We have been working with AI models for development a lot lately (yes, just like everyone else). And I’m seesawing between “damn, that’s impressive” and “damn, brainless fool” quite often.

I want to share a few scenarios in which we employed AI to write code, how it turned out, and what I think about the future of AI-generated code and its impact on software development in general.

Porting code between languages & platforms

One place where we are trying to use an AI model is making sure that the RavenDB Client API is up to date across all platforms and languages. RavenDB has a really rich client API, offering features such as Unit of Work, change tracking, caching, etc. This is pretty unique in terms of database clients, I have to say.

That is, this approach comes with a substantial amount of work required. Looking at something like Postgres as a good example, the Postgres client is responsible for sending data to and from the database. The only reason you’d need to update it is if you change the wire format, and that is something you try very hard to never do (because then you have to update a bunch of stuff, deal with compatibility concerns, etc.).

The RavenDB Client API is handling a lot of details. That means that as a user, you get much more out of the box, but we have to spend a serious amount of time & effort maintaining all the various clients that we support. At last count, we had clients for about eight or so platforms (it gets hard to track 🙂). So adding a feature on the client side means that we have to develop the feature (usually in C#), then do the annoying part of going through all the clients we have and updating them.

You have to do that for each client, for each feature. That is… a lot to ask. And it is the kind of task that is really annoying. A developer tasked with this is basically handling copy/paste more than anything else. It also requires a deep understanding of each client API’s platform (Java and Python have very different best practices, for example). That includes how to write high-performance code, idiomatic code, and an easy-to-use API for the particular platform.

In other words, you need to be both an expert and a grunt worker at the same time. This is also one of those cases that is probably absolutely perfect for an AI model. You have a very clearly defined specification (the changes that you are porting from the source client, as a git diff), and you have tests to verify that it did the right thing (you need to port those, of course).

We tried that across a bunch of different clients, and the results are both encouraging and disheartening at the same time. On the one hand, it was able to do the bulk of the work quite nicely. And the amount of work to set it up is pretty small. The problem is that it gets close, but not quite. And taking it the remaining 10% to 15% of the way is still a task you need a developer for.

For example, when moving code from C# to TypeScript, we have to deal with things like C# having both sync and async APIs, while in TypeScript we only have an async API. It created both versions (and made them both async), or it somehow hallucinated the wrong endpoints (but mostly got things right).

The actual issue here is that it is too good: you let it run for a few minutes, then you have 2,000 lines of code to review. And that is actually a problem. Most of the code is annoyingly boilerplate, but you still need to review it. The AI is able to both generate more code than you can keep up with, as well as do some weird stuff, so you need to be careful with the review.

In other words, we saved a bunch of time, but we are still subject to Amdahl's Law. Previously, we were limited by code generation, but now we are limited by the code review. And that is not something you can throw at an agent (no, not even a different one to “verify” it, that is turtles all the way down).

Sample applications & throwaway code

It turns out that we need a lot of “just once” code. For example, whenever we have a new feature out, we want to demonstrate it, and a console application is usually not enough to actually showcase the full feature.

For example, a year and a half ago, we built Hugin, a RavenDB appliance running on a Raspberry Pi Zero. That allowed us to showcase how RavenDB can run on seriously constrained hardware, as well as perform complex full-text search queries at blazing speed.

To actually show that, we needed a full-blown application that would look nice, work on mobile, and have a bunch of features so we could actually show what we have been doing. We spent a couple of thousand to make that application, IIRC, and it took a few weeks to build, test, and verify.

Last week, I built three separate demo applications using what was effectively a full vibe-coding run. The idea was to get something running that I could plug in with less than 50 lines of code that actually did something useful. It worked; it makes for an amazing demo. It also meant that I was able to have a real-world use case for the API and get a lot of important insights about how we should surface this feature to our users.

The model also generated anywhere between 1,500 and 3,000 lines of code per sample app; with fewer than 100 lines of code being written by hand. The experience of being able to go and build such an app so quickly is an intoxicating one. It is also very much a false one. It’s very easy to get stuck way up in a dirty creek, and the AI doesn’t pack any sort of paddles.

For example, I’m not a front-end guy, so I pretty much have to trust the model to do sort of the right thing, but it got stuck a few times. The width of a particular element was about half of what it should be, and repeated attempts to fix that by telling the model to make it expand to the full width of the screen just didn’t “catch”.

It got to the point that I uploaded screenshots of the problem, which made the AI acknowledge the problem, and still not fix it. Side note: the fact that I can upload a screenshot and get it to understand what is going on there is a wow moment for me.

