Of course, when we talk about utilizing AI as an efficient tooling, it is well expected to see the very outcomes you desire. But the point here is the disconnect between the perceived productivity gains and their corresponding measuring metrics. Remember the Goodhart's Law[1]? It becomes fairly unreliable when you try to measure productivity by a set of standards instead of actually producing productivity. When it comes to future about this issue, I'd hope to see the progress actually happening in open source, decentralized AI instead of the current way of using it.
replyYeah, I talk to normies and nobody still understands the utility of just having a ChatGPT desktop app being able to basically drive your computer and do whatever needs to be done.
replyWell is there any productivity gain from chatbots? So far, I mostly see a deluge of misleading content disguised as documentation for things that don't even exist. If anything, the amount of slop out there is slowing me down…
replyI can't imagine it can even feel productive to anyone, unless you use agents to automate re-rolling your slop machines, but then you're just going to end up with an unmaintainable ball of mud. Either way, it's definitely net negative.
Kind of same outcome as if you just generated lots and lots of random code (perhaps at AST level; but don't tell your Claude, that'd trick it into making itself obsolete tech!) and compiled it in parallel till it passed tests, but this time around, the computer also tells you that you hit the nail on its head, that you're totally right <s>and that you're such a good puppy. agent uses tool `clicker`</s>
I was in this camp until a few months ago, but LLMs have really gotten that good now that you can actually trust an agent with a ticket and it will do it cromulently. The code quality isn't any worse than if a junior did it. Sure, you'll sometimes want to refactor something, but then you can just tell the agent and it will do it for you.
replyUpdate your priors.