Reflection AI raises $2B to be America’s open frontier AI lab, challenging DeepSeek — marking one of the largest funding rounds for an AI startup in 2025. The one-year-old company, founded by former Google DeepMind researchers, has skyrocketed to an $8 billion valuation — a massive 15x jump from just seven months ago.
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The ambitious startup aims to position itself as both an open-source alternative to closed AI labs like OpenAI and Anthropic, and a Western response to China’s rapidly advancing AI firms, including DeepSeek.
Founded By DeepMind Veterans
Reflection AI was launched in March 2024 by Misha Laskin, who led reward modeling for DeepMind’s Gemini project, and Ioannis Antonoglou, co-creator of AlphaGo, the AI that famously defeated the world champion in Go.
Their mission is clear: to prove that top AI talent, given the right tools, can build frontier-scale models outside the walls of Big Tech.
Reflection AI’s Open Frontier Vision
Alongside the new funding round, Reflection AI revealed that it’s building an advanced AI training stack designed to be open and accessible. The company says it has identified a “scalable commercial model that aligns with our open intelligence strategy,” suggesting it’s pursuing a sustainable path for open-source frontier research.
The team now numbers around 60 researchers and engineers, many of them veterans from DeepMind and OpenAI. CEO Misha Laskin said Reflection AI is already operating a powerful compute cluster and plans to release its first frontier language model in 2026 — trained on “tens of trillions of tokens.”
Building Frontier Models At Scale
In a post on X (formerly Twitter), Reflection AI shared that it has built “a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale.”
This technology, once limited to the world’s top AI labs, is now being developed independently — a move that could reshape the competitive landscape of frontier AI research in the West.
The company first tested this system in autonomous coding, proving the potential of its large-scale architecture. With that success, Reflection AI is now moving toward general agentic reasoning — AI systems capable of more human-like understanding and decision-making.
Competing With DeepSeek And China’s AI Boom
The Mixture-of-Experts (MoE) architecture sits at the core of today’s frontier large language models. Until recently, only massive, closed labs could train them effectively. But DeepSeek’s open model success in China changed that narrative — followed by breakthroughs from Qwen and Kimi.
According to Laskin, these developments were a wake-up call:
“DeepSeek and Qwen and all these models are our wake-up call because if we don’t do anything about it, then effectively, the global standard of intelligence will be built by someone else.”
Reflection AI’s massive funding and open-source strategy signal that the U.S. isn’t backing down in the race for frontier AI leadership.
With $2 billion in fresh funding and a team stacked with elite AI researchers, Reflection AI is emerging as a key challenger in the global race for general-purpose intelligence. By combining open research principles with cutting-edge infrastructure, the company hopes to democratize access to frontier-scale models — keeping innovation in the open and the West in the lead.
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