July 11, 2024 Coding Languages You Need for AI

By Baxter Juds

Artificial Intelligence (AI) is the latest tech paradigm. AI has taken the world by storm due to the promise of being able to automate processes, improve systems, and create all new industries within the near future. Its growth has been exponential and is too important to ignore. What this means is that for a developer, or a business, AI represents a lucrative opportunity. We’ve broken down some of the most important programming languages across the use cases for AI development to help you decide what direction you want to go. So join us as we explore some possible options for you to access in your AI journey.


Python is a high-level programming language that is famously simple and readable, This makes it a favourite among beginner and professional developers.

Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Its massive library and third-party modules provide its user base with the opportunity to make use of a number of features, across a spectrum of tasks, like web development, data analysis, machine learning and automation.

Being an open-source, beloved language, Python’s community-driven development means there’s no shortage of support for developers and there are ongoing improvements and optimisation for the coding language.

Python is the undisputed leader in AI development due to its simplicity and a vast ecosystem of libraries and frameworks.

  • Machine Learning: Libraries like Scikit-learn and XGBoost help with the implementation of traditional machine learning algorithms.
  • Deep Learning: TensorFlow and PyTorch are used to develop and train neural networks.
  • Natural Language Processing (NLP): NLTK and SpaCy are used for text processing and linguistic data analysis.
  • Computer Vision: OpenCV and TensorFlow are used for image recognition and video processing.
  • Reinforcement Learning: Stable Baselines3 provides implementations for reinforcement learning algorithms.


C++ is used for system/software development and for programming games. It is efficient and has good control over system resources. C++ supports both procedural and object-oriented programming paradigms. It is used to develop operating systems, real-time simulations, and applications.

C++ gives direct access to hardware and memory management, making it great for applications that need to make the most of their performance, like AI. It has a large standard library, and the fact that it is compatible with C helps its functionality and support over time. This, plus its broad range of uses, means it has a large developer community of around 4.4 million people around the world.

It is a critical element in the development of AI robotics because of how much it helps performance, so if you’re wanting to live your sci-fi dreams, C++ is a good start for any developer.

  • Robotics: The Robot Operating System (ROS) uses C++ in its software development.
  • Game Development: Unreal Engine uses C++ to create AI in games, meaning all the bosses that killed you in Elden Ring were probably made with C++.
  • High-Performance Computing: CUDA with C++ allows for GPU-accelerated computing, critical for training large-scale AI models.


Julia was created in 2012 by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman to address the needs of high-performance numerical and scientific computing while offering simplicity and ease of use similar to Python. It is designed for numerical and scientific computing.

Julia supports procedural, functional, and object-oriented programming paradigms. It offers built-in parallel and distributed computing capabilities, making it great for large-scale data analysis and algorithm development. Julia’s straightforward syntax attracts both new and experienced programmers.

Its high-performance capabilities make it suitable for tasks that require intensive computations, like AI.

Julia's ecosystem includes a wide range of libraries for machine learning, data visualisation, and statistical computing, enabling efficient handling of complex computational tasks.

It is gaining popularity in the AI community for its performance and ease of use.

  • Scientific Computing: JuliaOpt and DifferentialEquations.jl are used for high-performance numerical and scientific computing.
  • Probabilistic Programming: Turing.jl allows for Bayesian inference and probabilistic modelling.


Rust was developed by Mozilla to ensure safe concurrency and memory management.

It emphasises speed, safety, and efficient use of resources. Rust achieves these goals by preventing null pointer dereferencing and buffer overflows through a strict borrowing and ownership system.

The language offers a powerful toolset for building reliable and efficient software, particularly in system-level programming. Rust's syntax is designed to be familiar to developers who have experience with languages like C++ or Python, making it accessible for a wide range of programming tasks.

Rust is known for its memory safety and performance, making it suitable for systems programming in AI.

  • Systems Programming: Rust’s features ensure safe and concurrent execution, critical for performance-critical applications.
  • WebAssembly: wasm-bindgen allows building high-performance web applications with Rust.


Go, also known as Golang, was created by Google engineers Robert Griesemer, Rob Pike, and Ken Thompson. They designed Go to simplify software development and improve productivity. Go compiles quickly, supports concurrent programming, and offers garbage collection.

The language's syntax is straightforward and easy to learn, making it suitable for large-scale system programming and web development. Go's efficient handling of concurrency and its robust standard library have made it popular for cloud services, networking tools, and other performance-critical applications.

Go is used for building scalable backend services that deploy AI models.

  • Backend Services: TensorFlow Serving and Gorgonia are used to deploy machine learning models and develop graph-based computational models.
  • Microservices: Go’s concurrency support makes it ideal for microservices architectures in AI.

Coding Languages You Need for AI

Understanding what languages you need to learn to develop for AI is critical for these next steps in your career, and your business. This is why AES Global has created this article to help you understand what you need to do next. Once you’ve made that decision, why not contact one of our talent consultants or client success managers to help you start your new career in AI, or build your world-class AI team.