Still Betting on Python? The AI Language Wars in 2025 Are Just Getting Started

Choosing a programming language for AI in 2025 is less about syntax and more about survival.

Back in 2018, saying “I’m building an AI product” usually meant strapping TensorFlow onto some Python spaghetti and praying your EC2 instance didn’t melt. Fast forward to 2025, and you’ve got a sprawling buffet of choices—frameworks for days, language-specific libraries, even LLMs that can write your code for you.

But the core question remains: which programming languages actually matter for building AI products this year? Not for PhD research. Not for viral GitHub demos. For actual, battle-tested systems—tools you can use to ship AI that works, scales, and doesn’t collapse at 3 a.m. during inference.

Let’s break down the contenders, the pretenders, and the surprisingly effective sidekicks.

Python: King of the Hill, Cluttered Throne Room

If AI had a lingua franca, it’s Python. Still.

Yes, people love to hate it. It’s slow. It’s dynamically typed. Half your environment setup time is spent arguing with pip, conda, and the ghost of some old CUDA driver. But none of that matters when your entire stack—from NumPy and pandas to PyTorch and LangChain—is written in it.

At 1985, we still reach for Python first when:

• Prototyping anything with an LLM or RAG system
• Training models where tensor logic trumps execution speed
• Integrating with the exploding Hugging Face + OpenAI ecosystem

The real reason it wins? The talent pool. You can find Python-literate ML folks in nearly every market. You can find contractors. You can find interns who ship. Even ChatGPT thinks in Python when you ask it to write code.

That said, if your inference workloads need to scream (think: drone navigation, on-device assistant), Python becomes a bottleneck. It’s the whiteboard, not the racetrack.

Rust: From Systems Geek to AI’s Infrastructure Darling

If Python is the idea board, Rust is the engine room.

Three years ago, we’d call Rust in AI a niche. Now? We’re seeing real traction—especially among infra-heavy teams tired of C++’s decades-old footguns.

Why it’s earning love:

• Memory safety + concurrency = massive win for parallelized inference systems
• Performance that rivals (and often beats) C++
• Modern tooling and growing support: Hugging Face’s Candle, Burn, and several vector DBs are Rust-first

We’ve used Rust in prod for:

• AI agents that need speed and fault tolerance (our LangChain-like system for internal task routing is part Rust)
• Lightweight inferencing services deployed to Kubernetes clusters
• Building a high-throughput image captioning API without burning 10x the compute budget

Rust is the language you choose when your AI stack becomes infrastructure. But good luck onboarding junior devs to it. The learning curve is real, and Stack Overflow often leaves you hanging.

Go: The Quiet Workhorse Behind LLM-Powered APIs

Go is that quiet team member who doesn’t speak unless spoken to—and still ends up shipping the most stable services.

It’s not flashy. It’s not topping any AI research papers. But it is the glue for a lot of real-world AI deployments.

Where Go shines:

• Serving AI inference APIs (especially in LLM pipelines)
• Vector database internals (Weaviate, Milvus have Go DNA)
• Connecting various services via gRPC, Pub/Sub, or Kafka

We’ve had success using Go when:

• Spinning up async task queues that pull from Pinecone and return LangChain responses
• Monitoring and logging GPU workloads at scale (with Prometheus/Grafana)
• Wrapping Python models behind efficient HTTP layers without reaching for FastAPI

For AI engineering teams scaling past the proof-of-concept phase, Go is often where the refactors start.

TypeScript: You Can’t Spell UX Without TS in 2025

Okay, that’s not true. But if you’re building anything customer-facing with AI under the hood, TypeScript is now unavoidable.

Frontend folks have gone from “add React” to “add embeddings, autocomplete, voice, translation, and a chatbot.” And they’re doing it all with:

• LangChain.js
• ONNX.js and TensorFlow.js
• Custom wrappers for OpenAI, Cohere, or Anthropic APIs

In our projects, we use TypeScript when:

• Building internal dashboards that let non-tech teams run prompt chains
• Embedding retrieval-augmented search in marketing tools
• Prototyping mini-chatbots that live on landing pages or inside CRMs

Fun fact: some of our fastest LLM feature experiments shipped as TS-only builds—with zero backend inference, all in-browser. Not scalable, but damn fast to test.

C++: Still Undefeated at the Metal

Look under the hood of any serious AI library—TensorFlow, PyTorch, ONNX Runtime, TensorRT—and you’ll find C++ doing the heavy lifting. It’s still the language of performance.

Nobody prototyping in C++. But when your AI model hits millions of users, inference time is measured in milliseconds, and hardware utilization matters, C++ re-enters the chat.

Where we see it:

• Optimizing GPU usage on inference servers
• Writing custom operators for model execution
• Extending native parts of PyTorch with better performance guarantees

But unless you have a team that eats pointer arithmetic for breakfast, it's a language best reserved for seasoned devs who don't flinch at segfaults.

Julia: Still the Underdog (But Finally Useful?)

Julia has always felt like a dreamer’s language—designed for numerical computation, built with elegance, but left out in the cold when it came to ecosystem traction.

2025 update: it’s gaining ground. Slowly.

