Opinion I’m willing to be impressed by AI products, but Anthropic’s AI‑built C compiler leaves me a bit cold. It’s little more than a clever demo. It is not the moment when software engineering as we know it flips over and dies. Not even close.
Anthropic proudly claimed its team of 16 Claude Opus 4.6 agents had written a Rust-based C compiler from scratch without any access to the internet. Really? That’s meant to impress me? Sure, as Anthropic claims, the AI-created C compiler can compile this, that, and the other thing. Yes, even Doom. But so what?
Anthropic’s Claude Opus 4.6 spends $20K trying to write a C compiler
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The C language is 53 years old. That’s older than many of you. The C ecosystem has torture‑test suites that encode the subtleties of the language. There are gold‑standard reference compilers, GCC and Clang, to compare against at every step. In addition, I have no doubt that all the many open source C compilers and tools were already in Claude’s LLM. How could they not be?
Saying it didn’t access the internet is like a student saying: “I had copies of the code I was working on, but I’d turned my Wi-Fi off.”
Some people seem to think writing a C compiler is hard work. Well, yes, if you’re writing it in assembly language, it is. But I hate to break it to you: Computer science undergraduates write C compilers every semester. Heck, you can learn to do it yourself thanks to Daniel McCarthy’s Developing a C Compiler From Scratch online class. And the class will cost you a lot less than the $20,000 it cost Anthropic.
Anthropic’s experiment starts after all of that hard work has been done, and it still fails. Sure, according to the headlines, it sounds great: “16 agents… two weeks… 100,000 lines of Rust… compiles Linux and Doom.” Drill into the details and community testing, and the story is far less impressive.
For example, as several people pointed out on GitHub, because the Anthropic compiler doesn’t include the path to the native C library on your system, it can’t compile “Hello World.” As one person put it: “Apparently compiling hello world exactly as the README says to is an unfair expectation of the software.” You can spell out the paths, of course, but as another person added, “you shouldn’t have to lmao!” Oh, the shame of it all!
That’s a nice litmus test. If your miracle compiler chokes on a canonical first example without hand‑holding, it’s not ready to stand next to GCC and Clang in anyone’s toolchain.
But, wait, there’s more! Over in the Programming subreddit, one top-rated comment spelled out in painful detail:
It lacks the 16-bit x86 compiler needed to boot Linux from real mode. For this, it calls out to GCC.
It does not have its own assembler and linker. The demo video was produced with a GCC assembler and linker.
The compiler successfully builds many projects, but not all. It’s not yet a drop-in replacement for a real compiler.
The generated code isn’t very efficient. Even with all optimizations enabled, it outputs less efficient code than GCC with all optimizations disabled.
The Rust code quality is reasonable, but it is nowhere near the quality of what an expert Rust programmer might produce.
Here’s what Anthropic really did. It managed to write a half-assed C compiler in Rust that, based on existing open source code and with a lot of hand-holding, can run test suites and successfully compile Linux and other programs.
The agent swarm did not wake up one morning and invent compilation. Reddit commenters noted that it was given “highly intricate and thorough test suites along with execution harnesses, all crafted by humans, with the harness designed specifically for the AI’s use.” Another comment calls it not much of a challenge “compared to beginning from the C specification when someone else has already broken down the issue, developed thorough tests, and provided a comparison with a recognized binary format. Their initial explanation of the task doesn’t quite stand up to scrutiny.”
On top of that, you have the training‑data problem. One skeptic notes that the system “was developed using the very code base it is supposed to replicate. Additionally, the validation set was included in the training data.” From that vantage point, what you’re seeing is not AI discovering compilers from first principles, but a sophisticated retrieval‑and‑refinement process over a world already filled with compilers and compiler tests.
Over on Hacker News, commenters distilled the two narratives nicely: “Pro-LLM coding agents: look! a working compiler built in a few hours by an agent! This is amazing! Anti-LLM coding agents: it’s not a working compiler, though. And it doesn’t matter how few hours it took, because it doesn’t work. It’s useless.” The truth is in between – but it’s much closer to an “interesting lab demo” than an “obituary for human programmers.”
My real worry remains that AI-crazy companies will fire developers willy-nilly and replace them with AI to make their bottom line look better. AI simply isn’t ready to take over programming jobs yet, no matter how many CEOs insist it’s already a game-changer.
No, it’s just a useful tool that requires careful, expert use to be effective. ®