California Legislature Passes AI Safety Verification Bill SB 813

August 31, 2026
California lawmakers passed SB 813, a Fathom sponsored bill that would create a state framework for independent verification organizations to assess AI safety claims if signed by Governor Gavin Newsom.
California Legislature Passes AI Safety Verification Bill SB 813

The California Legislature passed SB 813, a bill authored by Senator Jerry McNerney and sponsored by Fathom, the company said in a press release.

The bill now goes to Governor Gavin Newsom, who can sign or veto it. If signed, SB 813 would direct the Government Operations Agency to designate qualified independent verification organizations.

Those organizations would be outside experts able to test AI safety claims, measure risk, and assess AI models and systems using testing and analysis. The framework would create a state process for independent assessments rather than relying only on companies to evaluate and report their own results.

The bill follows California SB 53, which requires large frontier AI developers to publish a safety framework and report critical incidents.

We hope you enjoyed this article.

Consider subscribing to one of our newsletters like AI Policy Brief or Daily AI Brief.

Also, consider following us on social media:

AI Safety & Regulation AI Brief AI Brief (X)

More from: Regulation

09/04
Jason Isbell and Other Musicians Sue Suno Over Alleged Identity Misuse
09/04
OpenAI Builds Automated Shutdown Controls for AI Systems
09/03
Revelio Labs Finds Wider Employment Gap for Young Workers in AI Exposed Jobs
09/03
Trump Administration Backs OpenAI in New York Times Copyright Case
09/03
Lutnick Says Anthropic Has Repaired White House Ties

Subscribe to AI Policy Brief

Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You May Also Like