Comment by _aavaa_
5 days ago
The benefits of having a computer that we can now interact with in plain natural language, that can extract intent from vague questions/statements, and that can piece together answers is obvious.
The link talks directly about the disconnect between the supposed productivity benefits of a technology and the measured productivity benefits of it in practice. And provides historical context about why the “obvious” benefits of a computer did not materialize when it was introduced; business and their processes had to be rebuilt around the computer before real gains were seen.
But LLMs can't actually do that any better than a 30k secretary with no training.
It's a structural deficiency in the way they work that can't just be handwaived away.
Nobody is talking about hand waving. Look at progress in models between the original ChatGPT release and what came out this year. The progress is incredible, both in the frontier models and in the smaller ones that can be run in high end laptops (<100GB of ram).
There are new architectures, paper, and harnesses coming out weekly that improve performance, accuracy, and/or performance efficiency.
Whether they can do better or not than a secretary is a poorly defined metric. But by objective metric they are already producing less buggy code than $30k developers, and doing it faster.
Only if by "incredible" you mean it takes vastly more resources to do the same thing slightly better but with an even larger chance of completely fouling the bed.
But by objective metric they are already producing less buggy code than $30k developers, and doing it faster.
That's like bragging that a $1 trillion tank can go faster than a cheap budget car. For $1 trillion being slightly better isn't good enough.
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Neither can an accounting computer.