251–260 of 985 posts
Earlier quoted context omitted.
I'm looking forward to buying my own slightly used 5 million square ft data centre in Texas for $1
Tired: homelabbers bringing decommissioned datacenter gear home. Wired: homelabbers moving into decommissioned datacenters.
> Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said This doesn't seem correct to me, or at least is built on several shaky assumptions. One would have to 'refill' your hardware if: - AI accelerator cards all start dying around the 5 year mark, which is possib…
"It is 1958. IBM passes up the chance to buy a young, fledgling company that has invented a new technology called xerography. Two years later, Xerox is born, and IBM has been kicking themselves ever since. It is ten years later, the late '60s. Digital Equipment DEC and others invent the minicomputer. IBM dismisses the minicomputer as too small to do serious computing and, therefore, unimportant to their business. DEC…
I'm not sure these examples are even the gotchas you're positing them as. Xerox is a dinosaur that was last relevant at the turn of the century, and IBM is a $300bn company. And if it wasn't obvious, the Apple II never made a dent in the corporate market, while IBM and later Windows PCs did.
In any case, these examples are almost half a century old and don't relate to capex ROI, which was the topic of dicussion.
Reminds me of all the dark fiber laid in the 1990s before DWDM made much of the laid fiber redundant. If there is an AI bust, we will have a glut of surplus hardware.
The problem is that the laid fiber can be useful for years while data center hardware degrades and becomes obsolete fast. It could be a massive e-waste crisis.
So it's more a crisis of investors wasting their money rather than ewaste.
Earlier quoted context omitted.
I must be dense, why does this imply AI can't be profitable?
Tokens are, roughly speaking, how you pay for AI. So you can approximate revenue by multiplying tokens per year by the revenue for a token. (6.29 10^16 tokens a year) * ($10 per 10^6 tokens) = $6.29 10^11 = $629,000,000,000 per year in revenue Per the article > "It's my view that there's no way you're going to get a return on that, because $8 trillion of capex means you need roughly $800 billion of profit just to pay…
Earlier quoted context omitted.
Cheap compute would be a boon for science research.
It'll likely be used to mine bitcoin instead.
I would add an addendum to this -- there is no way the announced spending on AI data centers will all come to fruition. I have no doubt that there will be a massive build-out of infrastructure, but it can't reach the levels that have been announced. The power requirements alone will stop that from happening.
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This happened in the dotcom too btw. Companies built out fibre networks, it wasn't possible to actually build all the physical infra that companies wanted to build so many announced plans that never happened and then, towards the end, companies began aggressively acquiring stakes in companies who were building stuff to get financial exposure (an example was BT, which turned itself briefly into a hedge fund with a telephone network attached...before it imploded).
CEOs do not operate on the timescale of waiting and building. Their timescale is this year's bonus/share options package. Nothing else matters: announce plans to do X or Y, doesn't matter, they know they will be gone long before it happens.
"It is 1958. IBM passes up the chance to buy a young, fledgling company that has invented a new technology called xerography. Two years later, Xerox is born, and IBM has been kicking themselves ever since. It is ten years later, the late '60s. Digital Equipment DEC and others invent the minicomputer. IBM dismisses the minicomputer as too small to do serious computing and, therefore, unimportant to their business. DEC…
IBM released the 5100 in September 1975 [0] which was essentially a personal computer in feature set. The biggest problem with it was the price tag - the entry model cost US$8975, compared to US$1298 for the entry Apple II released in June 1977 (close to two years later). The IBM PC was released in August 1981 for US$1565 for the most basic system (which almost no one bought, so in practice they cost more). And the original IBM PC had model number 5150, officially positioning it as a successor to the 5100.
IBM’s big problem wasn’t that they were disinterested in the category - it was they initially insisted on using expensive IBM-proprietary parts (often shared technology with their mainframe/midrange/minicomputer systems and peripherals), which resulted in a price that made the machine unaffordable for everyone except large businesses, governments, universities (and even those customers often balked at the price tag). The secret of the IBM PC’s success is they told the design team to use commercial off-the-shelf chips from vendors such as Intel and Motorola instead of IBM’s own silicon.
> Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said This doesn't seem correct to me, or at least is built on several shaky assumptions. One would have to 'refill' your hardware if: - AI accelerator cards all start dying around the 5 year mark, which is possib…