Does AI Dominate All Programming Languages Equally?

No. AI performs far better in some languages (like JavaScript, Python, TypeScript) and far worse in others (like Rust, Haskell, COBOL, or highly specialized embedded languages).

AI Dominates Best in “High-Surface-Area” Languages With Huge Training Data

AI models perform best when a language:

• has massive open-source code ecosystems
• has lots of GitHub repositories (JS, TS, Python)
• appears everywhere in tutorials, forums, StackOverflow, docs
• is used in web development (simple, repetitive patterns ideal for LLMs)

The biggest beneficiaries:

• JavaScript / TypeScript
• Python
• SQL
• HTML/CSS (small domain, predictable)

These languages account for the majority of all public code on Earth, so AI models naturally excel at them. This is why AI coding demos overwhelmingly use JS, TS, or Python.

JavaScript Case

JavaScript is a dream for LLMs:

• It’s ubiquitous
• It’s forgiving
• It has millions of nearly identical code examples
• Prompts like “make a todo app” or “fetch this API” always produce workable patterns

This is why Bun, Vercel, Cloudflare, Anthropic, OpenAI, and GitHub all showcase AI coding features using JavaScript/TypeScript.

AI is Weaker in “Correct-by-Construction” or “Strict” Languages

Languages built around correctness, strict typing, manual memory management, or advanced type systems are harder for AI to generate correctly.

Examples where AI struggles more:

Rust

• Strong ownership and borrowing semantics
• Compiler is strict
• Memory safety rules are non-negotiable
• Small ecosystem relative to JS

AI can write Rust that looks right but fails to compile.

Haskell / OCaml / F#

• Strong functional paradigms
• Less real-world training data
• Abstract math-heavy patterns

C and C++

• Manual memory management
• Project structure complexities
• Undefined behavior hazards
• Inconsistent patterns across codebases

AI often generates code that segfaults or is not safe.

Go

• AI is moderately strong but weaker than in JS/Python because Go codebases are simpler but far fewer in number.

AI Is Worst at Specialized or Legacy Languages

Low-resource, domain-specific languages give available models very little to learn from:

• COBOL (mainframes, banks)
• ABAP (SAP)
• VHDL/Verilog (hardware description languages)
• MATLAB (scientific, proprietary)
• LabVIEW
• Embedded C dialects
  

These domains:

• Have minimal public code
• Often include proprietary/internal code
• Require deep domain knowledge beyond syntax
  

AI frequently generates syntactically valid but practically unusable code.

AI Performs Better at Glue Code Than Systems Code

AI excels at:

• CRUD
• API wrappers
• UI components
• CLI tools
• JSON transformations
• Refactoring
• Writing tests

But struggles with:

• Performance tuning
• Concurrency
• Systems-level code
• Distributed systems
• Memory-safety critical code
• Security-sensitive code
  

This is why AI produces React components with ease, but writing a correct multi-threaded C++ scheduler often fails.

Model Architecture Matters

Different AI models are better with different languages:

Claude, ChatGPT, Cursor

• Best at JS, TS, Python, SQL
  

DeepSeek Coder (latest generation)

• Stronger at C, C++, Rust, Go
  
• Explicitly trained on low-level code
  
• Still worse at JavaScript-scale “glue work,” but better at systems engineering tasks
  

Copilot (OpenAI o-series & gpt-4o)

• Extremely strong with TypeScript
  
• GitHub training bias

Tooling Influences AI Dominance

Languages with strong AI-integrated dev tools:

• JS/TS → Vercel AI, Copilot, Cursor
  
• Python → Jupyter, PyCharm, GitHub Copilot
  
• SQL → Natural language queries via AI
  

Languages lacking good AI-native IDEs (COBOL, ABAP) remain AI-poor.

Summary Chart

Language	AI Strength	Reason
JavaScript / TypeScript	⭐⭐⭐⭐⭐	Tons of training data, easy patterns, used everywhere
Python	⭐⭐⭐⭐⭐	Hugely popular, simple syntax, ML + scripting
SQL	⭐⭐⭐⭐⭐	Small domain, predictable patterns
Go	⭐⭐⭐⭐	Moderate data, simple syntax
Java	⭐⭐⭐⭐	Enterprise-heavy data, many repeatable patterns
C#	⭐⭐⭐	Good ecosystem, less open-source surface
C / C++	⭐⭐	Hard to reason, memory issues
Rust	⭐⭐	Strict compiler rules
Haskell / OCaml	⭐	Niche, complex semantics
COBOL, ABAP, HDL	⭐	Very limited public training data