How the Internet Got Infested With Garbage: The TikTok-ization of Everything

Exploring how social media platforms abandoned their original purpose as social networks to become algorithmic engagement machines that prioritize viral content over quality, truth, or human connection.

Key Takeaways

• All major platforms are undergoing "TikTok-ization" by prioritizing algorithmic content from strangers over actual social networks
• Facebook's 2012 traffic surge to media sites like Gawker marked the beginning of shareability-driven content over quality journalism
• Twitter no longer functions as the "assignment desk" for media due to anti-chronological feeds and engagement-driven payouts
• AI-generated "slop" is flooding platforms as creators worldwide exploit monetization programs to generate income through fake content
• Breaking news coverage has deteriorated dramatically with hoaxes and misinformation drowning out legitimate reporting
• Content moderation teams have been massively reduced across platforms, allowing garbage content to proliferate unchecked
• The media landscape is hollowing out the middle, leaving only giant national outlets and individual newsletter creators
• Platform algorithms cannot distinguish between quality engagement and rage-bait, treating both identically for monetization purposes
• The "dead internet theory" suggests we may be approaching a future where bots primarily talk to other bots

Timeline Overview

• 00:00–08:30 — Gawker's Facebook Revolution: Max Read's journalism origins, the 2012 Facebook traffic explosion that brought 15 million page views to blogs, and the beginning of platform-driven media
• 08:30–18:45 — The BuzzFeed Era and Shareability: How Facebook's algorithm rewarded affinity content like "23 things only people from upstate New York would know" over sophisticated insider journalism
• 18:45–28:20 — TikTok-ization and Platform Convergence: The "carcinization" process where all platforms evolve toward TikTok's model of algorithmic feeds from strangers rather than social networks
• 28:20–38:15 — Twitter's Decline as News Hub: How engagement payouts and anti-chronological feeds destroyed Twitter's function as the internet's assignment desk and breaking news source
• 38:15–48:30 — The Misinformation Economy: Why algorithms reward hoaxes and engagement bait equally with legitimate content, creating incentives for false information during disasters
• 48:30–58:45 — AI Slop Invasion: Facebook's creator bonus programs enabling worldwide operations to generate AI images about "Jesus, pets, the US Military and Manchester United" for monetary payouts
• 58:45–01:08:20 — The Dead Internet Theory: Whether human-created content matters for cultural consumption and advertiser concerns about quality versus quantity of engagement
• 01:08:20–01:18:00 — Media's Hollowed Middle: The collapse of mid-tier publications leaving only major national outlets and individual newsletter creators like substack writers
• 01:18:00–01:25:15 — Star Economy Tensions: How journalist personal branding creates conflicts within traditional newsrooms and drives talent toward independent platforms

The Gawker Revolution: When Facebook Broke Media

Max Read's experience at Gawker during the early 2010s provides a crucial window into how platform algorithms first disrupted traditional media economics and editorial priorities. The transformation from homepage-driven traffic to Facebook-distributed content fundamentally altered what publishers created and how they thought about their audiences.

• Gawker originally functioned as a "homepage site" where readers would regularly visit gawker.com directly, enabling a consistent editorial voice focused on "sophisticated witty cutting" content about media and political figures
• The 2011-2012 Facebook traffic explosion brought "15 million page views on a Blog totally unheard of for reasons we couldn't figure out we couldn't understand," creating unprecedented but mysterious audience growth
• Facebook's algorithm favored content optimized not for search or direct readership but for "what will people share and what will people click on if they come across it in their Facebook feed"
• This shift moved media away from insider content like "what's Rupert Murdoch up to" toward "sentimental content inspiration content" exemplified by BuzzFeed's viral list format
• The platform's power became evident when publishers realized "what was actually big was Facebook not BuzzFeed" despite BuzzFeed's appearance as a dominant media force
• Reed describes how the experience of managing traffic during this period made him "obsessed" with understanding platform-driven changes to culture and media business models

BuzzFeed's Affinity Content and the Shareability Revolution

The rise of BuzzFeed represented a fundamental shift in media strategy from creating content for dedicated readers to producing material optimized for social sharing and viral distribution across Facebook networks.

