Bend 2 and the Vibe-Coding Trap

If you are exploring AI-assisted coding, the tools you use might generate lengthy proofs when automated verification already exists.

Bend, a new programming language pitched for the AI coding era, allows humans to write "laws" which AI then implements and for which compilers generate proofs. The author, Liam Powell, criticizes Bend for falling into a "vibe-coding trap," arguing that its creator built the language and compiler without fully researching the existing field of formal verification. Powell points to the Bend demo, which requires 58 lines of code to state game rules and a substantial 442 lines for the AI to generate proofs. In contrast, Powell demonstrates a similar program in SPARK, a formal verification language, where the entire proof of correctness is automatically generated and verified by the compiler with minimal input, highlighting that Bend's approach is significantly less efficient and overlooks established solutions.

The "vibe-coding trap" described by Powell suggests that the ease of AI-driven development can lead creators to build complex systems without thoroughly researching or understanding the existing landscape of their chosen field. Bend's creator, for example, developed a language and compiler for AI-assisted formal verification without seemingly acknowledging the mature field of formal verification itself, which already offers more efficient solutions. This phenomenon implies that AI tools, while powerful, might inadvertently guide users toward reinventing existing wheels rather than building upon established knowledge.

This situation signals a broader challenge in the AI era: the potential for AI to accelerate development without necessarily imparting foundational knowledge or encouraging critical research. Developers may become adept at generating code and proofs via AI prompts, but without understanding the underlying principles or available tools, they risk creating inefficient or outdated solutions. Powell's SPARK example demonstrates that established tools can automate complex verification tasks far more effectively than a bespoke, AI-generated system built from scratch, suggesting a need for greater awareness of existing specialized tools.