According to Boris Churney, the specific programming language a developer uses is becoming less important because the AI model doesn't care. While features like type-checking currently help guide the model, future AIs will be sophisticated enough to write perfect code directly in low-level languages like assembly, abstracting away language choice entirely.

The cost of generating code with AI is trivial, shifting the primary expense to its maintenance, validation, and deployment. This inverts the traditional software engineering model where human code production was the main bottleneck, making code's complexity a liability.

Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.

The current software development workflow is paradoxical: engineers use natural language to prompt an AI, but then commit the resulting machine-readable code to a repository. This forces human teammates to interpret machine language instead of the original human intent. The future lies in committing the high-level intent itself.

As AI rapidly generates code, the challenge shifts from writing code to comprehending and maintaining it. New tools like Google's Code Wiki are emerging to address this "understanding gap," providing continuously updated documentation to keep pace with AI-generated software and prevent unmanageable complexity.

It's infeasible for humans to manually review thousands of lines of AI-generated code. The abstraction of review is moving up the stack. Instead of checking syntax, developers will validate high-level plans, two-sentence summaries, and behavioral outcomes in a testing environment.

As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.

AI agents can generate code far faster than humans can meaningfully review it. The primary challenge is no longer creation but comprehension. Developers spend most of their time trying to understand and validate AI output, a task for which current tools like standard PR interfaces are inadequate.

Programming languages like Python were designed for human readability. As AI models become the primary producers and verifiers of code, the dominant languages will likely shift to ones optimized for machine generation and formal verification. The focus will move from human convenience to provable correctness and efficiency for AI agents.