It's not exactly what it is; they now model an incredibly complex markov process, and harnesses that control how that thinking is done.
Is this any different than how a PM gets a programmer to work on a project? They think, then they deliver. If given more time, maybe they deliver something better. Maybe they consult some text and try to apply a design pattern.
The LLM in this use case is perfect because almost everything involved is text based, and the model is able to take in all the expressive that is language.
> Is this any different than how a PM gets a programmer to work on a project?
Yes, it's very different. You seem to be suggesting that the current frontier LLMs, when tied to their tools and harnesses, have emergent properties that are similar to human consciousness. If you truly believe that, I'm not sure how to have a productive discussion here.
I think they have the capabilities to execute a well defined plan. If you truly don't believe that, then you I suspect any work you do as a programmer will not survive the coming changes.
It's not just that, but the core is just that, even with reasoning models. Harness can only get you closer to the good result, but can't save you from every pitfall.
As for PM analogy - don't forget that models don't learn and keep doing same stupid stuff they were doing a month ago.
I would suggest you examine current harness memory persistence. Any reprimand you give your model will be remembered, in the same way a puppy that has a bad social experience will become more shy.
They will not save you from every pitfall, but that isn't the point; engineers walk into pitfalls all the time. This can get you in, and out, much much quicker.
But its not useful because even humans are like that - a bunch of neurons slapped together. Overall a tired analogy that is more suited to stay in 2024 where it belongs. Right now it is clear that it is _much_ more than a statistical model semantically. It is misleading to claim it is _just_ that just like a human is _just_ a statistical model.
