Which AI Programming Languages Are Best for Startups and Tech Companies?

Which AI programming languages should startups choose | TurnKey

There’s no doubt that artificial intelligence is a software development trend that will continue to shape all of our lives for the next several decades. But if you want to join the fray and start developing world-class AI software, it’s essential to understand which AI programming languages are best to use both today and tomorrow.


To this end, Python is one of the most popular programming languages for AI engineers due to its versatility when it comes to creating complex algorithms as well as its accessible syntax.


But Java, while slightly more difficult than Python, is another great programming language for those looking to develop robust artificial intelligence systems.


Lua and Go programming languages offer powerful libraries designed specifically for AI research with the former being incredibly efficient and fast whilst the latter provides an easy-to-master system focused on concurrency. 


Undoubtedly, all these programming languages offer AI developers valuable tools tailored for building intelligent systems. But let’s dig a bit more into the details to see which language may be best for you and your AI objectives.

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Background of the AI Programming Language Landscape

The promotion of AI-driven solutions brings with it an ever-growing need to understand what tools, frameworks, and programming languages developers should use. With IDC predicting that the total revenue of the AI market will be $554.3 billion by 2024, now more than ever startups and tech companies are looking to deploy better AI applications quickly and efficiently.

What Programming Language Do You Use on a Regular Basis | TurnKey
Source: 2018 Kaggle Machine Learning and Data Science Survey

Along with the surge in demand, technology has significantly advanced and there are many languages that have become available for developing AI software. Our list of the ten best programming languages will help you stay ahead when developing your next cutting-edge technology program. Time is of the essence – so let’s get coding!

The PYPL Popularity of Programming Language Index | TurnKey

Which Artificial Intelligence Programming Languages are Best?

There’s no shortage of programming languages (whereas the shortage of expert developers in AI and Data Science is very real) that are already being used to create the intelligent machines of today, and the ever-evolving AI programming languages of tomorrow. But what is the best language for programming Artificial Intelligence?

AI Programming With Python

According to numerous studies, Python is the closest candidate to be called the best programming language for AI.

It’s an easy-to-use language for complex applications thanks to its object-oriented design, simple syntax, and easy readability. With plenty of libraries and modules available in the programming world, Python really stands out for its ability to integrate with other programming languages like C, Java, and C++. Tons of online resources are available on the official Python website that can teach you everything from programming basics to AI programming techniques.

Using Python for AI provides many benefits, such as:

  • Ability to quickly develop complex applications
  • Easy readability
  • Object-oriented design
  • Wide range of libraries and modules
  • Ability to interface with other languages such as C, Java, and C++

One example of using Python for AI development is Google’s TensorFlow, which is an open-source software library for numerical computation. It allows developers to create powerful and sophisticated neural networks, which are then used to build complex AI systems. With the help of TensorFlow, software developers can create deep learning models that can be used in various applications such as natural language processing, speech recognition, computer vision, and more.

ChatGPT codebase is primarily written in Python. 

Guido Van Rossum on Python | TurnKey

Look no further than this informative video from TurnKey, where our CTO Boris Glants breaks down the reasons why Python is becoming a go-to programming language for many developers.

AI Programming With C++

C++ is a powerful and versatile programming language for AI development. It offers numerous benefits, such as high efficiency, portability, object-oriented programming capabilities, and the ability to manage memory directly. C++ is primarily used for designing and developing AI-based applications in areas such as robotics, natural language processing, machine learning, computer vision, and intelligent agents.

C++ is not a product and there is no company behind it, it’s a standard. So there is no official documentation.

Programming with C++ has a number of benefits that include:

  • Object-oriented programming capabilities: C++ allows you to create objects and classes, which makes code re-usability easier.
  • Compiled language: C++ is a compiled language, which means that it is converted directly into machine code and does not have to be interpreted at runtime. This makes it more efficient and faster than interpreted languages.
  • High performance: C++ is highly efficient and can produce code with excellent performance.
  • Cross-platform: C++ is a cross-platform language, which means that code written in C++ can be easily ported to other platforms and operating systems.
  • Large library: C++ has a large and comprehensive standard library, which makes it easy to find the functionality you need.
  • AI-friendly: C++ can be used to develop AI applications and algorithms, and its object-oriented capabilities make it a great choice for AI programming.

