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Ex-DeepMind teamโ€™s AI startup seeks $1b to rival Meta, DeepSeek

Reflection AI, a New York-based startup founded by former Google DeepMind researchers, is in talks to raise over US$1 billion to develop open-source large language models.

The company aims to compete with Meta, DeepSeek, and Mistral in the open-source AI space.

One source said Reflection AI has already secured most of its funding target.

The startup has spent the past year building a coding agent called Asimov, which analyzes corporate data to generate code.

It has generated a small amount of revenue from a preview with corporate clients. Investors include Lightspeed Venture Partners, Sequoia Capital, and CRV.

PitchBook previously valued the company at US$545 million.

The funding push comes amid rising demand from US companies for open-source AI models due to data security concerns with Chinese providers and the need for more customizable solutions.

๐Ÿ”— Source: The Information


๐Ÿง  Food for thought

1๏ธโƒฃ Cost efficiency gap drives urgent competitive response from US AI companies

DeepSeekโ€™s breakthrough demonstrates how dramatically lower development costs can disrupt established players in AI.

The Chinese company trained its R1 model for less than $6 million while achieving performance competitive with models that cost hundreds of millions to develop1.

This represents a cost advantage compared to traditional approaches, with OpenAI expecting to spend over $7 billion on training models this year alone.

DeepSeekโ€™s efficiency comes from its Mixture-of-Experts architecture, which activates only a subset of its 671 billion parameters during operation, and innovative techniques like FP8 mixed precision training that reduces energy and memory consumption2.

The rapid user adoptionโ€”10 million users within weeks of launchโ€”shows that cost-efficient models can quickly gain market traction when offered for free3.

This explains why Reflection AIโ€™s founders see an urgent opportunity to establish a US-based alternative, as the current cost structure puts American companies at a competitive disadvantage in the open-source market.

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