Dimension Capital, a New York venture firm investing at the intersection of science and compute, closed an $800 million third fund on July 21, a 60% increase over the $500 million second fund it raised just 18 months prior. The pace of that scaling is notable on its own: while many newer VC firms have struggled to raise any fresh capital in a tightening fundraising environment, Dimension has closed progressively larger funds in quick succession since its founding four years ago.
The firm was launched in late 2022 by Zavain Dar and Adam Goulburn, both former partners at Lux Capital, alongside Obvious Ventures alum Nan Li, on a thesis that founders would increasingly want to build companies crossing the boundary between deep-tech science -- biotech, materials, physics -- and modern AI compute infrastructure, rather than treating them as separate investment categories.
The firm's existing portfolio backs up that thesis with real markups: Dimension co-led a $30 million seed round in 2024 for Chai Discovery, an open-source AI foundation-model startup for drug development, which has since raised $400 million at a $3.8 billion valuation -- more than 100x the implied value at seed for a firm's earliest investors. Dimension was also an early investor in Anthropic, whose portfolio company Coefficient Bio was acquired by Anthropic itself this spring for a reported $400 million, and backs inference infrastructure company Modal Labs.
The competitive set for science-plus-compute investing includes established players like Lux Capital -- Dimension's founders' former firm -- and newer entrants like Convergent Research and a wave of AI-for-science funds launched over the past two years, but Dimension's track record of fast, large follow-on funds and a genuine seed-to-unicorn outcome in Chai Discovery gives it real differentiation heading into this raise.
For LPs, an $800 million close in a fundraising environment where most emerging managers are struggling is itself a signal that science-and-compute as a category has moved from a niche thesis to one institutional capital wants meaningful exposure to -- particularly as AI foundation models increasingly get applied to drug discovery, materials science and other historically slow-moving R&D categories. The risk is the same one every fast-scaling fund faces: deploying an $800 million check size responsibly across a category that's still genuinely early and unproven at scale.