11 of the Best Programming Languages for AI Development

Headshot of CareerFoundry contributor Eric Thompson.

ChatGPT has thrusted AI into the cultural spotlight, drawing fresh developers’ interest in learning AI programming languages.

Now corporations are scrambling to not be left behind in the AI race, opening doors for newer programmers with a solid grasp of the fundamentals as well as knowledge of how to work with generative AI.

The AI market size forecast is predicted to be $1.85 trillion in the next seven years, leading to a large demand for those that know AI programming. While AI won’t come and take developers’ jobs anytime soon, as Nils J. Nilsson, founding researcher in AI and Computer Science at Stanford says:

“In the future, AI will be diffused into every aspect of the economy.”

Here are the main points I’ll be covering today:

  1. What is AI programming?
  2. What are the best programming languages for AI development?
  3. What programming languages aren’t suited for AI development?
  4. AI programming languages FAQ
  5. What next?

1. What is AI programming?

AI programming is the development of any software under the AI umbrella. This can include:

  • Machine learning (ML), 
  • Computer vision (CV) 
  • Robotics
  • Artificial general intelligence (AGI)

Quite often these terms are conflated to just “AI”.

2. What are the best programming languages for AI development?

While there’s no single best AI language, there are some more suited to handling the big data foundational to AI programming. 

The languages you learn will be dependent on your project needs and will often need to be used in conjunction with others.

Let’s go through a few of the top ones:


Python, the most popular and fastest-growing programming language, is an adaptable, versatile, and flexible language with readable syntax and a vast community. 

It has thousands of AI libraries and frameworks, like TensorFlow and PyTorch, designed to classify and analyze large datasets.

Python can be found almost anywhere, such as developing ChatGPT, probably the most famous natural language learning model of 2023. Some real-world examples of Python are web development, robotics, machine learning, and gaming, with the future of AI intersecting with each. It’s no surprise, then, that Python is undoubtedly one of the most popular AI programming languages.


Java ranks second after Python as the best language for general-purpose and AI programming.

Released in 1995, it’s been extensively tested and proven reliable. Java has a steep yet quick learning curve, but it’s incredibly powerful with a simple syntax and ease of debugging.

You can find Java in web and mobile app development, two places where AI is growing.

It has a large and active community with a vast standard library and also has access to AI packages such as: 

  • Weka
  • Deeplearning4j
  • Deep Java Library
  • KubeFlow
  • OpenNLP
  • Java Machine Learning Library
  • Neuroph


JavaScript is a pillar in frontend and full-stack web development, powering much of the interactivity found on the modern web. A big perk of this language is that it doesn’t take long to learn JavaScript compared to other AI programming languages.

There’s a lot of excitement for Java concerning AI, with its ability to process AI on the client side through a web page. It has the capability to be the only language that you’d need to develop AI. Popular AI libraries and frameworks include: 

  • ConvNet.js
  • Math.js
  • Brain.js
  • Node-fann
  • TensorFlow.js

If you’re wondering if you should learn JavaScript, it’s also fantastic for software engineering and game development.


C++ is a fast and efficient language widely used in game development, robotics, and other resource-constrained applications. 

Although it isn’t always ideal for AI-centered projects, it’s powerful when used in conjunction with other AI programming languages. With the scale of big data and the iterative nature of training AI, C++ can be a fantastic tool in speeding things up.


Haskell is a functional and readable AI programming language that emphasizes correctness. Although it can be used in developing AI, it’s more commonly used in academia to describe algorithms. Without a large community outside of academia, it can be a more difficult language to learn.

Haskell does have AI-centered libraries like HLearn, which includes machine learning algorithms.


Scala is a user-friendly and dependable language with a large community but can still be complex to learn. It’s used for advanced development such as data processing and distributed computing.

Scala was designed to address some of the complaints encountered when using Java. It has a lot of libraries and frameworks, like BigDL, Breeze, Smile and Apache Spark, some of which also work with Java.

You can find many features suitable for AI development, such as building machine learning algorithms and handling big data.