• BuzzFeed's signature format—"23 things only people who live in Upstate New York would know"—worked because people would "share it if you're from upstate New York with everyone you know from upstate New York"
• This "affinity content" or "microtargeted identity content" dominated "the internet between say 2012 and 2014" and created massive viral reach through personal identification
• Industry observers viewed BuzzFeed as potentially "the next Disney" because of its apparent scale and cultural influence, creating "psychological damage" for journalists not working there
• The emphasis on shareability fundamentally changed editorial priorities from truth or insight to emotional resonance and personal identification with specific demographics
• Platform algorithms rewarded content that generated immediate sharing behavior rather than deep engagement or repeat readership, reshaping media economics
• The BuzzFeed model demonstrated how platform distribution could make content companies appear more powerful and influential than traditional media metrics suggested

TikTok-ization: The Algorithmic Convergence

Modern social media platforms are undergoing what Read calls "carcinization"—an evolutionary process where different species develop similar traits under comparable environmental pressures, resulting in all platforms adopting TikTok's core features and user experience.

• Platform carcinization creates "a focus on content from strangers and people you don't know rather than your sort of social networks" fundamentally abandoning the original social networking concept
• The "for you page" (FYP) model uses "heavily weighted algorithm that's supposed to give you exactly what you're looking for" but often creates unexpected rabbit holes and persistent content categories
• Platforms now emphasize "scrollable video feed that you just bounce through really quickly" rather than encouraging deeper engagement with specific creators or topics
• Direct creator monetization through engagement payouts incentivizes content designed specifically to generate clicks and shares rather than inform or entertain
• Users report that algorithmic feeds make their content consumption feel less intentional and more manipulative, with Reed noting the need to "consciously go and find something else to like scroll through click on" to cleanse unwanted content
• The shift represents platforms prioritizing user retention and engagement metrics over user satisfaction or meaningful social connection

Twitter's Fall from Assignment Desk to Engagement Trap

Twitter's transformation from journalism's primary news hub to an engagement-driven platform has fundamentally undermined its utility for breaking news coverage and real-time information sharing.

• Twitter historically functioned as "the assignment desk for much of the internet" and "the context for media" where journalists and observers went to understand developing stories
• The platform's anti-chronological FYP feed means "you're going to see stuff" from "12 hours ago from 20 hours ago from 5 minutes ago all jammed up against one another" destroying real-time news value
• Engagement-based payouts create "a huge number of people who are putting themselves out there not for any kind of Noble informational value but specifically to get followers to get engagement payouts"
• During recent hurricanes, Twitter's feed was "drowned in this flood of influencers and hoaxers and people just trying to get attention" making it "impossible to learn anything real or true about what was happening"
• The monetization structure "incentivizes hoaxes" because algorithms "can only measure engagement basically it can't measure quality of the Tweet"
• Users increasingly turn to traditional news sources like bloomberg.com for actual breaking news information rather than relying on Twitter's degraded signal-to-noise ratio

The Misinformation Economy: When Lies Pay Better Than Truth

Platform monetization structures have created economic incentives for false information that often outweigh the benefits of accurate reporting, fundamentally undermining the information ecosystem.

• Engagement-based algorithms treat correction and outrage identically, so "if someone is tweeting out something that's wrong and then a bunch of people come on and start pointing out that it's wrong it still counts as engagement"
• Viral misinformation during disasters includes completely fabricated claims like "FEMA is on the ground aiming snipers at anyone" that receive "15,000 retweets" despite being easily disprovable
• Many Twitter users view the platform not as "describing the world as it is" but as "engaged in a fight or a war between political parties" where truth becomes secondary to factional advantage
• The elimination of content moderation teams across platforms has removed institutional barriers to false information, as "people whose job is to try and sort of limit untrue information" have been "laid off"
• Platform owners like Elon Musk have "opened up space for meta for example to lay off a ton of its own content moderators" by demonstrating that reduced moderation doesn't significantly impact revenue
• Reed observes that many users may not actually care whether content represents "actual fact to be believed" as long as it serves their political or emotional purposes

AI Slop and the Global Content Farm Economy

Platform creator monetization programs have enabled worldwide operations to generate AI content for financial gain, flooding feeds with artificial material designed purely for engagement rather than human interest or value.

• Facebook's creator bonus payments create "pretty meaningful" income for "people all over the world" who can generate hundreds of dollars monthly through AI-generated content
• A Kenyan creator Reed interviewed identified "Jesus pets and animals the US Military and Manchester United" as his "four big engagement subjects" for AI image generation
• These operations can "make 500 or more bucks a month" which "is above minimum wage in a lot of places in Kenya" making AI content farming economically rational
• The "weird Facebook AI slop" phenomenon demonstrates how "platforms have created effectively infinite market for content of basically any kind as long as somebody will look at it"
• Combined with "effectively infinite content generator" capabilities in AI tools, this creates a system where "the threshold for creating this stuff is so minimal" that quality becomes irrelevant
• Reed warns that AI content generation may reach a tipping point where "the noise isn't going to drown out the signal" as human-created content becomes increasingly difficult to distinguish and discover

The Dead Internet Theory and Human Connection

The possibility that internet content will become primarily AI-generated raises fundamental questions about human cultural consumption and the value of authentic human creation in digital spaces.

