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How a former DeepMind researcher raised at a $300M pre-seed valuation before launching a product
Andrew Dai, a former Google DeepMind researcher, secured a $55 million seed round at a $300 million valuation for his new company, Elorian, just months after leaving Google. The deal, discussed on TechCrunch's Build Mode podcast, reportedly carries a more aggressive valuation-to-capital ratio than Thinking Machines' record-breaking raise, underlining how investors are racing to back experienced AI researchers before they even have a finished product.
Dai, whose earlier research contributed to work underpinning ChatGPT, said he is targeting visual AI, an area he argues has lagged behind advances in maths, physics and coding. In the interview, he explains how he translated a technical vision into a pitch nontechnical investors could grasp, and why he chose partners such as Nvidia and Menlo Ventures over rivals offering higher valuations, prioritising strategic value over price. He also discusses recruiting researchers away from Big Tech and why speed is a key competitive edge in AI development.
In short
- Ex-DeepMind researcher Andrew Dai raised $55M seed at $300M valuation
- New startup Elorian aims to advance "visual AGI"
- Dai chose Nvidia and Menlo Ventures over higher-valuation offers
Steelman: both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
Top AI researchers rarely leave entrenched positions at Big Tech companies, making such talent genuinely scarce and difficult to secure. Historical precedent—OpenAI's early funding, DeepMind's success—suggests that backing proven researchers early often yields better returns than waiting for a completed product. In rapidly evolving fields like AI, the team's capability and vision can matter more than an existing product, and strategic partnerships with industry leaders add concrete value beyond capital. The researcher's demonstrated track record and first-mover advantage in assembling talent justify aggressive valuation as a rational competitive move.
The case against
This valuation reflects venture bubble mentality, prioritising narrative and pedigree over evidence of commercial viability or product-market fit. Many brilliantly credentialed researchers have failed to translate corporate or academic excellence into successful commercial ventures, yet this model prices the investment as though success is assured. A £300 million pre-money valuation on an unproven concept creates unrealistic financial expectations unlikely to be met, risking capital destruction for investors. The urgency to fund talent quickly, rather than rational assessment of risk and return, suggests fear-driven decision-making that tends to precede market corrections.
Explained simply
Andrew Dai is a former researcher at Google DeepMind, a leading AI research lab, who recently left to start his own company called Elorian. His new venture has attracted $55 million in funding at a $300 million valuation, despite not yet having released a finished product.
This deal reflects a broader pattern in AI investment, where wealthy investors are competing to back experienced researchers and their new ventures before they establish themselves elsewhere. The $300 million price tag for a pre-product company is particularly steep, revealing how valuable investors consider established AI researchers to be in the current market.
Elorian is focused on visual AI, an area Dai argues has fallen behind recent progress in other domains such as mathematics, physics and coding. His earlier research contributed to foundational work in the field, giving him credibility with investors interested in backing the next generation of AI capabilities.