The Meta Leaks Are Worse Than You Think

One of the MANY reasons that I deleted my FB account.

From 80,000 Hours.-

Meta’s own internal documents show the company was aware it was profiting from $16 billion a year in scam ads — and that its leadership chose not to act. If this is how a social media company behaves when the stakes are ad revenue, how much should we trust AI companies when the stakes are far higher?

Leaked documents from Meta reveal that 10% of the company’s total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved scam prevalence from China, the documents suggest it was shelved after Zuckerberg was briefed. The team was disbanded, the freeze on fraudulent Chinese ad agencies was lifted, and within months fraud had bounced back to near its previous level. Meta also developed a global playbook for “managing” regulators — including altering its own ad library so that scam ads were removed from results whenever regulators came looking.

Host Rob Wiblin breaks down what the documents show and what they reveal about the limits of voluntary corporate self-regulation — then turns to the bigger question: How much do you trust companies like this — ones willing to put a dollar value on acceptable harm — to handle AI systems capable of making decisions about your healthcare, your finances, and your government?

Exposing Music’s Greatest Scam

From Asa Park,

Milli Vanilli rose fast and fell even faster, but their story says more about us than about them. It’s a tale of fame, illusion, and how the music industry quietly redefined what it means to be “real.”

AI Writing Essentially All The Code

From TheStandupPod; Will AI really be writing 90–100% of all code within a year? In this segment, the hosts react to bold claims from Anthropic’s CEO about the near-future dominance of AI-generated code. What starts as a discussion about productivity quickly turns into a deeper debate about what “writing code” even means. From glue code vs. real engineering, to entropy in codebases, legacy systems, and the economic incentives behind AI companies, the conversation breaks down why these predictions might be misleading—and what actually matters for developers in the AI era.