Rust is too low-level for typical enterprise app where requirements changes twice a day. You end up spending time and tokens fighting with borrow checker.
> Rust is too low-level for typical enterprise app where requirements changes twice a day. You end up spending time and tokens fighting with borrow checker.
In my experience, you do not spend tokens fighting with the borrow checker anymore, newer models are smarter. But it might not be ideal for a lot of CRUD applications.
> C# is MS product, which is no-go for some folks.
This is 2026, it's not 1996 anymore. .Net works on Linux and Microsoft is as friendly towards open source and open standards as a Big Tech company can be.
If anything, it was Oracle which more recently sued another company for using a JDK alternative. And this was a lawsuit that, if accepted, could have put the entire idea of API compatibility in danger and deal a severe blow to the Open Source movement.
Anyone who is morally bothered by MS but is unfazed by this is probably just mentally stuck in the 1990s.
> Kotlin probably would be the answer.
I love Kotlin, but I'm afraid that's not the case. The conservative organizations that choose Java out of inertia, would keep choosing Java over Kotlin, even if Kotlin is a better JVM language which is facing no downside.
For anyone who doesn't need to be on the JVM or work with JVM tooling, Kotlin doesn't cut it. It doesn't have null pointer dereference problem in theory... Only it does in practice if you're using any Java API that may return null (all these bang-decorated "Platform types"). Generic type erasure can only be overcome in inline functions with reified types. And building and deploying artifacts without docker is still a mess.
I found Kotlin extremely publishing for Java shops in the past, and I've converted multiple departments totaling over hundreds of employees to use Kotlin. But that was before AI. The rationale was simple: Java is an entrenched language that leads to bloated code, slow development cycles and way too many avoidable bugs in productions. Kotlin solves some if these issues, and it's very easy to learn for a Java engineer, while still letting you keep all of your tools and libraries. And as a language (putting ecosystem aside), I find it better than either Go or Typescript, and far more ergonomic than Rust[1].
But all of these arguments die with AI. Rust is just as ergonomic as any other popular language today if you're using an agent, and the fact that an engineer spent their lifetime writing Spring Boot programs in Java you don't have time to let them learn a new stack from scratch doesn't matter anymore.
Sure, there are many companies where letting AI write the code is still not acceptable, but most of these workplaces will accept AI agents sooner than they accept Kotlin.
I feel a bit sad since I like many ideas about Kotlin (especially how amenable it is for making DSLs) but we've lost that opportunity
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[1] Unless you have to write highly concurrent code without any data races.
> Rust is just as ergonomic as any other popular language today if you're using an agent
Have you worked on large (>500KLOC) codebases with an agent? Not only do you have to be an expert at the language, but even if you're lucky and everything is fine, Java code is likely to be particularly fast by comparison, because the agents aren't very good at manual optimisation, especially as the code grows (they're even worse than humans at that, and humans aren't great at manual optimisation of large codebases, either, which is one of the problems the JVM set out to solve; in fact, agent-written code in a low-level language gets pretty slow well below that size). Oh, and the long build times certainly don't help.
Have you worked on large (>500KLOC) codebases with an agent?
Yes. But keep in mind KLOCs are not easily comparable across languages. Java is notoriously verbose. A 500KLOC codebase in Java would usually be half that size in Rust. If your argument is that large codebases makes life harder for agents, you should go with a less verbose language.
I'm not sure what "manual optimization" means (isn't it a bit of an oxymoron when the agent does it?), but if your agent has the proper tools (e.g. ast-grep, rg, semble) it can deal with large codebases. Would the agent create slop? Yes. But it wouldn't be worse on the slop that humans created on every moderately-sized Java project I've worked on.
> in fact, agent-written code in a low-level language gets pretty slow well below that size
I've never seen this happening. I've seen agents writing suboptimal Rust code (e.g. copies instead of Cow). But while this occassionally happens with Rust, I've never seen an agent optimizing for Java where necessary (e.g. using object pools to avoid GC churn). Java is not magic.
> is notoriously verbose. A 500KLOC codebase in Java would usually be half that size in Rust
Lol, no way. Especially that rust is pretty verbose all things together (which makes sense, given it's a low level language - ergo you have to literally express more things about the code)
> it's a low level language - ergo you have to literally express more things about the code
That isn't really a comparison of the languages as much as the standard runtimes and ecosystems. It is important to consider that each have comparable components.
So you aren't comparing a no_std rust project against a comparable JavaCard, but say Diesel vs Hybernate code examples around ORM.
Comparable components, but almost every line of Rust code expresses information about the lifetime of objects - either implicitly (quite often), or explicitly.
Meanwhile in java it's a constant "Arc<JavaType>", and scopes don't mark "drop points"
> A 500KLOC codebase in Java would usually be half that size in Rust
Ok, so you barely know either Java or Rust.
> If your argument is that large codebases makes life harder for agents, you should go with a less verbose language.
You mean, like Rust??? But no, that's not my argument. Agents have a hard time keeping up the architecture in large software (and the differences between verbose languages like Java, Go, Rust, and C++ vs less verbose ones like Python and JS don't make much of a difference). So they either make a mess or they do the simple thing, and the simple thing in low-level languages is often slow.
> But it wouldn't be worse on the slop that humans created on every moderately-sized Java project I've worked on.
Yeah, I don't think you've actually tried it.
> I've never seen an agent optimizing for Java where necessary (e.g. using object pools to avoid GC churn). Java is not magic.
Object pools are far less efficient than Java's GCs, but while state-of-the-art compiler and memory management technology is certainly not magic, I suggest you learn more about these things if you want to make informed decisions.
Hmm? Java gets null dereferences all the time, that's what a NPE is. The VM takes on the extra plumbing to surface a dereference of a null pointer in a recoverable way to code. On Windows this is done using SEH, on Unix it is handling SIGFAULT - but each NPE corresponds to a null pointer dereference that java then tries to clean up.
That the language does not have a way to have compiler enforced "never null" is actually a huge productivity drain, specifically because you have to do your own defensive measures against null or attempt cleanup/recovery when it happens.
Even languages like Swift which use Optional (e.g. a maybe monad) to provide a concept of nilability still internally will hit null pointer dereferences on occasion with faulty bridged code/bindings. However, they treat this as a non-recoverable violation of invariants - a developer shouldn't be trying to recover from incorrect code at runtime.
That's a modular monolith, not microservices. It's a nice architecture for dev but it comes with a downside that you need to deploy everything whenever there's a change. You usually end up with a complex release train process. Again, the complexity is still there, but it moves. Where your complexity lives is a choice.
There is a reason why youngest developers are more productive. They accept everything Agent suggests. They don't understand or care. They rely on your review of the Agent output from prompt they copy-pasted from JIRA. So you spent your time reviewing it instead of your own work.
>> Without building this solution, and instead just taking what ChatGPT generated, they had skipped crucial steps in the programming problem solving process, and were now lost
When you skip the problem-solving process and take the LLM's first output, you're not building the cognitive framework needed to debug or maintain that code.
I'd side-eye that list as senior .NET dev full stack.
You're asking one person to do frontend/js/css, backend/databases/Redis/MQ, and Python with all that math behind data-science, but i doubt he can understand and review all of this
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