Much of Gen Z slang follows a specific diffusion pattern. It often originates in Black communities (AAVE) or on 4chan, gets adopted by gay communities, spreads to their social circles, and eventually diffuses into mainstream culture.

Classic regional accents (New York, Boston, Southern) are disappearing in younger generations. However, this isn't creating a uniform sound. Instead, new accent divides are forming along social lines like race, urban vs. rural, and even political affiliation.

Contrary to being a 'lesser' language, slang is arguably richer than standard vocabulary. A standard word often has only a specific referential meaning, whereas a slang term simultaneously communicates the speaker's identity (e.g., Gen Z), their attitude (contempt, affection), and their desired self-perception.

Accents weren't just a byproduct of geographic separation; they likely offered a survival advantage. Natural selection favored the development of accents and our ability to perceive them, as it allowed early humans to quickly identify potential collaborators or conflicts within and between groups.

Creole languages, born from language contact, strip away needless complexities like grammatical gender and irregular verbs. They are radically easier to learn than older, "gunked up" languages yet remain fully expressive, making them a model of efficient linguistic design.

Contrary to the belief that Shakespeare wrote purely for the stage, he was highly aware of his reading audience. He knew people copied speeches for pirated anthologies and that his plays were sold as quartos, so he intentionally included passages for a literate elite who would dissect the text.

Modern audiences struggle with Shakespeare because hundreds of words have subtly changed meaning over 400 years (e.g., 'generous' meant 'noble'). This cumulative semantic drift makes the original text functionally a different language, requiring prior study, not just cultural appreciation, to understand.

Playwrights like Shakespeare used the em dash to guide actors on performance. It visually represented aposiopesis—speech deliberately broken off—to show thinking pauses, interruptions, or shifts in thought, adding a new layer of expressiveness to written dialogue.

Building datasets for marginalized vernaculars like AAVE isn't just about representation; it's about ownership and safety. The risk of a language being co-opted for nefarious purposes means the community itself must control and benefit from any AI tools built on their linguistic data.