What Programming Language does AI use for Software Development?

Dinesh Thakur

12 Mar 2025

Artificial intelligence is an essential part of the modern software development process. The implementation of AI in programming languages helps developers build innovative software to fulfill users' requirements. 

With the use of AI in programming languages, software development is undergoing a significant shift. Also, learning AI has become more important for developers. AI is evolving rapidly, and likewise, AI jobs are also increasing steadily, requiring software engineers to learn AI technology. 

Developers working with AI technologies need to learn the new programming languages used for building AI feature applications. But do you know what programming language does AI use to build these superb applications? In this blog, we will explore all the languages AI developers use for software development. 

So What Programming Language Does AI Use in Reality?

Python

Python is one of the most popular programming languages in the development of AI software. It comes with a user-friendly syntax that developers can learn quickly. Also, it has a vast ecosystem of libraries and frameworks, which saves developers time as they can import and use them directly in their projects without the need to create everything from scratch. 

Data scientists, in addition to programmers, also use Python language to develop neural networks and machine learning algorithms. It is also used for data analysis to discover trends, patterns, and relationships. Leading platforms like PyTorch and TensorFlow also use the language to build powerful AI models. Libraries like sci-kit-learn offer tools to work on typical machine learning projects. The rich features and convenience of Python make it a perfect choice for AI software developers. 

Java

Java is a programming language that developers use to build web, mobile, desktop, and cloud applications. Java applications are easy to use on any computer system, and its object-oriented nature makes it convenient to write code, and you can update the code anytime. This also facilitates modularity, which allows developers to break an application into small files that are easy to manage. 

Java is commonly used for the development of robotics, simulations, and embedded applications. Extensions like RTSJ enable programmers to build real-time applications like bots. Various inbuilt modules and libraries help developers save time and create high-performance software solutions. Java is highly scalable and support multithreading that is important for development of AI applications. The best thing about Java is that it can be used to create simple, small, and large applications that are scalable for the future. 

R

R is another programming language that is widely used for building AI software. Most developers use it for data visualization and statistical analysis, which makes it a top choice for building AI applications. R is suitable for projects where you need to work on data, such as statistical computing and predictive modeling. 

Its comprehensive ecosystem with specialized tools for data visualization and manipulation streamlines the process of data analysis. This helps developers understand data patterns and anomalies, which allows them to develop high-quality AI models. 

R is used as an AI programming language in various industries, especially in education and research. R is used for machine learning, statistical computations, and the analysis of large data sets. It is also used for data handling and business intelligence. It can perform complex operations easily. 

While R may not work as fast as Python or Java, its advanced statistical capabilities make it perfect for AI software development. R is also used in the finance industry for credit scoring, risk analysis, and financial modeling. Another sector is e-commerce, which uses R-based applications for targeted advertising, cross-product selling, and enhancing user experience. 

JavaScript

JavaScript is used mostly to create highly interactive web browser applications. It's a client-side scripting language, and it runs on the browsers of end users. JavaScript is capable of performing multiple computation task and enhance the performance of AI applications. Custom Software Developers CA also use it to integrate AI functionalities and services into web applications using APIs. JavaScript makes it convenient to implement real-time AI applications like chatbots and virtual assistants.  

Using frameworks like TensorFlow.js, Brain.js, and ConvNetJS, developers can easily integrate various machine learning capabilities into their projects. Frontend developers use JavaScript to build interactive user interfaces, and full-stack developers leverage fast iteration cycles to build web applications. Web AI developers use JavaScript to create full-fledged applications in browsers using frameworks like Synaptic.js.

Visualization developers use JavaScript to visualize complex AI-generated datasets, which makes it easier for users to understand the model's output and insights. Conversational AI developers make use of JavaScript libraries to build chatbots capable of interacting with users naturally, like humans. 

Prolog 

Prolog stands for "Programming in Logic". It's a language designed for AI programmers to fulfill specific AI development needs, specifically in problem-solving, symbolic reasoning, and pattern matching. Unlike other languages that follow a command sequence, Prolog focuses on the relationship between rules and facts. 

