[mlsu]

I would almost invert that statement. Sorry if this comes off ranty, but what exactly are people doing in the "AI space" currently that isn't "undifferentiated spam/chatbot" being sold to non-techies who heard about AI on NPR? What are real people using "AI" for that is so insanely valuable today? How much "company Y: same product with a chat window, sparks emoji" do we all need before this thing levels out and we all take a breather on the hype?

[jdross]

personally?

- writing and refactoring code. probably 50 times a day now - improving documentation across the company - summarizing meetings automatically with follow ups - drafting most legal work before a lawyer edits (saved 70% on legal bills) - entity extraction and data cleanup for my users

[mlsu]

Put a number on it. How much value of this will they capture from you personally (we'll assume, very very charitably by the sound of it, that you represent an "average" user of AI products) when this market matures? Exactly how much will your employer pay for a meeting summarizer? $10/mo a seat, $20/mo a seat, $50/mo a seat? Could the product sustain a 5x, 10x, 50x price hike that is going to have to happen to recoup the investment being made today?

[fhd2]

Agreed. Even if right now this seems like stuff companies want to throw money at for novelty/FOMO related reasons, I think eventually reality ought to catch up.

Probably an unpopular opinion, but I think the most efficient companies of the future will tackle the ironies of automation effectively: Carefully designing semi automation that keeps humans in the loop in a way that maximises their value - as opposed to just being bored rubber stamping the automation without really paying attention.

[per1Peteia]

bingo

[marcosdumay]

I'd say that if your team needs a meeting summarizer, your team has a meeting problem.

It's a clutch that will help you cope with the problem. But the real value is on fixing the actual issue.

[fragmede]

I'd say if you're not using a meeting summarizer, you're wasting someone's time by having them write up notes. if you're not writing up notes, you're wasting someone else's time recapping the meeting for them. meeting notes are a 1 (meeting):many relationship for conveying information as to what was discussed. how else do you go back and see what the one person on the storage team talked to your the person on your team who left last week about so you can go into the next meeting with them prepared?

[marcosdumay]

If your meeting produces "notes", and those are relevant for people that were not in it, you are doing it wrong.

If your meeting is aimed at producing "general understanding", it's already a dangerous one, and the understanding should go to the correct documentation (what is best done during the meeting). Otherwise, it should produce "focused understanding" between a few people and with immediate application.

If all you take from it is notes, well, I'm really sure that your team won't go digging through meetings notes every time they need to learn about some new context. Meeting notes are useful for CYA only, and if people feel safe they'll be filled directly at /dev/null.

[swalsh]

Going to be vague, but I'm using it to scale out human processes in ways I couldn't using humans (because they cost too much) or regular code (because it's unstructured). Early results are promising, we've found a bunch of stuff which has been buried... and is potentially worth millions. Not a chat wrapper, just breathing new light into our regular old business.

[Art9681]

What do you consider "AI"? Because machine learning models have been deployed in enterprise systems for years. Video processing, security, data labeling, sentiment analysis. The sexiest one I can think of in recent memory is nVidia DLSS.

[mlsu]

Broadly, what marketing is saying is “AI.” There is huge value being created with deep learning today on internal systems. Recommenders, machine translation, computational photography… it is huge, improves people's lives, drives revenue.

None of that is marketed as "AI." It's just a thing the computer does. The single most valuable application of deep learning so far (content recommenders) is a cultural phenomenon, but it’s not referred to as “AI” but rather “the algorithm.”