Since there's a very broad spectrum of different kinds of computer programs with different constraints and desiderata, I think the transition will be very gradual. Consider the following things that are all computer programming tasks:

• Helping non-technical people set up a simple blog.
• Identifying and understanding the cause of unexpected behavior in a large, complicated existing system.
• Figuring out how to make a much cheaper-to-run version of an existing system that uses too many resources.
• Experimenting with a graphics shader in a game to see how you can make an effect that is really cool looking.
• Implementing a specific known cryptographic algorithm securely.
• Writing exploratory programs that answer questions about some data set to help you understand patterns in the data.

I have no doubt that sufficiently fancy AI can do or help human programmers do all these tasks, but probably in different ways and at different rates.

As an experienced programmer that can do most of these things well, I would be very surprised if my skillset were substantially obsolete in less than 5 years, and somewhat surprised if it was substantially obsolete in less than 10 years. It seems like GPT-3 and GPT-4 are not really very close to being able to do these things as well as me, or close to being able to help a less skilled human do these things as well as me.

What I expect to change quickly is that "programming languages" will go away completely. LLMs or similar tech will get us way closer to the DWIM level. Directly translating from a spec to executables will be something AI can excel at. The missing piece is the feedback: writing and executing unit tests and changing the executable (not the code!) to pass the tests.

Note that a lot of current CS concepts are human-oriented and make no sense when the human is not a part of the process: "architecture" is a crutch for the limitation of our brains. "Design" is another crutch. This can all be streamlined into "Spec->Binary". 

Even further, there is no reason to have a general-purpose computer when it is easy for an AI to convert a spec into actual hardware, such as FPGA.

Next on the list (or maybe even first on the list) is not needing the low-level executables at all: the LLM or equivalent just does what you ask of it. 

I have a major problem with the framing of your question.

Say we invented construction robots that given a blueprint for a building and an auto generated list of materials deliveries, take the materials off trucks and assemble the building.  This means you no longer need 'shift bosses', the computers do that.  You are essentially down to 5 main roles:

1.  The architects of the building 
2.  The site foreman (who oversees construction)
3.  lawyers to deal with getting permits and to sue the local jurisdiction when it denies them in violation of the local jurisdiction's or state laws (which can be automated)
4.  inspectors 
5.  Financing people

You may notice that the people replaced, welders and tradesmen and crane operators etc, are less skilled than the remaining people.  (not claiming blue collar work is unskilled but the time to learn to do it 'ok' enough to work independently is a few months of on the job training, with some skill gain over the years since)

This would be true for software also.  The remaining people required have to have more skills.  The idea of a "pointy haired boss" with no understanding of software designing a whole app that works to production scale reliability is false.

The main issue I see with this prediction is that 'computer programming becoming easier' has already happened before, and has not had this effect. 

Programming has become easier as new languages sanded the rough edges off and automated a lot of rote Assembly work, and as widespread Internet use allowed you to find solutions fast.  It was much harder to code on punch cards, or in Assembly, than to code today with StackOverflow's help. 

However, this didn't lead to programming becoming less of a career, and I don't think it led to programmers being less well paid either.

There's a possibility that 'the average programmer' might become less well-paid as the definition of 'programmer' expands, but I don't anticipate a given level of programming skill becoming less valuable until AIs reach a pretty-much-singularity level.

(Disclosure: am a programmer)

As a programmer, I extensively use GPT models in my work currently. It speeds things up. I do things that are anything but easy and repeatable, but I can usually break them into simpler parts that can be written by AI much quicker than I would even review documentation.

Nevertheless, I mostly currently do research-like parts of the project and PoCs. When I sometimes work with legacy code - GPT-3 is not that helpful. Did not yet try GPT-4 for that.

What do I see for the future of my industry? Few things - but those are loose extrapolations based on GPT progress and knowledge of the programming, not something very exact:

• Speeding up of programmers' work is already here. It started with GitHub Copilot and GPT-3 even before the Chat-GPT boom. It will get more popular and faster. The consequence is the higher performance of programmers, so more tasks can be done in a shorter time so the market pressure and market gap for employees will fall. This means that earnings will either stagnate or fall.
• Solutions that could replace a junior developer totally - that has enough capability to write a program or useful fragment based on business requirements without being baby-sitted by a more professional programmer - are not yet there. I suppose GPT-5 might be it. So I would guess it can get here in 1-3 years from now. Then it is likely that many programmers will lose their jobs. There still will be work for seniors (that would work with AI assistance on more subtle and complex parts of systems and also review work of AI).
• Solutions that could replace any developer, DevOps, and system admin - I think the current GPT-4 is not even close, but it may be here in a few years. It isn't something very far away. It feels like 2 or 3 GPT versions away, when they make it more capable and also connect it with other types of models (which is already being done). I would guess that scope of 3-10 years. Then we likely will observe most of the programmers losing jobs and likely will observe AI singularity. Someone will surely use AI to iterate on AI and make it refine itself.

