It is not yet good enough or there is not yet sufficient trust. Also there are still resources allocated to checking the code.
I saw a post yesterday showing Brave browser's new tab using 70mb of RAM in the background. I'm very sure there's code there that can be optimized, but who gives a shit. It's splitting hairs and our computers are powerful enough now that it doesn't matter.
Immateriality has abstracted that particular few line codes away.
I do. This sort of attitude is how we have machines more powerful than ever yet everything still seems to run like shit.
Were we advised to check compiler output every single time "in the early days"?
No, that's not the difference.
A compiler from whatever high/low level language is expected to translate a formal specification of an algorithm faithfully. If it fails to do so, the compiler is buggy, period.
A LLM is expected to understand fuzzy language and spit out something that makes sense.
It's a fundamentally different task, and I trust a human more with this. Certainly, humans are judged by their capability to do this, apply common sense, ask for necessary clarification, also question what they're being asked to do.
I understand the world is about compromises, but all the gains of essentially every computer program ever could be summed up by accumulation of small optimizations. Likewise, the accumulation of small wastes kills legacy projects more than anything else.
Supply and demand will decide what compromise is acceptable and what that compromise looks like.
Flagging something as potentially problematic is useful but without additional information related to the tradeoffs being made this may be an optimized way to do whatever Brave is doing which requires the 70MB of RAM. Perhaps the non-optimal way it was previously doing it required 250MB of RAM and this is a significant improvement.
I have been hearing (reading?) this for a solid two years now, and LLMs were not invented two years ago: they are ostensibly the same tech as they were back in 2017, with larger training pools and some optimizations along the way. How many more hundreds of billions of dollars is reasonable to throw at a technology that has never once exceeded the lofty heights of "fine"?
At this point this genuinely feels like silicon valley's fever dream. Just lighting dumptrucks full of money on fire in the hope that it does something better than it did the previous like 7 or 8 times you did it.
And normally I wouldn't give a shit, money is made up and even then it ain't MY money, burn it on whatever you want. But we're also offsetting any gains towards green energy standing up these stupid datacenters everywhere to power this shit, not to mention the water requirements.
I suspect there's a lot more use out there generating money than you realize, there's no moat in using it, so I'm pretty sure it's kept on the downlow for fear of competitors catching up (which is quick and cheap to do).
How far can one extrapolate? I defer to the experts actually making these things and to those putting money on the line.
It was basically a novelty before. "Wow, AI can sort of write code!"
Now I find it very capable.
The "early stages" argument means "not fit for production purposes" in any other case. It should also mean the same here. It's early stages because the product isn't finished (and can't be, at least with current knowledge)
It works, we are waiting for the infrastructure to support it to be put in place.
These are secondary concerns. We're past if it's useful or not.