Jack Clark, co-founder of Anthropic and former policy director at OpenAI, puts the probability of a fully automated AI research pipeline at 60% or higher before the end of 2028. The benchmark evidence he assembles - from coding agents to alignment research - suggests the transition is already underway.

Average transparency scores for major AI developers fell from 58 to 40 in a single year, reversing two years of measured progress. The companies building the most consequential models have decided, collectively, that the public does not need to know how they work.

After 15 years building Apple into a $4 trillion institution, Tim Cook is handing the keys to John Ternus, a mechanical engineer who has spent a quarter century shaping the products Cook sold. The transition says as much about where Apple has been as where it is going.

The Stanford AI Index 2026 documents record investment, rapid capability gains, and a narrowing U.S.-China model gap. It also documents an 89 percent collapse in AI scholar immigration, the dismantling of the government's only frontier model evaluation body, and a generation of entry-level workers being displaced before they form. The U.S. may still be winning. Whether anyone in power is paying attention to the score is a different question.

On April 8, Elon Musk listed seven models in simultaneous training on Colossus 2 and captioned the post "Some catching up to do." The cluster burns 400 megawatts, runs on an estimated 550,000 NVIDIA Blackwell GPUs, and is training a 10-trillion-parameter model. The question is whether scale alone can close the gap.