Arabic analysis asks if 2025 AI models truly ‘understand’ language

This AITnews piece captures a mood that’s been building all year: serious linguists are starting to find behaviours in cutting-edge LLMs that look uncomfortably close to real grammatical competence. By walking through a study where OpenAI’s o1 model parses entirely invented languages using classic syntactic trees and handles deeply nested recursive structures—a long-standing benchmark of human linguistic uniqueness—the article crystallizes a shift from “these models just mimic patterns” to “they might be learning something like the underlying rules.” ([aitnews.com](https://aitnews.com/2025/12/24/%D8%AD%D8%B5%D8%A7%D8%AF-2025-%D9%87%D9%84-%D8%A8%D8%AF%D8%A3%D8%AA-%D9%86%D9%85%D8%A7%D8%B0%D8%AC-%D8%A7%D9%84%D8%B0%D9%83%D8%A7%D8%A1-%D8%A7%D9%84%D8%A7%D8%B5%D8%B7%D9%86%D8%A7%D8%B9%D9%8A-%D9%81/))

For the race to AGI, that matters less as a philosophical argument and more as an empirical data point: if models can robustly infer the generative structure of unfamiliar symbolic systems from limited data, their ceiling on systematic generalization is likely higher than many critics assumed. That’s precisely the sort of capability you’d want in agents that must navigate new APIs, legal codes or scientific notations on the fly. At the same time, the piece wisely emphasizes that understanding syntax is not the same as having grounded concepts, goals or values—open problems that continue to justify caution.

The fact that this debate is happening in Arabic tech media, with references to Berkeley and Rutgers linguists, is itself a sign of how global the AGI conversation has become. It’s no longer confined to English-language policy briefs and Silicon Valley blog posts; it’s being refracted through different languages, cultures and risk perceptions, which will shape how frontier models are adopted and constrained.