Organizations

Organizations shaping AI

Explore companies, research labs, universities, nonprofits, and public institutions across the AI ecosystem.

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706 organizations · showing 145-168
Company🇺🇸 United States
4 people

AI safety and security startup founded by Bo Li, Dawn Song, Carlos Guestrin, Sanmi Koyejo, and collaborators, developing tools for automated red teaming, runtime guardrails, and AI governance.

Company🇺🇸 South San Francisco, CA
4 people

AI-native drug-discovery company launched with over $1 billion in initial funding. Led by CEO Marc Tessier-Lavigne (former Genentech chief scientific officer) and co-founded by protein-design pioneer David Baker, combining generative models with wet-lab drug development.

Company🇺🇸 United States
4 people

Startup accelerator whose batches have produced many AI companies and whose network is a major channel for AI founder talent.

Company🇺🇸 San Francisco
3 people

AI startup building enterprise-specific agents and infrastructure for training, deploying, and continuously improving models on company workflows and data. In April 2026 it announced an $80 million round led by Kleiner Perkins, bringing total funding to $160 million.

Non-profit🇺🇸 United States
3 people

Nonprofit founded by François Chollet and Mike Knoop to run the ARC-AGI benchmark and prize, which measures fluid intelligence and skill-acquisition efficiency on novel tasks rather than memorized knowledge.

Company🇺🇸 San Francisco
3 people

Arcee AI builds open-weight language models that organizations can download, adapt and run on their own infrastructure. Its Trinity family spans compact on-device models and larger reasoning systems, emphasizing efficient deployment and user control rather than dependence on a closed model provider.

Company🇺🇸 San Francisco
3 people

Arena operates a community-powered platform for evaluating frontier AI systems through anonymous comparisons and real-world human feedback. Originating as UC Berkeley’s Chatbot Arena, it publishes public leaderboards across language, vision, coding, search, video, and agentic tasks and offers evaluation services to model developers.

Non-profit🇺🇸 San Francisco
3 people

The Chan Zuckerberg Biohub Network is a group of nonprofit biomedical research institutes established starting in 2016 with a $600 million commitment from Priscilla Chan and Mark Zuckerberg, partnering with universities including UCSF, UC Berkeley, and Stanford to accelerate the understanding and treatment of disease.

Company🇺🇸 United States
3 people

AI startup pairing foundational research with product development around efficiency-first large language models and applications.

Academic🇺🇸 United States
Brigham Young University
3 people

Brigham Young University (BYU) is a private research university in Provo, Utah, founded in 1875 and sponsored by The Church of Jesus Christ of Latter-day Saints, enrolling approximately 33,000 students across engineering, sciences, humanities, and business.

Government🇺🇸 Washington, D.C.
Center for AI Standards and Innovation
3 people

CAISI is the US government's central technical body for testing commercial AI systems and developing voluntary standards for their security and evaluation. Based within NIST, it assesses capabilities and vulnerabilities with implications for cybersecurity, biosecurity, national security, and international competition.

Company🇺🇸 San Francisco
Coinbase
3 people

Coinbase is a San Francisco-based cryptocurrency exchange founded in 2012 by Brian Armstrong and Fred Ehrsam, providing retail and institutional customers with infrastructure to buy, sell, and hold digital assets; it became the first major U.S. crypto exchange to go public, listing on the Nasdaq in April 2021.

Company🇺🇸 San Francisco
3 people

Cruise was an autonomous-vehicle company founded in San Francisco and majority-owned by General Motors, developing self-driving technology and robotaxi services.

Non-profit🇺🇸 United States
3 people

Research non-profit investigating AI welfare and moral patienthood — whether and when AI systems could warrant moral consideration.

Company🇺🇸 San Francisco
3 people

Building the foundation of visual reasoning. Raised $55 million seed round in April 2026 with participation from Striker Ventures, Menlo Ventures, Altimeter, and Jeff Dean.

Non-profit🇺🇸 United States
3 people

Research organization producing data and analysis on AI trends, compute, benchmarks, timelines, and the trajectory of machine-learning capabilities.

Company🇺🇸 United States
Flapping Airplanes
3 people

"Flapping Airplanes is a frontier data efficiency lab that is currently in stealth."

Company🇺🇸 South San Francisco
3 people

Genentech is a biotechnology company founded in South San Francisco, a pioneer of recombinant-DNA medicine; it is now a member of the Roche Group.

Company🇺🇸 San Mateo
3 people

Generalist develops embodied foundation models that learn from physical experience, with an initial focus on giving robots the dexterity and adaptability needed for useful work across varied real-world tasks.

Company🇺🇸 San Francisco
3 people

GitHub is a code-hosting and developer collaboration platform founded in 2008 and acquired by Microsoft in 2018. Its Copilot product was one of the first widely adopted AI pair-programming tools.

Company🇺🇸 United States
3 people

AI interpretability company founded by Eric Ho, Daniel Balsam, and Tom McGrath. Builds Ember, a platform for inspecting and editing the internal mechanisms of neural networks; backed by investors including Anthropic and Lightspeed.

Company🇺🇸 United States
3 people

Harvey is a legal AI startup founded by Winston Weinberg and Gabriel Pereyra, building AI tools for law firms and professional-services work. It was an early portfolio company of the OpenAI Startup Fund.

Company🇺🇸 Yorktown Heights
IBM Research
3 people

IBM Research is the research division of IBM, established in 1945, with labs worldwide working on computing, materials, and artificial intelligence.

Company🇺🇸 Palo Alto
3 people

Inception develops Mercury, a family of diffusion language models that refine text in parallel rather than generating it strictly one token at a time. The approach is designed to make language-model responses faster and less costly for production use.