Premise VC Launches Early-Stage Fund Targeting Pre-Seed, Seed Startups

Vanessa Larco, a longtime venture capital investor best known for backing early-stage software and consumer technology, has launched a new firm aimed at a part of the startup market she argues has been underserved by mega-funds: the pre-seed and seed trenches where founders need time, attention, and hands-on support more than a brand-name logo.

Larco co-founded Premise VC with Mercedes Bent in early 2025, positioning the firm as a specialist fund that will invest exclusively at the earliest stages. The thesis is straightforward: as venture funds have ballooned into multi-billion-dollar vehicles, writing initial checks of around $2 million has become structurally less compelling inside large portfolios—often relegated behind later-stage reserves and bigger allocations where returns can “move the needle.” Premise’s answer is to treat technical founders as a ‘top-priority customer,’ building a fund model designed around what founders say they actually need.

Before striking out on her own, Larco spent nearly eight years at New Enterprise Associates, one of the industry’s best-known global venture firms, where she served as a partner and investment committee member. Her dealmaking spanned enterprise software, developer tools, and consumer technology, and she also participated as a board observer at Robinhood ahead of its public listing—experience that informs Premise’s emphasis on execution and long-term operating discipline, not just early hype.

Larco framed the decision to form an independent fund as a response to shifting market dynamics rather than personal reinvention. In her view, founders have increasingly begun to favor smaller, focused investors who can deploy real time and resources, instead of choosing the biggest name in a crowded cap table. That preference, she said, sharpened after the collapse of Silicon Valley Bank in 2023, when many startups scrambled to meet payroll and turned to existing investors for urgent support—an episode that made founders acutely aware of where they ranked inside an investor’s portfolio. Since then, she argues, pre-seed and seed rounds have more often been led by dedicated early-stage funds rather than by large, multi-stage firms.

Premise VC’s check sizes are designed for that market. The firm plans to invest between $500,000 and $3 million per deal, concentrating exclusively on pre-seed and seed rounds. Larco described the fund as something like a ‘startup’ itself—iterated through founder interviews and feedback loops to refine how Premise shows up in the earliest days, when recruiting, product iteration, and initial go-to-market decisions can be decisive.

The fund’s most distinctive filter is that it prioritizes founder potential over early ideas. Larco argued that at the pre-seed stage, the odds that a startup’s initial concept will match what the company becomes in five to 10 years are low. What matters more is whether the team can navigate uncertainty, find ‘product-market fit,’ and evolve the product and business model under pressure. To evaluate that, she looks for founder strengths across seven attributes, with the expectation that a top team doesn’t need to score highly on every dimension—but should display world-class edge in at least two or three.

One attribute Larco highlighted is ‘radical dissatisfaction’—a founder’s refusal to accept “good enough,” paired with the drive to push standards upward across the organization. She drew a careful distinction between productive insistence and ego: the trait only counts as an advantage, she said, when it reflects persistence toward outcomes rather than pride or performative intensity.

Larco also laid out a clear framework for AI investing at a moment when venture capital is saturated with generative AI pitches. In her view, breakout AI products should be meaningfully ‘faster,’ ‘cheaper,’ or ‘easier’ than existing workflows—and should meet at least two of those thresholds to establish defensible value. She did not dismiss startups that build on top of large language models as a “wrapper” by default, but signaled caution about teams dependent on a single model provider.

That caution, Larco suggested, comes down to technical fluency and cost control. When inference costs spike or model performance shifts, she expects strong teams to be able to adapt by mixing open-source and closed models, and by choosing among different ecosystems—including provider-specific options—rather than forcing every feature through one brittle dependency. Non-technical founders, she warned, can be more likely to treat one model as a universal solution, a habit that can erode long-term competitiveness as the AI stack commoditizes.

Despite the broader venture market’s coolness toward consumer technology—especially as capital has crowded into enterprise AI—Larco argued the pendulum could be swinging too far. She sees opportunity in consumer and fintech precisely because many investors have deprioritized them, even as AI accelerates shifts in user behavior and lowers the cost of building new products. A rapid retreat from the category, she suggested, may prove ‘short-sighted’ if the next wave of consumer adoption is being reshaped by AI-native experiences.

Larco also offered a view on how AI changes the role of product leadership. While AI tools can reduce repetitive work such as drafting requirements documents or tracking bugs, she said the core job of understanding what users want—and why—remains human. In practice, she expects top product leaders to spend less time producing documentation and more time defining performance standards for AI agents, then verifying whether systems actually deliver the intended outcomes in real-world use.

On the question of defensibility for early AI startups—often criticized for being easy to copy—Larco argued the old rules still apply. Durable moats, she said, are built less from the model itself and more from user workflows, accumulated data, network effects, and deep integrations that create switching costs. In a market where competitors can replicate surface features in days, the decisive advantage is the set of reasons customers continue to return.

Premise VC’s message, in short, is that the AI boom has not repealed the fundamentals of early-stage investing. The firm is betting that a small and specialized fund can win by committing to founders earlier, focusing on execution capacity and technical understanding rather than buzzwords—and by backing teams capable of outlasting the cycle and building products that endure.