AI Pullback Has Officially Started
The gulf between actual AI performance and AI hype is deep and cavernous, and we have the data to prove it. A recent MIT report found that 95% of AI pilots didn’t increase a company’s profit or productivity. A recent METR report also found that AI coding tools actually slow developers down. Why?
Well, generative AI models, even the very latest ones, often get things wrong and “hallucinate”, which requires considerable human oversight to correct. IT consultants Gartner attempted to quantify this and found that AI agents fail to complete office tasks around 70% of the time.
Simply put, the amount of human oversight necessary, even for simple tasks, almost always undermines whatever productivity gains are made. In other words, in the vast majority of cases, it is more productive not to use AI than to use AI. Yet despite all the evidence, AI is still being shoehorned in everywhere and being praised as the next industrial revolution. Or is it? Because there is also mounting data that the world is beginning to turn its back on this questionable technology.
Researchers are “coming to a better understanding of AI’s present limits and future potential” and are seriously reassessing how they use it and plan to use it.
. . . the AI adoption rate in large companies (250 employees or more) has dropped . . . .
This slowdown is made worse by the fact that the number of companies scrapping their AI initiatives has skyrocketed. Recent analysis found that back in 2024, only 17% were cancelled, meaning real-world ongoing use of AI was climbing fast. However, this year, the rate has surged to a staggering 42%. This, combined with the slowdown in AI adoption, means real-world corporate use of AI is witnessing a marked decline.
From data to anecdotes, we can see the tide is turning across the globe. The shine has tarnished, and everyone can see that this might actually be a bit of a turd after all.
AI isn’t the productivity machine they thought it was. And, because AI doesn’t improve productivity for the majority of use cases (MIT report), this leads to most AI programs being cancelled.
But, to bring this article full circle, the situation is slightly different in academia.
It wasn’t just researchers using AI to write their papers; those conducting the peer review also used AI to assess these papers. This meant that AI errors were not being picked up, as most research papers don’t spawn real-world implementations in which the errors become apparent.
This has created an enormous scandal, with thousands of obviously fake AI-generated papers being published in journals, and authors hiding AI prompts in their papers to get the peer review AI to give them a false good grade.
This is likely why many researchers thought AI was brilliant, as AI also compromised the academic feedback loop that should have caught their AI’s critical mistakes.
I know it is a tired cliché at this point, but it really is an emperor’s clothes moment, which is deeply worrying considering how dangerous the AI bubble is right now (read more here).
Could this alone pop the bubble? Truth be told, I don’t know. But either way, none of this is a good sign for AI.