We tend to think that markets reflect consumer choice. But the artificial-intelligence boom has been powered by the investment decisions of concentrated capital rather than by consumer demand.
Basically the general AI chat interaction gives a pretty good indication of capabilities and limitations as it pertains to various applications. It sounds right, but frequently is not right, and that has predictable implications.
Particularly it is really good at generating credible sounding content. It does not have a model for ‘facts’ so correctness is a persistent issue. For coding, the strategies involve having some sort of absolute validation to say whether it is “correct” or not, whether that’s some human reading it, or in the die-hard scenarios, someone writing a whole system to let it try, get pass/fail, try again on fail, and spin until it is done or you stop it because it just spins and spins. The former can be selectively useful, the latter I find to be more obnoxious than just doing the work.
So broadly, scenarios in which the “truth” is objective and/or in the input directly is where it can provide useful results. The examples of translation are an area where it is more helpful, since it isn’t tasked with adding any ‘facts’ merely reworking the input into another language. “Absolute do not ever warning” type stuff is the result of the human trying to write in non-native, so they would feed their native work into LLM and probably get better result.
The other super depressing thing is slop fiction and deep fakes. Here “facts” don’t matter, as long as it seems consistent. Even as it adds elements that may be ‘wrong’, well, it’s fiction so wrong doesn’t matter as much so long as it seems narratively consistent. Enabling people lacking in drive and creativity to pump out as much volume as people with a lot of drive and creativity with more interesting things to say.
Of course, the bubble is predicated on it working to replace most white collar work, which it is not poised to do. There’s a lot it can do, but it’s penchant for generating incorrect output is a liability that can’t be afforded in a lot of contexts. A lot of places just put the onus on the victim of the AI support bot by saying up front “you can’t trust anything this bot is going to say, but you are going to be getting the bot anyway”. Even as human support might be asked to intervene, the human is still likely to be able to review the chat quickly and maybe take a minute to see what the user wanted, click some button in the system the customer needed clicked, and move on, instead of 10 minutes of back and forth to figure out what the user needs. So they could still lay off 75% of those support people and, thanks to AI, still maintain apparent responsiveness, even if the users are annoyed. Lots of orgs have shifted to have sales be human because a customer gets pissed, but support from AI chatbot, because, well, you got their money, screw em, they can deal with some annoyance.
Basically the general AI chat interaction gives a pretty good indication of capabilities and limitations as it pertains to various applications. It sounds right, but frequently is not right, and that has predictable implications.
Particularly it is really good at generating credible sounding content. It does not have a model for ‘facts’ so correctness is a persistent issue. For coding, the strategies involve having some sort of absolute validation to say whether it is “correct” or not, whether that’s some human reading it, or in the die-hard scenarios, someone writing a whole system to let it try, get pass/fail, try again on fail, and spin until it is done or you stop it because it just spins and spins. The former can be selectively useful, the latter I find to be more obnoxious than just doing the work.
So broadly, scenarios in which the “truth” is objective and/or in the input directly is where it can provide useful results. The examples of translation are an area where it is more helpful, since it isn’t tasked with adding any ‘facts’ merely reworking the input into another language. “Absolute do not ever warning” type stuff is the result of the human trying to write in non-native, so they would feed their native work into LLM and probably get better result.
The other super depressing thing is slop fiction and deep fakes. Here “facts” don’t matter, as long as it seems consistent. Even as it adds elements that may be ‘wrong’, well, it’s fiction so wrong doesn’t matter as much so long as it seems narratively consistent. Enabling people lacking in drive and creativity to pump out as much volume as people with a lot of drive and creativity with more interesting things to say.
Of course, the bubble is predicated on it working to replace most white collar work, which it is not poised to do. There’s a lot it can do, but it’s penchant for generating incorrect output is a liability that can’t be afforded in a lot of contexts. A lot of places just put the onus on the victim of the AI support bot by saying up front “you can’t trust anything this bot is going to say, but you are going to be getting the bot anyway”. Even as human support might be asked to intervene, the human is still likely to be able to review the chat quickly and maybe take a minute to see what the user wanted, click some button in the system the customer needed clicked, and move on, instead of 10 minutes of back and forth to figure out what the user needs. So they could still lay off 75% of those support people and, thanks to AI, still maintain apparent responsiveness, even if the users are annoyed. Lots of orgs have shifted to have sales be human because a customer gets pissed, but support from AI chatbot, because, well, you got their money, screw em, they can deal with some annoyance.