AI Hype Or Crock?

AI Hype Or Crock?

Robert Gore 

Never has humanity expended so much on an endeavor for which it will receive so little as the Artificial Intelligence (AI) project. Its design rests on the assumption that the human intelligence (HI) it is attempting to mimic and surpass is analogous to its own operating protocols. In other words, humans take in data and process it in definable ways that lead to understandable outputs, and that is the essence of HI.

AI designers reverse the scientific process of exploring reality and then defining, modeling, and perhaps deriving something useful from it, instead assuming that the reality of HI conforms to the AI model they’re building. It’s like expecting a clock to reveal the nature of time. This may seem surprising because among AI designers are some of the brightest people in the world. However, they demonstrate a profound lack of those qualities that might lead them to further understanding of HI: self-awareness, introspection, humility, wisdom, and appreciation of the fact that much of HI remains quite mysterious and may always remain so. Alas, some of them are just plain evil.

AI looks backward. It’s fed and assimilates vast amounts of existing data and slices and dices it in myriad ways. Large language models (LLMs) can respond to human queries and produce answers based on assimilated and manipulated data. AI can be incorporated into processes and systems in which procedures and outcomes are dependent on data and logically defined protocols for evaluating it. Within those parameters, it has demonstrated abilities to solve problems (playing complex games, medical diagnosis, professional qualification exams, improving existing processes) that surpass HI. There is, of course, value in such uses of LLMs and AI, but that value derives from making some of the more mundane aspects of HI—data assimilation, manipulation, and optimization for use—better. Does that value justify the trillions of dollars and megawatts being devoted to AI? Undoubtedly not.

AI has been presented as a labor-saving miracle. But many businesses report a different experience: “work slop” — AI-generated content that looks polished but must be painstakingly corrected by humans. Time is not saved — it is quietly relocated.

Studies point to the same paradox:

• According to media coverage, MIT found that 95% of corporate AI pilot programs show no measurable ROI.

• MIT Sloan research indicates that AI adoption can lead to initial productivity losses — and that any potential gains depend on major organizational and human adaptation.

• Even McKinsey — one of AI’s greatest evangelists — warns that AI only produces value after major human and organizational change. “Piloting gen AI is easy, but creating value is hard.”

This suggests that AI has not yet removed human labor.
 It has hidden it — behind algorithms, interfaces, and automated output that still requires correction.

More...