Artificial intelligence (AI) is changing the world of software engineering and driving demand for particular skills. As AI continues its adoption across industries, Python has become the go-to programming language for AI and machine learning (ML) workflows. Already the most popular programming language – having taken over other languages in 2021 and continuing on this trajectory – Python’s growth marks a paradigm shift in the software engineering world, with its popularity also extending to AI workflows. The reasons for this are simple: Python’s usability and mature ecosystem are perfect for the data-driven needs of AI.
As its functionality evolves to keep up with the rise of AI adoption, demand for developers skilled in the language will increase. This provides a major opportunity for ambitious developers, enabling them to thrive in the ongoing AI and ML boom, but only if they invest in their AI knowledge to capitalise on this trend.
The language of AI development
The key feature of Python which has made it such a dominant force in today’s world is that it is easy to learn and simple to write. Even people without programming experience can get to grips with it. It doesn’t require developers to write complex boilerplate code. Also, developers can write 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 specialised 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 designed specifically 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.
Tomorrow’s skills
To thrive in the AI era, developers will need to focus on specific skills. Developers will need to write code that can 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 programmes 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 important to develop these skill sets, as they will be critical for developers of the future.
Getting started with AI
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. 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.
That’s not to say that companies will not help, and many now 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. 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.
Future ready
The synergies between Python and AI will only grow stronger as AI becomes integrated into new applications and across sectors. The simplicity and versatility of Python mean that it is the perfect choice for any ambitious developer hoping to build a career in AI, and the perfect launching point to deal with emerging technologies such as low-code and agentic AI.
By taking the initiative and getting to grips with Python and its AI capabilities, developers can ensure they have a powerful skill set which will keep them relevant in a fast-moving technology workplace.
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