AI doesn't think: the invisible labor propping up the bubble
Ramón López de Mántaras debunks AI myths: it neither thinks nor is intelligent. Behind it lies precarious human work and a stock market bubble that is starting to burst.
English · Original discussion in Spanish · Published
AI doesn't think: the invisible labor propping up the bubble
Behind every "intelligent" chatbot lie thousands of hours of poorly paid human work. And the industry carefully hides it. Ramón López de Mántaras, a veteran Spanish researcher with 50 years in AI, explained this in a talk that dismantles the sector's myths point by point.
The myth of artificial intelligence
López de Mántaras argues that today's systems neither think nor are intelligent in the human sense. They are statistical machines that process enormous volumes of data but lack real understanding. His argument is nothing new among those familiar with the fundamentals of LLMs, but it clashes head-on with the commercial narrative of the big tech companies.
The invisible labor behind the magic
The most uncomfortable point is the amount of precarious work that sustains artificial "superintelligence." Image labeling, content moderation, model training: repetitive tasks carried out by people working in often perversos conditions. Without that cheap labor, the algorithms would not work. The narrative of autonomous machines deliberately conceals this human dependency.
A stock market bubble?
Several analyses suggest the market is overvaluing AI's promises. "There's a reason the AI bubble is bursting on the stock markets," some observers note. If the future were so bright, prices would keep rising; but the recent correction suggests investors are starting to doubt the projections.
Against those who dismiss veteran computer scientists as "obsolete" because they were trained on punched cards, the data show that precisely those experts were the first to warn about the technology's real limitations. The question is not whether AI will advance, but whether the current narrative is more marketing than reality. With the real economy holding the whole thing up, the answer seems clear.
Related discussions in the forum
Behind every "intelligent" chatbot lie thousands of hours of poorly paid human work. And the industry carefully hides it. Ramón López de Mántaras, a veteran Spanish researcher with 50 years in AI, explained this in a talk that dismantles the sector's myths point by point.
The myth of artificial intelligence
López de Mántaras argues that today's systems neither think nor are intelligent in the human sense. They are statistical machines that process enormous volumes of data but lack real understanding. His argument is nothing new among those familiar with the fundamentals of LLMs, but it clashes head-on with the commercial narrative of the big tech companies.
The invisible labor behind the magic
The most uncomfortable point is the amount of precarious work that sustains artificial "superintelligence." Image labeling, content moderation, model training: repetitive tasks carried out by people working in often perversos conditions. Without that cheap labor, the algorithms would not work. The narrative of autonomous machines deliberately conceals this human dependency.
A stock market bubble?
Several analyses suggest the market is overvaluing AI's promises. "There's a reason the AI bubble is bursting on the stock markets," some observers note. If the future were so bright, prices would keep rising; but the recent correction suggests investors are starting to doubt the projections.
Against those who dismiss veteran computer scientists as "obsolete" because they were trained on punched cards, the data show that precisely those experts were the first to warn about the technology's real limitations. The question is not whether AI will advance, but whether the current narrative is more marketing than reality. With the real economy holding the whole thing up, the answer seems clear.
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Summary of a discussion on Burbuja.info - Foro de economía, actualidad y política., translated from Spanish and reviewed before publication. Read the full discussion (10 replies).
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