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GamingChairModel

@ GamingChairModel @lemmy.world

Posts
1
Comments
439
Joined
3 yr. ago

  • Anthropic accuses Chinese rival Alibaba of illicitly extracting AI capabilities

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  • Yeah, it wouldn't be copyright. It might be trade secrets, though. And trade secrets can be made out of public data, but arranged in a way that gives competitive advantage (for example, customer lists themselves might be trade secrets, even if each entry is a publicly available set of name/contact information/job title/company).

  • Valve Says The Companies Making RAM Give Them A Price And If They Say No, They ‘Never Talk To Us Again’

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  • The actual process of creating semiconductors is basically:

    1. Etch a stencil that has the pattern you want.
    2. Place the stencil over a piece of silicon.
    3. Bombard the silicon and stencil with radiation so that the chemical properties of the silicon change exactly under that stencil.
    4. Repeat the process with multiple other stencils, so that the resulting silicon has basically shapes of wires and logic gates that can perform different functions with the electricity running through those shapes.

    In recent years, step 3 has gotten so complicated, based on needing to create radiation of exactly a particular wavelength of extreme ultraviolet light focused exactly on the silicon (and the mask/stencil above it), because that wavelength allows for the smallest possible features on the silicon. So they take purified tin, melt the tin into molten liquid, and ejecting the molten tin in a liquid jet downward into a vacuum at exactly the right speed to where it forms into droplets of the exact size for the machine (about 50 μm), then blasts each droplet, mid-fall, with a 1.6kW laser that heats it up so hot that it vaporizes and ionizes into plasma at the exact position where a system of highly polished and precisely positioned mirrors focuses the UV radiation evenly onto the silicon surface.

    Oh, and the machine makes one tin droplet every 1/50,000 of a second, so in any given second it ionizes 50,000 droplets in the stream.

    The machine costs something like $300 million, and requires full time experts to make sure that it's working correctly.

    Everything else in the fabrication facility is similarly complicated, which is why a fab represents something like $30 billion in total costs over its lifetime.

  • We live in the future!

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  • crypto is untraceable (mostly)

    It is very traceable. It's just that the government doesn't have a special position with tracing transactions, so there's been a bunch of kludges built on top of the very transparent Bitcoin network to try to mask things.

  • Local Brand Realizes Customers Hate Its AI Ads, Switches to Charming Homemade Ones Instead

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  • "I outsourced the copywriting to the lowest bidder, who happened to be in Poland"

  • Your Career Isn't Eroding - You're Just Holding the Wrong Moat

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  • Because it obviously was.

    The dashes, the short sentences, the bullet points, the overly familiar tone that seems LinkedIn-ish. All of it sounds like AI.

  • macOS 27 requires Apple Silicon, as Apple draws down the Intel Mac era

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  • At least they moved onto year-based versioning. That was probably the best part about the 26/Tahoe release.

  • macOS 27 requires Apple Silicon, as Apple draws down the Intel Mac era

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  • It's called decoding and encoding.

    But the big data centers doing all the video processing for the big video services (including both permanent videos from a library and things like live streaming) are encoding the videos with settings that require less computational power to decode. The idea is to be able to let even old budget smartphones still be able to display the video with very low power requirements on the client device. There's no universe where consumers decoding digital video will be a high-power computational task.

    Restaurants have sharp knives in the kitchen, but generally serve food that requires only minimal cutting effort from the table knives set out with the rest of the table settings. Dining will always be easier than cooking, by a margin that makes the difficulty of dining not worth mentioning, so it would be bizarre to criticize a knife as being only good for cooking and eating food, when plenty of dining tableware knives out there would be insufficient for kitchen work.

    You've made the mistake of lumping decoding and encoding together based on the algorithmic/mathematical similarity of those tasks, when everyone else is more inclined to discuss the very different end user use cases of those computing needs.

  • what will happen to all the datacenters when the bubble bursts?

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  • Yes. But major differences:

    The dot com buildout of physical communications infrastructure involved basically 3 things:

    1. Switches/routers at the nodes for sending signals down the right route.
    2. Fiber optic cables connecting the nodes.
    3. Legal rights of way and easements for the legal right to keep the physical assets in that physical place, and to maintain/replace the stuff as needed.

    Category number 1? That stuff went obsolete quickly, and wasn't really reused after the crash.

    Category number 2 was better. Turns out, fiber optics can carry signals on a lot more channels than those fibers were originally designed for. And they're designed for useful lives measured in decades. So even if they sat dark from being unused for 5-10 years, eventually they could be used again.

    Category 3 is super important. That legal right is basically permanent, and so long as communications equipment needs to physically go from one place to another, having that legal right can be built on and profited on (including the ability to sell or lease those rights).

