Reflection AI is a New York-based artificial intelligence lab founded in 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou. It is building autonomous coding agents and open frontier language models, with the stated ambition of reaching superintelligence by first solving software engineering. Its flagship product, Asimov, is a code research and comprehension agent for large codebases. After raising $2 billion at an $8 billion valuation in October 2025, the company has positioned itself as an open, Western alternative to closed labs like OpenAI and Anthropic and to Chinese open-weight models like DeepSeek.
Misha Laskin is the co-founder and CEO of Reflection AI, a New York lab building open-weight frontier models and autonomous coding agents. A theoretical physicist by training (Yale, University of Chicago), he led reward modeling for Google DeepMind's Gemini and worked in reinforcement learning at Berkeley and DeepMind before launching Reflection in 2024 with AlphaGo co-creator Ioannis Antonoglou. In 2025 the company raised roughly $2 billion at an $8 billion valuation, positioning itself as America's open-source answer to DeepSeek.
AfterQuery is a San Francisco applied research lab that builds expert-generated datasets, benchmarks, and reinforcement-learning environments for the world's leading AI labs. The company recruits nearly 100,000 vetted domain experts - in finance, law, medicine, software, and beyond - to teach frontier models how specialists actually think.
Deccan AI is a Mountain View-based GenAI data company that runs the post-training and production layer for frontier labs and enterprises. It builds super-accurate SFT and RLHF datasets, reinforcement learning environments and agentic evaluation pipelines using an elite expert network and a purpose-built quality platform.
Mercor is an AI-powered talent marketplace that recruits domain experts - doctors, lawyers, engineers, scientists - to train and evaluate frontier AI models for labs like OpenAI, Anthropic, Google, Meta, and Amazon. Founded in 2023 by three Thiel Fellows, it has grown from a hiring tool into the human data backbone of the AI economy.
Rukesh Reddy is the Founder and CEO of Deccan AI, a Mountain View-based AI data and post-training company that raised a $25M Series A in March 2026 led by A91 Partners with participation from Susquehanna International Group and Prosus Ventures. Built as a 'born GenAI' company in October 2024, Deccan AI serves frontier AI labs and major tech companies - including Google DeepMind and Snowflake - with high-precision training datasets, reinforcement learning environments, and enterprise evaluation suites. Reddy brings over 15 years of experience spanning finance, strategy consulting, and digital transformation at firms including J.P. Morgan, Monitor Group (now Monitor Deloitte), and Citi, where he led CX and digital transformation for the global retail bank.
Edwin Chen is the Founder & CEO of Surge AI, the AI data infrastructure company that became Anthropic and Google's secret weapon for model training and evaluation. A former ML scientist at Google, Twitter, Dropbox, and Facebook, Chen bootstrapped Surge AI from his San Francisco apartment in 2020 to over $1.2 billion in annual revenue with fewer than 110 employees - no venture capital, no sales team. TIME named him one of the 100 Most Influential People in AI in 2025, and Forbes put him on the 400 list as one of the youngest billionaires. Surge's platform powers RLHF, supervised fine-tuning, and custom evaluations for the world's leading AI labs.
Nathan Lambert is a Senior Research Scientist and Post-Training Lead at the Allen Institute for AI (Ai2), where he leads open-source language model development on the OLMo and Tulu series. A UC Berkeley PhD, he previously led the RLHF team at Hugging Face, co-building the TRL library and the Zephyr model. He runs Interconnects AI, a Substack newsletter read by tens of thousands covering post-training, open models, and AI policy, and is the author of The RLHF Book (Manning Publications). With roughly 8,000 academic citations and a reputation for demystifying the hardest parts of modern AI, Lambert is one of the most trusted voices at the intersection of open-source AI research and public education.