Modern WisdomTHEY’RE BRAINWASHING YOU! (& other secrets that made you click) - Etymology Nerd
Chris Williamson and Adam Aleksic on how platforms, influencers, and AI reshape language, identity, and attention.
In this episode of Modern Wisdom, featuring Chris Williamson and Adam Aleksic, THEY’RE BRAINWASHING YOU! (& other secrets that made you click) - Etymology Nerd explores how platforms, influencers, and AI reshape language, identity, and attention “Word of the year” picks and viral nonsense terms (e.g., “six seven”) are framed as marketing and clip-farming tactics that exploit the attention economy.
At a glance
WHAT IT’S REALLY ABOUT
How platforms, influencers, and AI reshape language, identity, and attention
- “Word of the year” picks and viral nonsense terms (e.g., “six seven”) are framed as marketing and clip-farming tactics that exploit the attention economy.
- Different platforms and subcultures generate distinct “dialects,” where slang functions as identity signaling and in-group membership, accelerating language change via algorithms.
- Influencer and broadcaster voices are treated as engineered performance styles (floor-holding, uptalk, pacing, clarity) optimized for retention, authority, or excitement.
- AI is already feeding back into human language—detectably shifting word choice (e.g., “delve”) and writing patterns—raising concerns about hidden biases and homogenization.
- Language change is portrayed less as decay (“brain rot”) and more as adaptive creativity, while warning that distribution incentives privilege arousal (rage, fear, awe) over contentment and nuance.
IDEAS WORTH REMEMBERING
5 ideas“Word of the year” is often a distribution strategy, not a linguistic verdict.
Adam argues dictionary word-of-the-year selections can be marketing plays that ride controversy and meme momentum, similar to how creators “clip farm” to trigger sharing and engagement.
Absurd viral terms can still “mean” something socially.
Even vacuous phrases like “six seven” are described as meta-commentary on the information ecosystem—designed to provoke questions, generate clips, and signal awareness of the attention panopticon.
Platforms function like “houses” with expected registers and dialects.
Just as you speak differently at your grandmother’s than at a frat house, users adopt platform-specific norms (LinkedIn professionalism, Twitter play, fandom lexicons), with many micro-dialects inside each.
Influencer voices are engineered for retention and positioning.
Lifestyle influencer speech emphasizes warmth/relatability (uptalk, drag-out syllables), while educational influencer speech emphasizes authority (faster pacing, stressed keywords), and MrBeast-style delivery prioritizes shock-and-awe excitement.
Uptalk, filler words, and “No, because…” are attention tools—often unconscious.
“Floor-holding” keeps the listener from “scrolling away” by signaling the speaker isn’t finished; hooky openers create in-medias-res momentum that reduces friction from formal introductions.
WORDS WORTH SAVING
5 quotes“Whenever a dictionary chooses their word of the year, that’s a marketing ploy by big dictionary.”
— Adam Aleksic
“Absurdity is a meaning… The absurdity of the word is its own definition.”
— Adam Aleksic
“Dead silence is very bad on the algorithm… that uptalk… is very good for online hooking.”
— Adam Aleksic
“Every single term now is a search engine optimization term because the algorithm is looking at every single word you use.”
— Adam Aleksic
“We are now being trained by ChatGPT to use different language.”
— Adam Aleksic
QUESTIONS ANSWERED IN THIS EPISODE
5 questionsWhat exactly makes “six seven” meaningful beyond being “random,” and how is that different from older nonsense slang?
“Word of the year” picks and viral nonsense terms (e.g., “six seven”) are framed as marketing and clip-farming tactics that exploit the attention economy.
Can you map concrete examples of a TikTok dialect vs Twitter dialect vs LinkedIn dialect—what linguistic features change (syntax, tone, vocabulary, pacing)?
Different platforms and subcultures generate distinct “dialects,” where slang functions as identity signaling and in-group membership, accelerating language change via algorithms.
Which parts of the “lifestyle influencer accent” are most predictive of higher retention: uptalk, vowel lengthening, vocal fry, or pacing?
Influencer and broadcaster voices are treated as engineered performance styles (floor-holding, uptalk, pacing, clarity) optimized for retention, authority, or excitement.
How does “floor-holding” show up in scripted YouTube versus live streaming, and what are the most common audio cues creators use?
AI is already feeding back into human language—detectably shifting word choice (e.g., “delve”) and writing patterns—raising concerns about hidden biases and homogenization.
Is the ‘AAE → ballroom/gay slang → straight women → mainstream’ pipeline always true, and where does it break or get oversimplified?
Language change is portrayed less as decay (“brain rot”) and more as adaptive creativity, while warning that distribution incentives privilege arousal (rage, fear, awe) over contentment and nuance.
