The link he provides is to a paywall, but bouncing over that, you can see the economist's lack of proper citation too, when they note:
> A survey by Jon Hartley of the University of Texas at Austin and others has found that about 33% of people now use AI at work, down from a peak of 46% in mid-2025.
IDK man, I was just there for 2 weeks in Kobe, Osaka, Kyoto, and Tokyo, and there were so few publicly accessible trashcans that I started considering photographing the handful I saw (2-3 next to drink vending machines, and one (1) large one in Shinjuku's main luxury-tourism thoroughfare) for their shiny rarity. At one point we were joined by our third member, and we warned them first thing "there are no trash cans in Japan!" and sure enough, about 2 hours later, they were clutching an empty beverage, looking around futilely for somewhere to throw it out...
Personal anecdote, but I lived in Okinawa for two years, and can state that I _never_ saw a single trash can in the street. Inside buildings, sure, but not outside.
Australia leading the way - our fully informed and highly tech literate (avg age of House of Reps ~=52yo) government banned Deepseek in February last year, including downloadable GGUF's - with some nebulously hand-wavy 'it's just too dangerous' safety claims:
Isn't using age as a proxy for technical literacy rather fraught? It seems to me just as much an abuse of stereotypes as other sexist or racist comments.
I agree that the demographics are probably correct, but we're not supposed to assume that all individuals conform to group stereotypes. And that ought to be doubly true when we're not selecting those individuals at random, but we're (putatively) selecting them for their wisdom and other skills.
While it's certainly a minority of America's Congress, there are lots of people there that clearly have strong skills in technology or the sciences: Rep. Bill Foster (D-IL), Rep. Chrissy Houlahan (D-PA), Rep. Jay Obernolte (R-CA), all of whom are over 50 years old, just to name a few.
No, it's not particularly fraught. There are plenty of old people who love technology and stay current. They are also a tiny minority. It's pretty reasonable to expect that an octogenarian is not going to be technically literate, on average. The reverse is not true, of course. You can't assume that because someone is 30 years old they are technically literate.
USA has age limits for the executive and that isn't considered an issue. We should have age limits at the top end as well. It isn't only technology. I don't think 80+ year olds should be driving a car or even operating a McDonalds much less making sweeping long term decisions across dozens of disciplines with little to no research.
True, but that prevalence is not a constant. In fact, across the broader landscape of human history, that appears to be a relatively recent development, and almost certainly environmental/toxins related. For example: https://www.newscientist.com/article/2479344-dementia-cases-...
Until last year (when he was 95), he was operating the world's largest holding company by assets and revenue.
It was also the first non-tech company to reach $1T market cap.
And, its class A stock was by far the most expensive stock in the world at $740,000 per share, more than 100x more expensive than any other US stock.
So, yeah, I think he'd be qualified to operate maybe even two McDonalds. Maybe he could even be driven to them.(Coincidentally, he is a HUGE McDonalds and Coke fan and has eaten there almost every morning for over 50 years.)
Disrespecting older people seems to be a hobby as old as Father Time.
Can you articulate a clear rule that allows me to use age-focused stereotypes, but prevents me from using race- or sex- or gender- focused stereotypes? Why is one OK but not the other? Is it just that you perceive that the age ones are true in a greater proportion of cases, and if so, how do we determine where to draw that line?
These old people started their career when computer use became a job requirement for any office work. A few years more and homes got saturated as well. A 52 yo was 20 thirty years ago, in 1996. Have you heard about Napster, MS Office, Netscape, Windows 95 / NT, Hotmail, AOL, Messenger, ICQ, Doom, C&C, .... hundreds more. And everything else since. They had to live through those every day, keyboard+screen instead of pen+paper.
...aren't you confusing them with the generation above 80?
You're right, and as someone north of that age, and who self-assess as mildly competent with technology, I didn't mean to imply a linear correlated.
I'm aware our (AU) representatives & senate average age is relatively low compared to US / UK, and I think that figure (52 ish) is probably relatively healthy in terms of a spread of younger + experienced minds.
But OTOH, our government has form on this front (of doing as our friends over the pond instruct, and of not really understanding the technology around which they are formulating laws) and I should probably have pointed at that aspect, but it's harder to succinctly convey that track record.
50+ year olds seen and used and made more than 30 years worth of constantly evolving technology, their life was saturated with it for 3 decades. Was this what the poster wanted to say perhaps? ; )
What's (probably) incorrect is drawing conclusions based on A without considering the Bs in the population.
