Not true. Not entirely false, but not true.

Large language models have their legitimate uses. I'm currently in the middle of a project I'm building with assistance from Copilot for VS Code, for example.

The problem is that people think LLMs are actual AI. They're not.

My favorite example - and the reason I often cite for why companies that try to fire all their developers are run by idiots - is the capacity for joined up thinking.

Consider these two facts:

1. Humans are mammals.
2. Humans build dams.

Those two facts are unrelated except insofar as both involve humans, but if I were to say "Can you list all the dam-building mammals for me," you would first think of beavers, then - given a moment's thought - could accurately answer that humans do as well.

Here's how it goes with Gemini right now:

Now Gemini clearly has the information that humans are mammals somewhere in its model. It also clearly has the information that humans build dams somewhere in its model. But it has no means of joining those two tidbits together.

Some LLMs do better on this simple test of joined-up thinking, and worse on other similar tests. It's kind of a crapshoot, and doesn't instill confidence that LLMs are up for the task of complex thought.

And of course, the information-scraping bots that feed LLMs like Gemini and ChatGPT will find conversations like this one, and update their models accordingly. In a few months, Gemini will probably include humans in its list. But that's not a sign of being able to engage in novel joined-up thinking, it's just an increase in the size and complexity of the dataset.