• The "dead internet theory" envisions a future where "Bots talking to each other" dominate while "people just aren't going to be that engaged in terms of content creation anymore"
• Read argues that "it matters in some fundamental way to us that the things we see and engage about are human created" beyond simple entertainment value
• Cultural consumption depends on "our relationship to other people and the ability to kind of consume it in tandem with other people that we can talk about it with"
• The concern extends beyond content quality to social connection: "talking about AI generated content with AI Bots waiting for the next AI generated episode of a TV show" may not provide "the same level of satisfaction"
• Currently, advertisers appear unconcerned about content authenticity as long as they receive engagement, though Reed hopes for "recognition on the part of advertisers that these are not high-quality views"
• The economic sustainability of AI slop depends on whether "people who are scrolling through it are not looking for the stuff they're doing" will eventually seek higher-quality alternatives

Media's Hollowed Middle: Giants and Individuals

The transformation of platform economics has created a bifurcated media landscape where only the largest institutional outlets and individual creators can survive, eliminating the diverse middle tier of publications that traditionally served various communities and interests.

• The media industry is "hollowing out the middle" leaving "the Times probably the Wall Street Journal Bloomberg" as viable large institutions alongside individual "substacks and streamers and the sort of Internet personality model"
• The New York Times employs approximately "7% of everybody of all journalists" currently working in the United States, demonstrating extreme concentration in remaining institutional media
• Local and specialized publications—"magazines weekly even local daily newspapers"—have largely disappeared except for "local news networks" and NBC affiliates that "still exist" and remain "surprisingly powerful"
• Substack and similar platforms enable individual creators to own their audiences through email lists, providing "40,000 email list that I own that I can take" as a "Lifeboat" when institutional relationships end
• The tension between institutional media and star journalists creates conflicts as reporters who "built a brand for herself" run up "against the limits of what" traditional newsrooms "were willing to let her do"
• This dynamic affects newsroom culture broadly, as journalists with large followings "know that they have an audience know that they have some leverage with their bosses" and may consider independent alternatives

Conclusion

The internet's transformation from a social networking ecosystem to an algorithmic engagement machine represents one of the most significant shifts in how humans consume and share information. Max Read's analysis reveals that platform economics—rather than user preferences or technological necessity—drove this change, as companies discovered that algorithmic feeds and engagement-based monetization generated more revenue than authentic social connections. The TikTok-ization of all platforms has created a race to the bottom where AI-generated content competes with human creativity, misinformation spreads faster than truth, and breaking news becomes indistinguishable from engagement bait. While individual creators and major institutions have found ways to navigate this landscape through direct subscription models and brand recognition, the collapse of mid-tier media has eliminated much of the diverse, specialized content that once characterized internet culture. The resulting environment prioritizes viral engagement over quality, algorithmic distribution over community building, and monetizable attention over meaningful human connection.

Practical Implications

• For Content Creators: Building direct relationships with audiences through email lists and subscription platforms provides more sustainable revenue than relying on platform algorithms and engagement-based monetization
• For Media Consumers: Curating information sources through subscriptions, bookmarks, and community platforms like Discord or Reddit provides better signal-to-noise ratios than algorithmic feeds
• For Platform Users: Understanding that algorithms optimize for engagement rather than quality can help users make more intentional choices about content consumption and sharing behavior
• For Advertisers: Developing metrics that distinguish between quality engagement and bot-driven interactions becomes crucial as AI-generated content proliferates across platforms
• For Journalists: Building personal brands and direct audience relationships serves as professional insurance against institutional media instability while creating potential conflicts with traditional newsroom structures
• For Platform Companies: The tension between short-term engagement metrics and long-term user satisfaction may require rethinking monetization models that currently reward garbage content
• For Policymakers: The collapse of content moderation and rise of AI-generated misinformation suggests need for regulatory frameworks that address platform responsibility for information quality
• For Communities: Investing in heavily moderated, purpose-driven platforms and spaces provides alternatives to the degraded experience of mainstream social media algorithms