To me the issue is more that conceptualizing requires a certain state of mind. Before llms it was 10% hard thinking 90% implementing. Implementation was actually sort of a reward, it felt so good just being in the zone and fleshing out ideas.

Post llms I find myself walking up and down quite a lot, only doing the thinking. Now it's more like 40% thinking 60% reviewing plans/code. I haven't experienced flow state since. The thinking is fun but exhausting, the reviewing is just kind of annoying, especially as llms get into these weird failure modes. Before I could look at a bad piece of code and instantly tell what the author was thinking and why the thing doesn't work. Now I need to be a lot more careful because there is little code smell, but a lot of badly chosen abstractions.

Just exhausting...

At that point however open weight model providers will start to shine. All eyes on China.

I can spin this in a weirdly positive light though. With fertility rates going down, life becoming less and less meaningful and simultaneously a small and decreasing group of people becoming extremely productive.. maybe humanity will finally stop exploiting the planet and start a sort of transition.

AI enhanced increased lifespan forest elves watching over nature. Mm I'd prefer that over soma. We are the heralds of The Great Ones

But honestly I'm not sure this will be enough for people to spend on e.g. restaurants or activities or oh I don't know, children. I think this will imply a freezing or even stepping back on the Maslow pyramid, the majority of people consolidating in the middle.

What I'm mostly concerned about is not even economic, it's psychological. With nothing to do, people will not have purpose, and bored people are a gunpowder keg.

Help family? Sure, although you don't need that much money for that. Friends? Ehh not very smart, just think about the changes in the friendships' authenticity.

Wishing for 1B is completely nonsensical if you understand what kind of money that is.

Non-ambiguous is like a first semester algorithms class in university.

That said, I'd agree that people who currently claim 20x speedups will indeed be replaced.

I can give you many, many examples of where it failed for me:

1. Efficient implementation of Union-Find: complete garbage result 2. Spark pipelines: mostly garbage 3. Fuzzer for testing something: half success, non-replicateable ("creative") part was garbage. 4. Confidential Computing (niche): complete garbage if starting from scratch, good at extracting existing abstractions and replicating existing code.

Where it succeeds: 1. SQL queries 2. Following more precise descriptions of what to do 3. Replicating existing code patterns

The pattern is very clear. Novel things, things that require deeper domain knowledge, coming up with the to-be-replicated patterns themselves, problems with little data don't work. Everything else works.

I believe the reason why there is a big split in the reception is because senior engineers work on problems that don't have existing solutions - LLMs are terrible at those. What they are missing is that the software and the methodology must be modified in order to make the LLM work. There are methodical ways to do this, but this shift in the industry is still in baby shoes, and we don't yet have a shared understanding of what this methodology is.

Personally I have very strong opinions on how this should be done. But I'm urging everyone to start thinking about it, perhaps even going as far as quitting if this isn't something people can pursue at their current job. The carnage is coming:/

I understand the drive for stabilizing control and consistency, but this ain't the way.