Developers use Prolog to build expert systems and natural language processing algorithms. It facilitates pattern matching and automatic backtracking for developing complex algorithms that involve logical queries. The use of Prolog also extends to the wider Semantic Web and retrieval of information from large datasets and databases. Prolog is also used to create expert systems shells for specific problem-solving. 

Although Prolog is not as widely used as Python or Java, its unique problem-solving approach to AI development makes it highly valuable for programmers working on specific AI projects, such as building AI chatbots or rule-based systems. 

C++

C++ is well-known for its quick execution and command over system resources, which makes it perfect for creating AI applications where efficiency is crucial. Its proximity to the system hardware enables more effective AI algorithm processing, which is crucial for real-time applications and resource-constrained systems.

When latency is a major concern, AI experts utilize C++ to create neural networks and machine learning. For example, C++ leverages its speed and lower-level operations to fuel TensorFlow's effective matrix computations. This emphasizes how C++ serves as the foundation for AI's high-performance computation.

The fields of simulation and game development, which are progressively embracing AI to create more intricate and realistic settings, also frequently employ C++.

The allocation of RAM is more within the control of AI developers. To lessen lagging and other performance problems, they could, for instance, provide functions in charge of intricate calculations and extra memory. By utilizing computer hardware such as GPUs, machine learning programs can increase performance thanks to frameworks like OpenCL and CUDA that offer hardware acceleration.

C++ is best for creating high-performance AI applications with little memory management and hardware control.

Julia

Julia is popular for AI development due to its high performance and fast prototyping capabilities. Julia offers fast processing capabilities, that are required in AI development as large amounts of data needs to be processed for building AI applications. In addition, it has a user-friendly syntax similar to Python, which makes it easy for programmers to learn.

Julia also supports other programming languages such as C, R, and Python. This means that developers can leverage its rich libraries and dependencies such as TensorFlow and PyTorch which are written in other languages and use them in building their applications.

In addition, out-of-the-box parallelism and distributed computing capabilities of Julia enable AI platforms to handle high computational loads and large amounts of data without any significant degradation in the performance. 

Julia is open source, which simply means developers can use other community-maintained libraries, to save their time and effort during development. Some Julia based tools include Plots.jl and Gadfly.jl for data visualization and MLJ.jl for building machine learning and deep learning models. In addition, Julia has a library, Scikitlearn.jl, which acts as an interface to the Python library scikit-learn.

Julia is ideal for building powerful AI applications that require fast processing and easy integration with other languages.

Lisp

Lisp, a powerful and adaptable language, stands at the position of a programming language developed for AI purposes, which emphasizes its adaptability in machine learning and symbolic expression processing.

These rules could be basic types like the string and the numbers or a mix of types as shown in the list of data records and maps. This way, developers could build Lisp applications even if they were dealing with quality or quantity problems in complex calculations and data manipulation for vast data sets. This is why Lisp is very famous for the development of AI applications.

One of the best features of Lisp is that it allows developers to program Lisp applications that perform demanding calculations and manipulate large data sets. It is through this feature that Lisp can be used to develop AI applications.

Lisp makes it possible for a program to call itself, which is known as the recursive programming technique. It is a perfect memory management technique that allows programmers to avoid memory management-related tasks.

Sometimes, the type of variable is not known until the program is in execution, and this is precisely why Lisp is a dynamically typed language. This is a very helpful feature that gives the coder almost absolute freedom to orient the data structure in whatever way is found fit to the problem at hand. 

One of Lisp's characteristics is that it was one of the first programming languages to adopt the functional programming paradigm. Its clear syntax allows for the expression of high-level ideas and developing new functions. It achieves better solutions in AI and other research fields.

LISP is mainly suitable for research and experiments in artificial intelligence. Such cases could involve symbolic expressions processing.

Haskell

Haskell, as a functional programming language and functional language in particular, holds core principles like pattern matching, higher-order functions, and immutable data. These principles are of utmost importance when it comes to working with and manipulating complex data structures in the domain of AI development.

