Python isn’t going anywhere. Not yet. But the forces reshaping how software gets written — and who writes it — are putting enormous pressure on the languages developers use, the tools they rely on, and the assumptions they’ve held for decades about what programming even means.
The question facing the software industry in 2025 isn’t whether artificial intelligence will change programming. That’s settled. The real question is which programming languages will thrive as AI becomes both the builder and the thing being built, and which will fade into legacy maintenance roles, kept alive only by the sheer inertia of existing codebases.
According to a detailed analysis by The New Stack, the future of programming languages is being shaped by three converging forces: the computational demands of AI workloads, the rise of AI-assisted code generation, and a fundamental shift in what developers need from a language in the first place. The implications are significant for every company making long-term technology bets.
Python dominates AI and machine learning today with a grip that looks almost unbreakable. It’s the lingua franca of data science, the default for TensorFlow and PyTorch, the language every tutorial starts with. But Python’s dominance rests on a peculiar foundation: it’s slow. Remarkably slow, in fact, compared to systems languages. What saves it is an enormous collection of libraries written in C and C++ that do the actual heavy computation underneath. Python is, in many respects, a very popular steering wheel attached to an engine built by someone else.
That arrangement has worked. It may not work forever.
As AI models grow larger and inference costs become a serious business concern, the performance characteristics of programming languages matter more than they have in years. The New Stack reports that languages like Rust and Mojo are positioning themselves as alternatives that combine Python-like usability with the raw speed that AI infrastructure demands. Mojo, developed by Modular and led by Chris Lattner — the creator of Swift and LLVM — explicitly targets the AI space, promising Python compatibility with C-level performance. It’s an ambitious pitch. Whether Mojo can build the critical mass of libraries and community support needed to challenge Python remains an open question, but the fact that it exists at all signals real dissatisfaction with the status quo.
Rust, meanwhile, has been gaining ground steadily. Its memory safety guarantees without garbage collection make it attractive for the kind of high-performance systems work that AI infrastructure requires. Hugging Face has built key components in Rust. Parts of the AI toolchain at several major companies now run on it. Rust won’t replace Python as the language data scientists reach for when prototyping a model, but it’s increasingly the language engineers choose when that model needs to run efficiently at scale.
The more disruptive force, though, isn’t which language humans prefer — it’s what happens when AI writes most of the code.
Large language models are already generating enormous volumes of code. GitHub Copilot, Amazon CodeWhisperer, and a growing roster of AI coding assistants are changing the daily workflow of millions of developers. According to GitHub’s own data, Copilot suggests code that developers accept roughly 30% of the time, and that acceptance rate has been climbing. Google has reported that more than 25% of new code at the company is now generated by AI, a figure disclosed by CEO Sundar Pichai during a recent earnings call.
This changes the calculus around programming languages in ways that aren’t immediately obvious. When a human writes code, the ergonomics of a language matter enormously — syntax, readability, how quickly you can express an idea. When an AI writes code, those human-centric concerns become less important. What matters instead is how well the language maps to formal verification, how predictable its behavior is, and how large the training corpus was. Languages with vast amounts of existing open-source code — Python, JavaScript, Java — have a built-in advantage as training data for code-generating models. Languages with smaller communities may find themselves in a vicious cycle: less training data means worse AI assistance, which means fewer developers adopt the language, which means even less training data.
But there’s a counterargument. AI code generation could actually lower the barriers to using more complex languages. If an AI can write correct Rust code as easily as it writes Python, the traditional knock against Rust — its steep learning curve — becomes less relevant. Developers might increasingly choose languages based on runtime performance and safety characteristics rather than how easy they are to write by hand, because they won’t be writing much by hand.
This is already happening in pockets. Reports from developer communities on X and platforms like Hacker News describe teams adopting Rust or Go for new AI-adjacent projects specifically because AI coding tools have flattened the learning curve. The code is harder to write manually but not harder to review and maintain, and the performance benefits are real.