Here are some examples of AI Programming with C++:

  • Implementing AI algorithms such as decision trees, neural networks and genetic algorithms.
  • Developing applications for computer vision, natural language processing and robotics.
  • Creating intelligent agents for game playing and simulations.
  • Designing robotic arm control systems and self-driving cars.
  • Developing tools for facial recognition, sentiment analysis and text-to-speech.
  • Writing predictive models for data analysis and machine learning.
  • Creating machine learning applications for image recognition and object detection.

If you want to harness the power of AI, TurnKey is here to help! We specialize in assembling all-star AI development teams that outperform the rest.

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AI Programming With Java

Java is enjoying an ever-growing presence in the world of Artificial Intelligence (AI). It’s scalability, robustness, ease of use, and platform independence has made it the programming language of choice for many AI projects.


Whether you’re developing sophisticated AI applications such as natural language processing (NLP), computer vision, and robotic process automation (RPA), or just need a programming language that’ll help you get the job done right, look no further than Java.


Using Java for AI programming provides many benefits, including its ease of use and portability, as well as its wide range of libraries, tools, and frameworks. It is a stable, reliable, and well-supported language, with a large and active community. Java also provides good performance, scalability, and security, which make it well-suited for AI applications.


Additionally, Java is able to integrate with other languages and tools, making it versatile and suitable for a variety of use cases.


Here are some examples of AI programming with Java:

  • Developing AI algorithms such as neural networks, pattern matching, and fuzzy logic.
  • Creating machine learning models and applications. 
  • Implementing natural language processing techniques.
  • Developing AI-driven web and mobile applications.
  • Creating AI-driven game engines and AI-driven bots.
  • Using AI to analyze large datasets and generate insights.
  • Developing AI-driven chatbots and virtual assistants.
  • Building AI-driven recommendation systems.

AI Programming With Scala

Scala is rapidly becoming a popular programming language for artificial intelligence. From its highly efficient and scalable design to its comprehensive library of tools, Scala offers a great advantage over other programming languages when working with complex algorithms and models. Working with Scala gives developers a lot of flexibility with their projects, as its syntax is easy to interpret and understand.


Not only that, but with its ability for distributed computing and parallel processing, Scala is one of the best programming languages for large-scale AI and machine learning projects. It’s already proven itself in production systems, giving it an extra edge in reliability and support. All things considered, Scala is certainly one of the top programming languages you should consider for AI development.


Here are some libraries that help AI developers use Scala for their projects:

  • Apache Spark. Apache Spark is a popular open-source distributed computing framework that uses Scala for data processing and machine learning. It includes libraries like MLlib and GraphX that can be used for AI applications.
  • ScalaNLP. ScalaNLP is a library for natural language processing, which can be used to develop AI applications. It includes tools such as CoreNLP and Breeze for text processing and machine learning.
  • Akka. Akka is a distributed computing framework for building distributed applications. It provides an actor-based programming model which can be used to build AI applications.
  • Scala AI. Scala AI is an open-source library for developing AI applications. It includes tools such as A* search and Monte Carlo Tree Search for developing search algorithms and algorithms for probabilistic reasoning and machine learning.
Scala libraries for AI developers | TurnKey

AI Programming With JavaScript

JavaScript is among the best languages for AI. With libraries like TensorFlow.js, ConvNetJS and other offerings providing APIs and methods to create and train machine learning models, its popularity amongst developers makes it a smart choice given its smaller memory requirements and quick start-up time.


Privacy is also beneficial for programming in JavaScript since models can be hosted on the device itself, speeding up the process even more.


Examples of AI programming with JavaScript include using neural networks to classify images, using reinforcement learning to play games, and using natural language processing to build chatbots.


