On AI, GPT and the future of developers

When I started using GitHub Copilot, I was quite amazed at how good it was. Sessions using ChatGPT can be jaw dropping in terms of the generated content.

The immediate reaction from many people is to consider what the impact of that would be on the humans who currently fill those roles. Surely, if we can get a machine to do the task of a human, we can all benefit (except for the person made redundant, I guess).

I had a long discussion on the topic recently and I think that it is a good topic for a blog post, given the current interest in the subject matter.

The history of replacing manual labor with automated machines goes back as far as you’ll like to stretch it. I wouldn’t go back to the horse & plow, but certain the Luddites and their arguments about the impact of machinery on the populace will sound familiar to anyone today.

The standard answer is that some professions will go away, but new ones will pop up, instead. The classic example is the ice salesman. That used to be a function, a guy on a horse-drawn carriage that would sell you ice to keep your food cold. You can assume that this profession is no longer relevant, of course.

The difference here is that we now have computer programs and AI taking over what was classically thought impossible. You can ask Dall-E or Stable Diffusion for an image and in a few seconds, you’ll have a beautiful render that may actually match what you requested.

You can start writing code with GitHub Copilot and it will predict what you want to do to an extent that is absolutely awe-inspiring.

So what is the role of the human in all of this? If I can ask ChatGPT or Copilot to write me an email validation function, what do I need a developer for?

Here is ChatGPT’s output:

And here is Copilot’s output:

I would rate the MailAddress version better, since I know that you can’t actually manage emails via Regex. I tried to take this further and ask ChatGPT about the Regex, and got:

ChatGPT is confused, and the answer doesn’t make any sort of sense.

Most of the time spent on “research” for this post was waiting for ChatGPT to actually produce a result, but this post isn’t about nitpicking, actually.

The whole premise around “machines will make us redundant” is that the sole role of a developer is taking a low-level requirement such as email validation and producing the code to match.

Writing such low-hanging fruit is not your job. For that matter, a function is not your job. Nor is writing code a significant portion of that. A developer needs to be able to build the system architecture and design the interaction between components and the overall system.

They need to make sure that the system is performant, meet the non-functional requirements, etc. A developer would spend a lot more time reading code than writing it.

Here is a more realistic example of using ChatGPT, asking it to write to a file using a write-ahead log. I am both amazed by the quality of the answer and find myself unable to use even a bit of the code in there. The scary thing is that this code looks correct at a glance. It is wrong, dangerously so, but you’ll need to be a subject matter expert to know that. In this case, this doesn’t meet the requirements, the provided solution has security issues and doesn’t actually work.

On the other hand, I asked it about password hashing and I would give this answer a good mark.

I believe it will get better over time, but the overall context matters. We have a lot of experience in trying to get the secretary to write code. There have been many tools trying to do that, going all the way back to CASE in the 80s.

There used to be a profession called: “computer”, where you could hire a person to compute math for you. Pocket calculators didn’t invalidate them, and Excel didn’t make them redundant. They are now called accountants or data scientists, instead. And use the new tools (admittedly, calling calculators or Excel new feels very strange) to boost up their productivity enormously.

Developing with something like Copilot is a far easier task, since I can usually just tab complete a lot of the routine details. But having a tool to do some part of the job doesn’t mean that there is no work to be done. It means that a developer can speed up the routine bits and get to grips faster / more easily with the other challenges it has, such as figuring out why the system doesn’t do what it needs to, improving existing behavior, etc.

Here is a great way to use ChatGPT as part of your work, ask it to optimize a function. For this scenario, it did a great job. For more complex scenarios? There is too much context to express.

My final conclusion is that this is a really awesome tool to assist you. It can have a massive impact on productivity, especially for people working in an area that they aren’t familiar with. The downside is that sometimes it will generate junk, then again, sometimes real people do that as well.

The next few years are going to be really interesting, since it provides a whole new level of capability for the industry at large, but I don’t think that it would shake the reality on the ground.