Everything Wrong With the Internet and How to Fix It
Episode
87 min
Read time
3 min
Topics
Fundraising & VC, Design & UX, Software Development
AI-Generated Summary
Key Takeaways
- ✓Enshittification Pattern: Platforms follow a three-stage decay: first attract users with good service while creating lock-in through network effects, then degrade user experience to attract business customers, finally extract from both groups simultaneously. Facebook moved from promising no surveillance in 2006 to algorithmic feeds where under 20% of content comes from accounts users follow, with the remainder algorithmically selected for engagement maximization.
- ✓Amazon Marketplace Economics: Amazon's take from third-party sellers increased from below 20% around 2010 to over 50% today. The platform generated $56 billion in 2023 just from sellers bidding against each other for higher search rankings, making this more profitable than Amazon Web Services. The first search result averages 17% more expensive than the best match for user queries, demonstrating extraction over service quality.
- ✓Algorithmic Wage Discrimination: Nursing staffing apps purchase workers' credit histories from data brokers and offer lower wages to nurses carrying more debt or overdue payments. This desperation premium creates a vicious cycle where economically vulnerable workers receive progressively lower pay for identical work. The practice relies on lack of federal privacy law—the last consumer privacy legislation passed in 1988 covered only VHS rental records.
- ✓Anti-Circumvention Barrier: Section 1201 of the Digital Millennium Copyright Act makes it a felony to modify apps or devices without manufacturer permission, even for legal purposes. When two teenagers created OG App in 2022 to show chronological Instagram feeds without ads, Meta had Apple and Google remove it from app stores. This prevents competitive alternatives that serve user interests over platform extraction.
- ✓Utility Regulation Framework: Essential platforms should face non-discrimination duties preventing self-preferencing and potentially margin caps around 30%. The electric utility model succeeded not through perfect regulation but by preventing the network from extracting profits from innovations built on top of it. Platforms function as infrastructure where discrimination between users or products enables extraction rather than value creation for the broader economy.
What It Covers
Ezra Klein interviews tech critics Cory Doctorow and Tim Wu about platform decay through "enshittification" and "extraction." They trace how Facebook, Amazon, and other platforms shifted from serving users to maximizing profits, now taking 50%+ margins from sellers while degrading search quality. Solutions include interoperability mandates, privacy laws with private enforcement, and banning anti-circumvention rules that prevent users from modifying software they own.
Key Questions Answered
- •Enshittification Pattern: Platforms follow a three-stage decay: first attract users with good service while creating lock-in through network effects, then degrade user experience to attract business customers, finally extract from both groups simultaneously. Facebook moved from promising no surveillance in 2006 to algorithmic feeds where under 20% of content comes from accounts users follow, with the remainder algorithmically selected for engagement maximization.
- •Amazon Marketplace Economics: Amazon's take from third-party sellers increased from below 20% around 2010 to over 50% today. The platform generated $56 billion in 2023 just from sellers bidding against each other for higher search rankings, making this more profitable than Amazon Web Services. The first search result averages 17% more expensive than the best match for user queries, demonstrating extraction over service quality.
- •Algorithmic Wage Discrimination: Nursing staffing apps purchase workers' credit histories from data brokers and offer lower wages to nurses carrying more debt or overdue payments. This desperation premium creates a vicious cycle where economically vulnerable workers receive progressively lower pay for identical work. The practice relies on lack of federal privacy law—the last consumer privacy legislation passed in 1988 covered only VHS rental records.
- •Anti-Circumvention Barrier: Section 1201 of the Digital Millennium Copyright Act makes it a felony to modify apps or devices without manufacturer permission, even for legal purposes. When two teenagers created OG App in 2022 to show chronological Instagram feeds without ads, Meta had Apple and Google remove it from app stores. This prevents competitive alternatives that serve user interests over platform extraction.
- •Utility Regulation Framework: Essential platforms should face non-discrimination duties preventing self-preferencing and potentially margin caps around 30%. The electric utility model succeeded not through perfect regulation but by preventing the network from extracting profits from innovations built on top of it. Platforms function as infrastructure where discrimination between users or products enables extraction rather than value creation for the broader economy.
- •Surveillance-Driven Business Models: Over 96% of Apple users opted out of Facebook tracking when given a simple choice, disproving revealed preference arguments that users accept surveillance. Microsoft Office now offers bosses the ability to stack-rank divisions by mouse movements and typing patterns, then compare this data against competitors. Amazon drivers face facial recognition monitoring that previously docked them for driving with mouths open, representing technology adoption curves starting with prisoners and refugees before reaching mainstream workers.
Notable Moment
Doctorow reveals that Facebook internally calculates 10% of its advertising revenue comes from ads for actual scams, and a Reuters investigation found the company charged advertisers premium rates for fraudulent video view counts during the pivot to video era. This led news organizations to restructure their entire businesses around fabricated metrics, demonstrating how extraction harms not just users but entire industries dependent on platform data.
Episode Transcript
When was the last year that the Internet felt good to you? I think everybody has different answers to this. Mine, I think go fairly far back maybe to the heyday of blogging, at least before the moment when Twitter and Facebook went algorithmic. But whatever your answer to it is, I have not found many people who think 2026. Right now, this Internet, with all of its anger and its outrage and its AI slop, this is what we were promised. This is living at the technological peak. But even if there is this growing consensus that something went wrong with the internet somewhere and that it is driving our society somewhere we don't want it to go, there's not really a consensus of what to do about it. What to do about these giant platforms increasingly spammed up with ads and sponsored results, boosting content that will keep us hooked and angry, isolating and dividing us into ranging our politics. So making a few billionaires ever richer, held up by an army of low wage workers in warehouses and on delivery bikes. Something has gone so wrong, but But what do we do about it? My guests today have two theories of the case. Cory Doctorow is a longtime blogger, an activist with the Electronic Frontier Foundation, and a science fiction writer. His new book is why everything suddenly got worse and what to do about it. Tim Wu worked as a special assistant to president Biden for technology and competition policy. He's a professor at Columbia Law School and author of influential books on technology, including his latest, The Age of Extraction, How tech platforms conquered the economy and threatened our future prosperity, and shitification and extraction. Those are the ideas I wanted to put in play together here and to also think about what solutions they might present. As always, my email is reclineshow@nytimes.com. Tim Wu, Corey Doctorow. Welcome to the show. Thank you very much. Great to be here. So I just learned that you both went to elementary school together. Yep. Yeah. That's true. In Suburban Toronto, a a weird little school with, like, 80 kids. It was kindergarten to eighth grade in one classroom. Older kids taught the younger kids. We more or less were left to go feral and design our own curriculum. They chucked us out of the school on Wednesday afternoons to take our subway pass and find somewhere fun in Toronto to go do stuff. It was great. Is there anything about that school that would lead people to becoming sworn enemies of our tech overlords? Well, we love tech at the time. I mean, we had we were early in on Apple twos, and I frankly, that's where it all started in a way. Mhmm. You know, both of our our our books have this kind of pining for a lost age, and I think some of it is this early era of computing when we were just, …
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