The Rust Paradox: Why Programming is Both Being Automated and Getting Harder

2025-03-14

Let's talk about a strange contradiction we're seeing in 2025. On one hand, we hear that AI is automating programming jobs. On the other hand, Rust - a systems programming language - is considered "too hard to learn" by many. Both of these things can't be true at the same time. What's really happening, and why does it matter for your career?

Listen to the full podcast episode

Why Rust is Growing in the Age of AI

Rust is getting more popular despite all the AI coding tools coming out:

• New users joined Rust communities in 2024
• Major companies (Microsoft, AWS, Google, Cloudflare, Canonical) are all using Rust
• Even the Linux kernel now accepts Rust code - which is huge since Linux was C-only for decades

The tools built with Rust are incredibly fast and changing how we work:

• Ruff (a Python linter written in Rust) is 10-100x faster than traditional Python tools
• UV (a package installer built in Rust) blows away tools like Conda or Python virtual environments
• Polars (a Rust-based data analysis library) is much faster than pandas

The Programming Split Explained

Here's what's really happening: programming is splitting into two different paths.

• Application development: Things like websites, basic apps, and user interfaces are getting easier with AI help. This is like what happened with web frameworks and higher-level languages.
• Systems programming: Infrastructure, high-performance tools, and safety-critical code still need human experts. AI struggles here because the details really matter.

As I explain on the Pragmatic AI Labs platform (paiml.com), AI coding tools are pattern-matching systems, not truly intelligent. As I say in the podcast, "It would be like saying K-means clustering is a financial advisor." It's nonsense!

Why Learn Rust When AI Can Write Code?

Rust solves problems that AI can't handle well:

• Memory safety without slowing down performance
• Finding bugs at compile time instead of in production
• Thread safety for multi-core processing without race conditions

Yes, Rust has a learning curve. But at Pragmatic AI Labs, we've created comprehensive courses that make learning Rust much easier. The investment pays off big time:

• Less debugging after deployment
• Better performance for your applications
• Higher-paying job opportunities as basic coding gets automated

The Future: Rust + AI Together

Rust and AI will work together, not against each other:

• AI can help make Rust easier to learn and use
• Rust provides guardrails that make AI-generated code safer
• The most valuable developers will know when to use AI and when to code in Rust

At Pragmatic AI Labs (paiml.com), we're fully committed to Rust in the era of AI coding. We have more Rust courses than you'll find anywhere else, covering everything from basics to AI integration, data engineering, MLOps, WebSockets, and WebAssembly.

What This Means For You

As programming changes (like it has for decades), the hard parts become more valuable, not less. Rust excels exactly where AI currently falls short.

The paradox isn't really a paradox - it's just a natural split in how software gets built. If you want to future-proof your career, learning Rust alongside AI tools is one of the smartest moves you can make in 2025.

Check out our comprehensive Rust courses at paiml.com and join the revolution in systems programming!

// The future belongs to those who can do both
fn career_strategy() {
    learn_rust_at_paiml();
    master_ai_tools();
    combine_for_superior_results();
}

Want expert ML/AI training? Visit paiml.com

For hands-on courses: DS500 Platform

Recommended Courses

Based on this article's content, here are some courses that might interest you:

1. AWS Advanced AI Engineering (1 week) Production LLM architecture patterns using Rust, AWS, and Bedrock.

2. Python Essentials for MLOps (5 weeks) Learn essential Python programming skills required for modern Machine Learning Operations (MLOps). Master fundamentals through advanced concepts with hands-on practice in data science libraries and ML application development.

3. 52 Weeks of AWS: Complete Cloud Certification Journey (21 weeks) Complete AWS certification preparation covering Cloud Practitioner to Machine Learning specializations in 52 weeks

4. Rust-Powered AWS Serverless (4 weeks) Learn to develop serverless applications on AWS using Rust and AWS Lambda. Master the fundamentals of serverless architecture while building practical applications and understanding performance optimizations.

5. Rust Data Engineering (4 weeks) Master data engineering principles using Rust's powerful ecosystem and tools. Learn to build efficient, secure, and scalable data processing systems while leveraging cloud services and machine learning capabilities.

Learn more at Pragmatic AI Labs