Resolution criteria

This market resolves YES if, by December 31, 2035, a majority of accredited programming schools and bootcamps teach AI-assisted "vibe coding" as their primary method of instruction for teaching code generation and development. Resolution will be determined by:

1. Curriculum analysis of major programming schools (e.g., General Assembly, Flatiron School, Springboard, university computer science departments) showing vibe coding as the primary pedagogical approach

2. Industry surveys or reports from organizations like ACM or IEEE documenting that vibe coding is the dominant teaching methodology

3. Educational accreditation standards or guidelines that identify vibe coding as the primary instructional method

The market resolves NO if traditional syntax-focused or line-by-line manual coding remains the primary teaching method, or if AI-assisted coding is taught as a supplementary tool rather than the primary approach.

Background

Vibe coding was introduced by computer scientist Andrej Karpathy in February 2025, and refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing it. By early 2025, nearly 44% of developers had adopted AI coding tools, and 25% of Y Combinator startups in Winter 2025 had codebases that were 95% AI-generated.

Educational adoption is emerging but nascent. Stanford Continuing Studies offers a course teaching vibe coding where natural conversation becomes the primary programming language for creating applications, and some coding bootcamps are beginning to incorporate AI pair programming as a skill. However, as of 2025, vibe coding is at the forefront of software development, gaining traction but not yet universal.

Considerations

A key part of vibe coding's definition is that users accept AI-generated code without fully understanding it, distinguishing it from responsible AI-assisted development where code is reviewed and tested. Developers using AI-generated code without comprehension risk undetected bugs and security vulnerabilities, and while suitable for prototyping, experts consider it risky in professional settings where deep code understanding is crucial.

Research shows that while AI increases productivity for experienced developers, it can widen learning gaps for beginners, with junior programmers struggling to land entry-level roles since AI automates simple coding tasks that once served as training grounds. This tension between accessibility and foundational learning may influence whether schools adopt vibe coding as their primary method versus maintaining traditional fundamentals-first approaches.