My thoughts on AI in my XR job – Part 1: Development

“I HAVE JUST FIRED ALL MY DEVELOPERS AND SUBSTITUTED THEM WITH AI. NOW I AM 10X FASTER!”. This is how some LinkedIn posts sound these days. Talking about AI and especially making bold claims about it has become a popular sport, where the prize is not a gold medal, but thousands of likes. I’ve never been a fan of clickbait, and I’m more for analyzing things objectively, so in this series of posts, I’m going to tell you some opinions I’ve matured on by actually using AI in my work as an XR developer, entrepreneur, and blogger.

This is the first article of a series of posts: today I’ll speak about the use of AI in my work as a developer, while in the next ones, I’ll talk about the use of AI for other stuff, like XR app design, creative work, etc.. I hope you’ll read this article with interest… otherwise, YOU CAN MAKE AI READ THIS POST AND SUMMARIZE IT FOR YOU, SO YOU CAN GO 10X FASTER!

AI and development

Artificial Intelligence has become seriously good at writing code. 10 years ago, I was already pretty amazed when Resharper was able to do some smart autocompletion of the code, I already saw it as black magic… and now we are at the point where LLMs can flawlessly write entire classes. As a developer, of course, I started wondering how to use this new tool and also worrying about when this tool was going to replace me entirely…

The advantage of using GitHub Copilot & Co.

2 years ago, I wrote an article about my experience with GitHub Copilot. In case you don’t know GitHub Copilot, a short primer is that it is a plugin for the most popular development environments (e.g. Visual Studio, Rider) that uses AI to support you in your development. It has some interesting features, with the two most popular ones being:

• An autocomplete of the code so that while you’re writing your application, Copilot suggests the next lines of code. Sometimes the suggestions are like 10 lines, so that you can have entire functions written “for free” and spare a lot of time
• An integrated chat, so that you can ask Copilot questions like “Can you refactor this to add this functionality?” or “I have an error here, what can be the reason?”. The chat can provide verbose answers or propose entire chunks of code

When I wrote that article in 2023, I hinted that Copilot was making me 10-15% faster, which was already a lot, considering its ridiculous price of $10/month. Today, also thanks to some smart advice from friends and colleagues (in particular my first biz partner Gianni), I was able to step up my game with Copilot, and now I am like 30% faster, which is a lot. AI allowed me this year to handle more projects than I could before: the benefits have been much superior to the subscription price.

Copilot is the tool that I use the most, but sometimes I also use other tools, like ChatGPT or Claude, when I want to have multiple code stub files written at the same time. I also use them when I need to do non-development-related work, or when I need to do more open-ended tech brainstorming.

In general, I confirm what many other people are saying: AI is able to speed up my work by a lot, and I think that every developer should use it. That’s why I was incredibly surprised when I was at the XRCC hackathon as a mentor, and I saw various developers not using Copilot (or similar tools). When I tried to use their Visual Studio to help them with coding, it seemed to me to have returned to the ancient past when people were using Notepad to write code.

Vibe Coding

According to many “LinkedIn experts,” everyone can now write code without the need for developers. You just “follow your vibes” (whatever this means) and ask the AI to make the code for you, then you try it, flag the bugs to the AI, it gives you new code, rinse and repeat until you have a working program. But is it true? Well, sort of.

This “Vibe coding” shit is just an evolution of what was happening before, when creative people with some lightweight developer knowledge went around copy-pasting code from the web, mixing it with existing libraries, giving a few hammer hits to fix the errors, and obtained something that sort of worked. With AI, this process becomes much better and faster, but more or less, it is the same thing.

You can for sure obtain something that works. The problem is that there is a huge difference between something that works and something production-ready. I still remember when someone on LinkedIn posted how she had been able to make a working backend with Cursor without needing any help from a developer. She posted a video about it, and the most voted comment was by a guy saying that he had a lot of experience writing backend code, and that flagged that in the code written by AI, there was a huge backdoor for potential hackers. Let’s say it worked with her, but also for the hackers wanting to “vibe exploit” her product (maybe we should see that as a win-win after all, lol). Maybe AI could have fixed the backdoor, but the problem in the first place was that without the field knowledge, she had no idea that there was a backdoor there, so she couldn’t even ask the AI to fix it.

