AI NEWSLatest AI NewsArticleMove 1 Million Lines of Code in Two Weeks: Anthropic Used Claude to Migrate a Language They Had Been Afraid to Touch for Years into a WeekendPublished in Latest AI NewsTime :Jul 17, 2026Read :25minuteReplacing an entire programming language, this was once tantamount to issuing a two-year military order for a company. Engineers would turn pale at the thought, budget committees would be terrified, and codebases would remain stuck in place year after year. On July 16th, Anthropic came out with an article that turned this old account into a new chapter: In the past month, their internal developers used Claude Fable5, Opus4.8, and dynamic workflows to cleanly migrate 10 code packages, each ranging from tens of thousands to hundreds of thousands of lines, to a new language. This is no longer a hypothetical future scenario—it has already happened to them.The two most striking cases were like two nails that nailed imagination to reality. Jarred Sumner, co-founder of Bun, used Claude Code to migrate the entire Bun from Zig to Rust, producing a million lines of code in less than two weeks, with 100% of the CI test chain passing before merging into the main branch. After merging, 19 regressions surfaced, which have since all been fixed. The Rust version was released in June via Claude Code. On the other side, Mike Krieger, co-lead of Anthropic Labs, migrated a Python codebase to 165,000 lines of TypeScript over a weekend, including hundreds of proxies, eight stage gates, three rounds of adversarial review, and finally using a parity check to compare every command’s output line by line with the original Python version.Anthropic distilled this entire approach into one sentence, also the sharpest blade in the article: You are not fixing code, but the process (cycle) that produces it. Once you understand this, those migration projects that were indefinitely postponed are no longer off-limits.First clarify when and why to act, because the premise of this has changed long ago. Teams usually start a migration because the technical landscape at the project’s inception no longer aligns with today’s needs: either the original trade-offs that were known at the time have now become problematic, or better paths have emerged, or the original ecosystem is shrinking. Jarred originally chose Zig for its C-level performance and extreme simplicity, especially suitable for a founder in a small apartment in Oakland during the era before large models existed, who spent a year writing Bun. That simplicity had a cost, which he later openly admitted in an article.Fast forward to 2026, Bun’s command-line tool has exceeded ten million monthly downloads and is widely used internally within Claude Code. At the end of last quarter, these costs were still not enough to freeze the roadmap and invest several quarters of resources on a big task. Migrating languages can indeed lead to smaller, faster, and safer systems, but no one had the incentive to pay the price. Engineers also had to weigh the career risks: maintaining two sets of code for years and ending up with only 90% equivalence could be more troublesome than just leaving things as they are.Now it’s different. The worst-case scenario is simply deleting that branch and starting over. Of course, business calculations are still needed. A migration of a million lines does not require burning three or four million dollars in engineer resources as it did in a four-year project, but it still costs several thousand to tens of thousands of dollars. The migration of Bun consumed 5.9 billion uncached input tokens and 690 million output tokens, which amounted to about $165,000 based on API pricing; Mike’s main migration also burned 27 million tokens. What really made Mike decide to act was the compilation hurdle. His team delivered internal tools as single binary files, and compiling for each platform with the Python toolchain took about eight minutes, making the entire build matrix take thirty minutes. After the migration, the same compilation took only two seconds, and the binary started six times faster, while also eliminating a separate deployment pipeline.Why can AI balance this old account? Because large-scale code migration is precisely the ideal scenario for these advanced models. The work is naturally parallelizable, with hundreds or even thousands of independent units like files or crates that can be handled simultaneously without waiting for others. The context is clear and complete, with the old code itself serving as the best specification document for the model, and as the guidebook for the translation agent. Even more cleverly, there’s a built-in referee. Large codebases often have a suite of tests that the agent can use to self-score, and when validation is sufficiently objective, the model can focus on the truth for days without someone sitting beside it to judge quality. The queue writes itself: when a compiler or test fails, the next job automatically queues