Most likely, the APP-E or GAME-E, given a prompt generate an application or game, will not generate C++/JavaScript/Swift/Kotlin but directly target the pixel space, running in a 60+ FPS loop a single "function" such as `nextFrame(currentState)`.
It will probably be here in the next few years: write a prompt such as "2D game like Mario but with butterflies" and receive a 2GB blob which opens a window accepting inputs and changing the pixels accordingly. Or, something more serious, a prompt like "invoicing application following the laws of France, Material Design, store the database in AWS using <token>". APP-E or GAME-E doesn't need to totally replace software development, just be good enough to replace in 99% of use cases.
Bugs/tests could probably be solved by some mechanism for injecting localized prompts: given an already generated binary, fine tune it accordingly to a list of other prompts.
As for deployment, it's already pretty much solved with the CI/CD solutions galore all around, not sure why you would need generative statistics for it.
What DALL-E offers is a glimpse of the next 30 years, and probably 99% of the infrastructure required to run it to its full potential is not here yet. Just as in 1992 (3 years after the HTTP proposal, but 2 years before the launch of Netscape) there were only glimpses of what a connected world would look like.
My point was that something like APP-E or GAME-E seems very plausible in the near future and it is more likely to render pixels with the underlying logic encoded in an inscrutable sparse matrix, somewhat the consequence of a beefier DALL-E with regard to the data set, the learning modalities, and the attention span, than to write programs to be compiled/interpreted by any current language stack.
With software it might take days of testing to verify the result, and then repeat that for every iteration. Would be cheaper to build the thing!
Where AI might work is in some restricted subset of software, like a web CRUD app where you say "I want an app that stores a TODO list with dates". With the constraints of it being crud, it just needs to AI the database and arrangement of fields and so on.
The AI is not programming so much as it is choosing which "rails-like scaffolds" to initiate.
The serious engineers are all working on things that go far deeper, and they could never be replaced.
If you want to build a business big enough to be listed on the NASDAQ, you need real developers, and you need to pay them real money.
One could think that much of art is just pretty form without sense and that is why DALL-E works.
Most art is not "pretty form without sense". It actually has sense and meaning more often than not, so we can debate what a particular piece "means".
The difference with engineering is that art's meaning is way more subjective, and that if I "miss the point" or simply disagree with the consensus on its meaning, this doesn't make an airplane go down or a nuclear reactor to melt down.
Pick any random Jira ticket for a large software project. Could an AI understand and implement that feature into a larger project? Can it correctly deploy it without interruptions to production jobs? Will it correctly implement tests and decent code coverage? If there are regressions will it know how to go in and fix them? If there are bugs reported by users will it be able to investigate and accurately fix them? What about when multiple branches of feature development have to be merged, will it know how to do it correctly? Will it know how to write high performance software or just use shitty random algorithms?
If it can’t do these things AI is basically useless. Because this is basically 90% of software development.