Import AI 466: The bitter lesson for robotics, AIs complete week-long programming tasks; and OpenAI’s accidental AI hacker

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Epoch and METR release MirrorCode, a benchmark for seeing how well AI systems can do long-horizon programming tasks:
…AI systems can’t solve the hardest tasks yet (good!)…
Epoch and METR have released MirrorCode, a benchmark meant to see how well AI systems can do tasks that take humans a long time to do. The benchmark was first announced in April (Import AI #453) and has now been fleshed out and released with additional tests. The findings are already very striking; Opus 4.7 solved a task in 14 hours for $251 in inference cost which METR and Epoch believe would take a human 2-17 weeks to do. “We also found that AI models are improving rapidly over time. Leading models from a year ago would have scored about 30%, and were limited to simpler programs, such as a calendar utility.”

What MirrorCode is: MirrorCode sees how well AI systems can re-implement a software program based purely on CLI access. “Without access to the original program’s source code or the web, a full reimplementation requires devising a structure for the entire program, rather than merely translating the code piece-by-piece.”
Example programs: pkl (a programmable configuration language developed by Apple; 61k total lines of code); gotree, a program to parse and manipulate phylogenetic trees (16k lines of code); and qsv_select, a program to select and reorder columns of CSV data (87k lines of code).

Results: MirrorCode is a tractable but hard benchmark, though perhaps a little too easy. “Across all 25 target programs, 17/25 had at least one perfect-scoring run. Four more targets had a near-perfect run scoring over 99%. AI models successfully reimplemented large target program,s” the authors write. “Both Claude Opus 4.7 and GPT-5.5 successfully reimplemented gotree across several different programming languages, at costs…