Artificial intelligence is changing not only how we write code, but also which programming languages are in demand. If you are just starting your journey in development, the question of choosing a language has become even more difficult. Let's take a look at the prospects for three popular languages in the era of AI.
Python: the king of machine learning.
Python has firmly established itself as the main language for working with artificial intelligence. The reasons are obvious: a huge ecosystem of libraries (TensorFlow, PyTorch, scikit-learn), simple syntax, and a low entry threshold.
With the advent of ChatGPT and other AI assistants, Python has become even more popular. Most APIs for working with language models have official Python SDKs. If you need to quickly prototype a solution using AI, Python is the first choice.
However, there is a nuance: Python itself is slow. In production, critical parts are often rewritten in faster languages. But for most tasks, especially those related to AI, Python's speed is sufficient because heavy calculations are performed in optimized libraries written in C or C++.
Prospects: Python is not going anywhere. It will dominate data science, machine learning, and rapid development of AI applications. For beginners, it's a great choice to quickly enter the industry.
Go: speed and simplicity for backend.
Go was created at Google to solve specific problems: high-performance network services, microservices, and cloud infrastructure. In the era of AI, these tasks have not disappeared, but have become even more relevant.
While Python developers are training models, someone has to create the infrastructure to deploy them. Go is ideal for writing APIs that process millions of requests to AI models. It is fast, has built-in support for competitiveness, and is compiled into a single executable file, which simplifies deployment.
AI tools themselves are often written in Go. For example, Docker and Kubernetes, which are essential for modern development, were created in this language. As the number of AI services grows, the need for reliable infrastructure also increases.
Prospects: Go will be in demand for backend development, DevOps, and creating infrastructure for AI applications. It's a great choice if you're interested in system programming and high performance.
Rust: safety and performance.
Rust is a language that promises the performance of C++ without its main drawback: unsafe memory handling. In the era of AI, when both speed and reliability are critical, Rust is gaining momentum.
Many companies are rewriting critical components of their systems in Rust. It is used in systems where predictability and the absence of unexpected failures are important: operating systems, browsers, and tools for working with big data.
From an AI perspective, Rust is interesting for building high-performance libraries and tools. For example, the Polars data library (an alternative to pandas) is written in Rust and runs much faster. Rust is also actively used in the development of tools for edge computing, where AI models run on devices with limited resources.
Prospects: Rust will grow in system programming and where maximum performance is needed. The learning curve is steep, but the demand for Rust developers is constantly increasing.
So which language to choose?
The answer depends on your goals. If you want to quickly start working with AI and create prototypes, choose Python. If you are interested in backend development and infrastructure, Go will be a reliable choice. If you are ready for a challenging but promising path in system programming, learn Rust.
It is important to understand that in the era of AI, all three languages will survive because they solve different problems. Python will remain the language for experimentation and model development, Go for creating scalable services, and Rust for critical components where performance and security come first.
The most important thing is to start. And which language you choose first is not as critical as it seems. Programming skills are transferable, and having mastered one language, you will easily learn another.
Come to the courses in Codice - we have created clear materials specifically for novice developers. And we also have a cool Telegram channel with a friendly community where you can ask any question and get support from fellow beginner developers and experienced mentors!