I'm a lawyer, so a bunch of work--factual analysis, legal research, etc.--goes into the draft that isn't just the words on the page. At the same time, the work product is meant to persuade human readers, so a lot of work goes into making the words on the page perfect. (Perhaps past the point of diminishing returns, but companies are willing to pay for that incremental edge when the stakes are high.)
Programming is different in that you don't usually have senior engineers rewrite code written by junior engineers. On the other hand, look at how the Linux kernel is developed. You have Linus at the top, then subsystem maintainers vetting patches. The companies submitting patches presumably have layers of reviewers as well. Why couldn't you automate the lower layers of that process? Instead of having 5 junior people, maybe you have 2 somewhat more senior people leveraging AI.
This is probably not sustainable unless the AI can eventually do the work the more senior people are doing. But that probably doesn't matter in the short term for the market.
Maybe because code is different. A software is a recipe that an autonomous machine can follow (very fast and repeateadly).
But the whole goal of software engineering is not about getting the recipe to the machine. That’s quite easy. It’s about writing the correct recipe so that the output is what’s expected. It’s also about communicating the recipe to fellow developers (sharing knowledge).
But we are not developing recipe that much today. Instead we’ve built enough abstractions that we’re developing recipes of recipes. There’s a lot of indirection between what our recipe says and the final product. While we can be more creative, the failure mode has also increased. But the cost of physically writing a recipe has gone down a lot.
So what matters today is having a good understanding of the tower of abstractions, at least the part that is useful for a project. But you have to be on hand with it to discern the links between each layer and each concept. Because each little one matters. Or you delegate and choose to trust someone else.
Trusting AI is trusting that it can maintain such consistent models so that it produces the expected output. And we all know that they don’t.
I'm a lawyer, so a bunch of work--factual analysis, legal research, etc.--goes into the draft that isn't just the words on the page. At the same time, the work product is meant to persuade human readers, so a lot of work goes into making the words on the page perfect. (Perhaps past the point of diminishing returns, but companies are willing to pay for that incremental edge when the stakes are high.)
Programming is different in that you don't usually have senior engineers rewrite code written by junior engineers. On the other hand, look at how the Linux kernel is developed. You have Linus at the top, then subsystem maintainers vetting patches. The companies submitting patches presumably have layers of reviewers as well. Why couldn't you automate the lower layers of that process? Instead of having 5 junior people, maybe you have 2 somewhat more senior people leveraging AI.
This is probably not sustainable unless the AI can eventually do the work the more senior people are doing. But that probably doesn't matter in the short term for the market.
Maybe because code is different. A software is a recipe that an autonomous machine can follow (very fast and repeateadly).
But the whole goal of software engineering is not about getting the recipe to the machine. That’s quite easy. It’s about writing the correct recipe so that the output is what’s expected. It’s also about communicating the recipe to fellow developers (sharing knowledge).
But we are not developing recipe that much today. Instead we’ve built enough abstractions that we’re developing recipes of recipes. There’s a lot of indirection between what our recipe says and the final product. While we can be more creative, the failure mode has also increased. But the cost of physically writing a recipe has gone down a lot.
So what matters today is having a good understanding of the tower of abstractions, at least the part that is useful for a project. But you have to be on hand with it to discern the links between each layer and each concept. Because each little one matters. Or you delegate and choose to trust someone else.
Trusting AI is trusting that it can maintain such consistent models so that it produces the expected output. And we all know that they don’t.