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One of the more interesting questions emerging around AI-assisted software engineering isn't whether models can write code.
We've already crossed that threshold.
The harder question is:
Which programming languages are best suited for AI-generated software?
One lesson we've seen at GoDaddy is that AI-assisted development changes what we optimize for. It's no longer enough to generate code quickly—we also need systems that help AI continuously verify, refine, and improve what they produce.
I'm a huge Rust fan. It's extremely durable, and its lints and chatty compiler are exactly the kind of feedback loops LLMs devour. We're seeing systems running at one-tenth the cost with 10x the durability, all built through AI test-driven development with full documentation and backward/forward compatibility.
Rust offers an interesting answer.
Its ownership model, strict compiler, and memory safety guarantees create an environment where verification becomes part of the development loop rather than something added afterward.
For autonomous coding agents, that's a meaningful shift.
Instead of relying primarily on runtime failures or manual review, agents receive immediate compiler feedback that improves subsequent iterations. The compiler effectively becomes another participant in the reasoning loop.
This shifts the conversation beyond code generation.
It's about building engineering systems where generation, verification, and correction happen continuously.
As agentic development evolves, language design may become just as important as model capability.
Great technical write-up from the GoDaddy Engineering team.
🔗 https://www.godaddy.com/resources/news/rust-is-the-native-language-of-agentic-development
Curious whether others think AI-native development will influence programming language adoption—or whether models will simply adapt to the languages developers already use.
We've already crossed that threshold.
The harder question is:
Which programming languages are best suited for AI-generated software?
One lesson we've seen at GoDaddy is that AI-assisted development changes what we optimize for. It's no longer enough to generate code quickly—we also need systems that help AI continuously verify, refine, and improve what they produce.
I'm a huge Rust fan. It's extremely durable, and its lints and chatty compiler are exactly the kind of feedback loops LLMs devour. We're seeing systems running at one-tenth the cost with 10x the durability, all built through AI test-driven development with full documentation and backward/forward compatibility.
Rust offers an interesting answer.
Its ownership model, strict compiler, and memory safety guarantees create an environment where verification becomes part of the development loop rather than something added afterward.
For autonomous coding agents, that's a meaningful shift.
Instead of relying primarily on runtime failures or manual review, agents receive immediate compiler feedback that improves subsequent iterations. The compiler effectively becomes another participant in the reasoning loop.
This shifts the conversation beyond code generation.
It's about building engineering systems where generation, verification, and correction happen continuously.
As agentic development evolves, language design may become just as important as model capability.
Great technical write-up from the GoDaddy Engineering team.
🔗 https://www.godaddy.com/resources/news/rust-is-the-native-language-of-agentic-development
Curious whether others think AI-native development will influence programming language adoption—or whether models will simply adapt to the languages developers already use.
