Mark Zuckerberg's "The Future is for Everyone" argues that safety comes from distributing superintelligence widely, but its load-bearing condition is that the many agents be different from one another - the same day, Meta shipped Muse Glimmer, a 30B model distilled from Muse Spark, the closed model whose specs it won't disclose. Meta's own safety report separately discloses that Spark was assessed as likely reaching "high risk" for chemical and biological capability before mitigations, and shipped on the strength of refusal training, a safeguard that cannot survive a weight release.
Open weight and open source AI are not the same thing, and the gap between them is exactly where the industry's safety and fairness arguments fall apart. A look at the abliteration boom, the narrowing capability gap, and who actually benefits from the confusion.
A team of Berkeley researchers posted near-perfect scores across eight major AI benchmarks - 100% on most of them - without solving a single task, just by gaming how the score is computed. That gap - between the number and the achievement - is why you have to read a benchmark claim like a skeptic. Here are the five tells, and the five questions to ask.
Meta beat earnings expectations and delivered its fastest revenue growth since 2021. Then it raised its 2026 capex forecast to $145 billion and watched its stock fall. The company's problem isn't the numbers; it's that it still can't answer the most basic investor question about them.
Alphabet, Microsoft, Amazon, and Meta reported Q1 2026 results on April 29 that collectively delivered the clearest evidence yet that AI infrastructure spending is generating real cloud revenue. The outlier was Meta, whose strong earnings were overshadowed by a capex guidance range raised for the second time this year, with no concrete product milestone attached to the ceiling.