AI is fast. Much too fast. And, above all, much faster than the famous “human-in-the-loop” approach. And that’s becoming more and more of a problem.
It has become incredibly easy to generate high-quality results using AI (whether source code, text, scientific papers, test cases, presentations, marketing materials, annual reports, analyses, credit ratings, and much more)—and it has now become incredibly difficult to verify them.
If we insist on having a “human in the loop,” we must also give that person the necessary time and resources. Which, in turn, means that person will significantly slow down AI-optimized processes.
But if an AI suddenly produces 20,000 lines of source code per day (on average) instead of 200, and the “human in the loop” is supposed to handle quality assurance, that simply can’t work.
If it only takes 30 minutes (instead of 2 months) to write an AI-generated scientific paper, it will no longer be possible to properly review the sheer volume of additional papers.
And it’s not just a matter of time—in theory, that could be solved by having hundreds of “humans in the loop.”
The main point is that we need a completely new set of skills for AI-supported processes. In the past, you could tell pretty quickly from the result whether it was good or bad. Today (thanks to AI), everything looks great at first glance. When reviewing, we have to go into much greater detail, dig much deeper, and ask far more questions. We need the prompts that were used to identify potential risks, and we need the time to do so.
So anyone who calls for a “human-in-the-loop” approach today should be aware of what that means—and that it’s not something you can just do on the side.
To ensure that the “human in the loop” doesn’t end up with a problem, they must be “equipped” accordingly—with time, qualifications, and authority. Otherwise, the “human in the loop” will end up being the one left holding the bag…