Current AI Risk Assessment

27.38%

Chance of AI Control Loss

September 6, 2035

Estimated Date of Control Loss

AGI Development Metrics?

79.34%

AGI Progress

September 17, 2029

Estimated Date of AGI

Risk Trend Over Time

Latest AI News (Last 3 Days)

June 21, 2026
-0.03% Risk

US Export Control Forces Anthropic to Pull Advanced Models Offline Amid Political and Security Tensions

The US government forced AI safety lab Anthropic to take its advanced models, Fable 5 and Mythos 5, offline following national security concerns and bypassed guardrails. While cybersecurity experts warn that removing these models harms defense capabilities, others view the administration's actions as potentially politically motivated. The shutdown has sparked intense debate over AI regulation, national security, and the competitive landscape among major AI laboratories.

June 20, 2026
-0.03% Risk

Prominent Nobel Laureate John Jumper Shifts from DeepMind to Anthropic

John Jumper, the co-creator of the groundbreaking AlphaFold model and a 2024 Nobel laureate, has announced his departure from Google DeepMind to join competitor Anthropic. His transition coincides with other high-profile talent shifts in the AI industry, including Character AI co-founder Noam Shazeer moving to OpenAI. This high-profile migration highlights the intensifying war for elite talent among frontier AI laboratories.

June 19, 2026
-0.03% Risk

US Export Ban on Anthropic's Cyber Models Highlights Challenges of AI Control

The U.S. government recently banned Anthropic from exporting its powerful cyber-capable AI models, Fable and Mythos, over national security concerns. This move marks a major test of whether export controls can successfully contain frontier AI systems. However, historical precedents with encryption and spyware suggest that such governmental restrictions are often ineffective and easily bypassed by global actors.

US Government Intervention Halts Anthropic's Fable 5 and Mythos 5 Over Safety Concerns

The US government has ordered Anthropic to pull its newly released Fable 5 and Mythos 5 models due to national security concerns over easily bypassed guardrails. Cybersecurity researchers and Anthropic have criticized the ban, pointing out that similar jailbreak vulnerabilities exist in competitor models. This development raises questions about the balance between government intervention and AI security.

Anthropic Faces Commercial Backlash After Government Bans Fable 5

Anthropic was forced by the US government to withdraw its latest models, Fable 5 and Mythos 5, following discoveries of bypassable guardrails. Despite the regulatory action, industry observers note that commercial interest and market expectations remain largely unaffected. The incident highlights the growing friction between national security intervention and market momentum in the AI sector.

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AI News Calendar

January 2025
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AI Risk Assessment Methodology

Our risk assessment methodology leverages a sophisticated analysis framework to evaluate AI development and its potential implications:

Data Collection

We continuously monitor and aggregate AI news from leading research institutions, tech companies, and policy organizations worldwide. Our system analyzes hundreds of developments daily across multiple languages and sources.

Impact Analysis

Each news item undergoes rigorous assessment through:

  • Technical Evaluation: Analysis of computational advancements, algorithmic breakthroughs, and capability improvements
  • Safety Research: Progress in alignment, interpretability, and containment mechanisms
  • Governance Factors: Regulatory developments, industry standards, and institutional safeguards

Indicator Calculation

Our indicators are updated using a Bayesian probabilistic model that:

  • Assigns weighted impact scores to each analyzed development
  • Calculates cumulative effects on control loss probability and AGI timelines
  • Accounts for interdependencies between different technological trajectories
  • Maintains historical trends to identify acceleration or deceleration patterns

This methodology enables data-driven forecasting while acknowledging the inherent uncertainties in predicting transformative technological change.