
Did The Tech Bros Trick Us?
They are now trying to push through trillion-dollar IPOsThey are paying an army of influencers to push this year’s IPOs for OpenAI and Anthropic just as they did with SpaceX.
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They are paying an army of influencers to push this year’s IPOs for OpenAI and Anthropic just as they did with SpaceX.
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Some tech bros are even questioning the corporate benefits from generative AI, a year after academic analyses started questioning them.
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The frequency of AI slop is too high for AI to be successful and there are few solutions, except to wait until the labs produce better AI products.
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Will the bubble pop or is the pullback another false alarm? In any case, the extraordinary claims will likely continue and perhaps even accelerate as AI startups push their IPOs and companies become fearful of the bubble popping.
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Companies charge their customers enough money for them to pay their suppliers, and for those suppliers to pay their suppliers but this isn’t happening in AI.
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“The maximum hype you have right now, which is that AI is replacing people, is not true. But it’s also not true that AI will never threaten jobs. It’s going to be complicated.”
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They say Amazon is rolling out AI use in a haphazard way while also tracking their AI use, and they’re worried the company is essentially using them to train their eventual bot replacements.
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AI related companies have accrued at least $200 billion in debt and the figure is likely considerably higher because that estimate doesn’t count undisclosed private deals.
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People often invest in companies not because they think the companies will succeed, but because they think others will invest.
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Recently, talk of a bubble has increased as it has become clear that OpenAI, and likely other AI software companies, may never become profitable.
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AI software startups should be required to release audited numbers so that we can at least understand the current state of the AI economy.
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Circular financing is the last gasp for OpenAI and the cloud services. They need it to maintain the mirage of an economic boom despite big losses and small revenues for AI software.
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Companies can apply OpenAI’s models to a limited set of data and questions, creating a smaller model that is cheaper to train, update, and run.
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LLMs excel at simple coding tasks but are still too unreliable to use without extensive human supervision on complex tasks where mistakes are expensive.
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Users, whose output is used to train the AI systems, will pay higher electricity costs on account of them while experts eventually earn top salaries.
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Trying to understand the rationale of big monopolists such as Microsoft and Google is often a fool’s errand.
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The job market for recent college grads may be warning us that participation trophies for college and ChatGPT use have long-run costs.
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The tech sector can push AI hype that resembles cargo cult science to its investors because of changes in media resulting from 30 years of the internet.
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Individual workers may claim huge productivity gains for their work, ignoring the impact of AI on the work of downstream employees within the same process.
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Karikó’s story, as outlined in her book, highlights a big problem with universities: ideas matter less than sheer numbers of papers published.
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