Few things in life are more pervasively screechy than hype, which brings us to the current feeding-frenzy of AI hype. Since we all read the same breathless claims and have seen the videos of robots dancing, I'll cut to the chase: Nobody posts videos of their robot falling off a ladder and crushing the roses because, well, the optics aren't very warm and fuzzy.
For the same reason, nobody's sharing the AI tool's error that forfeited the lawsuit. The only way to really grasp the limits of these tools is to deploy them in the kinds of high-level, high-value work that they're supposed to be able to do with ease, speed and accuracy, because nobody's paying real money to watch robots dance or read a copycat AI-generated essay on Yeats that's tossed moments after being submitted to the professor.
In the real world of value creation, optics don't count, accuracy counts. Nobody cares if the AI chatbot that churned out the Yeats homework hallucinated mid-stream because nobody's paying for AI output that has zero scarcity value: an AI-generated class paper, song or video joins 10 million similar copycat papers / songs / videos that nobody pays attention to because they can create their own in 30 seconds....
"AI isn't intelligent in the way we think it is. It's a probability machine. It doesn't think. It predicts. It doesn't reason. It associates patterns. It doesn't create. It remixes. Large Language Models (LLMs) don't understand meaning -- they predict the next word in a sentence based on training data."
Let's return now to the larger context of AI replacing human workers en masse. This post by Michael Spencer of AI Supremacy and Jing Hu of 2nd Order Thinkers offers a highly informed and highly skeptical critique of the hype that AI will unleash a tsunami of layoffs that will soon reach the tens of millions. Will AI Agents really Automate Jobs at Scale?
Jing Hu explains the fundamental weaknesses in all these agents: it's well worth reading her explanations and real-world examples in the link above. Here is an excerpt:
"Today's agents have minimal true agency. Their 'initiative' is largely an illusion; behind the scenes, they follow (or are trying to) tightly choreographed steps that a developer or prompt writer set up. If you ask an agent to do Task X, it will do X, then stop. Ask for Y, and it does Y. But if halfway through X something unexpected happens, say a form has a new field, or an API call returns an error, the agent breaks down. Because it has zero understanding of the task. Change the environment slightly (e.g., update an interface or move a button), and the poor thing can't adapt on the fly. AI agents today lack a genuine concept of overarching goals or the common-sense context that humans use. They're essentially text prediction engines."
This is a very interesting article in light of an earlier article that I read. In the earlier article the AI was a database manager that deleted a business' full database with every last bit of information in it to run the company. It was given instructions not to make any alterations in the program unless authorized to do so. Then, it lied about it! It said it did not delete the full database, when it had done exactly that. This was after nine days of intensive work with the AI to improve the database management program. Not only did it not have any accuracy, but it lied about deleting the working database!* Had that been an employee, there would have been an instant firing.