Yeah it’s not even drift. It’s just smoke and mirrors that looks convincing if you don’t know what you’re talking about. It’s why you see writers say “AI is great at coding, but not writing” and then you see coders say “AI is great at writing, but not coding.”
If you have any idea what good looks like, you can immediately recognize AI ain’t it.
For a fun example, at my company we had a POC done by a very well known AI company. It was supposed to analyze a MS Project schedule, then compare tasks in that schedule to various data sources related to to tasks, then flag potential schedule risks. In the demo to the COO, they showed the AI look at a project schedule and say “Task XYZ could be at risk due to vendor quality issues or potential supply chain issues.”
The COO was amazed. Wow it looked through all this data and came back with such great insight. Later I dug under the hood and found that it wasn’t looking at any data behind the scenes at all. It was just answering specifically “what could make a project task at risk?” and then giving a hypothetical answer.
Anyone using AI to make any sort of decision is basically doing the equivalent of Googling your issue and then taking the top response as gospel. Yeah that might work for a few basic things, but anything important that requires any thought whatsoever is going to fail spectacularly.
It’s why you see writers say “AI is great at coding, but not writing” and then you see coders say “AI is great at writing, but not coding.”
I’ve always thought of this as being just like Hollywood. If you have expertise in whatever field they present an expert in, it’s painful how off they are but it lookks fine for everyone outside the field of expertise.
It was just answering specifically “what could make a project task at risk?” and then giving a hypothetical answer
It wasn’t even doing that. It was “looking” at training data for what a an analysis like that might look like, and then inventing a sequence of words that matched that training data. Maybe “vendor quality issues” is something that appears in the training data, so it’s a good thing to put in its output.
Yeah it’s not even drift. It’s just smoke and mirrors that looks convincing if you don’t know what you’re talking about. It’s why you see writers say “AI is great at coding, but not writing” and then you see coders say “AI is great at writing, but not coding.”
If you have any idea what good looks like, you can immediately recognize AI ain’t it.
For a fun example, at my company we had a POC done by a very well known AI company. It was supposed to analyze a MS Project schedule, then compare tasks in that schedule to various data sources related to to tasks, then flag potential schedule risks. In the demo to the COO, they showed the AI look at a project schedule and say “Task XYZ could be at risk due to vendor quality issues or potential supply chain issues.”
The COO was amazed. Wow it looked through all this data and came back with such great insight. Later I dug under the hood and found that it wasn’t looking at any data behind the scenes at all. It was just answering specifically “what could make a project task at risk?” and then giving a hypothetical answer.
Anyone using AI to make any sort of decision is basically doing the equivalent of Googling your issue and then taking the top response as gospel. Yeah that might work for a few basic things, but anything important that requires any thought whatsoever is going to fail spectacularly.
I’ve always thought of this as being just like Hollywood. If you have expertise in whatever field they present an expert in, it’s painful how off they are but it lookks fine for everyone outside the field of expertise.
It wasn’t even doing that. It was “looking” at training data for what a an analysis like that might look like, and then inventing a sequence of words that matched that training data. Maybe “vendor quality issues” is something that appears in the training data, so it’s a good thing to put in its output.