heating, and which side of the house to place them), I'm sure AI is fine. If you want AI to write a five hundred word story detailing a conversation between Kim Possible, Rufus, and a smoked herring, I'm sure it could cope. Just.

If you want it to do anything hard enough that a human could do with an assistant, the tests I've been using (meaningful rather than synthetic problems) suggest you've developed a fantastically expensive blue smoke generator. We are going to need to see the inference engines developed for previous generations of AI, and the reasoners developed for the semantic web, and perhaps tools that haven't begun to be imagined yet, before AI can do anything non-trivial.

A balloon is not static, it moves with the atmospheric system it is in. Ergo, it tells you how that system evolves. A drone cannot do this.

A drone can stay put, relative to the ground, telling you how a location's atmosphere evolves. A balloon cannot do this.

These are solving completely different types of problem.

You could, of course, construct a hybrid, as balloons are traditionally single-shot. If a balloon carries the measurement gear and a drone capable of carrying that same gear PLUS a deflated balloon, then you can have something that operates as a balloon until it reaches some pre-set location, whereupon the balloon's gas is released and the drone flies the ensemble to a collection point.

I don't have much more to say about it, except that if I was a billion-dollar AI company I wouldn't try and pass off my flaws as brilliance.

It obviously gives us guarantees in terms of what the source will do and what the compiled code should need.

These do not provide memory safety, so memory bugs would not be solved by these. They will find unexpected behavioural issues, though.

The precondition and postcondition statements are not cunningly-disguised assert statements because they're not asserted at runtime. They're enforced at static check and/or compile time.

The policy hints mean that if the kernel is doing something you're not expecting, you find this when testing the code. Equally, though, it means that users cannot cause the software to venture into realms unknown through many of the usual attack vectors because those will require permissions that aren't there.

They won't want to touch the other side of it either. If what Flock is doing is fine, so is the creeper snapping photos on a public playground and creating a giant database somewhere.

That seems...undesirable.

Agency is just it's ability to act upon it's conclusions. While there is a great argument that AI should not be given agency, it had it.

If you're trying to say it had no sentience, I'll agree with that.

In the same sense that a gerbil has agency but not sentience. It too might escape from a cage.