In the past year, myself and many other people who program for a living started using AI coding assistance. I’m just going to plainly discuss my experience, and ramble about some related issues.

This explanation is primarily for the benefit of people who do not code professionally, and thus have little idea what’s going on. If you code professionally, then you already know, and have formed your own opinion about it, which you are welcome to keep. But it’s worth noting that lots of different kinds of people code for a living. It’s not just software engineers, you know. I code on a daily basis but I’m a data scientist. The reader should be cautious about generalizations because what’s true of one profession may not be true of another.

Agentic AI

At work I use what’s called an “agentic” AI application. In practice, that means that the AI is embedded in the code editor. The AI can edit files and execute code. It’s using the same LLM technology as a chatbot, and you can freely choose which model to use, including ChatGPT, Claude, Gemini, and more.

Having the AI embedded in the code editor streamlines the process significantly compared to just having ChatGPT open in a separate browser window. If I’m just talking to ChatGPT in my browser, I might describe what I’m doing, maybe copy a relevant snippet of my code. It might suggest some code to use, which I can then paste into my editor. But with an agentic application, the LLM can just look through my existing code, and make a bunch of suggestions scattered across a dozen locations.

Here’s how I typically approach the AI. I describe a new feature that I want, as detailed as possible. This usually includes several links to specific documents, such as the files where I want the code to be located, files containing similar code that I want to emulate, and so on. Then I review the changes, approving some and rejecting others. I may ask for further adjustments if I want it to take another approach. Then I test the code, show the AI the error logs, and repeat until the feature is finished.

I’ve seen people skip some of these steps, and sometimes I skip some steps too. For example, instead of reviewing a feature piece by piece, you could just provide the AI with a whole design document, and let it chug. I am not convinced that you’ll get great results this way, but sometimes you only need it to be “good enough”. Or maybe the reason I don’t like that approach is because I’m not as good at outlining exactly what I want from the start.

There are also uses that don’t generate code at all. You can ask it general questions like: “My colleague wrote this code, what does it do?” “Here’s my plan for this project, do you see anything I’ve missed?” “What are the advantages and disadvantages of this design decision?”

Is it effective?

When it comes to using AI coding assistance, we’re pretty much immediately getting feedback on whether it works. People on the internet can write all the thinkpieces they want, but at the end of the day, either the software works, or it doesn’t. Programmers have to have open mind about it because for any specific task, perhaps the AI is surprisingly effective—or surprisingly ineffective. The rest of y’all can confidently spout opinions about AI coding, because you’re not actually doing it, so you never risk the humility of learning that you were wrong.

In my experience, agentic coding is fine. A lot of the time, it just works, and works very fast and clean. Of course, it doesn’t do everything right. Plenty of course correction is required. It’s often unaware of the larger context of my code. And some problems, it just doesn’t know how to solve, so it will talk around in circles proposing solutions that don’t work.  When the AI starts going in circles, I learned to stop wasting time asking it questions, and fall back to more traditional methods.

What about all the invisible ways that AI can hurt the code? For example, there was that study that found that when software engineers use AI, they think they’re working 20% faster, but they’re actually working 20% slower. This is invisible to the typical programmer, because there isn’t an easy way to compare how long you would have taken if you had approached the work differently. This is a legitimate concern, but I don’t put much stock in the specific result. The study is looking at a small sample of software developers, and I’m not even in that category, so how can I know whether it applies to me? This study obviously isn’t the last word; I easily found studies with different findings, and there are surely even more ongoing. Researchers need more time to cook.

There’s also the concept of “code debt”. Code debt is what you call it when you program faster, at the cost of future programmers’ time. For example, the code is buggy, or it’s hard to read, or it doesn’t leave room for expansion. Code debt is a universal problem across all programming. In a large organization, the problem is that programming speed is measurable, while code debt is not. So even if you want to minimize code debt, all incentives push in opposite direction.

Does AI coding make the code debt better or worse? Unclear. It pulls in both directions. For example, one thing it’s really good at is writing unit tests. This is slow and tedious work used to test for bugs. They’re common among many software engineers, although in my line of work we hardly ever use them! But with AI, it’s so easy. And this may result in less buggy code.

On the other hand, if the code is AI generated, you may have less of an understanding of what it’s doing. Therefore, you have less capability to modify it, or to explain to other programmers how to use it.

But really, code debt is an organizational problem. If an organization wanted to fight code debt, they could spend resources on it. If they don’t spend resources on it, how much do they really care? If they don’t care, why should we?

