🐍 Python is the king of the mountain (and he knows it)
Status: Undivided dictator
Market share: 26% in TIOBE Index, 47-58% AI/ML vacancies
Python in AI is like Nike sneakers in running: you can run in something else, but why? TensorFlow, PyTorch, scikit-learn, Keras, Hugging Face Transformers — if it's an AI library, it's in Python.
Python is especially popular in Russia: Yandex uses it for recommendation systems and Yandex.GPT, Sber uses it for GigaChat and data analysis, and VK uses it for ML models in social networks.
Advantages:
The syntax is so simple that your code reads like pseudocode
A library ecosystem the size of a small country
If you google an error, you will find 20 solutions on StackOverflow (and on Habr too)
Rapid prototyping: from idea to working model in an evening
Huge Russian-speaking community
Cons:
About 5 times slower than C++ (but who cares when there's a GPU?)
It eats up memory as if tomorrow won't come
GIL (Global Interpreter Lock) — a headache for multithreading
Who is it for: Data Scientists, ML engineers, everyone who wants to do NLP, computer vision, generative AI.
Average salary in Russia: 150-300 thousand rubles for ML engineers
Verdict: If you're only going to learn one language for AI, it's Python. Period.
💛 JavaScript/TypeScript — an unexpected hero
Status: "I'm here too!"
Market share: 66-69% of developers, 98% of websites
While Python spins models in the backend, JS/TS does what Python can't — it works in the user's browser. TensorFlow.js, brain.js, ml5.js — AI directly in Chrome without a backend.
TypeScript even temporarily surpassed Python in popularity on GitHub in August 2025. Respect.
Advantages:
AI directly in the browser, without servers
Huge ecosystem and community
Ideal for AI features in web applications (chatbots, recommendations, recognition)
TypeScript adds typing and saves from stupid bugs
Cons:
Not for heavy ML - training large models is not for him
Fewer specialized AI libraries than Python
Who is it for: Frontend and fullstack for developers who want to add AI to web applications.
Average salary in Russia: 120-250 thousand rubles for full-stack developers with AI
Verdict: Python makes AI, JavaScript delivers it to the user. Dream team.
☕ Java — enterprise does not give up
Status: Respectable uncle at a family celebration
Position: #4 in TIOBE Index
Banks, telecoms, insurance companies, state corporations - they do not rewrite their Java monoliths in Python. They take Deeplearning4j, Weka, Java-ML and push AI into the existing infrastructure.
In Russia, Java is especially popular in Sber, Tinkoff, VTB, MTS — wherever reliability and scalability are critical.
Advantages:
Cross-platform ("Write Once, Run Anywhere")
Automatic garbage collection
A huge ecosystem for the enterprise
Predictable performance
Many vacancies in Russian corporations
Cons:
The code is 5 times longer than in Python
Fewer AI-specific libraries
Not as active a community in ML as Python
Who is it for: Corporate developers who work with legacy systems.
Average salary in Russia: 150-280 thousand rubles for enterprise Java developers
Verdict: If your company lives on JVM, Java is your choice.
⚡ C++ — when speed is everything
Status: Old-school tough guy
Specialization: Performance
Autonomous cars, robotics, gaming AI, high-frequency trading — wherever milliseconds matter, C++ rules. TensorFlow and PyTorch are also written in C++ under the hood.
Yandex uses C++ for search algorithms, VK — for video processing, game studios — for AI opponents.
Advantages:
Maximum performance
Full control over memory
Libraries: TensorFlow Lite, MLpack, Shark, Torch
In demand in game development and robotics
Cons:
The learning curve is like Everest
Code is 5+ times longer than Python
More time for development
Who is it for: Developers of real-time systems, robotics, embedded systems, game developers.
Average salary in Russia: 180-350 thousand rubles for senior C++ developers
Verdict: Python for experiments, C++ for production and speed.
🗄️ SQL — yes, seriously
Status: Underestimated MVP
Use: ~50% of developers
Without data, there is no AI. Without SQL, there is no access to data. That's all.
Advantages:
Critical for working with data
Query optimization = fast data preparation
Integration with any ML pipelines
Mandatory requirement in 90% of data scientist vacancies
Cons:
It's not a language for ML as such, but you can't do without it
Who is it for: To everyone who works with AI (yes, everyone).
Verdict: Knowing SQL for AI is a basic requirement, not an option.
🦀 Rust — when security is not a bug, but a feature
Status: Fashionable hype
Trend: Growing
Rust promises C++ performance without its main pain — memory bugs. In AI, it is used for high-load systems and infrastructure.
Yandex is actively moving critical services to Rust, Russian startups use it for blockchain projects with AI.
Advantages:
Memory safety without garbage collector
C++ level performance
Modern ecosystem
Growing demand in Russia
Cons:
Fewer AI libraries than Python or C++
Steep learning curve
Young ecosystem
Who is it for: For developers of productive systems who are tired of segfaults.
Average salary in Russia: 200-400 thousand rubles (high demand, few specialists)
Verdict: A promising choice for AI system infrastructure.
🚀 Go — infrastructure hero
Status: Unobtrusive worker
Specialization: Backend and DevOps
Go is not for training models, but for orchestration, API and microservices around AI — a great choice. Ozon, Avito, and Wildberries actively use Go for their backends.
