Reflection AI, a startup founded just last year by two former Google DeepMind researchers, has raised $2 billion at an $8 billion valuation, a whopping 15x leap from its $545 million valuation just seven months ago. The company, which originally focused on autonomous coding agents, is now positioning itself as both an open source alternative to closed frontier labs like OpenAI and Anthropic, and a Western equivalent to Chinese AI firms like DeepSeek.
The startup was launched in March 2024 by Misha Laskin, who led reward modeling for DeepMind’s Gemini project, and Ioannis Antonoglou, who co-created AlphaGo, the AI system that famously beat the world champion in the board game Go in 2016. Their background developing these very advanced AI systems is central to their pitch, which is that the right AI talent can build frontier models outside established tech giants.
Along with its new round, Reflection AI announced that it has recruited a team of top talent from DeepMind and OpenAI, and built an advanced AI training stack that it promises will be open for all. Perhaps most importantly, Reflection AI says it has “identified a scalable commercial model that aligns with our open intelligence strategy.”
Reflection AI’s team currently numbers about 60 people — mostly AI researchers and engineers across infrastructure, data training, and algorithm development, per Laskin, the company’s CEO. Reflection AI has secured a compute cluster and hopes to release a frontier language model next year that’s trained on “tens of trillions of tokens,” he told TechCrunch.
“We built something once thought possible only inside the world’s top labs: a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale,” Reflection AI wrote in a post on X. “We saw the effectiveness of our approach first-hand when we applied it to the critical domain of autonomous coding. With this milestone unlocked, we’re now bringing these methods to general agentic reasoning.”
MoE refers to a specific architecture that powers frontier LLMs — systems that, previously, only large, closed AI labs were capable of training at scale. DeepSeek had a breakthrough moment when it figured out how to train these models at scale in an open way, followed by Qwen, Kimi, and other models in China.
“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,” Laskin said. “It won’t be built by America.”
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Laskin added that this puts the U.S. and its allies at a disadvantage because enterprises and sovereign states often won’t use Chinese models due to potential legal repercussions.
“So you can either choose to live at a competitive disadvantage or rise to the occasion,” Laskin said.
American technologists have largely celebrated Reflection AI’s new mission. David Sacks, the White House AI and Crypto Czar, posted on X: “It’s great to see more American open source AI models. A meaningful segment of the global market will prefer the cost, customizability, and control that open source offers. We want the U.S. to win this category too.”
Clem Delangue, co-founder and CEO of Hugging Face, an open and collaborative platform for AI builders, told TechCrunch of the round, “This is indeed great news for American open-source AI.” Added Delangue, “Now the challenge will be to show high velocity of sharing of open AI models and datasets (similar to what we’re seeing from the labs dominating in open-source AI).”
Reflection AI’s definition of being “open” seems to center on access rather than development, similar to strategies from Meta with Llama or Mistral. Laskin said Reflection AI would release model weights — the core parameters that determine how an AI system works — for public use while largely keeping datasets and full training pipelines proprietary.
“In reality, the most impactful thing is the model weights, because the model weights anyone can use and start tinkering with them,” Laskin said. “The infrastructure stack, only a select handful of companies can actually use that.”
That balance also underpins Reflection AI’s business model. Researchers will be able to use the models freely, Laskin said, but revenue will come from large enterprises building products on top of Reflection AI’s models and from governments developing “sovereign AI” systems, meaning AI models developed and controlled by individual nations.
“Once you get into that territory where you’re a large enterprise, by default you want an open model,” Laskin said. “You want something you will have ownership over. You can run it on your infrastructure. You can control its costs. You can customize it for various workloads. Because you’re paying some ungodly amount of money for AI, you want to be able to optimize it as much as much as possible, and really that’s the market that we’re serving.”
Reflection AI hasn’t yet released its first model, which will be largely text-based, with multimodal capabilities in the future, according to Laskin. It will use the funds from this latest round to get the compute resources needed to train the new models, the first of which the company is aiming to release early next year.
Investors in Reflection AI’s latest round include Nvidia, Disruptive, DST, 1789, B Capital, Lightspeed, GIC, Eric Yuan, Eric Schmidt, Citi, Sequoia, CRV, and others.
