I wonder if this will also have a reverse tail end effect.
Company uses AI (with devs) to produce a large amount of code -> code is in prod for a few years with incremental changes -> dev roles rotate or get further reduced over time -> company now needs to modernize and change very large legacy codebase that nobody really understands well enough to even feed it Into the AI -> now hiring more devs than before to figure out how to manage a legacy codebase 5-10x the size of what the team could realistically handle.
Writing greenfield code is relatively easy, maintaining it over years and keeping it up to date and well understood while twisting it for all new requirements - now that’s hard.
AI will help with that too, it’s going to be able to process entire codebases at a time pretty shortly here.
Given the visual capabilities now emerging, it can likely also do human-equivalent testing.
One of the biggest AI tricks we haven’t started seeing much of yet in mainstream use is this kind of automated double-checking. Where it generates an answer, and then validates if the answer is valid before actually giving it to a human. Especially in coding bases, there really isn’t anything stopping it from coming up with an answer compiling, running into an error, re-generating, and repeating until the code passes all unit tests or even potentially visual inspection.
The big limit on this right now is sheer processing cost and context lengths for the models. However, costs for this are dropping faster than any new tech we’ve seen, and it will likely be trivial in just a few years.
I wonder if this will also have a reverse tail end effect.
Company uses AI (with devs) to produce a large amount of code -> code is in prod for a few years with incremental changes -> dev roles rotate or get further reduced over time -> company now needs to modernize and change very large legacy codebase that nobody really understands well enough to even feed it Into the AI -> now hiring more devs than before to figure out how to manage a legacy codebase 5-10x the size of what the team could realistically handle.
Writing greenfield code is relatively easy, maintaining it over years and keeping it up to date and well understood while twisting it for all new requirements - now that’s hard.
AI will help with that too, it’s going to be able to process entire codebases at a time pretty shortly here.
Given the visual capabilities now emerging, it can likely also do human-equivalent testing.
One of the biggest AI tricks we haven’t started seeing much of yet in mainstream use is this kind of automated double-checking. Where it generates an answer, and then validates if the answer is valid before actually giving it to a human. Especially in coding bases, there really isn’t anything stopping it from coming up with an answer compiling, running into an error, re-generating, and repeating until the code passes all unit tests or even potentially visual inspection.
The big limit on this right now is sheer processing cost and context lengths for the models. However, costs for this are dropping faster than any new tech we’ve seen, and it will likely be trivial in just a few years.