I finally just used dev tools and figured out that there was a root div limiting the width of everything. Once I pointed this out, the model was able to figure out what magic CSS was needed to make it work.

A demo application is a perfect stage for an AI model, because I don’t actually have any other concern other than “make it work”. I don’t care about the longevity of the code, performance, accessibility, or really any of the other “-ities” you usually need to deal with. In other words, it is a write-once, then basically never maintained or worked on.

I’m also perfectly fine with going with the UI and the architecture that the AI produced. If I actually cared exactly what the application looked like, it would be a whole different story. In my experience, actually getting the model to do exactly what I want is extremely complex and usually easier to do by hand.

For sample applications, I can skip actually reviewing all this code (exceeding 10KLOC) and accept that the end result is “good enough” for me to focus on the small bits that I wrote by hand. The same cannot be said for using AI coding in most other serious scenarios.

What used to be multiple weeks and thousands of dollars in spending has now become a single day of work, and less money in AI spend than the cost of the coffee drunk by the prompter in question. That is an amazing value for this use case, but the key for me is that this isn’t something I can safely generalize to other tasks.

Writing code is not even half the battle

It’s an old adage that you shouldn’t judge a developer by how fast they can produce code, because you end up reading code a lot more than writing it. Optimizing code generation is certainly going to save us some time, but not as much as I think people believe it would.

I cited Amdahl's Law above because it fits. For a piece of code to hit production, I would say that it needs to have gone through:

• Design & architecture
• Coding
• Code review
• Unit Testing
• Quality Assurance
• Security
• Performance
• Backward & forward compatibility evaluation

The interesting thing here is that when you have people doing everything, you’ll usually just see “coding” in the Gantt chart. A lot of those required tasks are done as part of the coding process. And those things take time. Generating code quickly doesn’t give you good design, and AI is really prone to making errors that a human would rarely make.

For example, in the sample apps I mentioned, we had backend and front-end apps, which naturally worked on the same domain. At one point, I counted and I had the following files:

• backend/models/order.ts
• frontend/models/api-order.ts
• frontend/models/order.ts
• frontend/models/view-order.ts

They all represented the same-ish concept in the application, were derived from one another, and needed to be kept in sync whenever I made a change to the model. I had to explicitly instruct the model to have a single representation of the model in the entire system.

The interesting bit was that as far as the model was concerned, that wasn’t a problem. Adding a field on the backend would generate a bunch of compilation errors that it would progressively fix each time. It didn’t care about that because it could work with it. But whenever I needed to make a change, I would keep hitting this as a stumbling block.

There are two types of AI code that you’ll see, I believe. The first is code that was generated by AI, but then was reviewed and approved by a person, including taking full ownership & accountability for it. The second is basically slop, stuff that works right now but is going to be instant technical debt from day one. The equivalent of taking payday loans to pay for a face tattoo to impress your high-school crush. In other words, it’s not even good from the first day, and you’ll pay for it in so many ways down the line.

AI-generated code has no intrinsic value

A long time ago (almost 25 years) .NET didn’t have generics. If you wanted to have a strongly typed collection, you had a template that would generate it for you. You could have a template that would read a SQL database schema and generate entire data layers for you, including strongly typed models, data access objects, etc. (That is far enough back that the Repository pattern wasn’t known). It took me a while to remember that the tool I used then was called CodeSmith; there are hardly any mentions of it, but you can see an old MSDN article from the Wayback Machine to get an idea of what it was like.

You could use this approach to generate a lot of code, but no one would ever consider that code to be an actual work product, in the same sense that I don’t consider compiled code to be something that I wrote (even if I sometimes browse the machine code and make changes to affect what machine code is being generated).

In the same sense, I think that AI-generated code is something that has no real value on its own. If I can regenerate that code very quickly, it has no actual value. It is only when that code has been properly reviewed & vetted that you can actually call it valuable.

Take a look at this 128,000-line pull request, for example. The only real option here is to say: “No, thanks”. That code isn’t adding any value, and even trying to read through it is a highly negative experience.

Other costs of code

Last week, I reviewed a pull request; here is what it looked like:

No, it isn’t AI-generated code; it is just a big feature. That took me half a day to go through, think it over, etc. And I reviewed only about half of it (the rest was UI code, where me looking at the code brings no value). In other words, I would say that a proper review takes an experienced developer roughly 1K - 1.5K lines of code/hour. That is probably an estimate on the high end because I was already familiar with the code and did the final review before approving it.

Important note: that is for code that is inherently pretty simple, in an architecture I’m very familiar with. Reviewing complex code, like this review, is literally weeks of effort.

