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Comment by namaria | Hacker News Reader

So on the one hand, these newly publicized models can render convincing representations of realities we used to get from deterministic processes. On the other hand, it's probabilistic and fails to conform to logic quite often, and in a confident way.

We're building systems capable of programing computers non-deterministically. I think this is huge. But not because ChatGPT23 will be a CEO or a politician. But because this is a paradigm shift in compute similar to moving from integrator machines to general computers. I don't think LLMs will make programmers obsolete. I think large enough models will make programming something completely different from what it is now.

The days of sequencing tokens for compilers/interpreters seem to be drawing to an end as the dominant way of specifying software products.

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The LLM can act as a global cache for common solutions to common problems, with the ability to perform the integration work necessary to apply them.

That prime number example is a little bit like when you put a functools.lru_cache decorator on a function in Python. It's faster than computing the function call because it's able to recall the return value for the parameters from the cache "memory".

Of course, many skilled programmers are also mainly used a cache for common solutions to common problems organizations have in the programming domain. As humans we can derive satisfaction from being able to tackle the same task others can, as a confirmation of our own progress. We like "doing it ourselves". But globally that's not super interesting if lots of people are constantly re-developing the same solutions to the same problems for their own benefit. I guess that's the push-and-pull (and the anxiety generator).

>The LLM can act as a global cache for common solutions to common problems, with the ability to perform the integration work necessary to apply them.

In my opinion Stack Overflow does a fine job at that. And it's transparent in terms of solutions proposed being voted on and discussed. Turning that into sausage advice is a downgrade.

>But globally that's not super interesting if lots of people are constantly re-developing the same solutions to the same problems for their own benefit.

I'd argue this is how we train individuals thus globally quite relevant.

I'm sort of glad to be retiring soon. I have a feeling everything I enjoy about programming is going to be going away.

I get the feeling. But I've always enjoyed the abstract puzzles more then anything. Computers attracted me as a form of very complex abstract puzzles.

But when it comes down to it everything in life is just nth dimensional tensor puzzles. What I really cherish computers for giving me is fast and clear feedback.

I'm about 10-15 years from retiring, and lately, I've been thinking a lot about how to handle the rest of my career with all these new AI advancements.

This disruption does appear to be different than others prior. As it is not a narrow disruption with limited boundaries for which we can plan and organize our lives for some stabilization period to follow.

Instead, it is constant and accelerating destabilization. Hundreds of AI projects attempting to ride this accelerating wave were essentially just made obsolete yesterday - https://www.youtube.com/watch?v=DH-2BHDYNfk

I feel the excitement is going to very soon turn into frustration of attempting to remain relevant ahead of the accelerating technological curve. Humans need periods of stabilization to plan and reason about their lives.

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