From my experience, also, AI, at the time of writing, doesn’t always return optimized code or readable code. When writing C# code for Unity, sometimes it returns me some weird Linq concatenated stuff that is many lines long… which I have no idea if it works, and I even have no idea what it means… and considering that some C# purists consider Linq as the evil, I have to wash my PC with the water of the tears of Linus Torvalds to purify it again after seeing that blasphemy. When I ask it to refactor some unit tests to change them because the original code changed, sometimes I obtain such a mess that it’s faster if I fix the code myself. In fact, in a book I’m reading just now, I’ve read that currently, AI is better at writing code than refactoring it. So AI is not that coding silver bullet some people think, yet.

Don’t misunderstand me: sometimes it writes great code, and sometimes it even makes me discover some things I did not know. But the crucial thing is that, as a coding professional, I know how to understand what code is good and what is not. If you are from another field and just follow the “vibes”, you have no clue what you are adding to your application.

So, as much as I hate the term “vibe coding”, I still think it is a valuable thing. It can be used by non-tech people to make pseudo-functional prototypes to show their ideas to investors. It can be used by tech people to have some portion of code written in a language they don’t know well (I use it to write shaders for my prototypes, for instance). It can be used by tech people to learn a language they don’t know well faster, because AI can also explain how it has written the code. It can be used to make a porting of an application to another language, and so on. So I’m not against it… as long as it is clear to people that today is not something to safely use for writing production code.

Tomorrow it will become better, and maybe one day some mix of AI agents will be able to write production-ready code. But that day has still to come…

AI as a junior dev, the human as a lead dev

I have worked both as a developer and as a lead of developers: some people may think it is a similar job, but actually, it is very different. In the first case, you write the code; in the second case, you coordinate the people who write the code, giving them tasks, and checking their work. With AI, I think all developers should reinvent themselves and become some sort of hybrid figure with the lead developers.

When I ask the AI copilot to write some code portion for me, what I do is:

• Write the prompt (which is the equivalent of assigning the task to a developer)
• Wait for the answer
• Check the resulting code (which is the equivalent of checking the pull request of a developer)
• Fix the code myself, in case of small tweaks, or ask it to rewrite it following some suggestions, in case of big mistakes (which is the equivalent of leaving my comments on a pull request and asking a developer to improve the code following them)

As you can see, there is a lot of parallelism: using AI is a bit like being a lead developer and having a junior dev always available to take some work. A junior dev who you can assign all the most boring tasks, like writing unit tests, or writing comments, because it doesn’t complain. Since the copilot can take the most boring and dull tasks, I feel more relieved during my work because I can focus on the parts that I love the most.

And as it happens with real developers working with me, it offloads some work from me, but I also lose some ownership of the code. For instance, it doesn’t write code in my same style, so the code feels less “mine”, but I still approve it as long as it works well. I think it’s important to underline that I always review the AI output so that I verify that it is functional and it complies with my coding guidelines. I’ve found that writing unit tests in the AI era is even more important, because sometimes the code that Copilot writes seems to make sense and passes the human review, but it actually has some small bug (e.g. a +1 instead of a -1) that prevents it from working as intended. My advice for you all is: never skip the review and testing phase of the code written by AI. It should be standard practice in general, but with “vibe coding”, it is even more important.

Since I use AI more, I have become a sort of hybrid figure, because sometimes I write code and other times, I manage my Copilot minion and ask it to write the code for me. I guess in the future we won’t have one copilot, but various copilot agents that can even work at the same time… so the more we go on, the more we developers will become managers of AI developers that will make the hard work for us. In the meantime, we can focus on the most important part of the development, which is designing a proper architecture for the application and ensuring that everything works as expected, following high-quality standards.

