Zhenzhi Venture Capital
真知创投
Zhenzhi Venture Capital (真知创投) is an early-stage fund that appears in elsewhere's corpus primarily through its co-investment activity. In June 2024, it participated as a follow-on investor in Deep Principle Technology's nearly $10 million seed round, which Linear Capital led, with Taihill Venture also joining . The fund's profile in the bundle is thin: the only other mention places it as the early backer of an AI companion startup called Ropet, where the founder describes the investors as having "some technical accumulation" and a focus on making "machines more like living organisms" . Beyond these two data points—one in AI-driven materials science, the other in consumer AI companions—elsewhere's corpus does not offer a broader picture of its vintage, AUM, or founding team.
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Coverage
From Foundation Models to AI Companions: What Cyclical Patterns Lie Behind the Rotation of AI Hype Cycles?
First figure out how to "make something people love," then consider "what role AI plays."
Transforming the Industrial Landscape of Chemical Materials with AI | Linear Capital Portfolio Interview Series: "Deep Principle Technology"
Today, Deep Principle, a company focused on AI-driven scientific research in chemistry, announced the completion of a nearly $10 million seed funding round. Linear Capital led the round, with Zhenzhi Ventures and Taihill Venture participating as co-investors. XtalPi and DP Technology also joined as strategic industry investors. Founded in 2024, Deep Principle's founding members are all graduates of MIT. The company's vision is to integrate artificial intelligence, quantum...
Using AI to Accelerate Discovery of Efficient Catalytic Materials, "Deep Principle Technology" Raises Nearly $10 Million in Seed Funding Led by Linear Capital
By Chen Sida | Edited by Deng Yongyi Source: Intelligent Emergence From household daily chemical products to high-efficiency catalysis for energy use, nearly every aspect of life and production depends on emerging materials. The old "needle in a haystack" trial-and-error approach can no longer meet today's materials R&D demands. But the AI boom sparked by the large model wave is bringing "AI alchemy" closer to reality.
