This is the 2026 edition of Ken Olsen: "There is no reason anyone would want a computer in their home"
Digging into this:
> In conclusion, there is evidence that Ken Olsen did doubt the need for computers in the home, but the evidence is based primarily on the testimony of David Ahl who was perturbed when the personal computer project he championed at DEC was not supported by Olsen in 1974.
> Olsen’s resistance may have been similar to that expressed by another DEC executive, Gordon Bell. In 1980 Bell thought home terminals would act as gateways to remote computers which would provide appropriate services.
* https://quoteinvestigator.com/2017/09/14/home-computer/
It was supposedly said in 1977: most computers at that time were not small, and so it would not be surprising that people would not expect the general public to desire a large, power-hungry, noise-y apparatus in their house.
And, like the overly large machines of 1977, models are getting faster, leaner, and better. It's happening a lot quicker, though.
People take these quotes out of context all the time. Said in a business context, there was no need, at that time, for someone to have a personal computer.
There's no business justification in 1977 for a personal computer department at a business. It's similar to the gates quote about RAM (I think it was 64KB?).
These statements aren't meant to be forever quotes. Their business plan quotes.
640, and Bill Gates said he either never said that, or at least never remembered having said it. I think there is no evidence anywhere that he did.
https://www.computerworld.com/article/1563853/the-640k-quote...
The early popularity of Minitel, the continued popularity of ssh/tmux, and the web browser itself indicates that bespoke client applications are not the only way. He wasn’t directionally wrong.
Nobody ever said that, at least not as an assertion or prediction. The actual instances of similar language are from multiple people describing their earlier thoughts before they learned it wasn’t true.
Local models aren’t deterministically equivalent in capabilities to foundation models. Home computers are turing complete; just like a mainframe. They are just slower. Often not slower enough to matter.
I think there’s a sweet spot currently with munging your data blindly on the server so that your client device battery still lasts all day.
Meanwhile Apple and others push on with making client side models more efficient so that eventually the server costs and complexities go away.
You could run a pretty good home server on $50 of gear and yet we never saw any real adoption of OwnCloud/NextCloud style products as an alternative to Google Drive/Photos or Apple Cloud.
Why should LLM/Transformers be any different? Especially when you need a proper expensive GPU to run them instead of a Raspberry Pi?
On-device AI is going to be important, I think. It doesn't have to take the form of a chatbot UI to be useful.
Maybe if you ask them that question, but if you show them two products, they'll definitely prefer the faster one. 30 seconds is a long time to watch a progress bar.
People definitely aren't going to accept more expensive + slower ...
I suspect personal privacy and need to run AI workflows to handle the litany of administration tasks of a household will be what result in regular need for local AI.
Apple is already out front with this on a personal, individual level, but they are not obviously headed toward multiuser/family-level ~biz admin with a persistent server running local LLM.
Very significant improvements may be viable for unattended inference via large-scale batches, which can reuse sparse experts and thereby mask some of the latency involved - this is quite unique to DeepSeek, again due to its efficient KV cache.
2. Qwen is much more demanding and borderline unusable on consumer hardware because it's a dense model. The 27B parameters are active all time for each token. It's not a MoE architecture where a router activates only some of them.
3. Qwen doesn't like quantization at all.
Settings: RTX 5090, 5-bit weights (Unsloth), FP8 KV cache.
Last time I tried running large MoEs on this PC, they had inferior quality at 2-3 bits compared to much smaller dense models at 5-6 bits, and were slower anyway.
But yeah, the Qwen line is pretty impressive on commodity hardware.
To me, LLMs are for asking research questions + exploring design spaces + pointing at codebases to investigate bugs. And those all benefit from the model being as "smart" (in terms of both fluid intelligence and burned-in knowledge) as possible.
I'm guessing there exist problems where "intelligence past a certain point" doesn't matter, so these medium-sized models can match the performance of the bigger models. But what problems might those be?
I have a hard time believing running a model on a laptop will be cheaper than running it in a datacenter. Why wouldn't economies of scale apply here as with every other computation?
The vision NVIDIA is selling is pure marketing IMHO
Local may or may not be cheaper than remote now, depending on the details, but the factors you describe won't affect the math nearly as much as they will once that subsidization ends.
You're going to need to analyze the problem much more deeply because it sound like the standards you are implicitly applying would result in "economically, everything should be centrally hosted" but that is clearly not the result that obtains. Even a modern mid-grade cell phone is no slouch; you may not be running a current-gen frontier AI on it but you certainly can do a lot of other rather intense things locally that would have been laughable 10 years ago, like suprisingly high powered games.
Do you think he's in mensa too?
But they also want to taste the sweet fruit of AI so the only way to do this that a CISO will approve is on local air gapped hardware. It’s a niche but still a billion dollar niche.
Where you will need games to be rewritten for ARM to get full performance, just like on Apple's M series chips.
This made me laugh. I can only image how insufferable this person is to deal with.
Especially on Dwarfstar.
anyone whose addicted to token theoughput is losing the operational knowledge and offline capabilities.
if you arent moving to the AMD 395 or MACs then youre hitching aride on the expensive calory ride
But watching everyone flounder because claude goes down or forcing you on API costs.
I'm programming things that'd take me days with a PC that, without OpenAI's VRAM shenagans, would cost you $2k.
It's more than just 'this is what I could do' it's definitely about 'this is what anyone could do with a new PC purchase'.
You're doing what the IT industry has been addicted to for decades: number goes up.
No, I have a hands on experience with bigger models, and understand the advantages of using them.
Not everything I want to use an LLM for requires "PhD level intelligence", and increasingly I'm finding more uses that involve sharing my personal data.
Yesterday my local model helped me when looking for a doctor who is in-network for my insurance. I threw it a screenshot from the providers search results and it looked up reviews for all of them.
I own the DVDs so I'm OK upscaling/editing my own copies for my own use. But if I ran the task on an ai service I would no doubt trigger copyright issues.
Lol yeah seriously, that stinks "I ask AI to generate a huge amount of bullshit and upload it to pad irrelevant stats".
Absolute loser.
As to why he now has this on his blog? I also cringe when I read it. I presume someone told him he should self-promote more, and this is his lame attempt to do so. He's almost certainly the most cited person in his department, but it's entirely possible that none of his colleagues actually know this. Cut him some slack. Self-promotion is not his strength. He's a nerd's nerd, and not a marketer. I'll mention to him that his attempt here might be backfiring when I'm next in contact with him.
He doesn't just have it on his blog, he has it EVERYWHERE. Sometimes 2 or 3 times on the same page.
It sounds like he's gotten bad advise about how to market himself /or/ this is being marketed to people who have bigger checks to write and whom he believes will be responsive to this kind of marketing. As an academic, it rubs me very wrong - I think it's detrimental to the field when we get into h-index stacking contests or citation count comparisons. But I don't know what incentives he's responding to, which seems important for putting this stuff in context.
(as an aside, it turns out that polars + fastexcel is about 10x faster than pandas + openpyxl for searching that dataset, if anyone else is curious what he was actually talking about. :)
Being the top x% is what OnlyFans girls brag about, professor...
And it's not exactly brain surgery, is it? https://www.youtube.com/watch?v=THNPmhBl-8I
Citation needed