Python.

Why: biggest AI/ML ecosystem (PyTorch, TensorFlow/JAX, scikit‑learn, Hugging Face, OpenAI/Anthropic SDKs), fastest to prototype, easy to ship with FastAPI/Docker, huge community.

Pick something else if: - Web app mostly calling hosted LLM/vision APIs: TypeScript/JavaScript (Next.js/Node, Vercel AI SDK, LangChainJS). - Mobile/on-device AI: Swift (iOS with Core ML/MLX) or Kotlin (Android with TensorFlow Lite/NNAPI). - High‑throughput backend/services: Go or Java/Kotlin. - Maximum performance/custom inference/runtime work: Rust or C++.

Practical starter stack: Next.js (TypeScript) frontend + Python (FastAPI + PyTorch/Transformers) inference service + Postgres/Redis (+ pgvector or a vector DB).

Want a tailored pick? Is your app web or mobile, will you use hosted APIs or train your own models, and do you need offline/on-device inference?

I’ll keep this response streamlined since this is a repeated question, focusing on the core points with a fresh perspective. The best programming language for building an AI app depends on your project’s needs and your experience level, but here are the top choices:

1. Python (Top Choice)

• Why: Beginner-friendly with a simple syntax, and it dominates AI development due to powerful libraries like TensorFlow, PyTorch, and scikit-learn. It supports machine learning, deep learning, NLP, and more.
• Best for: Most AI apps (e.g., chatbots, recommendation systems, image processing), rapid prototyping, and learning.
• Drawbacks: Slower runtime compared to lower-level languages (often offset by optimized tools).

2. C++

• Why: Offers unmatched speed and control, crucial for performance-intensive tasks. Supports AI via libraries like TensorFlow’s C++ API and OpenCV.
• Best for: Real-time systems, embedded AI, robotics, and gaming AI.
• Drawbacks: Steeper learning curve and slower development process.

3. Java

• Why: Robust and scalable, with libraries like Deeplearning4j. Ideal for cross-platform and large-scale deployments.
• Best for: Enterprise AI applications and Android AI apps.
• Drawbacks: More complex and less agile for prototyping than Python.

4. JavaScript

• Why: Enables AI directly in web browsers using TensorFlow.js, making it accessible for web-based solutions.
• Best for: Web AI apps and client-side machine learning.
• Drawbacks: Not suited for heavy computational tasks.

5. R

• Why: Excels in statistical analysis and data science with packages like caret.
• Best for: Statistical AI models and research-focused projects.
• Drawbacks: Limited for general-purpose AI or production environments.

Recommendation:

Python is the best starting point for most people. It’s the industry standard for AI, widely used in academia and business, and offers the most resources and community support. If you need high performance (C++), web integration (JavaScript), or enterprise scalability (Java), you can explore those later or combine them with Python.

If you’d like a more tailored suggestion, could you tell me what kind of AI app you’re aiming to build or your programming background? I’m happy to dive deeper!