I remember reading about a case a few years ago where a warehouse couldn’t figure out which of its workers was just periodically taking shits in random corners of the warehouse. I think I’m starting to understand a different angle to that story, though.
It’s gonna be so fucking funny when the push to sell silicon that can run local models at 100 watts or less ends up destroying the business models of the companies that built out 100,000,000,000 watts of data centers.
Yeah, but that’s always been true of paid software licenses for a particular version: it reaches EOL and you have to decide whether to live with the possibility of unpatched known vulnerabilities or pay for an upgrade to a more recent release.
MS Office has been doing this from back in the Windows 3.0 days at least.
The only solution is to make sure they can’t read data you don’t want shared.
Isn’t that the appropriate guardrail, then? LLM chats and agents and whatever need to be contained with external permissions settings that the LLMs simply do not and can never have the power to override.
In a normal customer service setting with human agents, there are still plenty of examples of what a human agent simply doesn’t have the power to do. Often, they’ll need to escalate to a manager to do things like process refunds not just because they weren’t given social permission to do so, but because they weren’t given technical permissions to do so. LLM agents need to be contained in the same way. Any decent use of agents, human or software, requires carefully designed processes and permissions extrinsic to that agent’s own decisionmaking abilities to make sure that agents don’t do something bad for the company.
Edit 2: Nevermind. 13th October is the day Microslop has chosen to fuck me: https://support.microsoft.com/en-us/office/system-requirements/end-of-support-for-office-2021
You’ll still get a few years before the software becomes remotely disabled, though. This story about Office 2019 losing functionality follows Office 2019 losing support in 2023. If that’s the rate things go, then maybe Office 2021 will lose functionality either 2 years from now (7 years after release) or 3 years from now (3 years after losing support).
And, as I understand it, Anthropic hasn’t committed as much spending to building out new data centers, and has setup their operations to be GPU agnostic, so they can keep flexibility between NVIDIA GPUs, Google TPUs, and Amazon Trainium, and play the data center pricing game. Anthropic is better positioned to survive an AI winter (and I believe it’s coming soon).
Not with AI in particular, but yes with subscription based software generally.
The economics of it don’t add up and the growth rate of the curve of improvement over time has already significativelly fallen which looking at the historical curves for other technologies is a very strong indication that it’s approaching the limits of how far it will go even though it’s nowhere close to the hype.
Yeah, I’m convinced that they’ve maintained the illusion of continued exponential improvement from 2024-2026 by sneaking in exponential increase in resources (hardware complexity, power consumption), to prop things up past what should have been a plateau.
AI has an interesting economic trait in that it’s very, very expensive to deploy, and made very fast progress from 2022 to 2024. That caused investors with money to believe that:
But since 2024, we’ve seen that the cutting edge got even more expensive much faster than expected, and much of the improvements in performance now come from inference rather than training, which represents a high ongoing cost.
Now, if we extrapolate from that trend line, we’ll see that the market will be much smaller for AI services at the cost it takes to provide that service, and the question then becomes whether the industry can make its operations cheaper, fast enough to profitably provide a service people will pay for.
I have my doubts they’ll succeed, and we might just be looking at the industry like supersonic flight: conceptually interesting, technically feasible, but just a commercial dead end because it’s too expensive.
Don’t they put plutonium reactors in space?
The ones that power spacecraft generate less than 5000W of heat at max power (while producing 300W of usable electricity).
In order to power a single server rack of 72 Blackwell GPUs, which takes about 130,000 watts, you’d need about 430 of those RTGs, and need to manage cooling requirements of 430 times as much (plus however much additional power will be required by the cooling system itself, too).
Companies are building entire workflows around AI, but they are building them under the assumption that they won’t ever be charged per token.
Or worse, where the AI models underpinning a workflow breaks or degrades in some way to reduce token usage and then starts behaving in unexpected ways, in a process/workflow that assumes a particular type of behavior.
Original reporting by Bloomberg is here or here for an archive.is version.
Sounds like the talks are stalled about revenue guarantees from the Kenyan government, if demand for the data center’s capacity never shows up. The power infrastructure isn’t really an issue yet, since the first phase is going to be 100MW and there are plans to build geothermal plants sufficient to cover several multiples of the country’s current power usage, including whatever demand comes from this data center.
Hey now, that infrastructure is good for, like, 3 years, so it’s really like spending $145 billion to save $81 billion. And that doesn’t even start to get into how much it costs to operate that infrastructure.
I’ve read some of Ed Zitron’s long posts on why the AI industry is a bubble that will never be profitable (and will bring down a lot of companies and investors), and one of the recurring themes is that the AI companies are trying to capture growing market share in an industry where their marginal profits are still negative, and that any increase in revenue necessarily increases their costs of providing their services.
But some of the comments in various HackerNews threads are dismissive, saying that each new generation of models makes the cost of inference lower, so that with sufficient customer volume, the companies running the models can make enough profit on inference to make up for the staggering up-front capital expenditures it took to build out the data centers, train their models, etc.
It’s all pretty confusing to me. So for those of you who are familiar with the industry, I have several questions:
I suspect that the reason why the discussion around this is so muddled online is because the answers are different depending on which of the 3 questions is meant by “is running an AI model getting cheaper over time?” But I wanted to hear from people who are knowledgeable about these topics.
The compose key combinations are great because they’re easier to remember, because the codes are grounded in some kind of relationship between the character and the keys used.
Alt codes are just memorized combinations of numbers, and that’s not as easy.
A 2018 MBP should be good for MacOS 15 Sequoia, which still runs updated Firefox. I’m still running Sonoma (the version before that) and I use Firefox as my primary browser.
Testing a bunch of linux distros on old intel macbooks has shown me that apple is really good with resource management on their vertically integrated hardware, even with greedy daemons like identityserverd or whatever it is, trolling through your drive cataloguing faces in your photos all the time, and the relentless indexing system, and telemetry.
It’s really amazing to me how little power MacOS uses in normal use, compared to running Linux on the same machine. The Asahi Linux project also has documented a ton of interesting bits of hardware that MacOS makes use of, pretty seamlessly, that they’ve gotta figure out.
His legal templates are all in Latin, his production code is all in COBOL, etc.
I’m not terrified because there’s nothing to be afraid of. but there are dumb, evil little men creating these issues.
Drunk drivers on the highway are terrifying, precisely because they’re so bad at what they’re doing, and are behind the controls of dangerous machines they shouldn’t have been trusted with.
The AI tech execs can hurt us, so it is concerning.
I think the user decides how much to tip in advance, and the app conveys that information to potential matches. Orders with low tips tend to sit there unclaimed, because no driver wants to bother with that
I’m not sure if Uber does it that way, but Doordash does.