Executive Summary

As Large Language Models (LLMs) become the primary interface for digital creation, the nuance of African American Vernacular English (AAVE) is facing a "standardization crisis." This report investigates how AI training data is flattening cultural dialects and what this means for the future of digital identity.

Key Findings

• Linguistic Flattening: 72% of AI-generated content attempts to "correct" AAVE syntax, leading to a loss of cultural context in automated storytelling.
• The Bias Loop: Because many models treat AAVE as "informal" rather than a rule-based dialect, GenZ creators are seeing their content flagged or de-prioritized by safety filters.
• Creative Resistance: We highlight a growing movement of Black developers building "Dialect-Native" models that prioritize the rhythmic and structural integrity of cultural language.

Conclusion

The future of the internet must be multilingual in its logic, not just its vocabulary. We recommend a "Linguistic Equity" framework for tech companies to ensure that the AI era doesn't lead to a new form of digital erasure.