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AI Bubble Is Ignoring Michael Burry's Fears

2 months 1 week ago
An anonymous reader shares a report: Costing tens of thousands of dollars each, Nvidia's pioneering AI chips make up a hefty chunk of the $400 billion that Big Tech plans to invest this year -- a bill expected to hit $3 trillion by 2029. But unlike 19th-century railroads, or the Dotcom boom's fiber-optic cables, the GPUs fueling today's AI mania are short-lived assets with a shelf life of perhaps five years. As with your iPhone, this stuff tends to lose value and may need upgrading soon because Nvidia and its rivals aim to keep launching better models. Customers like OpenAI will have to deploy them to stay competitive. So while it's comforting that the companies spending most wildly have mountains of cash to throw around (OpenAI aside), the brief useful life of the chips and the generous accounting assumptions underpinning all of this investment are less consoling. Michael Burry, who made his name betting against US housing and who's recently turned to the AI boom, waded in this week, warning on X that hyperscalers -- industry jargon for the giant companies building gargantuan data centers -- are underestimating depreciation. Far from being a one-off outlay, there's a danger of AI capex becoming a huge recurring expense. That's great for Nvidia and co., but not necessarily for hyperscalers such as Google and Microsoft. Some face a depreciation tsunami that's forcing them to be extra vigilant about controlling other costs. Amazon has plans to eliminate roughly 14,000 jobs. And while Wall Street is used to financing fast-depreciating assets such as aircraft and autos, it's worrying that private credit funds are increasingly using GPUs as collateral to finance loans. This includes lending to more speculative startups known as neoclouds, who offer GPUs for rent. Microsoft alone has signed more than $60 billion of neocloud deals.

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IBM Lets Fly “Nighthawk” And “Loon” QPUs On The Way To Quantum Advantage

2 months 1 week ago

Quantum computing is finally heating up. There is a heady mix of high-profile and highly resourced big tech players like Google, Microsoft, Amazon Web Services, and Nvidia either building QPUs, simulating  them, or integrating them with classical supercomputers in addition to well-funded younger companies and startups, such as QuEra, IonQ, Quantum Computing, Quantinuum, D-Wave, and Alice & Bob. …

IBM Lets Fly “Nighthawk” And “Loon” QPUs On The Way To Quantum Advantage was written by Jeffrey Burt at The Next Platform.

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Researchers Surprised That With AI, Toxicity is Harder To Fake Than Intelligence

2 months 1 week ago
Researchers from four universities have released a study revealing that AI models remain easily detectable in social media conversations despite optimization attempts. The team tested nine language models across Twitter/X, Bluesky and Reddit, developing classifiers that identified AI-generated replies at 70 to 80% accuracy rates. Overly polite emotional tone served as the most persistent indicator. The models consistently produced lower toxicity scores than authentic human posts across all three platforms. Instruction-tuned models performed worse than their base counterparts at mimicking humans, and the 70-billion-parameter Llama 3.1 showed no advantage over smaller 8-billion-parameter versions. The researchers found a fundamental tension: models optimized to avoid detection strayed further from actual human responses semantically.

Read more of this story at Slashdot.

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