Dennis Ritchie’s Legacy Lives On
The Enduring Relevance of C in the Era of Large Language Models
Back in October 2011, when we lost Dennis Ritchie, the world was a very different place. I remember that time clearly — we were all excited about the iPhone 4S, cloud computing was just starting to take off, and if you’d mentioned “large language models” to most developers, you’d have gotten blank stares. That same month we lost Steve Jobs.
Later that month, I delivered a eulogy for DMR at our local C/C++ meetup. I have put the text at the end of this article. You can also find it in the archive — https://www.sanjaysays.com/2011/10/
Now, sitting here in 2024, surrounded by AI breakthroughs and specialized hardware accelerators, I find myself thinking about how Ritchie’s creation — the C programming language — isn’t just surviving; it’s thriving at the very heart of these innovations.
You know what’s fascinating? While all the AI headlines focus on Python code and fancy neural networks, C is quietly powering the entire show behind the scenes. Every time a data scientist writes some PyTorch or TensorFlow code, they’re standing on top of a mountain of C and C++. Those blazing-fast NVIDIA CUDA drivers? C. Google’s TPU kernels? C. The core of Python itself? You guessed it — C. It’s almost poetic how Ritchie’s work continues to be the foundation upon which we’re building the future.
What really gets me excited is how relevant C remains in 2024, especially as we push the boundaries of computing. When you’re trying to squeeze every ounce of performance out of a GPU for AI training, there’s still nothing quite like C’s ability to speak directly to the hardware. I see this every day in my work — when performance really matters, when every microsecond counts, we inevitably find ourselves reaching for C.
The explosion of edge computing and embedded AI has been particularly interesting to watch. These small, resource-constrained devices running AI models simply can’t afford the overhead of higher-level languages. Try running Python on a tiny sensor with real-time requirements, and you’ll quickly understand why C remains indispensable. It’s remarkable how Ritchie’s focus on efficiency and direct hardware control seems almost prescient now.
Let’s talk about the infrastructure running all these AI systems. Whether you’re using Windows, Linux, or macOS to train your next neural network, you’re relying on operating systems built predominantly in C. Sure, Rust is making inroads in systems programming, and that’s exciting, but if anything, it’s validating the principles Ritchie championed — the importance of direct hardware access, explicit control over resources, and clean, efficient code.
Consider the tech giants’ AI models for a moment. When OpenAI runs GPT, or when we interact with Anthropic’s Claude, the high-level interfaces might be in Python, but the critical, performance-sensitive paths? They’re often running optimized C code. The same goes for the neural network compilers, memory management systems, and hardware abstraction layers that make modern AI possible. It’s like C is the silent partner in every major AI breakthrough.
What really strikes me is how Ritchie’s philosophy continues to shape modern software development. Even as we build increasingly complex systems, his emphasis on simplicity and clarity echoes through modern API design. The Unix philosophy he helped develop — small, focused tools working together — feels more relevant than ever in our age of microservices and distributed systems.
Looking ahead, as we venture into quantum computing and neuromorphic hardware, I’m convinced C’s influence will persist. The language might be older than many of the developers using it, but its principles are timeless. In my years of programming, I’ve seen frameworks and languages come and go, but the fundamentals Ritchie taught us — that efficiency matters, that simplicity beats complexity, that understanding hardware is crucial — these things never go out of style.
What I find most heartening is seeing new programmers still learning C. Not just because they have to, but because understanding C means understanding how computers actually work. In our world of increasing abstraction, this fundamental knowledge becomes more valuable, not less. It’s like learning to drive a manual car — it gives you an appreciation for what’s really happening under the hood.
Ritchie’s C is like Latin in natural languages — not just a historical artifact, but the root from which modern computing has grown. As we push forward with AI and hardware acceleration, we’re not moving away from C’s influence; we’re building upon its foundation in ways that Ritchie might never have imagined, but would surely appreciate. When I write code today, whether it’s for AI systems or embedded devices, I still find myself thinking in C-like terms, appreciating the elegant simplicity of Ritchie’s design.
In 2024, as we watch transformers transform and neural networks evolve, we’re still speaking Ritchie’s language. The tools may have changed, but the principles he established — efficiency, clarity, and elegant simplicity — remain as relevant as ever. In the end, that may be his greatest legacy: not just creating a language that endures, but establishing principles that transcend time and technology. Every time I open a terminal or write a line of code, I’m grateful for the foundation he laid. The future of computing may be uncertain, but one thing’s clear — we’re still standing on the shoulders of this giant.
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