Then there’s the question of entirely new languages designed from the ground up for an AI-first world. The New Stack points to domain-specific languages (DSLs) gaining traction in AI workflows — languages and frameworks purpose-built for tensor operations, neural network architectures, or data pipeline orchestration. Triton, the GPU programming language developed by OpenAI, is one example. It allows researchers to write highly optimized GPU kernels without dropping into CUDA, Nvidia’s proprietary toolkit. That matters because access to efficient GPU computation is the bottleneck for most AI work today.
Julia, the scientific computing language that has long promised to solve the “two-language problem” — prototyping in Python, then rewriting performance-critical code in C++ — continues to find adherents in AI research. Its multiple dispatch system and JIT compilation make it genuinely fast for numerical work. Yet Julia’s adoption curve has been frustratingly gradual. The community is passionate but small relative to Python’s massive installed base.
The economics here are unforgiving. Programming language adoption follows power laws. Network effects dominate. A language with ten times the libraries, ten times the Stack Overflow answers, and ten times the experienced developers available for hire has an advantage that’s nearly impossible to overcome through technical superiority alone. Python’s position is fortified by exactly this kind of compounding advantage.
So what actually changes?
The most likely outcome isn’t a wholesale replacement of Python but a stratification. Python remains the interface layer — the language humans use to direct AI systems, specify what they want, and inspect results. Below that interface, performance-critical components increasingly get written in Rust, C++, or specialized languages like Mojo and Triton. And the glue code that connects everything — the API endpoints, the data pipelines, the deployment scripts — gets written more and more by AI itself, in whatever language the target system requires.
This stratification has implications for hiring, for education, and for how companies structure engineering teams. If AI handles routine code generation, the premium shifts toward developers who can architect systems, evaluate AI-generated code for correctness and security, and work across multiple languages as needed. The polyglot developer — comfortable reading Rust, writing Python, and understanding GPU kernels — becomes more valuable than the specialist who knows one language deeply.
There are risks embedded in this transition that the industry hasn’t fully grappled with. AI-generated code is already introducing subtle bugs and security vulnerabilities at scale. A recent analysis by researchers at Stanford found that developers using AI coding assistants produced code with more security flaws than those writing code manually, in part because they placed excessive trust in the AI’s output. As AI generates a larger share of production code, the languages and tools that make it easier to catch these errors — through strong type systems, formal verification, or built-in safety checks — will have a structural advantage.
Rust’s borrow checker, often cited as the language’s most frustrating feature for newcomers, may turn out to be its greatest asset in an AI-driven world. It catches entire categories of memory bugs at compile time. When code is being generated by an AI that doesn’t truly understand what it’s writing, that kind of automated safety net isn’t a nice-to-have. It’s essential.
The competitive dynamics among cloud providers add another dimension. Google has been pushing Go and has invested heavily in AI tooling around its own languages. Microsoft, through GitHub and its partnership with OpenAI, has enormous influence over which languages AI coding tools support best. Apple’s Swift, with its roots in the same LLVM infrastructure that Mojo builds on, could become more relevant if on-device AI processing grows as expected. And Nvidia’s dominance in AI hardware gives CUDA — and by extension, any language that interfaces well with it — an outsized role in determining what’s practical.
None of these outcomes are predetermined. The history of programming languages is littered with technically superior options that lost to inferior but better-marketed or better-timed alternatives. What’s different now is the speed of change. AI is compressing technology adoption cycles in ways the industry hasn’t experienced before. A language that barely existed two years ago — like Mojo — can attract serious attention from well-funded companies almost overnight. And a language that seemed permanently entrenched can find its relevance eroding faster than anyone expected.
For technology leaders making decisions today, the practical advice is straightforward but uncomfortable: don’t bet everything on one language. The era of the monoglot codebase is ending. Build teams that can work across the stack, invest in languages with strong safety properties for critical infrastructure, and keep Python for what it does best — rapid experimentation and human-AI interaction. And watch Rust. Closely.
The programming languages that will define the next decade of AI aren’t just tools for writing software. They’re the medium through which humans and machines will negotiate the terms of an increasingly automated future. The stakes, for once, match the hype.
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