LLMs certainly hold potential, but as we’ve seen time and time again in tech over the last fifteen years, the hype and greed of unethical pitchmen has gotten way out ahead of the actual locomotive. A lot of people in “tech” are interested in money, not tech. And they’re increasingly making decisions based on how to drum up investment bucks, get press attention and bump stock, not on actually improving anything.
The result has been a ridiculous parade of rushed “AI” implementations that are focused more on cutting corners, undermining labor, or drumming up sexy headlines than improving lives. The resulting hype cycle isn’t just building unrealistic expectations and tarnishing brands, it’s often distracting many tech companies from foundational reality and more practical, meaningful ideas.
That wouldn’t address the bulk of the issue, only the most egregious examples of it.
For every funny output like “I asked for 1 ice cream, it’s giving me 200 burgers”, there’s likely tens, hundreds, thousands of outputs like “I asked for 1 ice cream, it’s giving 1 burger”, that sound sensible but are still the same problem.
It’s simply the wrong tool for the job. Using LLMs here is like hammering screws, or screwdriving nails. LLMs are a decent tool for things that you can supervision (not the case here), or where a large amount of false positives+negatives is not a big deal (not the case here either).