I know someone who works in law and deals particularly with an area of US benefits and healthcare law. One of their workflows for lower-level employees at their firm involves taking in documents from healthcare plans and organizations, analyzing them for certain kinds of data, and then importing that data into an internal system they use to analyze and provide guidance on plans. The internal system can contain hundreds of documents for an individual client. All of the documents have the same information (roughly) but in totally diverse formats and styles. Once it's in the system, it's easy to compare and analyze across documents and the research process is much faster.
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
I just realized how refreshing it is to read an honest take like "from 2-3 documents an hour to 8-10 documents an hour" instead of "it's doing the work of a month in 5 minutes!!!!1".
That's a factor of 3 to 5 improvement reported on here. If you keep stacking these kinds of improvements, you end up at month to minutes pretty quickly.
Sorry for being so detached from the discussion and your actual point but if someone had made a dystopian sci-fi novel with the tagline following your sentence with "...or can they?", and made it all cheesy, I'd read it in one sitting.
I'm too bad of a writer to be scared of giving my ideas away, but I'd explore a pupae stage, where (layman disclaimer, if it isn't abundantly clear from my incoming butchering of the science) the butterfly is basically melted down into a goop with no "brain part" or "feet part" of the goop existing until reformed, yet retain their memories. If you divided that goop up and imagined there was a gestation time reduction, before combining it for the final stage of formation, you'd have some "plausible" (loose definition here) venue for it.
ivraatiems · · focus · HN ↗
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
ManuelKiessling · · focus · HN ↗
eru · · focus · HN ↗
That's a factor of 3 to 5 improvement reported on here. If you keep stacking these kinds of improvements, you end up at month to minutes pretty quickly.
monax · · focus · HN ↗
egeozcan · · focus · HN ↗
lukan · · focus · HN ↗
croon · · focus · HN ↗
eru · · focus · HN ↗
marcta · · focus · HN ↗
eru · · focus · HN ↗