Advantages:
Fast compilation
Excellent performance
Built-in concurrency support
Simple syntax
Popular in Russian startups
Cons:
Not many ML libraries
Not suitable for scientific computing
Who is it for: DevOps and backend developers who build infrastructure for AI.
Average salary in Russia: 150-300 thousand rubles.
Verdict: Ideal for orchestrating AI services, but not for ML itself.
🔥 Mojo is a newcomer with ambitions
Status: Rising star
Year of appearance: 2023
Mojo from Modular is an attempt to create a language with Python syntax and C++ performance. By September 2025, it can already be installed via pip. Ambitious!
Advantages:
Syntax compatible with Python
Performance is close to C++
Native GPU support
Cons:
Still not production-ready in 2026
Small ecosystem
Experimental frameworks and libraries
There are almost no vacancies in Russia
Who is it for: For enthusiasts and those who love bleeding edge technology.
Verdict: Keep an eye on him, but it's too early for production.
🧮 Julia — scientific speed
Status: Scientists' favorite
Specialization: Scientific computing
Julia was created specifically for scientific computing and data science. Fast as C, convenient as Python. In Russia, it is used at Moscow State University, Moscow Institute of Physics and Technology, and research centers.
Advantages:
High performance
Excellent for mathematical calculations
Good for parallelism
Popular in Russian science
Cons:
Fewer libraries than Python
Smaller community
Steeper learning curve
Few vacancies in commercial companies
Who is it for: Data scientists and researchers working with scientific computing.
Average salary in Russia: 100-200 thousand rubles (mainly in scientific organizations)
Verdict: An excellent choice for academia and scientific ML.
📊 R — a statistical mastodon
Status: Academic legend
Specialization: Statistics and data analysis
R is the language of statisticians. If your job is data analysis and visualization, R is unsurpassed. In Russia, it is used in pharmaceuticals, bioinformatics, and sociological research.
Advantages:
Unsurpassed for statistical analysis
Excellent libraries for visualization
A huge community in the academic environment
Cons:
Not a universal language
Slower than Python in production
Less suitable for general-purpose tasks
Who is it for: Statisticians, bioinformaticians, researchers.
Average salary in Russia: 100-180 thousand rubles.
Verdict: A specialized tool for specific tasks.
🎭 Scala — big data and functionality
Status: Enterprise big data
Specialization: Apache Spark and distributed systems
Scala + Apache Spark = powerful ML pipelines for big data. Yandex, VK, Mail.ru use Scala to process petabytes of data.
Advantages:
Great for big data
Compatibility with the Java ecosystem
Functional programming
Strict typing
Cons:
Steep learning curve
Not so friendly for beginners
Fewer vacancies than Python/Java
Who is it for: Big data for engineers and those who work with distributed ML systems.
Average salary in Russia: 180-320 thousand rubles.
Verdict: The choice for enterprise big data ML.
🎓 C# — Microsoft ecosystem
Status: Stable corporate player
Specialization: .NET and Azure
ML.NET will have matured into a normal framework by 2026. If your company is on the Microsoft stack, C# is a logical choice. Many banks and state corporations in Russia use .NET.
Advantages:
Excellent integration with Azure
ML.NET for AI inside .NET applications
Microsoft's large ecosystem
Many vacancies in the enterprise
Cons:
Fewer AI libraries than Python
Microsoft ecosystem binding
Who is it for: .NET developers in the enterprise.
Average salary in Russia: 150-280 thousand rubles.
Verdict: A logical choice for corporate .NET teams.
🎯 How to choose your language?
Now the most important question: what exactly to teach?
If you are new to AI:
Python. No options. Quick start, huge Russian-speaking community, a bunch of tutorials on Habr and YouTube.
If you are making web applications with AI:
JavaScript/TypeScript + Python in the backend. Perfect for startups.
If you work in a Russian enterprise:
Java (Sber, VTB, MTS) or C# (state corporations, banks).
If you work in e-commerce (Ozon, Wildberries, Avito):
Python for ML + Go for the backend.
If you need speed:
C++ (Yandex, game dev) or Rust (modern startups).
If you work with big data:
Scala + Apache Spark (VK, Yandex).
If you are engaged in science:
Julia or R (universities, research centers).
📚 Do you want to improve your programming skills?
Theory is cool, but without practice it is useless. That's why we created Code - an application where you learn programming not through boring lectures, but through real practice.
Plus, we have Telegram channel
Community 2000+ Russian developers, where:
🔥 Useful posts about programming are coming out
💡 New technologies and trends in the Russian market are discussed
🚀 Real developers from Yandex, Sber, VK, and startups share their experience
📖 You can repeat the material in a convenient format
💼 Vacancies and internships are published
Subscribe to Kodik and join the community is a great way to not just read about programming languages, but to really master them!
🎬 Final thoughts
2026 showed: there is no perfect language for AI. There is a right tool for a specific task.
The main thing is not to get stuck in the paralysis of choice. Start with Python, make your first project with real data (for example, classification of reviews on Ozon or prediction of prices on Avito), and then expand the stack for tasks.
AI is not the future. It is the present of the Russian IT market. The demand for AI developers is growing faster than for any other specialty. Now is the time to learn.