Where Julia works:

• In academic ML research, where performance and math readability matter
• In teams doing scientific simulations that now want to bolt AI on top
• Where Python bottlenecks are unacceptable but Rust/C++ is overkill

Will we hire for Julia? Maybe. But only when the candidate is a wizard with it. Otherwise, it's still an edge case in the real-world AI playbook.

Wild Cards: Niche Picks With Surprising Use Cases

Let’s rapid-fire a few more:

R: Great for data analysis, still terrible for prod ML. Your data science lead probably uses it in secret.
Swift: Core ML is deeply entrenched in Apple’s ecosystem. We’ve built offline inference into iOS apps this way.
Kotlin: For Android devs working with TensorFlow Lite. Stable, familiar, and way better than Java.
Shell/Bash: Yes, half the AI pipelines we’ve seen are orchestrated by duct tape and cron jobs. Don’t judge.

Scorecard: Who Wins Where?

Here’s our 2025 cheat sheet. Feel free to yell at us on Reddit later.

Our Stack Recommendation? Go Polyglot or Go Home

If you're building AI in 2025, you're likely already polyglot—even if you don't know it.

• Your data scientists are in Python
• Your infra team is sneaking in Rust or Go
• Your frontend lives in TypeScript
• Your DevOps team curses in Bash
• And your LLM wrappers are... ChatGPT-generated YAML now?

The key isn’t picking the "best" language—it’s knowing when to hand off.

Wrap-up: Ship in the Language That Ships for You

Picking the right AI language in 2025 is like picking a Formula 1 engine. It’s not about which one looks coolest in the garage. It’s about what gets you around the track fastest, given your team, your tech, and the corners you're about to take.

At 1985, we’re stack-agnostic but opinionated. We’ve shipped AI features in all of the above—some elegant, some duct-taped, all working.

Need a team that can wrangle Rust runtimes, speak Python fluently, and make TypeScript do LLM magic? Ping us. We’ll build, debug, and deploy it—whatever your flavor.

1. Is Python still the best language for AI in 2025?
Yes—for most use cases. Python remains dominant thanks to its mature ecosystem, enormous community, and support from frameworks like PyTorch, TensorFlow, and Hugging Face. It's ideal for prototyping, model development, and integrating with modern AI tools. However, it lags in performance, so for edge or high-throughput applications, you'll need to pair it with something faster under the hood.

2. When should I choose Rust over Python for AI?
Choose Rust when performance, memory safety, and concurrency are critical. It's a solid choice for building inference runtimes, AI agents, and model-serving infrastructure. Rust isn’t great for fast experimentation, but it shines once your models are production-bound and you need reliability at scale.

3. How is Go used in real-world AI projects?
Go is increasingly used for backend AI services—especially where scalability and concurrency matter. It’s a popular choice for orchestrating inference APIs, handling data pipelines, or gluing together vector search, caching, and LLM APIs. You likely won’t train models in Go, but it’s excellent for running them reliably in prod.

4. Is C++ still relevant in AI today?
Absolutely. C++ powers the performance-critical internals of nearly every major AI framework. If you’re writing custom ops, optimizing for GPUs, or building edge-AI systems with tight latency constraints, C++ is still your go-to. Just know that it’s harder to work with and slower to iterate on compared to modern languages.

5. Can I use JavaScript or TypeScript for AI development?
Yes, especially for AI products with a strong UX layer. With tools like TensorFlow.js, ONNX.js, and LangChain.js, you can now bring real-time AI inference into browsers and apps. TypeScript is increasingly common for orchestrating LLM interactions on the frontend. Just don’t expect to train models or run heavyweight inference with it.

6. What’s holding Julia back from wider AI adoption?
Ecosystem maturity and developer adoption. Julia is elegant, performant, and great for math-heavy workloads, but it still lacks the deep library support and broad hiring pool of Python. It’s best suited for research or numerical computing projects where both speed and precision matter.

7. How do I decide which AI language to use for my project?
Start with your primary constraint—team expertise, performance requirements, or time to market. Use Python for prototyping and research, Go or Rust for backend AI infrastructure, TypeScript for frontend LLM features, and C++ when inference needs to be blazing fast. In most real-world teams, you’ll end up mixing two or three.

8. Is there a “full stack” AI stack you recommend in 2025?
For most teams, a pragmatic AI stack looks like: Python for model development and orchestration, Go or Rust for serving and infra, and TypeScript for user interfaces and LLM interaction. This gives you velocity, scalability, and product polish without locking you into one brittle ecosystem.

9. Will newer languages replace Python in AI anytime soon?
Not in the short term. Python has inertia, tooling, and community support that’s hard to beat. Languages like Rust and Go are gaining ground in specific areas (like serving and edge AI), but Python remains the best entry point and orchestration layer for now. Think “complement,” not “replace.”

10. What language should I invest in learning for a career in AI?
Start with Python—it’s the universal AI toolkit. If you’re leaning toward backend systems, learn Go or Rust. If you’re more product- or UX-oriented, add TypeScript to your stack. And if you want to go deep into performance optimization or compiler-level AI work, C++ is still worth the pain.