Consider: the leading cause of death for young black men is being killed by other black men. Not all young black men! Lots of them are pretty good! Both of those statements are true. What's incorrect is to use it to draw conclusions like "so police should stop and frisk young black men" or "we should avoid endangering the rest of our employees by hiring young black men".
The top-level comment was using age stereotypes to "prove" that an older legislative body is going to misunderstand technology, and therefore produce bad regulation. Showing that the argument was wrong because stereotypes aren't universal, and because in this case there's reason to believe that the samples are selected in a way that would tend to avoid the stereotype, isn't "tone policing". It's directly attacking the argument itself of that parent comment.
no, you did not prove anything. Boomers are indeed bad in tech in general, and the presence of few tech literate does not change the general population
Nope, both statements are completely wrong because 52 years old, and thereabouts, is GenX not Boomer.
And yes it makes a difference. They would have grown up with the first home computers like the 8 bit C64 or ZX Spectrum, and later the 16 bit Amiga A500 or Atari ST.
>"while the U.S. House is 59 and the Senate is 65."
The average is probably highly misleading. Young folks have started filtering in from the oldest retiring or expiring, but all of the institutional power still lies in the 70+ crowd. Chuck Grassley, 95, is third in line for the presidency right now.
> If u lost ur pet pidgin /it’s dead in front yard my Iowa farm JUST DISCOVERED here r identifiers Right leg Blue 2020/3089/AU2020/SHE ///LEFT LEG GREEN BAND NO PRINTED INFO. Sorry for bad news
It is typical that they conflated the website chat interface to DeepSeek-hosted inference with Amazon and other hyperscaler inference, and self-hosted / on-prem inference. Of course they also confused testing vs production, and left grey what is means when another company re-spins an open weight model (e.g. Cloudflare Clef is Alibaba Qwen derived).
They haven't officially banned Qwen, Kimi etc, but the same risks apply, and I think a ban is coming (certainly is in the USA). It is quite possible for any or all of these models to have a Ken Ritchie style self-propagating back door[1] or a simple phone home baked into the weights[2], and possibly triggerable misalignment in specific circumstances caused by poisoned training data.
Do you mean Ken Thompson?
There is apparently a famous talk named "Reflections on Trusting Trust"by Ken Thompson - the one where he describes how a compiler can be tricked to insert a Trojan horse that reproduces itself even when the source is "clean".
I think K&R (Ken Thompson and Dennis Ritchie) are very close in my brain's feature space. Reference [1] is the article you are referring to. An AI trojan could conceivably insert itself into the model weights / training data on systems it had access to.
>"government banned Deepseek in February last year, including downloadable GGUF's - with some nebulously hand-wavy 'it's just too dangerous'"
With the latest research on backdoored weights this really isn't tinfoil hat territory at all, and trusting every individual agency to fully vet what they're hosting and not use 3rd party inference is a tall order. I can't blame them.
Other than "it's cheap!", what is there to giving your data to a non-deterministic program written by a geopolitical rival that has a long history of malicious intrusion on Western networks?
You realize the whole reason people use deepseek is that you can download and run it offline where the spooky Chinese people can't get your data, right?
First, the Chinese people aren't spooky. That's not what I'm getting at. I think you know that, too.
Second, you can run it offline, but you aren't going to run the machine completely offline. You're going to have it on the internet. LLMs are probabilistic, not deterministic, programs. Evaluating them for what sort of security vulnerabilities they present - no matter what country made them - is still a fairly young field of information security, and isn't going to be as easy as evaluating a deterministic program.
Do I know for sure that there's some combination of states within a DeepSeek LLM that opens up an internet connection and sends information back to external servers? No. Do I know for sure that there's no combination of states within a DeepSeek LLM that opens up an internet connection and sends information back to external servers? Also no.
What I do know is that the People's Republic of China has an extensive history of offensive cyber operations against American and other Western computer networks. I also know that these efforts are likely ongoing.
Know who I don't have the same concerns about? Mistral.
Given that agentic AIs have shown an ability to exploit known vulnerabilities in software, it probably wouldn't be too far of a stretch to think they could get around firewalls in one way or another.
Which takes us back to the "it's hard to properly secure non-deterministic software" point.
No, it's definitely easy enough to build a container that doesn't let LLMs out. The problem is that these AI companies are building a playpen made of wet noodles and expecting AI to stay out of trouble
The weights contain malware? What security vulnerability do they contain? Is the claim that they will write insecure code? This seems easy enough to test - run a sufficiently large sample through another LLM
IIRC Europe was on this as well e.g. Meloni regarding OpenAI.
