Back to school: the best AI programming languages to learn first

AI is not a speciality anymore; it is a foundation of modern software. After years of shipping ML systems and helping thousands of developers build AI features, I have learned that language choice is strategic, not just technical. If you are prototyping an agent or hardening production inference, your language stack will either speed you up or slow you down.

The question I hear most in our masterclasses and developer Q&As is deceptively simple: What's the best programming language for AI?

The real answer depends on workflow, deployment constraints, and how fast your team needs to move. Here is the practical 2025 view.

What makes a language great for AI?

After years of wrangling ML infrastructure and collaborating with AI teams across industries, five factors consistently determine whether a language empowers or frustrates AI developers.

Five things matter in practice:

• Ecosystem maturity, the libraries, model APIs, and community patterns you can rely on.

• Developer experience, how quickly you can iterate, debug, and ship.

• Performance and deployment, latency, memory, and runtime footprint.

• Platform integration, from notebooks and IDEs to CI, GPUs, and cloud runtimes.

• Learning curve, especially for mixed teams of app devs, analysts, and domain experts.

These factors define whether a language helps you move fast and scale, or becomes friction in your workflow.

💡 For beginners, I always suggest starting with the [AI API Development Course] I put together. It distills what I’ve learned from years of building ML systems and teaching developers into a practical, project-based path.

And now, most importantly the languages…

Python

Python remains the best starting point in 2025. It dominates experimentation and production glue code. With PyTorch and Hugging Face for modeling, LangChain or LangGraph for agents, FastAPI for services, and official SDKs for model APIs, Python is unmatched in breadth.

Its magic lies in context continuity. You can begin with exploratory code in a notebook, wrap it into a FastAPI service, and deploy it on a GPU server, all without leaving the ecosystem. Teams that attempt to “translate” prototypes into another language often waste weeks on reimplementation instead of shipping value.

Where Python struggles is in low-latency serving or tight memory environments. The typical pattern is to prototype in Python, then deploy with optimised runtimes such as vLLM or NVIDIA Triton, or export to ONNX Runtime for portability. On mobile or embedded systems, exports target ONNX Runtime Mobile, TensorFlow Lite, or Core ML.

Bottom line: Start with Python. Profile bottlenecks, then optimise them with specialised runtimes or native code as needed. Selecting the IDE for Python also matters.

C++

Most teams don’t use C++ for everyday business logic. Instead, it powers the fast paths: kernels, runtimes, and performance-critical serving layers. Frameworks like PyTorch and TensorFlow expose C++ APIs, and production builds often ship models into C++ runtimes for tight latency and memory control. On NVIDIA GPUs, the high-throughput path typically runs through TensorRT or TensorRT-LLM.

Reach for C++ when Python hits its limits, when every microsecond matters, when you need deterministic memory behavior, or when writing device code. In practice, you’ll rely on high-performance libraries and exported models, while focusing your C++ code on kernels and serving layers.

JavaScript & TypeScript

A major 2025 trend is credible client-side inference. With WebGPU, models can now run directly in the browser delivering zero server round trips and strong privacy.

Practical options include TensorFlow.js with WebGPU, ONNX Runtime Web with WebAssembly or WebGPU, Transformers.js for Hugging Face models, and WebLLM for in-browser LLMs. These enable autocomplete, private chat, local embeddings, and responsive AI UIs.

Entire SLM agents (Small Language Models) now run client-side, validating inputs, generating suggestions, and processing media in real time. The experience feels magical, but hardware limits force models to be small and inference to be fast.

The bigger challenge for frontend engineers isn’t inference, it’s code organization. Prompts, embeddings, API calls, and inference logic quickly grow unwieldy. Structured workflows and memory tools are critical.  Tsavo (our CEO) and Scott Hanselman from Microsoft share tons of experience about it in this podcast.

Java

Java rarely shows up in AI hype threads, yet it quietly powers a lot of real systems, especially in financial services, logistics, and enterprise SaaS. In these environments teams often keep their existing JVM microservices and add inference, instead of rewriting stacks. That path is practical because Java has strong type safety, mature build tools like Maven and Gradle, and first class integration with the data plumbing many enterprises already run, Kafka, Hadoop, and Spark. 

In practice, you are not training the latest research models in Java. You are loading pre-trained models and serving them behind familiar frameworks. Teams use ONNX Runtime's Java bindings, TensorFlow Java, or engine-agnostic DJL, often inside Spring Boot services. Older but still useful libraries like Weka and Tribuo cover classic ML and tabular workflows. This keeps deployment simple: a WAR or fat JAR, a CI build, and the same observability and rollout procedures you already trust. 

