VCs deploy ‘kingmaking’ strategy to crown AI winners in their infancy

In early October, DualEntry, an AI enterprise resource planning startup, announced a $90 million Series A round led by Lightspeed and Khosla Ventures. This funding values the one-year-old business at $415 million. The company aims to replace legacy software like Oracle NetSuite with an offering that automates routine tasks and provides predictive insights. The substantial investment from top-tier venture capital firms suggests the startup is likely experiencing strong revenue growth.

However, one venture capitalist who declined to invest told TechCrunch that DualEntry’s annual recurring revenue was just around $400,000 when he reviewed the deal in August. DualEntry’s co-founder, Santiago Nestares, denies that figure. When asked about revenue at the deal’s closing, Nestares stated it was considerably higher.

Even so, an extremely high valuation relative to revenue is becoming an increasingly common investment strategy among top-tier VC firms. This tactic is known as “kingmaking.” The approach involves deploying massive funding into one startup within a competitive category, aiming to overwhelm rivals by granting the chosen company such a significant financial advantage that it creates the appearance of market dominance.

Kingmaking is not new, but its timing has shifted dramatically. Venture capitalists have always evaluated competitors and bet on a perceived winner in a category. The difference now is that this is happening much earlier, according to Jeremy Kaufmann, a partner at Scale Venture Partners.

This early aggressive funding contrasts with the last investment cycle. The 2010s version of this strategy was often called “capital as a weapon.” A canonical example was the massive funding into Uber and Lyft, though that capital weaponization did not begin until their Series C or D rounds.

As with Uber versus Lyft, investors in DualEntry’s competitors, Rillet and Campfire, are evidently eager to see their bets succeed with substantive capital. In early August, Rillet raised a $70 million Series B led by Andreessen Horowitz and Iconiq, just two months after closing a $25 million Series A led by Sequoia. Similarly, Campfire AI secured a $65 million Series B in October, only a couple of months after announcing a $35 million Series A round led by Accel.

AI ERP is just one of several AI application categories where startups are raising funding in rapid succession. Series B rounds now often happen 27 to 60 days after Series A rounds regularly, with no new operational data required between them. Besides AI ERP, this pattern is seen in categories such as IT service management and SOC compliance.

While some startups have reportedly grown at a breakneck pace between their back-to-back rounds, several venture capitalists note that is not the case for all. Many AI ERP startups and others that raised multiple rounds in 2025 still have annual recurring revenues in the single-digit millions.

Although not all venture capitalists agree that kingmaking is a sound investment strategy, there are reasons why offering large amounts of capital can be beneficial even when a startup maintains a modest burn rate. For instance, well-funded startups are perceived as more likely to survive by large enterprise buyers, making them the preferred vendor for significant software purchases. That is a strategy that helped legal AI startup Harvey attract large law firm customers.

Still, history shows that massive capitalization offers no guarantee of success. There have been notable failures, including the logistics company Convoy and the bankruptcy reorganization of the scooter company Bird.

But those precedents do not faze major venture capital firms. They prefer to bet on a category that seems well-suited for AI, and they would rather invest early. As one investor noted, everyone has internalized the lesson of the power law. In the 2010s, companies could grow faster and become bigger than almost anybody had realized. You could not have overpaid if you were an early investor in a company like Uber.