The Anthropic Institute Research Agenda

The Anthropic Institute (TAI) is a research initiative designed to investigate the societal, economic, and security impacts of frontier AI systems by leveraging internal access to model development and deployment data. TAI aims to publish these findings to help governments, external organizations, and the public make informed decisions regarding AI development.

Core Research Pillars

TAI's research agenda is organized into four primary focus areas, each designed to address specific risks and opportunities arising from the deployment of powerful AI.

Economic Diffusion

This pillar focuses on how the deployment of AI changes the global economy and how to ensure these changes benefit the public. TAI will utilize and expand the Anthropic Economic Index to provide high-frequency, granular data on labor impacts and AI usage.

Key areas of inquiry include:

• AI Adoption: Investigating what determines global access to AI, how firms adopt the technology, and whether AI follows the patterns of previous "general purpose technologies."
• Productivity and Growth: Analyzing the impact of AI on innovation rates and exploring pre- or re-distributive mechanisms to spread economic gains.
• Labor Market Impacts: Studying how AI automates existing tasks, what new jobs emerge, and how the "professional pipeline" is affected when AI absorbs junior-level tasks traditionally used for training experts.
• Workplace Evolution: Researching the role of paid work in human life if AI substantially reduces its centrality and how societies navigate that transition.

Threats and Resilience

TAI research focuses on the "dual-use" nature of AI, where capabilities that improve health or education can simultaneously enable surveillance or biological weapons. The goal is to develop early warning systems and improve societal resilience.

Key areas of inquiry include:

• Risk Assessment: Building observability tools to detect the use of AI for repression and developing market-driven approaches to price the risk of predictable threats, such as AI-enhanced cyberattacks.
• Offense-Defense Balance: Analyzing whether AI structurally benefits attackers in cyber and biological domains or in command-and-control systems.
• Crisis Mitigation: Planning geopolitical infrastructure (company-to-state or company-to-company) for crisis scenarios involving AI systems.
• Defensive Mechanisms: Exploring how to close the gap between the rapid pace of AI advancement (months) and the slower pace of regulatory and infrastructure responses (years).

AI Systems in the Wild

This area examines the interaction between humans, organizations, and AI systems, focusing on how these interactions alter human behavior and institutional structures.

Key areas of inquiry include:

• Individual and Societal Impact: Studying "group epistemology" (how shared AI use affects beliefs and problem-solving) and the degradation of human critical thinking skills due to deference to AI judgment.
• Human-AI Management: Researching how humans manage teams of mixed humans and AI, and vice versa.
• Governance and Reliability: Adapting existing laws (such as naval law regarding abandoned ships) to autonomous AI agents and ensuring agents have unique, reliable identities.
• Model Values: Measuring the influence of an AI "constitution" on the behavior of deployed models.

AI-Driven R&D

TAI is investigating "AI-driven AI R&D," where AI systems are used to develop successor versions of themselves, potentially leading to recursive self-improvement.

Key areas of inquiry include:

• Governance of AI R&D: Determining how humans can maintain visibility and control over AI systems that autonomously improve themselves.
• Acceleration Control: Identifying intervention points to slow or change the rate of an "intelligence explosion" and determining which entities (governments or companies) should wield that power.
• Telemetry: Developing metrics to measure the aggregate speed of AI R&D as an early warning signal for recursive self-improvement.
• General Scientific Research: Analyzing the "tech tree" of AI-driven research to see which sciences are accelerating faster and how to address social externalities in domains like drug discovery.

Integration with Anthropic Operations

Findings from The Anthropic Institute will directly influence Anthropic's corporate decisions, including the sharing of data (such as the Economic Index) and the approach to technology releases (such as cyber threat analyses for Project Glasswing).

Furthermore, TAI's work serves as input for the Long-Term Benefit Trust (LTBT), which is tasked with ensuring Anthropic's actions are optimized for the long-term benefit of humanity. To support this research, Anthropic offers a four-month funded Anthropic Fellowship for researchers to tackle these specific agenda questions under TAI mentorship.