What I do see in the comments: subjective improvement in text generation, possibly lower cost, some optimism about code generation, but some skepticism too.
I use coding agents. To me they are very useful. But what I spend on them isn't going to support trillions of dollars in investment.
I summarized each into new fable 5.1 sessions, and both seem to have arrived at reasonable solutions that only need a few nits revised before they are commit worthy.
I get your point, but we can only have groundbreaking leaps once in a blue moon. That doesn’t mean incremental improvements aren’t useful.
The problem frontier LLMs face is that they are hundreds of billions to trillions of dollars short of finding that market that's big enough to sustain capex commitments and further product development. If they don't find something groundbreaking, they are going to have a very painful year next year, maybe even starting this year for some of them and their data center partners.
Anthropic and OpenAI can't afford to live in a world where LLMs are at or near the top of their S curve.
I upgrade from dumb brick phone to 3G-capable nokia, then to touch screen smartphone, then touchscreen with LTE, then one with fingerprint sensor, etc.
And just when the smartphone begins running out of stuff to add, i also slow down my upgrade cycle.
So, i guess after 5 years in LLM, we are at the end of the curve
But yes, we might end up hitting the issue of "most jobs aren't solving hard problems" increasingly. The bigger potential benefit is higher trustworthiness, reliability/thoroughness, and squishy human things; people will likely continue to pay large premiums for those. "Solve it well and save time, long term". Those can be harder to see on a benchmark.