Current AI Risk Assessment

23.91%

Chance of AI Control Loss

December 27, 2035

Estimated Date of Control Loss

AGI Development Metrics?

74.57%

AGI Progress

January 9, 2030

Estimated Date of AGI

Risk Trend Over Time

Latest AI News (Last 3 Days)

February 3, 2026
+0.01% Risk

Apple Integrates Agentic AI Coding Assistants into Xcode Development Environment

Apple has released Xcode 26.3, integrating agentic coding tools from Anthropic (Claude Agent) and OpenAI (Codex) directly into its development environment. These AI agents can autonomously explore projects, write code, run tests, fix errors, and access Apple's developer documentation using the Model Context Protocol (MCP). The feature aims to automate complex development tasks while maintaining transparency through step-by-step breakdowns and visual code highlighting.

February 2, 2026
+0.08% Risk

SpaceX Acquires xAI to Build Space-Based AI Data Centers

SpaceX has acquired Elon Musk's AI startup xAI, creating a combined company valued at $1.25 trillion with plans to build data centers in space. The merger aims to address AI's massive electricity demands by moving computational infrastructure to orbit, requiring continuous satellite launches that will provide SpaceX with sustained revenue. The deal combines xAI's current $1 billion monthly burn rate with SpaceX's satellite-dependent business model, though concerns exist about both companies' near-term objectives and xAI's content safety issues.

OpenAI Releases MacOS Codex App with Multi-Agent Coding Capabilities

OpenAI has launched a new MacOS application for its Codex coding tool, incorporating agentic workflows that allow multiple AI agents to work independently on programming tasks in parallel. The app features background automations, customizable agent personalities, and leverages the GPT-5.2-Codex model, though benchmarks show it performs similarly to competing models from Gemini 3 and Claude Opus. CEO Sam Altman claims the tool enables sophisticated software development in hours, limited only by how fast users can input ideas.

January 31, 2026
+0.04% Risk

Physical Intelligence Raises $1B to Build General-Purpose Robot Foundation Models

Physical Intelligence, a two-year-old San Francisco startup valued at $5.6 billion, is developing general-purpose foundation models for robots similar to ChatGPT for language. The company has raised over $1 billion and operates without providing investors a commercialization timeline, instead focusing purely on research and cross-embodiment learning that allows robots to transfer knowledge across different hardware platforms. Founded by UC Berkeley and Stanford robotics researchers alongside former Stripe employee Lachy Groom, the company faces competition from Skild AI, which has already deployed commercially and raised $1.4 billion at a $14 billion valuation.

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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.