Research
Our research teams investigate the safety, inner workings, and societal impacts of AI models—so that artificial intelligence has a positive impact as it becomes increasingly capable.
Alignment
The Alignment team works to understand the risks of AI models and develop ways to ensure that future ones remain helpful, honest, and harmless.
Economic Research
The Economic Research team studies how AI is reshaping the economy, including work, productivity, and economic opportunity.
Frontier Red Team
The Frontier Red Team analyzes the implications of frontier AI models for cybersecurity, biosecurity, and autonomous systems.
Interpretability
The mission of the Interpretability team is to understand how large language models work internally, as a foundation for AI safety and positive outcomes.
Societal Impacts
Working closely with the Anthropic Policy and Safeguards teams, Societal Impacts is a technical research team that explores how AI is used in the real world.
Anthropic Economic Index report: Cadences
In our latest Economic Index report, we sample hourly for the first time to ask: When do people come to Claude? What do they produce with it? And how do they perceive AI's impact on their work?
Teaching Claude why
New research on how we've reduced agentic misalignment.
Project Deal
We created a marketplace for employees in our San Francisco office, with one big twist. We tasked Claude with buying, selling and negotiating on our colleagues’ behalf.
What 81,000 people want from AI
We invited Claude.ai users to share how they use AI, what they dream it could make possible, and what they fear it might do. Nearly 81,000 people participated—the largest and most multilingual qualitative study of its kind. Here's what we found.
Publications
- How Canada uses Claude: Findings from the Anthropic Economic Index
- Claude’s values across models and languages
- Claude plays robotics
- An off switch for dual-use knowledge in AI models
- A global workspace in language models
- Anthropic Economic Index report: Cadences
- Project Fetch: Phase two
- Agentic coding and persistent returns to expertise
- Paving the way for agents in biology
- Measuring LLMs’ impact on N-day exploits