Is Python Still the Hot Shot Programming Language for AI?

Is Python Still the Hot Shot Programming Language for AI?

We hear a lot about Python and its uses, including that it’s the most useful and commonly-used language in AI. However, since the AI industry is developing at such a rapid rate, we wanted to stop and ask ourselves: is Python still the hot shot programming language for AI? Or are there other contenders that are gaining significant traction in the space? 

Let’s first take a look at where Python came from and what it’s used for. We’ll then dive into a few other staple as well as less-common programming languages that are increasingly being adopted.

Python Basics

Python was created by Guido van Rossum in 1991. It can be used for web development (server-side), software development, mathematics, and system scripting. This means Python can be used to:

  • Create web applications
  • Be used alongside software to create workflows
  • Connect to database systems
  • Read and modify files
  • Handle big data
  • Perform complex mathematics
  • Be used for rapid prototyping, or for production-ready software development

Python and AI

Python is the programming language of choice for many artificial intelligence, machine learning, deep learning, and other such projects. In fact, a StackOverflow poll found that Python is the most popular programming language for data science and machine learning. Here are the main reasons why:

1. It’s easy to learn.

Python uses a very simple syntax that’s similar to the English language and can be used to implement simple computations; this allows developers to write programs with fewer lines than some other programming languages. Which brings us to our next point…

2. It requires less code.

Thanks to Python’s support for pre-defined packages, developers don’t have to code algorithms. Additionally, its syntax allows them to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.

3. It’s platform independent.

Python works on multiple platforms, including Windows, MacOS, Linux, Unix, Raspberry Pi, and more. Furthermore, packages such as PyInstaller can make transferring code from one platform to the other a breeze, as they take care of any dependency issues.

4. It has hundreds of prebuilt libraries.

Python’s prebuilt libraries—including Tensorflow, NumPy, Pytorch, and Keras—allow for the implementation of various Deep Learning and Machine Learning algorithms. You can install and load the necessary packages with a single command to run an algorithm on a data set.

In addition to these reasons, Python also has a massive community of users who are happy to help when programmers encounter coding errors.

Other Staple Programming Languages


C++ is a complex language, can be difficult to learn, and doesn’t have a lot of quality-of-life features—meaning a lot of things have to be handled manually by the programmer. However, if this is the language the programmer knows, C++ is a fast, powerful language that’s well-supposed, well-documented, and can be used effectively for AI development.


While Java is also verbose, has a steep learning curve, and very few quality-of-life features, it’s a popular, general-purpose language that’s statically typed—meaning you can catch errors early and run programs faster.


Whereas JavaScript is a popular language for web development and is regularly used in machine learning libraries like TensorFlow.js, it too is more complex and challenging to learn than Python. While it’s robust, this means it has so many options that it may confuse non-developers.


R’s robust data processing needs, integration with other languages, and many available packages make it excellent for AI. However, it also has a steep learning curve, can be slow, and isn’t well-supported.

Less Common Programming Languages


Go was introduced by Google in 2009. It compiles to machine code incredibly fast, is a fairly simple code, and is an efficient, concise, and pragmatic programming language. However, Go isn’t a system language and doesn’t have enough immutability.


First appearing in 2010, Rust focuses on enhancing the security, performance, parallelism, and modularity of existing frameworks. R has an amazing run speed, is easily interoperable with C, FFI, and more, reduces the chances of crashing, and saves debugging time. However, the process of learning Rust can be difficult and it is slower than the C and C++ language.

The Bottom Line

Yes, there are other useful languages when it comes to programming and development, but Python is still the most popular and useful language for AI. That’s because the main libraries, frameworks, and tools for AI are built into Python—which means it won’t be going anywhere.

If you’re an engineer and want to work in AI and remain at the forefront of competitive business development, then it’s time to perfect your Python.

If you’re looking to hire engineers and analysts to help build out your AI function, book a meeting with us today.

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