Why AI Won’t Replace Venture Capitalists Any Time Soon

The Wall Street Journal recently reported on a data company using AI to forecast startups’ financial futures, and it caused quite a stir in venture capital circles. After all, that level of in-depth analysis has long been a core advantage afforded to a venture firm’s crack analytics team. With that edge somewhat offloaded to AI, it invites the question: Is it reasonable to think AI will replace VCs entirely? Not in the foreseeable future. How do I know? I have been building custom AI models for Salesforce Ventures since joining the team back in 2022 and have seen first-hand how vital the human element is in investment decisions. The models my team builds help inform our investment strategy, making sure we find the right investment opportunities at the right time, and this is complemented by the expertise of our investors. While AI excels at recognizing patterns from historical data—valuable input for investment decisions—relying on past performance alone in venture is a strategic misstep. In fact, the most successful venture investors make calculated bets on novel ideas that historical patterns would caution against. Venture deals are based on far more than just the terms of a financial transaction—investors and founders alike consider a wide range of qualitative factors before striking a deal, including communication style, personal chemistry, range of relationships, and much more. This process requires judgment that transcends algorithms; experienced investors must quickly assess the potential for novel markets and products alike, while ascertaining critical founder qualities that defy quantitative analysis, such as grace under fire, hunger, and maturity. Critically, venture capital is unique in that access to critical information is typically asymmetrical. Founders meticulously control what information about their company is publicly available, which can bias training datasets—and subsequent AI models—in substantial and unpredictable ways. Both founders and investors strategically curate how information appears in the broader market, with databases functioning more as carefully managed signaling platforms. This is especially true for early-stage startups, where data platforms predominantly showcase what founders and investors want the market to perceive rather than providing neutral fact-based insights into actual developments and outcomes. As with any job, AI is best viewed as a powerful enabler, enhancing efficiency and helping investors focus their limited time on the companies and founders that make the most sense for their investment mandate. At Salesforce Ventures, for example, we focus our AI tools on amplifying investor capabilities and automating repetitive back office work rather than attempting to replace human interaction, connection, common sense, and, ultimately, shrewd judgment. Our AI models help categorize companies based on their innovations and surface opportunities that align with our investment theses in specific sectors. Yet we never over-rely on these metrics, as models that attempt to predict startup outcomes can make success feel like a foregone conclusion—and short-circuit critical investor engagement and ongoing support. Critically, AI tools free our investors of spreadsheet analysis and number-crunching that too often get in the way of what our team values most: meeting founders shaping the future. Our investors are better prepared to ask smart questions informed by AI, while gaining more bandwidth to focus on connection and empathy that builds foundational relationships. Yet human expertise and capabilities remain indispensable. Investors bring holistic evaluation skills to make sense of factors that are difficult to capture in a machine-readable format: the founding team’s social dynamics, the depth of previous experiences, and product-market fit now and in the future. These attributes are difficult to discern even for the most experienced investors; trying to extract these qualitative features for the training dataset of an AI model has been an impossible task so far. Importantly, these human dynamics that deeply influence the investment process don’t stop once the wire is sent. The best investors understand that venture is not a passive asset class and work tirelessly to support their founders. While an individual investor can have only so much impact, a critical component of the overall success of an investment decision is the follow-through: creating value for the company wherever possible, through introductions, advice, candid feedback, and support during the inevitable tough times. The best investors create differentiated value for founders through a robust community and privileged access to the resources founders need to scale their businesses. Those who believe success in venture capital can come from following the public equity market quant playbook—investing based on large-scale data, methodological expertise, toeing ownership thresholds, and repeatable experimentation—fundamentally misunderstand venture as an asset class. Venture dollars are deployed to back people, who in turn build companies. These decisions are made based on human evaluation, asymmetrical information, and an opinionated view of the future. That’s not to say fast-improving AI models won’t play an increasingly meaningful role in shaping investment decisions. But as long as human-led companies are raising capital, it’s vital to have another human in the room to make an optimal decision. EXPERT OPINION BY BRIAN MURPHY