The tradeoff is velocity. But if you're running large-scale systems where AI is one part of a bigger pipeline, and reliability is king, Java keeps things running. If your priority is stable, observable services that plug into existing JVM estates, Java is a very reasonable choice.

Julia

Julia set out to solve the two-language problem, you prototype and deploy in the same language, so you keep high level ergonomics while compiling to fast native code through LLVM. That goal makes Julia attractive when you want research speed without giving up performance, especially in scientific computing and numerical work.

In practice Julia shines when the math is the product. Teams use SciML’s toolchain for differential equations and model building, Flux or Lux for custom architectures, Zygote for differentiable programming, and CUDA.jl when kernels need to run on GPUs. 

The tradeoff is adoption and ecosystem depth. Julia’s community is smaller than Python’s, survey responses highlight “not enough users” as the biggest non technical issue, and popularity indices and broad developer surveys still place Julia well behind the mainstream choices used in industry. If your work is paper first or framework building, Julia fits well.

R

R isn’t trying to compete with Python for deep learning supremacy, and that’s exactly why it still matters. In domains like healthcare, public policy, and scientific research, R remains the go-to language for interpretable models, statistical rigor, and reproducible analysis.

In day-to-day projects teams use R for experiment analysis, model validation, and explainable ML. You can run A/B tests and Bayesian comparisons, cross-validate models with tidymodels or caret, and add local and global explanations with DALEX, iml, or lime. It is a good fit when the goal is to understand why a model behaves the way it does, not just to get a fast score. 

But the limitations are real. R doesn’t have deep support for LLMs or modern neural architectures. Production deployment options are limited. And development workflows can feel disconnected from the fast-moving world of LLM-based systems.

If your work centres on data quality, interpretability, and transparent results, R delivers. Use it to analyze, explain, and publish with confidence, and keep high-throughput LLM serving or custom neural work in ecosystems that specialise in that.

💡 High-quality, interpretable models depend on the data you feed them. If you want to learn how to collect and manage better datasets, check out my new video on Crowd-sourcing Data for Machine Learning. It dives into practical strategies for building reliable datasets in domains like healthcare, policy, and research.

Go 

You'll rarely see Go make AI Headlines, yet it appears behind many production inference services. This is where Go shines. Goroutines and channels make concurrency simple; the standard library gives you a solid net/http, and gRPC in Go is first class. In cloud native stacks, Go also fits naturally alongside Kubernetes and Docker, which themselves are written in Go. Put together, you get high throughput, low latency, and code that is easy to reason about.

In the field, I've seen Go used for inference microservices, streaming pipelines, and edge compute workloads. If you're wrapping a model with a scalable API or integrating AI into a cloud-native architecture, Go gets the job done with minimal overhead.

In practice, Go shows up as the serving tier, not the research sandbox. You call out to framework runtimes over HTTP or gRPC, for example NVIDIA Triton’s endpoints, or you hit hosted model APIs using the official OpenAI Go SDK. If you want an on-box option, there are community bindings for engines like llama.cpp, but most teams still prefer Python for training and heavy experimentation.

 If your goal is reliable serving, strong concurrency, and easy ops, Go is a pragmatic middle ground when Python is too heavy in production and C++ is too painful to maintain.

The multi-language reality of AI teams

In the real world, AI teams don’t use a single language, they use stacks. Research and exploration usually start in Python. Then, the model is wrapped behind an API in FastAPI or a Go service. The UI is TypeScript, and performance-critical paths are rewritten in C++ where latency and memory control matter.

Data teams often mix Python (often with long term memory) for feature work, R for validation and reporting, and Java for Spark jobs. On mobile, you deploy on device with Swift and Core ML on iOS, Kotlin and TensorFlow Lite on Android, or ONNX Runtime Mobile when you want a common path across platforms. The point is not to crown one language; it is to give each part of the system the runtime it needs

The most successful teams aren't dogmatic about languages; they're fluent in trade-offs, and that's the same advice our team gave when sharing about their choice of AI assistants. 

They use the right tool for the task, and rely on systems that help maintain context across language boundaries. That might be code snippets, prompt templates, experiment tracking, or workflow memory systems like Pieces.

As the AI stack fragments, context continuity becomes a superpower, and modern tools can now automate much more of that memory than ChatGPT alone.