    What's that gonna look like for the AI infrastructure? The servers full of GPUs are the bulk of the cost, and the GPUs are replaced with a new generation every 1-2 years, seem to require all new power and cooling infrastructure every 1-2 generations or so.

    Plus the AI buildout looks to be several trillion dollars. Even adjusting for inflation, that's so much more than the tens of billions that each telecom company built out that infrastructure.

    And it's hard to see how the servers themselves will be useful for regular businesses, much less consumers. A Blackwell 72-GPU server is $3 million and takes 130 kW to run. A residential electrical line maxes out at about 48kW. The newest Vera Rubin servers are projected to be up to 600kW, with all the power and cooling management that comes with that, plus all the ultra high end networking stuff built into that rack. Even deep pocketed businesses will have trouble finding a use for that server rack worth millions, requiring a ton of supporting infrastructure that not even normal pre-2025 data centers have.

  • 'At some point you've got to make money': Goldman's top AI skeptic warns the clock is running out ahead of OpenAI and Anthropic IPOs

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  • I don't think government funding can actually offset the crash in consumer and business demand being insufficient to cover the cost of the most expensive models on the most expensive GPUs. But if you look through my comment history I've made the comparison to supersonic flight, because I genuinely believe there's a possibility that governments fund the expensive branch of this technology for their own military or surveillance or law enforcement purposes without the benefits necessarily actually spilling out into normal commercial applications.

    We've hit the point where training a model (both pre training and post training) isn't the expensive part, and the expensive part is actual inference, which makes it hard to scale the most expensive models to where it's useful for a lot of people. So it might be that the companies and governments that can afford to operate an expensive model might be the only ones to do it. And they'll be able to, without necessarily the public being able to have access to the same tech.

  • 'At some point you've got to make money': Goldman's top AI skeptic warns the clock is running out ahead of OpenAI and Anthropic IPOs

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  • Plenty of examples of companies spending more than they earn for decades. Before OpenAI and Anthropic, though, nobody has ever needed to raise more than $100 billion from investors before turning a profit, though. The scale is immense, enough to where it affects the liquidity of the investors that have funded their rise.

  • 'At some point you've got to make money': Goldman's top AI skeptic warns the clock is running out ahead of OpenAI and Anthropic IPOs

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  • The business model should be that with economies of scale they could provide compute much cheaper than average consumer can buy to run locally.

    That business model assumes that the huge cloud models will always maintain a gap worth paying for, compared to the local models. I'm just not convinced that the average consumer will need cloud models for summarizing their emails or the news of the day.

    And for actual costs of their data centers, there literally aren't enough humans in the world where $20/month AI spending per person will help them break even. They'll need to sell big accounts (many businesses spending billions per year) in order to break even.

  • 'At some point you've got to make money': Goldman's top AI skeptic warns the clock is running out ahead of OpenAI and Anthropic IPOs

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  • There's just no way to pay for the cost of these services, though.

    When someone constructs a 100 MW data center (now considered a smaller one for new construction), that's about $2 billion in total costs to outfit the whole operation. And then once it's on, we're talking something like $10-20 million/month in electricity alone, and a few million in other costs. How many $20 subscriptions do you need to sell just to break even with your operating expenses? How many $100/month subscriptions do you need to sell to make a dent on your interest payments on the construction? Will there be a market for $1000/month subscriptions from millions of customers? If not, how's this all going to be paid for?

  • 'At some point you've got to make money': Goldman's top AI skeptic warns the clock is running out ahead of OpenAI and Anthropic IPOs

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  • Once you get into things with useful generation and large context windows, or things like video generation, suddenly you need one or more $10,000+ pieces of hardware to run it.

    A Blackwell server with 72 GPUs costs about $3 million, plus requires 130 kW of power (about 3 residential homes' max rated power through a residential 200A circuit box, for about $600-$1000/day in electricity cost).

    You're gonna need to sell a lot of $20/month subscriptions to get that paid for, assuming that the server is good for 5 years. If it's only good for 3 years, the economics are basically impossible.

  • Uber caps employee AI spending after blowing through budget in 4 months

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  • But you're seeing a screenshot of an unmatched order that no driver has claimed yet. I'm saying that unless an actual match is accepted, that's not really evidence that people in a place don't tip well, just that some people don't get their orders filled.

    If you never give less than $5, then any order you're involved in will involve at least a $5 tip. That may not be representative of the orders you're not involved with.

  • Uber caps employee AI spending after blowing through budget in 4 months

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  • 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.

  • Amazon Shuts Down Internal AI Leaderboard After Employees Cheated

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  • 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.

  • Nvidia launches ‘superchip’ putting AI power into laptops and PCs

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  • 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.

  • Microsoft is intentionally bricking all Office for Mac 2019/2021 installations

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  • 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.

  • Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked

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  • 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.