Creating a C compiler cost 2 million dollars and took 2 years. Claude Opus 4.6 did it in two weeks for $20,000

We are facing a technological inflection point. Uo in which software engineering, one of the most complex and demanding technical tasks in history, little by little It is becoming the “killer app” of AI. It is clear that generative AI models are not perfect, but we continue to see extraordinary evolution. The latest example? The C compiler that Claude Opus 4.6 programmed all by himself.

what has happened. Nicholas Carlini, researcher at Anthropic, I counted yesterday how “I’ve been experimenting with a new way of monitoring language models that we’ve called “agent teams””. What it has done is ensure that several programming agents work in parallel using the recently released Claude Opus 4.6, and thanks to that it has developed something exceptional with 16 of these agents: a C code compiler.

Hello CCC. At Anthropic they have called it Claude’s C Compiler (CCC), and they have published the code, completely generated by Opus 4.6, on GitHub. The project consists of 100,000 lines of Rust code that were generated in two weeks with an API cost of $20,000. And it works: with it they have compiled a functional Linux 6.9 kernel on x86, ARM and RISC-V.

Before it was (at least) two million dollars and two years. What this experiment has achieved is to demonstrate how software development can be much cheaper and faster thanks to the use of these agents. Although there is no readily available data on how much time and money compilers cost in the past, the size of these products was enormous, as is the case with Microsoft Visual C++For example. It is difficult to know how much it cost, but it is estimated that it involved 15-20 people working for five years. That’s a lot of man hours and a lot of money to develop and polish that compiler. The estimate of two years and two million dollars may in fact be overly optimistic.

another example. Historically, building a C compiler from scratch was considered one of the pinnacles of systems engineering. Not only was in-depth knowledge of processor architecture required, but thousands of man-hours were required to manage optimization and machine code generation. In the 90s the company Cygnus Solutions (clue in compiler development gcc) came to invest more than 250 million in a decade to maintain and port build tools. The real cost was not just in the final lines of code, but in countless hours analyzing CPU and memory patterns to make the resulting binary efficient.

Far from perfect, but… Carlini himself explained in the post that this compiler had serious limitations and for example “it does not have a 16-bit x86 compiler which is essential to start Linux outside of “real mode”, and it does not have its own assembler nor its linker“. It is probably far from mature compilers, but even so the achievement remains exceptional and points to that future in which even very complex developments can be supported with AI. They will be expensive, no doubt, but their total development will probably be a fraction of what they cost a few years ago.

Cursor already demonstrated it. Before Anthropic launched its AI-programmed compiler, Cursor completed a similar project, combining GPT-5.2 agents into its development platform to create a working browser in a week. In total the AI ​​programmed three million (!) lines of code in Rust, and although it was again far from being perfect or competing with Chrome, it demonstrated the current capacity of these agentic programming systems.

Turning point (especially for Anthropic). For the SemiAnalysis experts Claude Code, current leading exponent of this new era of AI-driven programming, is a paradigm shift: “We believe that Claude Code is the turning point for AI agents and is a glimpse into the future of how AI will work.” This prestigious newsletter predicts an exceptional 2026 for Anthropic, and so much so that they believe it will “dramatically surpass OpenAI.”

You ask, the AI ​​programs. If you have tried the vibe codingI’m sure you agree with me: AI allows you to do things you would never have dreamed of. What I did a few weeks ago with Immich made it clear to me, and I continue experimenting with AI and programming “custom” things that solve real problems and needs for me. Yes, for now they are for me and therefore they are not large and complex systems that need to be put into production as happens in professional environments, but I am clear that this is being done little by little and more will be done. In fact, both OpenAI and Anthropic have stood out how in the development of their latest models part of the work has been done, paradoxically, by those same models, which have fed back to each other. And the result is in production and used by millions of people. Something is changing. And it’s something big.

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