Welcome to the AI Safety Newsletter by the Center for AI Safety. We discuss developments in AI and AI safety. No technical background required.
In this edition we discuss the new AI Dashboard, recent frontier models from Google and Anthropic, and a revived push to preempt state AI regulations.
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CAIS launched its AI Dashboard, which evaluates frontier AI systems on capability and safety benchmarks. The dashboard also tracks the industry’s overall progression toward broader milestones such as AGI, automation of remote labor, and full self-driving.
How the dashboard works. The AI Dashboard features three leaderboards—one for text, one for vision, and one for risks—where frontier models are ranked according to their average score across a battery of benchmarks. Because CAIS evaluates models directly across a wide range of tasks, the dashboard provides apples-to-apples comparisons of how different frontier models perform on the same set of evaluations and safety-relevant behaviors.
Ranking frontier models for safety. The AI Dashboard’s Risk Index offers a view of how today’s frontier models perform across six tests for high-risk behaviors. It then averages the scores and ranks them on a 0–100 scale (lower is safer). Here are the benchmarks and hazardous behaviors they measure:
Across these tests, Anthropic’s recently-released Claude Opus 4.5 is currently the safest frontier model, with an average score of 33.6.
Ranking the frontier systems’ technical capabilities. The Dashboard’s Text and Vision Capabilities Indexes each test systems across five benchmarks. The text-based evaluations test systems on coding, systems administration, expert and abstract reasoning, and performance in text-based adventure games. The vision evaluations measure embodied reasoning, navigation, mental visualization, intuitive physics, and puzzle solving.
Measuring progress toward broad automation. The AI Dashboard also monitors progress toward three key automation milestones. It measures the industry’s overall advancement toward AGI using CAIS’s recently published definition. It evaluates progress on fully automating remote work through CAIS’s Remote Labor Index, which tests AI agents’ ability to complete paid, remote freelance projects across 23 job categories. Finally, it tracks development of autonomous vehicle safety using data from a community-run project documenting Tesla’s Full Self Driving disengagements.
A leaked draft executive order from a member of the Trump administration details a plan to prevent U.S. states from regulating artificial intelligence. Meanwhile, some congressional lawmakers are trying to pass a similar law by including it in a sweeping defense bill.
The executive order would empower federal agencies to preempt state AI laws. The draft executive order would require federal agencies to identify state AI regulations deemed burdensome and push states to avoid enacting them.
The draft order directed federal agencies to take the following actions:
It also ordered the creation of a nationwide, lighter-touch regulatory framework for AI, though it lacked specifics.
Congress revives its own efforts for a moratorium. House leaders are considering using the annual defense spending bill as a vehicle for a moratorium on state AI regulations. The National Defense Authorization Act (NDAA), a must-pass measure, is often used to advance other policy priorities. Specifics of the proposed language remain unclear. An earlier attempt called for a 10-year ban, later shortened to five years and limited to states seeking federal broadband funds. It was ultimately defeated by a bipartisan coalition of senators.
57% of American voters oppose inserting preemption into the NDAA. The same poll, from YouGov and the Institute for Family Studies, found that 19% supported the measure and 24% were unsure. Citing voter concerns, a coalition of over 200 lawmakers urged congressional leaders to drop the provision. Due to stiff opposition—and the fact that its controversial nature would likely delay the must-pass NDAA—Axios has characterized this effort as a long shot. Voting is expected in early December.
Google’s Gemini 3 Pro is now the strongest frontier system on nearly all general-purpose capability benchmarks—but trails other frontier systems in safety. Anthropic’s new Claude Opus 4.5 is close behind in capabilities but topped the frontier rankings in safety.
Gemini 3 Pro tops text and vision leaderboards. In independent evaluations performed by CAIS and posted on the new AI Dashboard, Gemini 3 Pro achieved state-of-the-art scores on both text and vision benchmarks. In some tests, it scored double-digit improvements over models released just weeks earlier.
Claude Opus 4.5, released a week after Gemini 3 Pro, averaged second place on both the text and vision capability indexes, and beat Gemini 3 Pro by 0.2 points at SWE-Bench.
What’s new in Gemini 3 Pro and Claude Opus 4.5. Google has positioned Gemini 3 Pro as having improved reasoning, broader agent capabilities, and expanded control settings. The company also released a new coding agent, Antigravity, based on the model. Google also notes that an enhanced reasoning version — Gemini 3 Deep Think — is still under safety testing before full release.
Anthropic highlighted Claude Opus 4.5’s productivity‑focused enhancements along with its high coding scores. New features include a larger context window and a new “effort” parameter that allows developers to adjust their speed, cost, and depth of processing.
There is significant safety variation across frontier models. Claude Opus 4.5 scored lowest on the AI Dashboard’s risk capabilities index, making it the current safest frontier model. Anthropic’s internal safety audit noted that Claude Opus 4.5 was measurably safer than earlier models, but somewhat vulnerable to certain jailbreaking techniques. They noted it showed a tendency toward evaluation awareness and dishonesty.
Gemini 3 Pro ranked ninth on the risk capabilities index, underperforming relative to other recent frontier models. Gemini 3 Pro’s safety report acknowledges that the model exhibits risky behaviors in certain capabilities (for example, cybersecurity) and says extra mitigations have been deployed as part of its “Frontier Safety” framework. Internal evaluations also showed that the model can manipulate users.
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See also: CAIS’ X account, our paper on superintelligence strategy, our AI safety course, and AI Frontiers, a platform for expert commentary and analysis.