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What's the most expensive thing one can do with AI and how do we do it on a mass scale to one company at at time?
Maybe 16k image/video upscaling of super low quality shitposts?

File sizes are going to be huge! 2K is already a lot to upload, couldn't imagine 16K right now.
Just expend some more compute time on doing compression and we'll get those filesize numbers to a workable level.
$ stat -c %s enhance.png
276773
$ convert enhance.png enhance.avif
$ identify enhance.avif
enhance.avif AVIF 1164x558 1164x558+0+0 8-bit sRGB 14391B 0.000u 0:00.000
$ stat -c %s enhance.avif
14391
$

zoom and enhance
$ identify enhance2x.avif
enhance2x.avif AVIF 2328x1120 2328x1120+0+0 8-bit sRGB 32448B 0.010u 0:00.000
$ stat -c %s enhance2x.avif
32448
$

zoom and enhance
$ identify enhance4x.avif
enhance4x.avif AVIF 4656x2232 4656x2232+0+0 8-bit sRGB 50758B 0.000u 0:00.000
$ stat -c %s enhance4x.avif
50758
$

Okay, that last one took 17 minutes to upscale on my GPU, so I'm not going further. But I'm using SD Ultimate Upscale, which is tile-based, so in theory that could be farmed out over a collection of GPUs and parallelized. Just need more compute hardware.
But as to filesize, that's under 50kiB.
I know a company that burned $100k in tokens after they they let like 50 worker bees using general AI for OCR, simply converting images and PDFs to text.
They didn't bother to create a skill, or teach the AI how to reuse a shared script so every request resulted in it writing a new python project, pulling libraries, using a frontier model rather than offloading a dumb one etc.
Basically find a business process that happens often and let em at it inefficiently, it'll happily chew through the budget.
Thats pretty much what people freaked out about llms doing at my work and all they use it for. I'm here like...we have had OCR for over 20 years.
People are duuumb.
There has been some serious leaps in terms of quality. It couldn't read human writing or half the fonts for that matter like 5 years ago, let alone 20.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone... there's few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It's an egregiously stupid use of resources.
have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone..
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM's do OCR. LLM's convert images into tokens and then treat them like text input. I can't see how it works but it does. It's why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
Burn your AI tokens and get promoted for using more AI!
I sadly don't find the GitHub repo with the bash script anymore, but that thing seems to do the same thing
(Maybe I've misread though, I'm just going to sleep)
Edit: ha! Found it!
https://github.com/dtnewman/burn-baby-burn
have it end to end build a fully featured web browser that works on Windows, MacOS, and Linux from scratch.
Thus transferring their money to openAI, Anthropic etc? How does that help?
They actually lose money as well, because they're proving those tokens at below cost price.
More companies with less money is better than a few companies with all the money.
Ultimately distributed power has to be more democratic, and centralized power has to be more fascistic.
That's part of why governments having large distributed bureaucracies each with their own authority and independent ability to intervene is better than say; a single executive office/president controlling everything directly.
Distribution also leads to stability though (making it harder to challenge the status quo), so it's a double edged sword.
those companies arent profitable either and they have same problems in which it costs them more to run their products than they are currently charging people to use it.
What do you mean by either? Walmart and Amazon make tens of billions in profit a year if not a quarter.
i thought we were talking about OpenAI, antrhropic, etc, not Walmart?
Did you read the title? It says to spend Walmart and Amazon's money on AI. And you said "those companies aren't profitable either" which would mean, using normal rules of English grammar, that "Walmart and Amazon aren't profitable and OpenAI and Anthropic aren't profitable either". So what are you talking about? What does "either" mean?
ooh I see. my bad. erase the word either from my comment, it wasn't necessary.
okay, thanks for clarifying. Sorry about having to be kind of pedantic.
"The public really didn't like it when we shoved AI down their throats. Whats the solution?" "How bout a lil' 'artificial scarcity?'"
I am not a CEO and I hope this AI bubble bursts already.
That said, if I were a CEO using all possible tokens while they are heavily subsidized and tightening the purse when they get more expensive does not sound like the worst strategy to me. You get all your teams to build some expertise and (hopefully) get a sense of where the technology might have some ROI.
If only they had presented it this way (and not "AI therefore layoffs") probably a lot of us would hate it much less now.
There is a limit to how long an AI company can keep subsidizing the tokens, eventually the financial ruin ensues. Every chat request that goes to the LLM has an actual physical compute and RAM cost, which scales to hilarious levels as you keep chatting in the same thread and the context size widens. It scales to astronomical levels when the query requires high-level analytical or reasoning skills like thinking..., pondering..., bloviating..., etc. - which is exactly what enterprise users intend on doing. The Uber story made it quite clear - even some of the big techs don't have the stomach for that kind of unlimited resource drain.
Article text?
So there is a key message in this article which heralds big problems for the AI bubble: AI is being commoditised. I.e. AI users are no longer just going for the biggest/best model overall but instead looking for the "good enough" models for specific uses.
This matters because the entire AI speculative boom has been predicated on the idea that the AI industry is not a commodity but a monopoly in waiting like Search or Social Media - one winner will take all, and so investors are betting heavily on their picks to be The One. But instead it looks like AI is more like a commodity - it will instead be a market of many players, with specalised models and differing levels of sophistication for different tasks. In that world, there isn't going to be a "one winner takes all". Instead there will be choice, and people will go for the cheapest model that is "good enough" for the task at hand. Do you need the most cutting edge expensive model to proof read an email?
The crazy valuations of big AI companies was already a problem even if the idea one winner takes all was even correct. It would have meant investors were speculating on huge rewards for backing the right horse, and inevitably many investors were going to lose out as only one horse can be the winner. Everyone of course believes that their company is The One, and everyone else will be the ones that take the hit - typical of a bubble like this. But if AI is a commodity, then no investors are going to strike it super rich as no one company will be dominant and essentially print money for them in the future. So what you have a load of massively over-valued companies that will instead be bit-players in a big market, and never reach the income levels that justify trillion dollar stock floats.
It's not dissimilar to the dot-com bubble. The Internet didn't go anywhere but the first speculative investors were burnt hard when the bubble burst. Other companies came along and built the world the first investors hoped to own. It's looking like AI is going to same way - there won't be one Google dominating everything, instead there will be lots of failures like boohoo.com or pets.com or go.com.
I think we'll see the big companies like OpenAI and Anthropic rush quickly to get their floats done, because sensible investors know now is the time to cash out. Float the company, quietly sell your shares and let others take the hit as it all comes crashing down.
Instead there will be choice, and people will go for the cheapest model that is "good enough" for the task at hand. Do you need the most cutting edge expensive model to proof read an email?
That’s already happening now, with companies telling their employees to be mindful of their usage and use lower reasoning levels for mundane/basic stuff (documents, summaries, etc.) and reserve the more costly models and reasoning for the extensive tasks (i.e. coding). The AI companies are now starting to charge money and suddenly tokenmaxxing isn’t such a good idea for management.
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