Julia is a newer language with a small yet rapidly growing user base that’s centered in academic computing. It’s fast and flexible, which allows quick iterations, ideal for AI. Julia tends to be easy to learn, with a syntax similar to more common languages while also working with those languages’ libraries.

Julia isn’t yet used widely in AI, but is growing in use because of its speed and parallelism—a type of computing where many different processes are carried out simultaneously.

Beyond Julia’s large standard library, there are machine learning libraries and frameworks including:


Created for statistics, R is used widely in academia, data analysis, and data mining. 

Although R isn’t well supported and more difficult to learn, it does have active users with many statistics libraries and other packages. It works well with other AI programming languages, but has a steep learning curve.

R performs better than other languages when handling and analyzing big data, which makes it excellent for AI data processing, modeling, and visualization. Although it’s not ideal for AI, it still has plenty of AI libraries and packages.


Lisp is the second-oldest programming language, used to develop much of computer science and modern programming languages, many of which have gone on to replace it. 

A flexible and symbolic language, learning Lisp can help in understanding the foundations of AI, a skill that is sure to be of great value for AI programming.

Lisp is difficult to read and has a smaller community of users, leading to fewer packages. It’s faster for computers to process, which leads to quick iterations.


Go was designed by Google and the open-source community to meet issues found in C++ while maintaining its efficiency. It has a high performance and is quite scalable. Go’s popularity has varied widely in the decade since it’s development.

It shares the readability of Python, but is much faster with the speed of C, making it ideal for beginner AI development. Its speed makes it great for machine learning, which requires fast computation. Go has a growing number of libraries for AI.


Developed by Apple and the open-source community, Swift was released in 2014 to replace Objective-C, with many modern languages as inspiration. 

It has a simple and readable syntax that runs faster than most readable languages. It works well in conjunction with other languages, especially Objective-C. You can start getting into it via our beginner’s guide to Swift.

Swift has a high-performance deep learning AI library called Swift AI.

A programmer codes at their desk.

3. What programming languages aren’t suited for AI development?

Php, Ruby, C, Perl, and Fortran are some examples of languages that wouldn’t be ideal for AI programming. 

These languages have many reasons why you may want to consider another. A language like Fortran simply doesn’t have many AI packages, while C requires more lines of code to develop a similar project. A scripting or low-level language wouldn’t be well-suited for AI development.

Many of these languages lack ease-of-life features, garbage collection, or are slower at handling large amounts of data. While these languages can still develop AI, they trail far behind others in efficiency or usability.

4. AI programming languages FAQs

Does AI need coding?

AI does need coding to develop. While some specific projects may not need coding, it’s the language that AI uses to speak and interact with data. There may be some fields that tangentially touch AI that don’t require coding.

Is AI written in Python?

AI is written in Python, though project needs will determine which language you’ll use. Currently, Python is the most popular coding language in AI programming because of its prevalence in general programming projects, its ease of learning, and its vast number of libraries and frameworks.

Which language is best for AI robot?

The language that would be best for AI robots would be a language that’s generally used in both AI and robotics individually, like Python or Java.

Is Java or C++ better for AI?

Determining whether Java or C++ is better for AI will depend on your project. Java is more user-friendly while C++ is a fast language best for resource-constrained uses. The uses of both languages can overlap.

5. What next?

Now that you’ve learned what makes each language distinct in how it develops and interacts with programming generative AI, you are well on your way to making an informed decision on which one best fits your needs and experience level.

There’s no one best AI programming language, as each is unique in the way it fits your specific project’s needs. With the ever-expanding nature of generative AI, these programming languages and those that can use them will continue to be in demand.

At its basic sense, AI is a tool, and being able to work with it is something to add to your toolbox. The key thing that will stand to you is to have a command of the essentials of coding.

It’s no surprise, then, that programs such as the CareerFoundry Full-Stack Web Development Program are so popular. Fully mentored and fully online, in less than 10 months you’ll find yourself going from a coding novice to a skilled developer—with a professional-quality portfolio to show for it.

If you’d prefer to read more about the world of development and generative AI, check out these articles:

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