Anthropic's Claude Opus 4.6 Attempts Autonomous C Compiler Development at $20,000 Cost

An Anthropic researcher's ambitious experiment with the newly released Claude Opus 4.6 model has demonstrated both the potential and limitations of autonomous AI software development. Nicholas Carlini, a member of Anthropic's Safeguards team, tasked 16 AI agents with creating a Rust-based C compiler from scratch capable of compiling the Linux kernel. The result, after nearly 2,000 Claude Code sessions and $20,000 in API costs, was a 100,000-line compiler that successfully builds Linux 6.9 on x86, ARM, and RISC-V architectures.

The experiment leveraged what Carlini called "agent teams" - multiple Claude instances working in parallel on a shared codebase without active human intervention. To achieve sustained autonomous progress, he developed a harness that placed Claude in a simple loop, allowing it to immediately pick up the next task upon completing the previous one. "I leave it up to each Claude agent to decide how to act," Carlini explained. "In most cases, Claude picks up the 'next most obvious' problem."

This autonomous approach yielded several important insights. First, the necessity of writing extremely high-quality tests became apparent. Carlini advised that the test harness should avoid printing thousands of useless bytes, making it easier for Claude to find what it needs. Additionally, he discovered that "Claude can't tell time and, left alone, will happily spend hours running tests instead of making progress" - a quirk that might make working with Claude feel closer to working with a regular human than expected.

The project consumed 2 billion input tokens and generated 140 million output tokens over nearly 2,000 sessions across two weeks. While Carlini acknowledged this made it "an extremely expensive project" compared to the priciest Claude Max plans, he noted that the total cost was still "a fraction of what it would cost me to produce this myself – let alone an entire team."

However, the results were mixed. The compiler successfully builds many projects but not all, and it's "not yet a drop-in replacement for a real compiler." The generated code is not very efficient, and while the Rust code quality is "reasonable," it's "nowhere near the quality of what an expert Rust programmer might produce."

Carlini's conclusion was nuanced: "Agent teams show the possibility of implementing entire, complex projects autonomously." Yet as a former pen-tester, he expressed genuine concern about the risks of fully autonomous development. "The thought of programmers deploying software they've never personally verified is a real concern," he stated. Ultimately, the experiment "excites me, [but] also leaves me feeling uneasy."

The GitHub community's reaction was notably more skeptical. Many commenters felt the $20,000 price tag ignored other significant factors, particularly the vast amount of other programmers' code the model was trained on initially. As mohswell put it: "If I went to the supermarket, stole a bit of every bread they had, and shoved it together, no one would say I made bread from scratch. They'd say I'm a thief. If this is 'from scratch,' then my cooking is farm-to-table."

Others took a more humorous approach to the implications. Sambit003 observed: "The comment section and the issue itself is 'absolute cinema' moment everyone living through... the longer the AI generated codes I see... the safer I feel. 😂 Still we have the jobs (for long enough years)... just enjoy the overhyping bruh."

The experiment raises important questions about the future of software development. While AI agents can now tackle complex programming tasks with minimal human intervention, the quality, efficiency, and security implications of such approaches remain significant concerns. As autonomous development tools become more sophisticated, the balance between productivity gains and the need for human oversight and verification will likely become an increasingly important consideration for the software industry.

This experiment with Claude Opus 4.6 represents a milestone in AI-assisted development, demonstrating that autonomous agents can indeed create functional, complex software systems. However, it also highlights that we're still far from the point where AI can fully replace human developers, particularly when it comes to producing high-quality, efficient, and secure code that meets professional standards.