The Altair 8800 came out in 1974 with no video output. The Commodore PET and Apple II were released in 1977 as non-kit computers with video output. VisiCalc came out in late 1979. We are approximately at the VisiCalc release date now with LLMs.
I think a lot of the reasons for this is because AI helps provide a productivity boost to non-profitable sectors of most businesses, i.e. software development, finance, HR, etc. Since these departments do not directly drive profits, there's no visible bottom line to make meaningful observations on.
I do software for a retail company now and we've been having a similar debate: AI helps me and other departments do work more efficiently, but me getting a feature out the door faster is better for the business but doesn't get more products off the shelves. So, to the shareholders and the C-suite, is AI doing anything for the company?
My guess is they don't really know how to price it in yet - but also they seem to massively reduce headcount in areas where you can loftily make assumptions that "ai is boosting productivity"
Seems like hand waving and layoffs will have to stop before we get real data
Wrapping business processes around these LLMs is the same kind of hard organizational problem plaguing most internal IT projects. People are still the bottleneck.
You also run into the issue of accuracy compounding. Running multi step flows with AI compounds the success rate and dramatically increases the chances of a full-job failure. E.g. even at 99% success rate for any single step, a 30-step process is only likely to succeed 75% of the time without errors. If you go down to 95% success for each, you only have a 75% likelihood of flawless execution at about 6 steps.
So it’s also about getting those per step success rates way up.
If it isnt showing up the they are probably depreciating the massive hardware spending over a long time, assuming that the announced billions are actually occuring.
Lots of companies are using AI already, which means there is no competitive edges against another company.
It mainly helps with mundane tasks. I think mostly employees have better life within the company do those stupid task or another email or meeting notes.
Someone I know works for a municipality in digital transformation. They have a public facing website where local residents can report things to the municipality, such as potholes, water system issues, noise complaints, etc.
The UI has this huge taxonomy with like 200 categories with three levels of nesting that route to different departments. There are multiple "other" categories.
When a resident chooses other, it becomes an employee's job to choose a different category. But 30% of the categories employees choose are wrong (about the same for residents).
It's a UX problem, a taxonomy problem, a training problem, a change management problem, a work routing problem, and all these contribute to longer resolution times.
My friend started tinkering with a small quantized local LLM, testing whether it could classify the reported issues more accurately than the public/staff. Of course it could. They're preparing to integrate it into production. Installing it will dramatically improve the UX for the residents, save staff time (at least hundreds of hours a year), improve resolution time, etc.
My friends boss mentioned it at a conference and apparently "no one else is doing this".
So yeah, we are just barely scratching the surface of the productivity opportunities LLMs offer. It's early, not because the technology isn't developed enough to help (it is), but because most people are still figuring out how it can help.
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[ 3.2 ms ] story [ 30.2 ms ] threadI do software for a retail company now and we've been having a similar debate: AI helps me and other departments do work more efficiently, but me getting a feature out the door faster is better for the business but doesn't get more products off the shelves. So, to the shareholders and the C-suite, is AI doing anything for the company?
Seems like hand waving and layoffs will have to stop before we get real data
Wrapping business processes around these LLMs is the same kind of hard organizational problem plaguing most internal IT projects. People are still the bottleneck.
You also run into the issue of accuracy compounding. Running multi step flows with AI compounds the success rate and dramatically increases the chances of a full-job failure. E.g. even at 99% success rate for any single step, a 30-step process is only likely to succeed 75% of the time without errors. If you go down to 95% success for each, you only have a 75% likelihood of flawless execution at about 6 steps.
So it’s also about getting those per step success rates way up.
It mainly helps with mundane tasks. I think mostly employees have better life within the company do those stupid task or another email or meeting notes.
Someone I know works for a municipality in digital transformation. They have a public facing website where local residents can report things to the municipality, such as potholes, water system issues, noise complaints, etc.
The UI has this huge taxonomy with like 200 categories with three levels of nesting that route to different departments. There are multiple "other" categories.
When a resident chooses other, it becomes an employee's job to choose a different category. But 30% of the categories employees choose are wrong (about the same for residents).
It's a UX problem, a taxonomy problem, a training problem, a change management problem, a work routing problem, and all these contribute to longer resolution times.
My friend started tinkering with a small quantized local LLM, testing whether it could classify the reported issues more accurately than the public/staff. Of course it could. They're preparing to integrate it into production. Installing it will dramatically improve the UX for the residents, save staff time (at least hundreds of hours a year), improve resolution time, etc.
My friends boss mentioned it at a conference and apparently "no one else is doing this".
So yeah, we are just barely scratching the surface of the productivity opportunities LLMs offer. It's early, not because the technology isn't developed enough to help (it is), but because most people are still figuring out how it can help.