AI가 코드를 진화시키거나 멸종하게 만들까요?

Will AI force code to evolve or make it extinct? What would an AI-first language look like? Last year, a developer in Spain warned that our human-friendly syntax consumed an “excessive” number of tokens — thereby increasing costs — and prevented complex programs from fitting within existing AI context windows. “I asked Claude to invent a programming language where the sole focus is for LLM efficiency,” the developer explained on Reddit, “without any concern for how it would serve human developers.” And their attempt at an “AI-first native language wasn’t the last. Just last week, a developer announced plans for a new language that addressed the needs of autonomous AI agents with “deterministic” syntax that clarified developer intent and a small language surface to reduce edge cases. “And I think that says a lot,” Griffiths says. “The gravitational pull of existing ecosystems is enormous — libraries, tooling, community knowledge, production infrastructure. A new language doesn’t just need to be better for AI. It needs to justify abandoning everything developers already have, and that shift is not gonna happen overnight.” “A new language doesn’t just need to be better for AI. It needs to justify abandoning everything developers already have, and that shift is not gonna happen overnight.” Will we one day develop an AI-optimized language at the expense of human readability? Or will AI coding agents make it easier to use our existing languages — especially typed languages with built-in safety advantages? Could we even imagine a world with AI-first languages that abstract away everything, generating compiler-ready modules without source code? Developers, language designers, and developer advocates are now beginning to ask those questions… Chris Lattner’s Mojo vs. Rust How should programming languages look in the age of AI? There’s more than one answer. During a recent episode of The Hanselminutes Podcast hosted by Scott Hanselman, Microsoft’s VP of developer community, Hanselman broached this topic with Chris Lattner, the co-founder and CEO of the AI tools company Modular AI. Lattner’s career includes creating the Swift programming language and the LLVM compiler toolchain — but he’s focusing on how hardware is changing, arguing that with today’s multi-core and AI-optimized chips, “We have all these crazy GPUs and all this compute out there that nobody knows how to program!” “We have all these crazy GPUs and all this compute out there that nobody knows how to program!” So while Lattner’s company builds AI tools for developers, it’s also working on its new programming language Mojo, which Lattner suggests is “LLVM but for AI chips, basically… a way to program it that scales across all the silicon.” Hanselman’s podcast dubbed it “a programming language for an AI world.” But others still see AI nudging coders toward existing programming languages with built-in memory safety — including Peter Jiang, the founding engineer of Datacurve (which sells high-quality/complexity data). Writing earlier this month in Forbes, Jiang describes Rust as ” the unlikely engine of the vibe coding era… When AI writes the code, Rust’s strictness stops being a hurdle and becomes free quality assurance,” with Rust’s compiler acting as “a guardrail that forces the LLM to prove its logic is sound.” It’s an attractive advantage, notes GitHub’s senior director for developer advocacy, Cassidy Williams. In January, Williams cited a 2025 academic study that found 94% of LLM-generated compilation errors were type-check failures.” Typed languages for the win? There’s data suggesting developers are acting on those advantages — and not just by moving to Rust. Williams added that TypeScript “is now the most used language on GitHub, overtaking both Python and JavaScript as of August 2025,” crediting as one factor “a boost from AI-assisted development… TypeScript grew by over 1 million contributors in 2025 (+66% YoY, Aug ’25 vs. Aug ’24) with an estimated 2.6 million developers total.” And other typed languages prove the trend, Williams believes, sharing more examples from GitHub’s data: - “Luau, Roblox’s scripting language, saw >194% YoY growth as a gradually typed language.” - “Typst, often compared to LaTeX, but with functional design and strong typing, saw >108% YoY growth.” - “Even older languages like Java, C++, and C# saw more growth than ever in this year’s report.” So while AI may be impacting programming languages, Griffiths says, it’s not necessarily through a move toward new AI-optimized languages. “What actually happens is more subtle: languages that are already structured, strongly typed, and explicit become more attractive because AI tools work better with them. TypeScript over JavaScript. Rust over C. Python’s type hints are becoming standard practice. The change isn’t a new language. It’s a shift in which existing languages win.” “The change isn’t a new language. It’s a shift in which existing languages win.” Griffiths spelled it out las