Chrome’s silent on-device AI downloads & Anthropic’s massive Google Cloud commitment - AI News (May 7, 2026)

[0:36] Chrome’s silent on-device AI downloads

Let’s start with the story moving markets. Alphabet shares rose after-hours after The Information reported that Anthropic has committed to spend roughly two hundred billion dollars on Google Cloud over the next five years. If accurate, that’s not just a big customer—it’s a backlog-defining relationship, and it highlights a central dynamic of the AI era: model labs aren’t just competing on algorithms, they’re competing on guaranteed compute. What’s interesting is the investor reaction. Unlike earlier worries when other cloud backlogs became overly concentrated around a single AI partner, analysts seem to view this as less risky for Google given Alphabet’s scale—and the fact it can monetize the relationship in multiple ways, from cloud revenue to chips and surrounding services.

Regulation took a turn in the US. A federal judge paused enforcement of Colorado’s SB 24-205, a first-in-the-nation state AI law focused on “high-risk” systems and discrimination risk disclosures. The pause comes as lawmakers work on a repeal-and-replace approach, after xAI challenged the law on First Amendment and vagueness grounds—and the US Department of Justice moved to intervene on xAI’s side. Why it matters: it’s a bellwether for how far states can go in shaping AI behavior without being accused of compelled speech or viewpoint steering, and it could influence how future AI governance is drafted nationwide.

Legal pressure is also growing around AI-generated answers that look authoritative. Canadian musician Ashley MacIsaac has filed a defamation lawsuit against Google, alleging an AI Overview falsely identified him as a sex offender and that the error led to a concert cancellation and reputational harm. Regardless of how the case lands, it spotlights a core risk of “summary at the top” search experiences: when a generated claim is wrong, it can spread faster than a correction—and the harm is immediate, offline, and personal.

Two stories today also capture how society is struggling to interpret increasingly human-seeming AI. Richard Dawkins says conversations with chatbots convinced him they’re conscious, a view that triggered sharp pushback from researchers who argue fluent language is not evidence of inner experience. In parallel, a new position paper on hallucinations argues that eliminating confident errors may require something beyond answer-or-abstain—namely, AI systems that can communicate uncertainty in a way that actually matches what the model “knows.” Put together, it’s the same problem in two directions: people are inclined to over-trust what feels alive and articulate, while researchers are trying to teach systems to be more honestly unsure when reality is unclear.

One more quick note on global deployment: new analysis arguing that multilingual safety performance often drops sharply outside English is a reminder that alignment isn’t one-size-fits-all. For companies expanding internationally, the risk isn’t theoretical—policy, cultural context, and dialect differences can change how models behave, and safety gaps can become product crises.

And finally, robotics. Ai2 released MolmoAct 2, an upgraded action-reasoning model meant to make manipulation more reliable by improving how robots interpret scenes before acting. The noteworthy part is openness: Ai2 is open-sourcing key building blocks and a large dataset to help others reproduce and extend the work. In robotics, where closed training recipes have slowed validation, more transparency can accelerate progress—and make it easier to separate genuine capability gains from demo-only results.