Now that governments are banning remote LLMs controlled by authoritarian governments by proxy (CN, USA) it is time for companies to realize they shouldn't do that either.
The alternative? On-prem open models. Performance a little lower? Too bad, it ain't worth the risk. A privacy officer can value the risk, but that would be a case by case scenario and is expensive employee (lawyer), so not worth the effort.
You're conflating - 'Do we understand that Holmes did these things?' - with 'Were those specific charges brought against her and were they successfully prosecuted?'.
Reading John Carreyrou's book, it's pretty clear she was guilty of (lots of bad) things beyond the lone 'defrauding investor' charge that was successfully carried.
The _other_ option is to shift, perhaps in two x half-hour stages over a month or longer, but around the equinoxes - the 'standard operating hours' for services and businesses.
This is often overlooked as an alternative to shifting everyone's timepiece at the same instant -- I've never understood why there's so much much emotion and/or inertia around clock changes.
I'd much prefer this option; businesses open an hour after sunrise, close an hour before sunset. That's more natural and human, to actually follow nature's cues.
But you know, why inconvenience social constructs for wealth generation when you can just literally inconvenience every human alive instead?
People - usually suit-wearers - have been making this spurious claim for decades, but it doesn't hold water.
The largest of the finest print reminding you that it's 'sold as is' (or more encompassing variants that might continue '... with no warranty for fitness of purpose') means that liability remains in the lap of the purchaser / consumer / operator.
(This has been a source of immense frustration over my career - where such people have assured me that they have 'recourse' (it's always vaguely described) by spending money on proprietary products & services, rather than opting for functionally equivalent or superior free options.)
I think your third paragraph is implying a distinction (or conflating the difference?) between LLMaaS's and self-hosting publicly available models.
If it's just where it's hosted that provides the legal insulation then things like OpenRouter would give you that. (But again, I suggest that it would not.)
If a frontier lab is willing to draft an SLA that assumes liability, corporate will pay for it as long as the cost/benefit is in favor of it over insourcing.
Sure, but that's quite a fanciful universe you're imagining there - the feasibility of a corporation obtaining insurance to cover that offer of liability ownership has got to be close to zero.
Peter Zeihan did a 5m video [1] on this 10 days ago (give or take) - and the history of how they've been used, and why the original design (big draw-downs during major emergencies - perhaps 5 of them, ever) hasn't lent itself well to the way they ended up being used.
The title is 'for single node local S3' but what they actually mean is 'for minio-compatibility', which is an entirely different question.
When I abandoned minio a year or so ago, I also surveyed the options, and settled on Garage. It lacked the GUI, but felt about the same complexity as minio. Perhaps a smidge more complexity, as I moved to 3-node and 5-node separate instances of garage, running as containers under Nomad.
I don't recall it being onerous, but I was looking for some basic S3-alike capabilities, not just minio-alike.
(How many people set out to build an object storage system with some number of AWS S3 primitives, but primarily try to match a third-party proprietary system's foibles?)
> So, Garage does work, but gosh…it is not just a drop-in replacement in terms of code changes.
I think in terms of actual code that uses local S3, it pretty much was a drop-in replacement. (I have multiple distribution/registry, Grafana Loki / Mimir, influx3 - all backing onto my object storage system, and the config changes there were modest - key+secret, and url - just as you'd expect.)
Yeah, whilst I probably wouldn't go that far I take your point. I suppose the difference is in how people respond to the way games dress up their mechanisms and how interesting they find the decisions around the processes they perform.
> VMware to Proxmox migrations are not affected ...
Amusingly - and acknowledging tiny scale and modest automation requirements of home lab compared to enterprise - as I started migrating to Proxmox a year or two ago, the process of shuffling VMs off my ESXi servers and onto Proxmox was surprisingly painless. Effectively mount the ESXi as storage systems, and then 'copy to here'.
IIRC the only small challenges were a conservative choice on target VM's CPU type (Proxmox seems to dislike defaulting to 'host' CPU) and the network / SCSI controller defaults. I assume designed to prioritise robustness over performance.
Proxmox been great for decades. I used it back in 2011 when I was sysadmin for https://sahanafoundation.org/eden/ - we had multiple live instance running for the site, wiki, translation, GIS. Just when kvm was getting support too (not openvz) so it was nice an snappy running on some donated poweredge 2950's (thanks zen.co.uk!)
Deployed for Haiti, Chengdu etc. All ran perfectly.
> A survey by Jon Hartley of the University of Texas at Austin and others has found that about 33% of people now use AI at work, down from a peak of 46% in mid-2025.
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