Giving AI a Language of Its Own

Giving AI a Language of Its Own explores whether AI agents could benefit from a computational language designed around learned operations, compact expressions and reusable functionality. AIN (AI Native Language) is the reference concept for this proposed language and native compilation architecture. Short references represent known capabilities: for example, !501(config) could invoke an approved database connection method. A compiler would resolve these references into native implementations. AICC (AI Computational Commons) complements this approach through shared, approved and versioned capabilities that agents can discover, bind and reuse. The paper presents examples across C#, Python, C++, Ruby and Java, alongside processing experiments, bounded native compilation tests, small model adaptation studies and comparisons of alternative code representations. It distinguishes measured results from conditional calculations and proposed future work. Current text-token comparisons favor compact Python; idealized workflow calculations show potential savings under explicit assumptions, with equivalent ideal Python and intermediate representations matching AIN. The complete language and compiler remain conceptual. The contribution is a research approach supported by bounded prototypes, documented evidence and a plan for further evaluation. A reproducible test package accompanies the article, including protocols, scripts, recorded results and instructions for independent review by researchers and AI agents. This working paper is released to invite criticism, replication and collaboration.