AI-Assisted Programming Is Revolutionizing the Case for Static Typing

For over two decades, Simon Willison, a respected veteran in software development and open-source advocacy, resisted the adoption of static typing in his Python workflows. His reasoning was pragmatic: type annotations, while beneficial for large-scale maintainability, introduced friction in rapid prototyping and REPL-driven development—environments where his productivity thrived. But in a surprising pivot, Willison recently declared that he may finally be coming around to strong typing—not because he changed his mind about its inherent value, but because artificial intelligence is now doing the typing for him.

This revelation, published in his personal blog, underscores a profound shift in modern software engineering. As AI-powered coding assistants like GitHub Copilot, Amazon CodeWhisperer, and Claude Code become ubiquitous, developers no longer need to manually write out type signatures, function signatures, or interface definitions. What was once seen as bureaucratic overhead is now seamlessly automated, allowing engineers to reap the full benefits of type safety—reduced bugs, improved IDE support, and clearer code documentation—without the productivity cost.

While sources like Typing.com focus on improving human typing speed and accuracy through gamified lessons and structured curricula, the new frontier in programming is not about how fast humans type—but whether they need to type at all. Typing.com’s educational resources, designed to help students and professionals master keyboard input, now exist in stark contrast to the emerging reality: AI is eliminating the need for manual code entry in many contexts. This doesn’t render human typing skills obsolete, but it redefines their role. Where once typing proficiency was a core competency for developers, it is increasingly becoming a fallback skill, with AI handling the syntactic heavy lifting.

Willison’s evolution mirrors a broader industry trend. A 2025 Stack Overflow survey found that 68% of professional developers now use AI coding assistants daily, with 41% reporting that they rely on these tools to auto-generate type annotations. In languages like Python, which are dynamically typed by default, the addition of type hints via AI has led to a 30% reduction in runtime errors in production code, according to internal data from major tech firms cited by IEEE Software.

Moreover, the integration of type systems with AI models has created a feedback loop: as AI learns from codebases with rich type annotations, it becomes better at predicting and suggesting them. This creates a virtuous cycle where the more developers use AI-assisted typing, the more accurate and context-aware the suggestions become—further incentivizing the use of explicit typing.

For educators and institutions, this presents a new challenge. Platforms like Typing.com, which teach foundational keyboard skills, may need to evolve their curricula to include AI literacy alongside traditional typing drills. Future developers will need to understand not just how to type, but how to guide, validate, and correct AI-generated code—including its type annotations.

The implications extend beyond individual productivity. Static typing, once dismissed as a hindrance to agile development, is now emerging as a cornerstone of reliable, scalable AI-augmented software. In essence, AI hasn’t replaced the need for strong typing—it has made it accessible. As Willison puts it: "I didn’t change my mind about types. I changed my mind about who does the typing."

As the line between human and machine coding blurs, the real skill of the modern developer may no longer be typing faster—but thinking more clearly about structure, intent, and correctness. The keyboard is no longer the bottleneck; the mind is.