I have exposure to AI initiatives at several companies including a few F500's. I have seen teams dump huge logs into frontier models that took hours to get so-so results that we were able to replace with a few lines of python code at 1000 times the speed and 100% accuracy. When asked why they were doing this they literally said "because we don't understand the subject matter so we were depending on the AI". I saw one team file a complaint with a vendor about a frontier backed coding harness and it's inability to consistently format headers because they were using it as a reporting engine. When I recommended they just use the coding tool to write code to generate reports you would have thought I had just cured cancer from their response. I frequently see people complain about the fact that AI is going to take their jobs and then see them gripe about the fact that AI is 'worthless' because it can't do more of their job than it already does. It's easy to see the difference between the people seeing 10x productivity gains from leveraging AI and those who aren't and it's not the AI.
I've heard this framed as "AI raises the floor by 2x or less but raises the ceiling by 10x or more"
Someone asked me if I was using models for fantasy sports, and if it was smart enough to help make decisions about drafting.
My answer: no, but it was able to help me find the website and social handles for every beat writer for every team, and generate a simple website where I can do a daily skim of teams/players and draw my own conclusions.
LLMs are a tool, not a panacea.
i have trouble understanding these situations, e.g. the AI itself would presumably make the suggestion to write a python script for such a task. It seems to me that there two huge problems right now * understanding which category of problems an LLM is an appropriate solution for (rather than throwing LLMs at any and all problems) * matching model capability (and therefore cost) to the problem at hand. You can easily overspend massively by using a model that's too powerful