I wouldn’t want to be getting into the software engineering business right now. I have been doing this for close to 40 years. Current systems can’t replace a senior level developer, but CharGPT is close to a junior developer. I expect that in 2-3 years, we’ll have systems that can do the work of a junior dev.

I expect that my job will become describing specs and test cases, and more of the architectural stuff. I expect to be mostly obsolete in 6 years, but maybe as long as 10 for niche solutions.

The most accurate answer is also the least helpful: none of us really know. Guido van Rossum has an opinion about GitHub Copilot in this interview: 

but he's really just talking about what LLMs can do know, not what they'll be able to do in five or ten years.

Chris Lattner has an opinion about Software 2.0 here:

but Software 2.0 isn't really the same thing. But he's talking about Software 2.0, which is a little different. More info about Software 2.0 here:

and if you watch Chris Latter and Lex talk for a little while longer, you'll see that Chris has no idea about how you can tell a computer to build you a webpage with a red button using just text, and admits that it's out of his area of expertise.

I bring up these examples mostly to illustrate that nobody has any clue. Sam Altman addresses the topic the most out of all the people I've linked, in this video:

and the TLDR is that Lex and Sam both think LLMs can make programmers 10x more productive. Sam also thinks that instead of hiring 1/10th the number of programmers, we'll just have 10x more code. He thinks there's a "supply problem" of enough software engineers.

One thing I would advise is to make yourself more than just a software engineer. Lex says in his talk with Sam that he's not worried because he's an AI guy, not just a programmer. You might want to learn more about how AI works and try to get a job in the space, and ride the wave, or learn about information security in addition to software engineering (that's what I'm doing, in no small part because of the influence of a one-on-one chat with 80,000 Hours), or maybe you learn a lot about oceanography or data science or something else in addition to software engineering.

Then I'd also just say that we have no idea and if anyone says they know, they really don't, because look a bunch of smart people discussed it and they have no clue either.

"Computer programming" describes a pretty wide range of cognitive work.  Stack Overflow has already reduced the training and knowledge required for many tasks, and LLMs push this a lot further.  The next level of abstraction, understanding program flow and correctness of results hasn't seen much evidence that LLMs can plan or understand things well enough to do.  And the elements of understanding user and business needs and deciding what to build in the first place hasn't really had a start.

As far as timelines, I suspect it'll transform some amount of coding into being focused on prompt generation and testing, speeding devs up by a lot, but the work will expand to fill the space rather than putting very many out of work.  As we figure out how to bolt on planning, modeling, and strategic modules coordinated with LLMs, it'll move up the stack, but this is probably decades in the making.  

Note that some of this coincides with "when does GPT become self-improving"?  

Here's how I'm tentatively thinking about it:

• As it gets cheaper to build more and better features, businesses will face stiffer competition to deliver superior products. This will create work for SWEs to implement those features.
• My coding work has already shifted heavily away from writing code first drafts and toward importing, tweaking, debugging, polishing and integrating LLM outputs. 
• Because I'm more productive, my build-test-refine cycle shortens, so I wind up putting more attention into thinking about business objectives.
• Other SWEs may gravitate toward questions like "how do I build a plugin-based pipeline to integrate software A B and C, and hardware X Y and Z, using LLM outputs to control the pipeline?" And there are all kinds of interesting technical issues there for SWEs to solve.
• Companies will increasingly focus on continuously importing their data into formats compatible with LLMs - everything from emails, to office layouts, to employee personalities and skill assessments, making it all searchable and interpretable. Despite this, there will always be a bleeding edge of data that hasn't been integrated into the system (which right now is just the familiar everyday knowledge we have rattling around in our heads, or in our inboxes), and that data-edge will be a thing that individual people define their job roles around. What exactly that is, of course, will be in continuous flux.

I think that in general, people who work in tech will be OK as long as they're keeping up with the new LLM-based tools and ways of working.

The shift we're looking at is going from program code that's very close to a computer's inner workings to natural human language for specifying systems, but where the specification must still unambiguously describe the business interest the program needs to solve. We already have a profession for unambiguously specifying complex systems with multiple stakeholders and possibly complex interactions between its parts in natural language. It's called a legislator and it's very much not an unskilled job.