Zhenzhi Venture Capital
真知创投
elsewhere's corpus covers Zhenzhi Venture Capital (真知创投) only lightly — mainly through its deal activity. In June 2024 it co-invested in Deep Principle Technology (深度原理科技), an AI-for-chemistry startup, joining a nearly $10M seed round led by Linear Capital alongside Taihill Venture, with XtalPi and DP Technology participating as industry backers, per Linear Capital's portfolio coverage. It also appears as an early investor in ROPET, the AI companion robot maker, per a FreeS Fund piece. Beyond those two bets — both in the AI-for-science / AI-hardware orbit — the corpus holds no dedicated profile of the fund itself: no founding date, team, fund size, or stated strategy. What can be read from the record is a fund willing to write seed-stage checks in frontier AI, but that's inference from two data points, not a portrait.
AI-generated — may contain errors, please verify.
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.