I also haven’t touched on debugging the code, verifying that it does the right thing, and ensuring proper performance - all the other “-ities” that you need to make code worthy of production.

Cost of changing the code is proportional to its size

If you have an application that is a thousand lines of code, it is trivial to make changes. If it has 10,000 lines, that is harder. When you have hundreds of thousands of lines, with intersecting features & concerns, making sweeping changes is now a lot harder.

Consider coming to a completely new codebase of 50,000 lines of code, written by a previous developer of… dubious quality. That is the sort of thing that makes people quit their jobs. That is the sort of thing that we’ll have to face if we assume, “Oh, we’ll let the model generate the app”. I think you’ll find that almost every time, a developer team would rather just start from scratch than work on the technical debt associated with such a codebase.

The other side of AI code generation is that it starts to fail pretty badly as the size of the codebase approaches the context limits. A proper architecture would have separation of concerns to ensure that when humans work on the project, they can keep enough of the system in their heads.

Most of the model-generated code that I reviewed required explicitly instructing the model to separate concerns; otherwise, it kept trying to mix concerns all the time. That worked when the codebase was small enough for the model to keep track of it. This sort of approach makes the code much harder to maintain (and reliant on the model to actually make changes).

You still need to concern yourself with proper software architecture, even if the model is the one writing most of the code. Furthermore, you need to be on guard against the model generating what amounts to “fad of the day” type of code, often with no real relation to the actual requirement you are trying to solve.

AI Agent != Junior developer

It’s easy to think that using an AI agent is similar to having junior developers working for you. In many respects, there are a lot of similarities. In both cases, you need to carefully review their work, and they require proper guidance and attention.

A major difference is that the AI often has access to a vast repository of knowledge that it can use, and it works much faster. The AI is also, for lack of a better term, an idiot. It will do strange things (like rewriting half the codebase) or brute force whatever is needed to get the current task done, at the expense of future maintainability.

The latter problem is shared with junior developers, but they usually won’t hand you 5,000 lines of code that you first have to untangle (certainly not if you left them alone for the time it takes to get a cup of coffee).

The problem is that there is a tendency to accept generated code as given, maybe with a brief walkthrough or basic QA, before moving to the next step. That is a major issue if you go that route; it works for one-offs and maybe the initial stages of greenfield applications, but not at all for larger projects.

You should start by assuming that any code accepted into the project without human review is suspect, and treat it as such. Failing to do so will lead to ever-deeper cycles of technical debt. In the end, your one-month-old project becomes a legacy swamp that you cannot meaningfully change.

This story made the rounds a few times, talking about a non-technical attempt to write a SaaS system. It was impressive because it had gotten far enough along for people to pay for it, and that was when people actually looked at what was going on… and it didn’t end well.

As an industry, we are still trying to figure out what exactly this means, because AI coding is undeniably useful. It is also a tool that has specific use cases and limitations that are not at all apparent at first or even second glance.

AI-generated code vs. the compiler

Proponents of AI coding have a tendency to talk about AI-generated code in the same way they treat compiled code. The machine code that the compiler generates is an artifact and is not something we generally care about. That is because the compiler is deterministic and repeatable.

If two developers compile the same code on two different machines, they will end up with the same output. We even have a name for Reproducible Builds, which ensure that separate machines generate bit-for-bit identical output. Even when we don’t achieve that (getting to reproducible builds is a chore), the code is basically the same. The same code behaving differently after each compilation is a bug in the compiler, not something you accept.

That isn’t the same with AI. Running the same prompt twice will generate different output, sometimes significantly so. Running a full agentic process to generate a non-trivial application will result in compounding changes to the end result.

In other words, it isn’t that you can “program in English”, throw the prompts into source control, and treat the generated output as an artifact that you can regenerate at any time. That is why the generated source code needs to be checked into source control, reviewed, and generally maintained like manually written code.

The economic value of AI code gen is real, meaningful and big

I want to be clear here: I think that there is a lot of value in actually using AI to generate code - whether it’s suggesting a snippet that speeds up manual tasks or operating in agent mode and completing tasks more or less independently.

The fact that I can do in an hour what used to take days or weeks is a powerful force multiplier. The point I’m trying to make in this post is that this isn’t a magic wand. There is also all the other stuff you need to do, and it isn’t really optional for production code.

Summary

In short, you cannot replace your HR department with an IT team managing a bunch of GPUs. Certainly not now, and also not in any foreseeable future. It is going to have an impact, but the cries about “the sky is falling” that I hear about the future of software development as a profession are… about as real as your chance to get rich from paying large sums of money for “ownership” of a cryptographic hash of a digital ape drawing.