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I understand that we are slowly taking away our own jobs but I do not find it depressing. I do find it concerning since most people do not talk about this openl

I understand that we are slowly taking away our own jobs but I do not find it depressing. I do find it concerning since most people do not talk about this openly. We are not sure how we are restructure so many jobs. If we cannot find jobs, what is the financial future for a large number of people across the world. This needs more thinking, honest acceptance of the situation. It will happen, we should take a positive approach to finding a new future.

seanhandley 1y ago
Read up on the Jevons Paradox
- freedomben 1y ago
  > In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes Jevons effect) occurs when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application); however, as the cost of using the resource drops, if the price is highly elastic, this results in overall demand increasing, causing total resource consumption to rise. Governments have typically expected efficiency gains to lower resource consumption, rather than anticipating possible increases due to the Jevons paradox.[1] I do think there will be some Jevons effect going on with this, but I think it's important to recognize that software development as a resource is different than something like coal. For example, if the average iPhone-only teenager can now suddenly start cranking out apps, that may ultimately increase demand for apps and there may be more code than ever getting "written," but there won't necesarily be a need for your CS-grad software engineer anymore, so we could still be fucked. Why would you pay a high salary for a SWE when your business teams can just generate whatever app they need without having to know anything about how it actually works? I think the arguments about "AI isn't good enough to replace senior engineers" will hold true for a few years, but not much beyond that. Jevon's Paradox will probably hold true for software as a resource, but not for SWEs as a resource. In the coal scenario, imagine that coal gets super cheap to procure because we invent robots that can do it from alpha to omega. Coal demand may go up, but the job for the coal miner is toast, and unless that coal miner has ownership stake, they will be out on their ass. [1] https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
  - elbear 1y ago
    > Why would you pay a high salary for a SWE when your business teams can just generate whatever app they need without having to know anything about how it actually works? It depends on how good the AI is. The advantage of an SWE is that they have a systems thinking mindset, so they can solve some problems more efficiently. With some apps in won't matter, but with others will. One potential positive outcome is that we will be able to solve more and bigger problems, since our capacity for solving problems has been augmented with AI.
  - mxkopy 1y ago
    The coal miner would have to pivot to being someone who knows a lot about coal instead of someone that actually obtained it, they’d become more of a coal-advisor to the person making decisions about what type of or how much coal to get/what’s even possible with the coal they’re getting. The future I’m seeing with AI is one where software (i.e. as a way to get hardware to do stuff) is basically a non-issue. The example I wanna work on soon is telling Siri I want my iPhone to work as a touchpad for my computer and have the necessary drivers for that to happen be built automatically because that’s a reasonable thing I could expect my hardware to do. That’s the sort of thing that seems pretty achievable by AI in a couple turns that would take a single dev a year or two. And the thing is, I can’t imagine a software dev that doesn’t have some set of skills that are still applicable in this future, either through general CS skills (knowing what’s within reasonable expectations of hardware, being able to effectively describe more specific behavior/choosing the right abstractions etc) or other more nebulous technical knowledge (e.g. what you want to do with hardware in the first place). Another thing I will mention is that for things like the iPhone example from earlier, there are usually a lot of optimizations or decisions involved that are derived from the user’s experience as a human which the LLM can’t really use synthetically. As another example if I turned my phone into a second monitor the LLM might generate code that sends full resolution images to the phone when the phone’s screen is much lower, there’s no real point for it to optimize that away if it doesn’t know how eyes work and what screens are used for. So at some point it needs to involve a model of a human, at least for examples like these.
    - freedomben 1y ago

seanhandley 1y ago

Read up on the Jevons Paradox

freedomben 1y ago
> In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes Jevons effect) occurs when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application); however, as the cost of using the resource drops, if the price is highly elastic, this results in overall demand increasing, causing total resource consumption to rise. Governments have typically expected efficiency gains to lower resource consumption, rather than anticipating possible increases due to the Jevons paradox.[1] I do think there will be some Jevons effect going on with this, but I think it's important to recognize that software development as a resource is different than something like coal. For example, if the average iPhone-only teenager can now suddenly start cranking out apps, that may ultimately increase demand for apps and there may be more code than ever getting "written," but there won't necesarily be a need for your CS-grad software engineer anymore, so we could still be fucked. Why would you pay a high salary for a SWE when your business teams can just generate whatever app they need without having to know anything about how it actually works? I think the arguments about "AI isn't good enough to replace senior engineers" will hold true for a few years, but not much beyond that. Jevon's Paradox will probably hold true for software as a resource, but not for SWEs as a resource. In the coal scenario, imagine that coal gets super cheap to procure because we invent robots that can do it from alpha to omega. Coal demand may go up, but the job for the coal miner is toast, and unless that coal miner has ownership stake, they will be out on their ass. [1] https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
- elbear 1y ago
  > Why would you pay a high salary for a SWE when your business teams can just generate whatever app they need without having to know anything about how it actually works? It depends on how good the AI is. The advantage of an SWE is that they have a systems thinking mindset, so they can solve some problems more efficiently. With some apps in won't matter, but with others will. One potential positive outcome is that we will be able to solve more and bigger problems, since our capacity for solving problems has been augmented with AI.
- mxkopy 1y ago
  The coal miner would have to pivot to being someone who knows a lot about coal instead of someone that actually obtained it, they’d become more of a coal-advisor to the person making decisions about what type of or how much coal to get/what’s even possible with the coal they’re getting. The future I’m seeing with AI is one where software (i.e. as a way to get hardware to do stuff) is basically a non-issue. The example I wanna work on soon is telling Siri I want my iPhone to work as a touchpad for my computer and have the necessary drivers for that to happen be built automatically because that’s a reasonable thing I could expect my hardware to do. That’s the sort of thing that seems pretty achievable by AI in a couple turns that would take a single dev a year or two. And the thing is, I can’t imagine a software dev that doesn’t have some set of skills that are still applicable in this future, either through general CS skills (knowing what’s within reasonable expectations of hardware, being able to effectively describe more specific behavior/choosing the right abstractions etc) or other more nebulous technical knowledge (e.g. what you want to do with hardware in the first place). Another thing I will mention is that for things like the iPhone example from earlier, there are usually a lot of optimizations or decisions involved that are derived from the user’s experience as a human which the LLM can’t really use synthetically. As another example if I turned my phone into a second monitor the LLM might generate code that sends full resolution images to the phone when the phone’s screen is much lower, there’s no real point for it to optimize that away if it doesn’t know how eyes work and what screens are used for. So at some point it needs to involve a model of a human, at least for examples like these.
  - freedomben 1y ago