It is based on a solid mathematical foundation, and allows for the easy creation of elaborate algorithms and even deep learning architectures. To avoid the costly overhead of computing resources, the lazy evaluation only evaluates the expression when it is absolutely necessary.

Thanks to Haskell's strong type system and type safety features, developers cannot assign the wrong values to the variable, and thus, the applications developed in this language are much more robust and reliable.

Haskell is suited not only to complex AI systems, but also to AI solutions that are focus centered on the programming paradigm of functional programming.

Scala

Scala's functional and object-oriented programming features make it a suitable language for AI development. This enables programmers to write clear and efficient code, resulting in high-performance applications.

Scala, by default, supports immutability; this means that the object's state should not change after they are declared. This makes a system that is capable of tracking and interacting with them throughout the whole development process, from initial development to production. Also, similar to giving support to multiprocessing and parallel computing in C++, Scala consequently walks the way of AI development to enable the creation of programs that are able to accomplish numerous tasks at a time.

Developers can benefit from Java-based frameworks and technologies that allow them to process big data in Java. The above-mentioned ability is one of the main reasons data analysts can resolve challenges efficiently by unveiling unnoticed dependencies or patterns in large irregularities of input data.

It is Scala's ability to recognize the patterns that make it a good choice for tasks like natural language processing (NLP), in which AI systems have to process user input and produce relevant results. The reason behind setting Scala as a good choice for this technology is its Object-Oriented methodology. 

For example, you can use Scala to develop research and prototyping tools.

For creating high-performance, scalable AI applications that interface with large data processing frameworks, Scala is the ideal choice. 

How DITS Can Help?

Choosing an AI language for software development requires knowledge, experience, and strategic thinking. It's not only about writing code for building AI applications but also about meeting specific demands and user needs. At Ditstek Innovations, we have a team of software developers who not only build software but also understand user requirements and problems that need to be addressed. 

We clearly communicate with clients to understand their specific needs and objectives and come up with a perfect solution customized to cater to their unique requirements. Also, AI application development may not be possible using just a single programming language but a combination of two or more.  

Our team of developers creates a full-fledged strategy from thinking of innovative ideas to deployment and post-delivery support. Choosing the right language is just a part of an AI development project. We take care of the integrations and support to ensure our applications work precisely and efficiently to fulfill the goals for which they are developed. 

Looking to build an AI application for your business? Contact us now for a detailed discussion with our experts to build our next AI application and get ready to experience the AI revolution. 

Conclusion

Choosing the right programming language is crucial for building innovative AI applications. Each language—whether Python, Java, R, JavaScript, Prolog, C++, Julia, Lisp, Haskell, or Scala—has something unique suited to different AI tasks. From machine learning and data analysis to real-time processing and natural language processing, every language offers some unique solutions.
 
As AI continues to advance, software developers need to adapt by learning new languages and tools to meet the market competition. Also, the continuously increasing demand for AI-driven solutions demands expertise in these languages. By selecting the right programming language, businesses and developers can create powerful, efficient, and scalable AI applications that drive technological progress and innovation in various industries.

FAQ’s

What Programming language does AI use for building software?

Software developers use many languages to build applications. Python is the most popular, followed by Java, JavaScript, R, Prolog, Julia, and many more. Choosing a language depends on the specific type of AI task and the libraries and frameworks required for software development. 

Why is Python so popular in AI?

Python is so popular in AI development due to its simple and readable syntax, which makes it accessible to beginners. Also, its extensive libraries boast a rich ecosystem of libraries specifically designed for AI development. Python runs on various operating systems, which makes it versatile for different environments. 

Is C++ used in AI?

Yes, C++ is used in AI to build applications that require high performance and a low level of control. C++ is often used in robotics, games, and embedded systems where efficiency plays a vital role. 

What language should beginners learn if they want to get into AI?

Python is a recommended AI language for beginners due to its simplicity, rich libraries, and access to a large community, which makes it excellent for learning AI concepts.