> There's a divide between people who enjoy the physical experience of the work and people who enjoy the mental experience of the work. If the thinking bit is your favorite part, AI allows you to spend nearly all of your time there if you wish, from concept through troubleshooting. But if you like the doing, the typing, fiddling with knobs and configs, etc etc, all AI does is take the good part away.
I don't know... that seems like a false dichotomy to me. I think I could enjoy both but it depends on what kind of work. I did start using AI for one project recently: I do most of the thinking and planning, and for things that are enjoyable to implement I still write the majority of the code.
But for tests, build system integration, ...? Well that's usually very repetitive, low-entropy code that we've all seen a thousand times before. Usually not intellectually interesting, so why not outsource that to the AI.
And even for the planning part of a project there can be a lot of grunt work too. Haven't you had the frustrating experience of attempting a re-factoring and finding out midway it doesn't work because of some edge case. Sometimes the edge case is interesting and points to some deeper issue in the design, but sometimes not. Either way it sure would be nice to get a hint beforehand. Although in my experience AIs aren't at a stage to reason about such issues upfront --- no surprise since it's difficult for humans too --- of course it helps if your software has an oracle for if the attempted changes are correct, i.e. it is statically-typed and/or has thorough tests.
I happen to be a shareholder in EA as part of a diversified portfolio. Not that my vote matters but I'll definitely be taking PE money. +20% in one day is nothing to sneeze at.
From the other comments about EA's games, it's not like EA is that special of a company. There's always going to be some other company (not necessarily an AAA-games maker) worth putting your capital in and end up doing as well as what the hypothetical EA could do if it were not taken private. (Obviously finding such market-outperformer isn't easy but by the same argument I'm not convinced that EA would be obviously that outperformer either.)
Speaking as a C# developer, who had in the past wanted to learn F#, but never got very far. What discourages me every time:
- C#'s good enough. Nothing's stopping you from writing functionally-oriented code in C# (and I do prefer that over traditional "enterprisey" object-orientation.)
- It's relatively difficult to have a codebase that is partly in C# and F# for incrementally trying things out. (I understand this is not really F#'s fault, owing to the .NET compilation model where C# and F# compilers each produce their own assemblies. And that Microsoft hardly cares about F#, and the tooling leaves a lot to be desired - admittedly I'm spoiled by C# tooling. )
- F# having its own implementations of concepts like async, option types introduces friction with C# obviously. I get that F# async is more powerful in some ways, but then again... F#'s option type is a reference type unlike C#'s Nullable<> value type, it's hard to see what's the advantage in that other than worse performance. One almost gets the impression that F# designers don't care about performance (while C# designers do in the past few years with additions to the ecosystem like Span<T>). This makes it hard to justify coding in F# for infrastructure libraries (which is what I often do).
C# 1.0 did not have generics, period. So the standard dictionary (Hashtable†) type took keys and values typed as "System.Object". As seen in the linked documentation this class still exists in the latest .NET to this day.
Occasionally one would still encounter non-generic classes like this, when working with older frameworks/libraries, which cause a fair bit of impedence mismatch with modern styles of coding. (Also one of the causes of some people's complaints that C# has too many ways of doing things; old ways have to be retained for backwards compatibility, of course.)
I don't know... that seems like a false dichotomy to me. I think I could enjoy both but it depends on what kind of work. I did start using AI for one project recently: I do most of the thinking and planning, and for things that are enjoyable to implement I still write the majority of the code.
But for tests, build system integration, ...? Well that's usually very repetitive, low-entropy code that we've all seen a thousand times before. Usually not intellectually interesting, so why not outsource that to the AI.
And even for the planning part of a project there can be a lot of grunt work too. Haven't you had the frustrating experience of attempting a re-factoring and finding out midway it doesn't work because of some edge case. Sometimes the edge case is interesting and points to some deeper issue in the design, but sometimes not. Either way it sure would be nice to get a hint beforehand. Although in my experience AIs aren't at a stage to reason about such issues upfront --- no surprise since it's difficult for humans too --- of course it helps if your software has an oracle for if the attempted changes are correct, i.e. it is statically-typed and/or has thorough tests.