Podcast Summary: TED Radio Hour – “How the creator economy is making you talk like the internet”

Date: November 14, 2025
Host: Manoush Zomorodi
Guest: Adam Aleksic (“Etymology Nerd”), linguist, author, and social media influencer

Episode Overview

This episode explores how the creator economy and the algorithms that power social media platforms are fundamentally reshaping not just how people make a living, but how we talk. Manoush Zomorodi and Adam Aleksic delve into "algospeak"—the ways platforms incentivize new styles of expression, slang, and even voice. They discuss how language evolves rapidly online, how memes and trends spread, the consequences for culture (good, bad, and weird), and why paying attention to these changes matters.

Key Discussion Points and Insights

1. The Rise of the Creator Economy and Algospeak

• Over half of Gen Z in the US aspires to be influencers, making a living from short-form online content (01:11).
• Adam Aleksic (aka "Etymology Nerd") has built a career by decoding how language spreads on the internet, amassing over 3 million followers.
• “Algospeak”: Language shaped by the pressures and incentives of social media algorithms. Creators intentionally adjust their style, word choices, and delivery to maximize reach.

Quote:
"I speak in what I call an educational influencer accent. I'll talk very quickly, I'll stress more words to keep you watching my video. And all of that is part of this expected way to talk through the algorithm."
— Adam Aleksic (01:28)

2. How Algorithms Shape Language and Communication

• Algorithms do not merely distribute content—they affect how creators communicate (10:44).
• Social media content must be fast-paced, simple, and attention-grabbing to succeed.
• Euphemisms like “unalive” replace forbidden words (“kill”) for algorithmic compliance—and these words spread beyond the platform (06:41, 07:22).
• Trending words and phrases (e.g., "slay," "riz," "skibidi") are both a medium for and result of viral culture and platform metadata.

Quote:
"We are entering an entirely new era of language change driven by social media algorithms."
— Adam Aleksic (TED Talk excerpt, 08:38)

3. How Virality Drives Linguistic Change

• Words that trends—or have a “story”—spread much faster now.
• Creators optimize content with strong hooks, second-person pronouns, and relatable narratives (03:29–03:53).
• The importance of “trend-hopping”: Creators analyze, explain, and even create trends for engagement.

Example:
Adam's viral “chai vs. tea” video (16 million+ views) succeeded because it used a storytelling hook and invited user identification (02:36–03:29).

4. Platform-Specific Censorship and Workarounds

• Literal censorship triggers linguistic workarounds: Words like “unalive,” born out of TikTok’s moderation regime, end up adopted offline and by younger generations (06:41–07:44).

Quote:
"The middle schoolers don't know this. They see the word online or hear it from friends and assume it's a word like any other." (08:06)

5. Acceleration and Amplification of Slang

• Songs, memes, and hashtags spread words and behaviors “like a virus”; success for one piece of content incentivizes copycats (16:46–19:07).
• Example: Words like “riz,” “gyat,” and “skibidi” explode thanks to algorithmic promotion.

Quote:
"The algorithm is the culprit. But influencers are the accomplices." (18:30)

6. Algorithmic Personalization: For You, or for Them?

• While users believe they “train” the algorithm, personalization is ultimately for the platform’s commercial benefit (11:44–12:48).
• Platforms monetize attention; viral content is what benefits the platform, not necessarily the user.

7. Generational Labels and Category Creation

• The algorithm encourages hyper-categorization (Gen Z, “cottagecore,” etc.) for targeted content and advertising (35:40–40:12).
• Identity and group trends become more pronounced—and more commodified.

Quote:
"If I'm a demisexual goblincore Gen Z Swifty... those are all little identifiers, little pieces of metadata about myself as a user."
— Adam Aleksic (39:04)

8. Media Theory: From Printing Press to Algorithms

• Aleksic frames the algorithm as a new “medium,” analogous to printing, radio, TV, and the internet.
• The medium is not neutral; it affects what language spreads and persists, and what does not (25:09–28:15).

9. Emerging Accents and Voice Trends

• Not just what we say, but how we say it: The “influencer accent,” “lifestyle accent,” or “educational accent” are strategies for engagement (29:45–31:41, 30:00–30:31).

