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.

As the universe expands the gap between galaxies widens until they start "disappearing" as no information can travel anymore between them. Therefore, if we assume that intelligent lifeforms exist out there, it is likely that these will slowly converge to the place in the universe with the highest mass density for survival. IIRC we even know approximately where this is.

This means a sort of "grand meeting of alien advanced cultures" before the heat death. Which in turn also means that previously uncollided UUIDs may start to collide.

Those damned Vogons thrashing all our stats with their gazillion documents. Why do they have a UUID for each xml tag??

In other words, people claiming these high productivity increases may be the ones at actual risk. Why employ 3 people when 1 can write the prompts?

Funny you say that, I encountered this in a seemingly simple task. Opus inserted something along the lines of "// TODO: someone with flatbuffers reflection expertise should write this". I actually thought this was better than I anticipated even though the task was specifically related to fbs reflection. And it was because I didn't waste more time and could immediately start rewriting it from scratch.

After all, it's through failure that we learn the limitations of a technology. Apparently some people encounter that limit more often than others.