Why Python is the language of choice for AI

The widespread adoption of AI is creating a paradigm shift in the software engineering world. Python has quickly become the programming language of choice for AI development due to its usability, mature ecosystem, and ability to meet the data-driven needs of AI and machine learning (ML) workflows. As AI expands to new industries and use cases, and Python’s functionality evolves, the demand for developers versed in the language will balloon. Python developers who invest in their AI and ML knowledge will be well-positioned to thrive in the era of AI. 

Python is the most popular programming language, according to the TIOBE Programming Community Index. Python took its first lead over the other languages in 2021 and continued to explode in popularity as the growth of other languages largely remained stagnant. Meanwhile, nearly 30% of the searches for programming language tutorials on Google were for Python, nearly double the percentage for Java, which is ranked second, according to the PYPL Index, which is based on data from Google Trends. It’s no wonder that the popularity of Python has extended to AI workflows too.

Why Python leads for AI development

There are a number of factors that make Python ideal for AI development including its ease of use, its rich and growing ecosystem of AI libraries and toolkits, and the libraries and tools available to improve its execution speed and scalability.

Usability and ecosystem

Python is easy to learn and a simple language to write, which makes it accessible to people without programming experience. It doesn’t require developers to write complex boilerplate code, and it can be written iteratively. Libraries in the many AI development toolkits available for Python are typically lightweight and don’t require building or training AI models. Instead, Python developers can use specialized tools from vendors to accelerate AI app development using available models.

The ecosystem around Python is massive. There is a rich set of libraries and frameworks that are specifically designed for AI and ML, including TensorFlow, PyTorch, Keras, Scikit-learn, and Pandas. Those tools provide pre-built functions and structures that enable rapid development and prototyping. In addition, packages and libraries like NumPy and Pandas make data manipulation and analysis straightforward and are great for working with large data sets. Many Python tools for AI and ML are open source, fostering both collaboration and innovation. 

User base and use cases

As AI development evolves, Python is opening the doors for more people and more use cases. Today, Python can be used for exploratory or even low-code solutions. The majority of AI applications that will be built in the future won’t require the level of customization and power that PyTorch and TensorFlow do. And future AI apps will use a different set of libraries, such as LangChain or LlamaIndex for building applications that use large language models (LLMs). 

Meanwhile, new packages for Python are being added all the time and will expand the horizons beyond just AI to include more common use cases, like building advanced websites. There isn’t a task that developers do with Python today that won’t be impacted by AI in some form. 

Performance aids

Python can be extended with libraries like Cython to nearly meet the performance of the C language, and just-in-time compilers like PyPy can significantly improve code execution speed. Critical performance components can be written in C or C++ and wrapped in Python, combining performance with Python’s ease of use. Python makes it easy to transition from a prototype to a production-ready solution, especially with tools designed to scale Python applications, such as Dask or Ray.

What AI projects require from Python developers

While Python’s usability makes it easy for even relatively unskilled developers to learn the language, there are specific skills that developers will need to focus on for the AI industry of the future. Developers will need to write code that can quickly and efficiently process large data sets through AI. Understanding concepts like parallel programming, throttling, and load balancing will be necessary. Python developers have the foundational knowledge to succeed at these tasks, but they need to build upon their skill sets to effectively pivot to AI projects, and set themselves apart in a crowded job market.

One area where there may be a skills gap for Python developers is working with AI agents, which is the next wave of AI innovation. With agentic AI, software agents are designed to work autonomously toward an established goal rather than merely provide information in reaction to a prompt. Developers will need to understand how to write programs that can follow this sophisticated orchestration or sequence of steps. 

AI is taking on a more active role in the development process itself too. It’s working much like a copilot in doing the legwork of looking up code samples and writing the software, and freeing up developers so they can focus on code review and higher-level strategic work. There’s an art to getting AI to generate reliable and safe code. It’s an important skill set to develop and it will be critical for developers of the future.

How to kickstart your AI learning journey

My advice? Developers should always be learning how to use new technologies and supplement their skill sets, but the fast pace of AI innovation brings even more urgency. I’m a strong believer in continuous learning, and I believe the responsibility to learn and grow lies with the individual rather than the company they work for. In today’s world, there are a plethora of free, extremely valuable learning resources at everyone’s fingertips; accessibility and cost are not valid excuses to opt out of upskilling. If developers can begin to chip away at their AI learning goals now — even if only for 15 minutes per day — they will reap the rewards down the line.

Many companies offer professional development stipends and opportunities for employees, and even the general public, like Google, Snowflake University, and MongoDB University. Coursera and Udemy offer certifications and courses that are both free and fee-based. YouTube offers a lot of tutorials, including one from freeCodeCamp.org, and Codecademy offers a free course on its website. Major universities also offer free Python classes for the public. These resources are everywhere.

Nothing beats hands-on training, though. If you can weave AI tasks with Python into your tool set at work and learn on the job, that will benefit you and your company. For those who don’t have that option, I recommend rolling up your sleeves and getting started on Python projects on your own. About a year ago, I spent some time over a few weekends using Python to build a personal AI-based widget to help me with my workout training and nutrition suggestions. This is just one example of how you can take the initiative to learn AI skills in a hands-on, engaging way. I encourage all of the folks I manage and meet with to do the same.

The synergies between Python and AI are strong, and they are expected to become stronger as AI gets integrated into additional applications and industries. Python’s simplicity and versatility make it an ideal choice for developers looking to harness the power of AI. As AI technologies evolve and become more prevalent, Python developers have a chance to take the initiative to learn about them and remain relevant and adaptable in a rapidly changing landscape.

Jeff Hollan is head of apps and developer tools at Snowflake.

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Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.