You may ask me why I still write code and don’t just do the “minion” thing. Well, there are a few reasons for that:

• First of all, I like it. I mean, I chose to become a developer because I like to be a developer. I like creating things with code. Developing is a form of art by itself. Reviewing code is usually super-boring… so if I spent the time just reviewing Copilot code, I guess I would become a psychopath like Jack Nicholson in the movie Shining
• Sometimes it’s easier to just write the code instead of explaining AI in a long prompt what it should do
• AI is very good at writing code related to well-known problems, e.g. sorting a vector, but it becomes worse when you have to do something less common in your application, like using the latest features of Oculus SDK to find how many potatoes are on the table of a user. So, for the parts of your applications that are quite “unique” to your use case, you had better write the code yourself
• I still want to know how to code. It is suggested that CTOs/tech leads/lead devs still spend some time writing code (at least 20% of the time), so they are connected to the code, plus they remember how to write good code, and their knowledge of coding does not become obsolete. If you just make the AI write the code, you forget how to code… and then you finish doing blind “vibe coding”, with all the problems described above
• Solving tech problems helps in keeping the brain trained. A recent research paper that went viral showed that if you use too much AI, your brain becomes less good at solving problems because the brain is like a muscle, and the less you train it, the weaker it becomes. So it’s better not to make AI make everything

The thing I pay the most attention to now is architecture: architecture is still a form of “art”, and it requires good knowledge of different systems and the interaction with different modules developed by different people. Gen AI can handle some of that, but for now, I think we humans are still better at it. This is the same thing I did when I was working as a CTO, so the comparison of AI as my helpful junior dev while I am the tech lead still holds.

Google search vs AI search

When ChatGPT became famous, many people started using it to ask questions about coding practices. It started substituting the usual Google search, in which 90% of the results were some Stack Overflow threads.

For questions with a straight answer, the questions to ChatGPT (or Copilot Chat, Claude, or whatever you want) are very efficient: they just tell you the right answer and why that is the right answer. For instance, if you ask if you should write meaningful names for variables, the answer is, of course, yes, and AI can reliably explain to you why. But for questions with nuanced answers, or where there is an open debate, I’ve always found a huge difference between AI search and Stack Overflow search. On Stack Overflow, you can read debates between super-talented developers explaining why one option is better than the other, and from these debates between these geniuses, you can truly learn all the tradeoffs of using one solution or the other. Delving into Stack Overflow forums, I learned a lot about good coding practices, especially for what concerns C++, which requires (or at least, required) much more technical skills than a language like C#. I don’t think I would have learned the same with the short summaries returned by GPTs.

I have to say, anyway, that LLMs have gotten better in this sense. Gemini AI provides you with not only a summary, but also the links to the references so that you can go further. This way you can have the best of both worlds. When I had a problem with Unity Multiplayer Play Mode not working with SteamVR, Gemini AI said that it was a problem other people were having, but there was no solution. Navigating the proposed links and reading carefully what the people were saying, I was able to understand the problem, fix it, and publish a fix for all the community to enjoy. I couldn’t do that using only a summary, but thanks to the provided links, I could analyze the problem in detail and fix it. By the way, now if you look for the same problem, Gemini AI has some links to my blog in its answer.

Will developers disappear?

So, what is the future for us developers? The reality is, no one knows. This is something I will reiterate multiple times in this series of articles: AI is such an impactful technology that no one knows what its impact will be in the next 10, 20, or 50 years. It’s the same with the Internet: when the Internet was invented, no one predicted that one day, thanks to it, some people would live by just selling their bath water and pictures of feet. But hey, here we are.

So I don’t know what the long-term future is for developers. I know that the present and short-term future will be about an increase in the time spent managing the agents writing code and a decrease in time spent writing the actual code. Devs will have to gain some management skills if they want to be efficient in this new era. They had better learn more about how to design architectures because since the minions writing the code will be the same for everyone (ChatGPT, Claude, etc…), the difference between the various applications will be on the infrastructure side and the architecture side.

