11–15 of 15 posts
"All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in2, but even within projects that we have observed in passing while doing totally unrelated work."
Sounds like a skill issue
just don't use AI
don't use computers
go run a farm or something
stop complaining and stop using
just enough with your AI hate
if it's not for you, fine, go do something else
another world-is-ending chicken screaming just don't use AI don't use computers go run a farm or something stop complaining and stop using just enough with your AI hate if it's not for you, fine, go do something else
I work at F500 company and this is not my experience at all. We are rolling out claude to basically everyone in my division and people are doing pretty amazing things with it and the uptake is high. Building “skills” around specific workflows has been very useful. We have regular training where we meet to share “skills”. Its exciting. Its not all rainbows but the impact and growth potential is very clear to me. This…
Agree. It’s good and exciting. It is so strong at information retrieval. Much enterprise work is finding, categorising, slicing, summarising info. Querying SQL, searching Sharepoint, AWS/Azure CLI, log analysis… etc. Amazing. ABAC/RBAC, data labelling, scoped tokens, Purview all v helpful in managing risk.
There is no fundamental guarantees provided by LLM architecture, so to assert anything of sorts would require manually executing the operation and comparing the results. And why not asserting the correctness is not negligence or fraud?
I am not saying it is impossible, but so far I have not found any explanations that would close this huge gap in logic for me. LLMs themselves "admit" the gap cannot be explained rationaly.