[posix86]

But that's how progress works! To me it makes sense that llms first manage to do 80% of the task, then 90, then 95, then 98, then 99, then 99.5, and so on. The last part IS the hardest, and each iteration of LLMs will get a bit further.

Just because it didn't reach 100% just yet doesn't mean that LLMs as a whole are doomed. In fact, the fact that they are slowly approaching 100% shows promise that there IS a future for LLMs, and that they still have the potential to change things fundamentally, more so than they did already.

[camdenreslink]

But they don’t do 80% of the task. They do 100% of the task, but 20% is wrong (and you don’t know which 20% without manually verifying all of it).

So it is really great for tasks where do the work is a lot harder than verifying it, and mostly useless for tasks where doing the work and verifying it are similarly difficult.

[ytpete]

Right — and I'd conjecture until LLMs get close to the accuracy of an entry-level employee, they may not have enough economic value to be viable beyond the hype/novelty phase. Why? Because companies already chose a "minimum quality to be valuable" bar when they set the bar for their most junior entry level. They could get lower-quality work for cheaper by just carving out an even lower-bar hiring tier. If they haven't, maybe it's because work below that quality level is just not a net-positive contribution at all.

[posix86]

I would go so far as to say that the reason people feel LLMs have stagnated is precisely because they feel like they're only progressing a few percentage points between iteration - despite the fact that these points are the hardest.

[baby]

The people who feel that LLMs have stagnated are similar to the ones who feel like LLMs are not useful.