Additionally, AI programming with JavaScript is being used in many web applications, from search engines to recommendation systems. A few examples of web applications that utilize AI programming with JavaScript include Amazon’s product recommendations, Netflix’s movie recommendations, and Google’s search engine.

AI Programming With Prolog

Developing AI in Prolog involves using logic programming techniques, such as rule-based programming, to create solutions that can solve problems.


The main benefit of using Prolog to create an AI system is that it offers a powerful and expressive language for defining rules and logic. Prolog is particularly well-suited for developing rule-based systems, as it allows for easy and efficient representation of complex rules and relationships.


One example of this is a program that can play a game of chess. Other real-world applications can include natural language processing, computer vision, and robotics.


To create AI in Prolog, you will need to develop a set of rules and facts that will be used to solve the problem, as well as a set of algorithms and search strategies for finding solutions. Additionally, you will need to create a knowledge base that contains all of the facts, rules, and algorithms necessary to solve the problem. Once you have everything set up, you can then test and debug the system until it is ready to use.

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AI Programming With R

R is a popular programming language that has emerged as one of the top choices for developing AI applications.


It has many benefits, such as its simple syntax, ease of use for statistical analysis, access to vast amounts of data analysis and manipulation capabilities, and compatibility with other popular programming languages such as Python. R can be used for various AI applications, including natural language processing (NLP), computer vision, machine learning (ML), and more. 


AI Programming with R has a number of benefits that include:

  • R is a powerful language for data analysis and manipulation, making it ideal for working with machine learning and AI algorithms.
  • R is open source and free to use, making it accessible to everyone.
  • R has a large and vibrant community of developers who can provide support and advice.
  • R has a wide range of libraries and packages that allow you to quickly and easily create powerful AI models.
  • R is a highly versatile language, allowing you to easily switch between different programming paradigms and techniques.
  • R provides powerful visualization tools, allowing you to easily evaluate the performance of AI models.

Examples of using R for AI development include using the caret package for building and evaluating machine learning models, using the tidytext package for natural language processing, and using the Keras package for deep learning. Additionally, R can be used to create AI-powered dashboards with interactive visualizations and to power AI-driven decision-making processes and scientific computing.

AI Programming With Lisp

Lisp is a dynamic AI programming language that unlocks powerful capabilities for developers. It has an expressive syntax, making it easy to express complex ideas quickly and efficiently. Lisp also supports extensibility, so new features can be added with ease. On top of this, its range of data manipulation options makes work involving large sets of information easier than ever before!


In practical scenarios, you’ll find Lisp being used in natural language processing (NLP), and reinforcement learning activities like building agents or robots interacting within their environment – all made possible by the power of Lisp.

Lisp is a language for doing what you’ve been told is impossible. - TurnKey

AI Programming With Julia

Julia is a programming language designed specifically for AI, machine learning, and data science applications. It combines the ease of use of Python with the speed and performance of C/C++. The main benefits of using Julia for AI programming include:

  • Increased speed and efficiency: Julia has been designed from the ground up to optimize AI programming tasks, and it often performs faster and more efficiently than other languages.
  • Easier to read and write code: Julia has a less verbose syntax than other languages, making it easier to read and write code.
  • Powerful data structures: Julia has a number of powerful data structures such as tuples, dictionaries, and arrays that can be used to represent and manipulate data quickly and easily.
  • A rich ecosystem of libraries: Julia has a large and growing ecosystem of libraries and packages that can be used to extend its capabilities.

Examples of AI programming with Julia include using it to create decision trees, neural networks, and natural language processing systems. It can also be used to optimize hyperparameters, generate text, and much more.

AI Programming With Haskell

If you’re looking to develop AI, Haskell may be the way to go. Not only does it support powerful functional programming and a robust type system for structuring codel, but its lazy evaluation feature allows for efficient data processing– so there won’t be any lag time when running your algorithms. Plus, thanks to the concise syntax and readability of the codebase in Haskel means less debugging down the line.