Chapter Breakdown
“Word of the Year” as virality marketing: the meaning of nonsense
The episode opens by dismantling “word of the year” announcements as marketing tactics rather than linguistic authority. The guests use “six seven” as an example of a deliberately empty phrase whose real function is to provoke curiosity, clipping, and algorithmic spread.
TikTok as a global slang engine: accelerated cycles and echo chambers
They argue TikTok is currently the most powerful driver of slang creation and diffusion. The platform interface, comment culture, and algorithmic trend loops speed up adoption and turnover of new terms.
Platform dialects and micro-communities: Twitter vs LinkedIn vs fandom speech
The conversation broadens into how each platform shapes expectations of tone and vocabulary, creating recognizable dialects. They emphasize that within-platform subcultures (K-pop, Swifties, etc.) produce even finer-grained micro-dialects.
Keyword virality and “algorithm wink” language (six seven, incel lexicon, ‘maxing’)
They unpack viral jargon as a set of algorithm-friendly keywords that creators and users deploy to trigger distribution and recognition. Some terms are harmless meta-jokes; others are tied to more toxic subcultures, but the mechanism is similar.
The influencer voice: founder effects, relatability vs authority, and floor-holding
They analyze “influencer accents” as evolved performance strategies shaped by early successful creators (founder effect). Lifestyle influencers aim for parasocial warmth, while educational creators use speed, stress, and clarity to project authority.
MrBeast, livestream ‘edging,’ and retention-first vocal performance
MrBeast is presented as a deliberate vocal code-switcher: calm in interviews, high-arousal in videos. They connect this to livestream formats that continually delay payoff, using language and pacing to keep viewers from scrolling away.
Broadcast voices: why newscasters and sports commentators sound engineered
They compare broadcaster speech to influencer archetypes: newscasters resemble “educational authority,” while sports commentators resemble high-excitement performers. These norms persist because audiences expect them and newcomers imitate proven templates.
Distribution over content: TED Talks, clip farming, and viral misalignment
They argue modern media rewards distribution mechanics more than message quality. Viral spread is biased toward high-arousal emotions (anger, fear, awe), creating a mismatch between what’s good for people and what platforms amplify.
Can you hear sexuality? Gay speech cues, lesbian accent uncertainty, and coded identity
They explore research and stereotypes around identifying sexuality by voice, noting gay male speech is more recognized than lesbian speech in studies. They frame such features as identity signaling shaped by history, safety, and community norms—not monolithic traits.
Emojis as language: substitution, tone-tags, and legal ambiguity
The episode treats emojis as meaningful linguistic units used for censorship evasion and emotional framing. They highlight how emoji meanings shift quickly, creating real-world confusion—including court cases hinging on interpretation.
Etymology as a mirror of reality: shortening, loss, and youth-driven change
They discuss whether language has a direction, concluding it mainly tracks changes in lived experience. Youth are described as the main engine of slang innovation due to identity formation and the desire to diverge from parents, while institutions mostly legitimize after the fact.
Filler words and “in medias res” hooks: like, you know, and creator openers
They reframe filler words as functional tools for turn-taking and maintaining attention. Creator patterns like starting with “No, because…” manufacture immediacy and pull the viewer into a story already underway.
AI’s linguistic fingerprints: ‘delve,’ em dashes, and humans learning from models
They argue AI is not just generating language but feeding back into human usage patterns via writing assistants, politicians, academia, and platforms like LinkedIn. “Delve” becomes a case study in how training and reinforcement biases propagate into real speech.
Social media vs AI: bottlenecks, homogenization, language death, and shaping thought
They conclude social media is the bigger driver because it captures and amplifies everything, including AI outputs. Concerns broaden from words to ideas: algorithms shape the Overton window, incentivize manipulation, and may contribute to homogenization amid rapid language extinction.
Gen Z as a constructed label: identity buckets, ‘umwelt,’ and resisting commodification
They challenge the reality of generations as natural categories, calling them modern marketing constructs that people are nudged to perform. The discussion ties back to language-as-identity and the tension between individuality and belonging.
Rapid-fire etymology and playful linguistics: word origins, conlangs, and QWERTY
The episode shifts into a fast, entertaining segment on surprising word histories, then expands into constructed languages and design constraints. They use QWERTY and Esperanto/Ithkuil to illustrate that “efficiency” is not language’s only goal—human bonding is.
Does ChatGPT speak English? Tokenization, embeddings, and why meaning can distort
They explain how LLMs transform text into tokens and mathematical embeddings, then back into text—suggesting the model isn’t “speaking” in a human sense. This pipeline clarifies how subtle biases or distortions (word choice, tone, ideology) can slip in and scale.
Wrap-up: where to follow Etymology Nerd and the core warning about attention systems
They close by pointing viewers to Adam’s Substack and book ‘AlgoSpeak,’ reinforcing the episode’s theme: language is being reshaped by attention incentives and intermediaries. The send-off mirrors the earlier discussion of performative communication and media-aware sign-offs.
EVERY SPOKEN WORD
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