Emerging Programming Languages and Frameworks in AI
For over a decade, Python has been the undisputed champion of artificial intelligence development. Its simple syntax, massive ecosystem, and community support made it the go-to language for data scientists, ML engineers, and AI researchers. But as AI use cases become more diverse and demanding, developers are increasingly turning to emerging programming languages in AI that offer better performance, flexibility, and scalability.
Whether you’re a developer exploring your next language, or a business seeking to build a future-proof AI team, understanding these new tools is crucial. Below are emerging programming Languages to explore
Julia
Julia was designed with high-performance numerical analysis in mind. Unlike Python, which often requires libraries like NumPy or Cython for speed, Julia delivers C-like performance without leaving the language.
AI researchers working on complex simulations, scientific models, and algorithm-heavy environments are increasingly adopting Julia. Its just-in-time (JIT) compilation and multiple dispatch features make it ideal for linear algebra and large-scale machine learning models.
Although the ecosystem is still growing, Julia is gaining traction in academia, finance, and quantum computing. If your AI project is computation-heavy, Julia deserves your attention.
Rust
Rust is emerging as a serious contender in AI systems programming, offering memory safety without garbage collection and incredible concurrency handling.
Where Python may choke under the demands of real-time AI systems (e.g., robotics, edge AI, autonomous vehicles), Rust shines. Libraries like tch-rs and rust-bert have made it possible to leverage AI models while maintaining Rust’s legendary safety.
More AI engineers are exploring Rust for mission-critical systems where failure isn’t an option.
Go (Golang)
While Go may not be the first name in AI model development, it’s gaining popularity for AI infrastructure—API layers, microservices, and cloud-native AI workflows.
Its lightweight syntax and fast compilation time make Go a smart choice for deploying scalable AI systems. Frameworks like Gorgonia and Fuego aim to bring deep learning capabilities to Go, while developers appreciate its built-in concurrency model.
As teams demand faster iteration cycles, integrating Go into hybrid AI stacks is becoming more common—especially in production environments.
Swift for TensorFlow (S4TF)
Google’s now-sunsetted Swift for TensorFlow project was ambitious, bringing full differentiable programming into a compiled language. Though the project didn’t gain critical mass, the idea behind it; type-safe, fast AI development has not died.
Developers building cross-platform AI applications (especially iOS ML apps) continue to explore Swift’s potential, and a small but passionate open-source community keeps S4TF’s ideas alive. Expect a second wave as Apple and other vendors look to deepen AI-native integrations in consumer tech.
ONNX + C++
As AI models are increasingly moved across platforms—from training on cloud GPUs to running on mobile or IoT interoperability is key. ONNX (Open Neural Network Exchange) bridges the gap, allowing models trained in one framework to be used in another.
Paired with C++, ONNX allows developers to take high-level models from PyTorch or TensorFlow and deploy them on efficient, compiled runtimes. This is especially useful for businesses that require AI across devices, platforms, and edge systems.
C++’s speed and ONNX’s portability make them a serious combo for production-ready AI at scale.
Honorable Mentions: Frameworks Worth Watching
Alongside language shifts, new frameworks are shaping the way developers build AI:
- JAX (Python-based, but designed for accelerated scientific computing with automatic differentiation)
- Hugging Face Transformers (dominant in NLP)
- TVM (deep learning compiler stack for optimization across hardware)
These tools may not require a new language, but they redefine what’s possible within existing ones—and signal where developer ecosystems are heading.
The Language of AI is Evolving, Are You?
Python will remain a major force in AI, but the horizon is expanding. Whether you’re optimizing performance, ensuring safety, or building AI into new environments, the next wave of innovation will be written in Julia, Rust, Go, and beyond.
For developers, mastering one of these languages could open doors to cutting-edge roles. For companies, building teams fluent in these tools will be a key differentiator.
Need help sourcing talent skilled in emerging AI languages? Connect with Loopp and we’ll help you stay ahead of the AI curve.