Some examples of AI applications written in Haskell include natural language processing, computer vision, machine learning, and robotics. In addition, Haskell has also been used to develop AI agents for video games, such as chess AI.

Bonus Tip: AI Programming With Lua and Go

Lua is a powerful and versatile language designed to make creating embedded applications fast, efficient, and enjoyable. Combining the best of multiple programming paradigms – procedural programming, object-oriented programing, functional programming – it’s easy to see why this multi-faceted language has become so popular with developers. With its simple C API for platform integration across many operating systems plus dynamic typing and automatic memory management with incremental garbage collection, all rolled into one small package – Lua deftly bridges the gap between complexity and capability.


Go programming language is beloved by millions of users for the numerous features that make it perfect for machine learning. It’s statically typed so your code will work exactly how you want, plus with the ability to handle concurrency, it opens up plenty of possibilities when developing applications. On top of all this, Go is super efficient – gets more done in fewer lines than other languages like Python or Java.


Perhaps surprisingly, neither Lua nor Go are popular choices for AI developers yet. The 2022 report from Statista paints a clear picture – Python is the programming language of choice with more than half of developers relying on its power and versatility. Meanwhile, only one in ten specialists choose Go to tackle their toughest coding challenges.

Golang vs. Python | TurnKey

How to Build a Team of AI Development Experts

Ready to take your startup or tech company up a notch with AI? Let’s go! 

To get started with selecting an AI language, make a brief description of what you plan to accomplish with this language if needed. Then, evaluate the work to be done and identify resources needed for the job. Consider the tools and libraries for each AI language and choose the language that best fits your specific development needs.


At TurnKey, we know the struggle of getting a startup or tech company humming – especially when it comes to offshore development. That’s why our Yourshore model is here! It offers an economical and effective solution for startups and high growth businesses that need top notch remote AI dev teams from day one. We’ll take care of recruiting your team, give you transparent pricing options tailored to fit any budget and support you in reducing churn with all sorts of helpful tools so everything runs as smoothly as possible.


We’ve supported a wide range of leading companies in getting innovative features and functionalities out the door – plus, the highly skilled offshore AI teams that we help you build can help turn any strategy into reality.


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Key takeaways: It notes that traditional programming languages like Python and Java are commonly used for AI development, but several new languages have emerged to address specific challenges in AI development.


What programming language is best for artificial intelligence?

Depending on your plans for developing AI, there are multiple options. Artificial intelligence programming languages are typically based on advanced mathematics, statistics, and machine learning algorithms. The most popular languages for AI are Python, R, Java, C++, and Lisp, but there are many other languages that can be used for AI development, including Prolog, Haskell, Scala, and Julia. Additionally, some languages, such as JavaScript, can be used for AI development with the help of libraries such as TensorFlow.js.

Who developed the Python programming language?

The Python programming language was developed by Guido van Rossum in the late 1980s. Van Rossum is a Dutch computer scientist known for his work on the Python language and its various implementations. He released the first version of Python in February 1991.

Is Python fast enough for AI?

Yes, Python is fast enough for AI. It has the necessary libraries and modules to build and develop AI models, and its high-level programming language makes it easy to write code. Additionally, Python has a wide range of libraries specifically designed for AI, Machine Learning, and Deep Learning, making it an ideal language for most AI projects.

How hard is AI code to develop?

AI code can be both simple and complex, depending on the task or project being implemented. Generally, AI code requires a deep understanding of algorithms and data structures, as well as programming experience in multiple languages. It is also important to understand the underlying principles of AI and machine learning, such as deep learning and reinforcement learning. AI code can be tricky to debug and optimize, so it helps to have an in-depth understanding of the algorithms being used. Additionally, AI code often requires extensive data manipulation and processing, so it is important to have a good understanding of data structures and algorithms.

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Daria Spizheva

Technology and software development expert and author

I’m a writer with a background in — and passion for — applied linguistics, programming, and technology. I’m a firm believer that the key to explaining detailed technical and business information is to simplify it into concepts that everyone can understand. Hope you enjoy!