Quote:
"There's actually a bunch of different types of influencer accents... It has that uptalk, it has the rising tone..."
— Adam Aleksic (29:45)

10. Language Change and Social Group Dynamics

• Slang both distinguishes in-groups and disappears when adopted by out-groups (21:04–21:41).
• Words with more subtle, lasting shifts (e.g., “low key”) may have greater future impact on the language than obvious memes.

11. Dark Sides of Algorithmic Language

• Extreme and polarizing language is algorithmically rewarded; moderate voices get lost (46:05–47:32).
• Hate-group terminology can “launder” into mainstream via algorithmic spread—e.g., “pilled” from incel culture (48:58).
• The same pipeline that spreads fun memes can also radicalize or harm.

12. Algorithmic Literacy and Optimism

• Younger generations show more media savvy—aware of curation and manipulation by the platforms (48:14).
• Nonetheless, the pressure for engagement and temptation toward extremism remains.

Quote:
"Older generations are maybe more concerningly illiterate of the new medium... Young people generally have a decent understanding of, of, oh, this could be AI. This might not be real."
— Adam Aleksic (48:14)

13. Advice for the Future

• Aleksic encourages consuming “time-biased” (books, oral tradition) and “space-biased” (viral, fast) media for balanced literacy (41:48–42:12).
• He points to the necessity of code-switching—adapting mode, tone, vocabulary to context and medium (42:46).

Notable Quotes & Memorable Moments (with Timestamps)

• On algorithmic optimization:
  “There's sort of a deal with the devil that has to happen, right? You have to accept as a creator... I'm playing into what the algorithm is rewarding.” (19:20)

• On generational buckets:
  “It's so strange to me that we put ourselves in buckets like that and they actually serve as algorithmic trend bait...” (36:26)

• On the "core" phenomenon:
  “TikTok algorithm has decided that words like cottagecore qualify as trending metadata... You might even start to identify with a cottagecore aesthetic. But here's the thing. It's all fake." (37:33–38:40)

• On code-switching:
  “A good communicator should know to adapt to different media and to different types of people and to different social settings. And we are constantly code switching all around us.” (42:46)

• On media variety:
  “I really hope that we come to realize as a culture that we should not just be relying on algorithms for our news... It's good to get news from as many places as possible and build a greater picture of reality.” (50:54)

• Signature sign-off:
  “Thanks for coming to my TED Talk.” (51:53)

Important Segments & Timestamps

• Creator economy/Algorithmic virality explained: 01:08–04:24
• Algospeak and euphemisms (“unalive”): 06:41–07:44, 08:06
• TED Talk segment on unalive and language change: 07:51–09:26
• Impact of personalization, for-who personalization serves: 11:44–12:48
• Demure, nonchalance, memeification of words: 15:36–16:26
• Rizzler song, meme virality, “skibidi” and “riz”: 16:46–20:19
• Algorithmic “in-group / out-group” cycle: 21:04–21:41
• Media theory (printing press, radio, etc.): 25:09–28:15
• Influencer accent and voice: 29:45–31:41
• Generational labeling, "core" phenomenon: 35:40–40:12
• Cultural and political ramifications (extremism, radicalization): 46:05–49:52
• Algorithmic literacy, mixing media, code-switching: 48:14–42:46

Takeaways

• Algorithms are actively shaping not just what goes viral, but how we communicate and what language “succeeds” online.
• Slang is incubated, accelerated, and sometimes commodified by these dynamics, with memes and in-jokes gaining dictionary status in record time.
• Censorship and content moderation create euphemisms that travel offline.
• Generational, aesthetic, and even consumer identities are being manufactured and reinforced as algorithmic “metadata,” creating new opportunities—and dangers—for personalization and polarization.
• There are risks: echo chambers, the spread of extremist terminology, and the flattening or commercialization of identity and language itself.
• The solution, Aleksic suggests, is literacy—awareness of the algorithm’s influence—and a deliberate practice of media variety and code-switching for a richer, more resilient language and culture.

For More

• Follow Adam Aleksic as “Etymology Nerd”
• Read his book: Algospeak: How Social Media Is Transforming the Future of Language
• See his talk at ted.com

“We should be aware when the way we're talking may have been conditioned by the algorithm... We should be aware when our language regurgitates extremist rhetoric. And we should be aware when that language can be used to harm other people...” — Adam Aleksic (51:12)