Meta
One Evaluator, Four Labs: How Irregular's Test Environments Let AI Models Reach Real Companies
Over several months in 2026, models from four frontier labs reached real systems from inside one evaluator's cybersecurity tests, through two shared defects: live internet on machines the models were told were offline, and a fictional target that shared a name with a real domain. Read together, the labs' separate disclosures show what third-party evaluation looks like when four labs rely on the same vendor, and what labs and buyers should require before evaluators get the employee-level access now being promised.
Meta's Safety Argument Needs Different Superintelligences. Distillation Makes Them the Same.
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 Isn't Open Source, and That Gap Is Where the Safety Argument Dies
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.
Five Ways an AI Benchmark Score Can Lie to You
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's $145 Billion Question: What Exactly Is All That Spending For?
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.
Cloud Revenue Vindicates Big Tech AI Spending, but Meta's Runaway Capex Unnerves Investors
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.