Guardrails

What’s to stop the AI from doing something destructive, like deleting your whole database? It’s worth noting that this is not actually a new problem. Humans do it too. I have an anecdote from when I first started.  One of my colleagues accidentally deleted the team’s data. And then the next day, I did it again! We were both following a tutorial, and at the end of the tutorial there was an overly broad command to clean up the workspace. There hadn’t been much important lost, but after that we started backing that data up.

Every software developer knows that the way to address these failures is not to magically write flawless code. The solution is to implement multiple layers of controls and failsafes. When you hear about some catastrophic failure where an AI deletes a database, that isn’t just an AI failure, it’s multiple layers of failure that you’re not hearing about.

There are a lot of standard failsafes that long predate AI. Like, none of it is running on my computer, it’s all running on a computer on the cloud. Every project is regularly backed up to Github. All our permissions are carefully curated by a dedicated team to mitigate risk of accidents.

An agentic application ought to have additional controls. So, there’s a very long list of rules about what the AI can and can’t do. There’s a long list of files it can and can’t access. And when it does access files, that data does not get used to train future models.

At least, that’s what the AI providers claim, and of course they could be lying or overselling. But that’s not really my responsibility nor my concern, since the risk is to the company not to myself. They pay infosec experts to think about it.

Ethics

Although the ethics of AI is a hot topic, almost all the attention goes towards AI art. If people think about AI coding at all, it’s often as an afterthought, relying on generalizations from art into programming.

People also often conflate the question of ethics with the question of efficacy. People say, AI coding is wrong because the code it generates is slop. Okay, but slop isn’t unethical?

Suppose that AI coding results in worse outcomes. Maybe that study is right, and programmers are 20% slower when using AI. Maybe it also results in additional code debt. So, all these tech companies that are purchasing enterprise subscriptions to agentic applications are basically shooting themselves in the foot. So… what? Why are we crying for tech companies? I get that people are worried about the enshittification of social media. But let’s be real, what’s good for Facebook isn’t what’s good for society, so if Facebook makes a bad investment, then boo hoo for them. I work for a tech company, my wellbeing is pretty directly tied my employer’s wellbeing, but there’s got to be a separation, and even more so for mere users of social media.

Or take the question of jobs. To be quite honest, I don’t think this AI technology is good for our jobs. However, my belief is premised on the efficacy of AI coding. If AI is not good at coding, then I’m not worried about our jobs, at least not in the long run. (In the short run, I’m more worried about Trump’s economic policies, I think that’s actually what’s killing the job market right now.)

One of the contentions about AI art is that the AI is just copying stuff from around the internet and pasting it into a Frankenstein’s monster. That’s not really how generative AI works. But it kind of is how traditional programming works. Traditional programming literally involves copying a bunch of code around the internet and pasting it into a Frankenstein’s Monster. Everyone who programs knows this. It’s part of the day-to-day workflow, and it’s also built into the foundations of how coding even works. We put code into packages, and use package management software to make sure we have all the packages we need. And there’s a whole category of bugs where some of the packages aren’t the right version, or the versions are incompatible, etc. That’s just programming life.

So, arguing that plagiarism is an issue for AI coding is much more of an uphill battle than it is for AI art.

What about the environmental impact? I’m not going to rehash the discussion about environmental impact, and I don’t really have the relevant expertise anyways. My two cents: the environmental impact comes entirely from computation, and everything that programmers do is already computational. As a data scientist, my job is all about processing large amounts of data hosted in data centers.

The reason to be concerned about the environmental impact of computation is that it’s an “externality”. The cost isn’t born by the corporation, but by the world at large. However, the cost isn’t entirely external. Corporations are explicitly billed for computation time. That’s how Amazon and other cloud computation centers make their money. So corporations would already like to reduce computation time if they could. It’s just a matter of not prioritizing it. You could get them to prioritize it more by implementing a carbon tax policy, although that may also be bad for jobs. That’s my favored solution.

Conclusion

This was a fairly positive post about AI coding. It just seems pretty effective in my experience. It’s impossible to ignore that the AI gets a lot of stuff wrong, and yet it still seems useful. I acknowledge and am concerned about the possibility that it is less effective than it appears, but I won’t believe it on the basis of a single study.

This seems to be a common opinion among professional programmers I know. But some people have more negative or positive opinions on the efficacy than others. And people who are not programmers are left to pick and choose whichever opinions they want to believe. Personally, I believe both the positive and negative experiences. It could be that it’s good at some things and bad at others.