Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech. Subscribe for daily updates.
As someone that works in tech, this doesn’t really surprise me at all.
Stuff like Claude Code is very useful to me both professionally and sometimes with tech hobby projects. But outside of that it is just a worse, albeit faster, version of a standard web search.
The problem is, the places where it could be the most effective are places where we already have processes in place, like the medical field and engineering.
I’m sure LLMs are helping sift through ideas in those fields, but they don’t reduce the amount of work that needs to be done by actual people to produce actual results; they just help select the best potential new idea.
Most stuff in life is already solved, and so AI can’t contribute there.
I work in tech and the biggest use case for AI for me is searching for information about the codebase. Essentially a fuzzy search and finding related information (ex: how does X service do Y operation).
I find if I use it for coding tasks it frequently does a worse job and ultimately slower than me doing it myself. But I also have over a decade of experience just in the language I’m using. Sometimes I use it to write a shell script of repetitive work, or writing some unit tests, but rarely larger more complex things. I really try to avoid using it for things that I can do myself in vim or whatnot because of the environmental impact that a LLM has.
This is what’s been annoying me since the beginning. Anytime someone comes up with an example of where AI can be useful it’s always coding. It’s like they think that literally everyone programs things all the time and does nothing else.
I think it’s because LLMs in single shot scenarios are always liars, fabulists, or both.
The only reason (and only way) it works with coding is because you can hook it up to tools to run static analysis on its bullshit and it can guess enough derivatives in a loop to eventually build something that kinda sorta works.
This doesn’t apply for many fields outside of writing software. There just aren’t many fields where it applies to have a machine produce permutations and then automatically run validation on the result.
Imagine if you put it in charge of manufacturing or something, you’d have so much wasted raw material before you got to an acceptable result that you’d go out of business.
As someone that works in tech, this doesn’t really surprise me at all.
Stuff like Claude Code is very useful to me both professionally and sometimes with tech hobby projects. But outside of that it is just a worse, albeit faster, version of a standard web search.
The problem is, the places where it could be the most effective are places where we already have processes in place, like the medical field and engineering.
I’m sure LLMs are helping sift through ideas in those fields, but they don’t reduce the amount of work that needs to be done by actual people to produce actual results; they just help select the best potential new idea.
Most stuff in life is already solved, and so AI can’t contribute there.
I work in tech and the biggest use case for AI for me is searching for information about the codebase. Essentially a fuzzy search and finding related information (ex: how does X service do Y operation).
I find if I use it for coding tasks it frequently does a worse job and ultimately slower than me doing it myself. But I also have over a decade of experience just in the language I’m using. Sometimes I use it to write a shell script of repetitive work, or writing some unit tests, but rarely larger more complex things. I really try to avoid using it for things that I can do myself in vim or whatnot because of the environmental impact that a LLM has.
– Diogenes, probably
i wouldnt even use that as “Faster web search” just a quick glance, of where the source is.
This is what’s been annoying me since the beginning. Anytime someone comes up with an example of where AI can be useful it’s always coding. It’s like they think that literally everyone programs things all the time and does nothing else.
I think it’s because LLMs in single shot scenarios are always liars, fabulists, or both.
The only reason (and only way) it works with coding is because you can hook it up to tools to run static analysis on its bullshit and it can guess enough derivatives in a loop to eventually build something that kinda sorta works.
This doesn’t apply for many fields outside of writing software. There just aren’t many fields where it applies to have a machine produce permutations and then automatically run validation on the result.
Imagine if you put it in charge of manufacturing or something, you’d have so much wasted raw material before you got to an acceptable result that you’d go out of business.