Some people argue that the future is about having only one senior dev managing the work of 10 AI agents, which substitute the current junior devs. I think this is a possibility, but as my previous lead dev Denis made me notice, how can we create new senior devs if junior devs are substituted by AI? To become a senior dev myself, I had to start as a junior dev and do a shitload of errors. If AI had done all the work for me, I could never have become a senior. I honestly don’t have an answer for this… a possibility is that the “senior dev” of tomorrow is not the “senior dev” of today: it is more someone who is an expert in architectures and in code reviews and not much in actual writing code him/herself. So the junior dev may review the AI code, and the senior dev tutoring him/her may review his/her reviews, teaching him/her how to review the code.

We should also remember that unless we reach AGI/ASI, AI will just regurgitate previously known code. This means that if there are no people writing code, we could have no new optimization algorithms, new coding strategies, or programming patterns that the AIs themselves could use. So I guess someone should still work on the coding side, but again, it is more specialized work than writing ordinary code.

In my opinion, the job of the dull code writer is destined to die. Pure code writing is just translation work, and it is even easier than language translations for an AI because human languages have nuances, dialects, and such, while a C# code segment just follows exact rules. Computers are already pretty good at writing code. And AI is already creating job losses in this sense: if all developers become 30% faster with Copilots like me, companies will need 30% fewer developers to do their work. The other option is that companies will become 30% faster. Or that there will be 30% more tech companies. Most probably, it will be a mix of all of them. But even if it were 10% fewer dev jobs, it is still a huge number of people losing their jobs.

There is and will still be work to do for domain experts, tech architects, senior devs, specialized optimized code developers, COBOL developers (they will never be replaced)… people that know what they are doing, and that have learned enough craftmanship that they can’t be replaced by a machine, yet. These people are going to work at a higher level and use AI in their work. AI will make the low-level work for them.

Do not forget that until we arrive at the Terminator stage, people will still work with people, so having some humans that can work with other humans will still be a good thing at a personal level. If I had to spend my whole day just chatting with my boss AI agent and dealing with the AI dev agents, I guess I would just kill myself (and be substituted by an AI senior dev the day after). The human factor will still be relevant: humans want to work with humans, and I think it’s much better to have a human lead dev dealing with the senior devs than having some creepy AI overlord.

I have no idea what will happen next, though, that is when (and if) AI will also be very good at being the senior dev or the dev architect… when, with just a prompt, you can obtain a working application. I guess we have to wait and see: the right attitude to have now is to be open to changes and to adapt fast to every new situation. For now, this means adopting some sort of AI in our working pipeline. For the future, let’s be prepared and try to ride whatever tide will come against us. In the worst-case scenario, I’ll just resort to selling bath water and feet pictures, too. The good news is that neither AI nor robotics can create human bath water, so I should be safe. Subscribe to my Patreon to support me and delay my OnlyFans moment as long as you can.

A final word

I have one final thing to say, about something that grinds my gears. Reading some social posts, it seems that the moment everyone is waiting for is when we all developers lose our jobs. I understand the people praising the democratization of tech skills, but some people speak as if they are happy that there is no need for devs anymore. I find it pretty weird, and you should, too. How would you feel if I wrote a LinkedIn post saying, “Thanks to robotics and AI, in a few years we can get rid of all doctors and nurses”? It sounds disturbing, doesn’t it? Doctors and nurses work a lot to save lives, and we should praise them. We devs do not save lives and are not superheroes like them, but we do our job at our best, we try to be helpful, we share our knowledge in free open-source repositories, and usually overwork to get the job done: the stories of underpaid, burned-out devs in the gaming industry are well-known to everyone. I myself have worked with developers who stayed up with me until 4 am because we cared about delivering a good product in time. We are specialized workers, and we deserve respect.

And if someone is happy that we are going to lose our jobs, well, I think he/she should remember that AI is already much better at writing dull social posts than at writing good code…

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I hope you have enjoyed this (human-written) post, and if that is the case, please subscribe to my newsletter so as not to miss the next one in the series, which will be out before the end of the month. And since I like debates, let me know your opinion here in the comments below or on my social media channels! I am interested in your opinion, both whether you are a human or an AI bot…

(The header image used the logo of GitHub Copilot)

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