The Asset Changed Faster Than the Institution

Abstract

Generative artificial intelligence is changing the economics of venture formation, but unevenly. At the application-software layer, AI-assisted development, cloud infrastructure, reusable models, and automated commercial tools can reduce the labour and time required to produce early product and market evidence. The same shock does not remove the physical work required to establish reproducibility, fabricate devices, qualify materials, engineer production, complete regulated validation, integrate into industrial systems, or build infrastructure. This article asks what that asymmetry does to venture capital. Institutional venture capital co-evolved with ventures that could turn relatively small, staged commitments into frequent and legible evidence. If some of those ventures require less capital before demonstrating viability, the investable asset supply relevant to large funds changes even if startup formation rises. Hardware-intensive science ventures remain able to absorb substantial capital, yet prior European evidence shows that they can also scale through licensing, customer-funded development, grants, procurement, strategic co-development, revenue reinvestment, and debt. Deep tech is therefore capital-needing but not automatically venture-capital-native. The article theorizes a two-part institutional risk: funds may migrate toward physically constrained sectors partly because they preserve capital-absorption capacity, while their declared theses move faster than the capability required to select, sequence, and govern those ventures. The resulting thesis-capability gap should bias allocation toward hardware ventures that look familiar under software-derived heuristics and can support large follow-on rounds, rather than toward ventures whose technical-commercial architecture is strongest. Integrating research on experimentation costs, fund economics, specialization, organizational capability, construct clarity, science commercialization, and alternative capital architectures, the paper develops seven propositions, boundary conditions, falsification tests, and a multi-study empirical design.