VCs are implementing a “reigning” strategy to crown AI winners in their infancy

In early October, DualEntry, an AI-powered enterprise resource planning (ERP) startup, announced: Series A for $90 million round led by Lightspeed and Khosla Ventures, valuing annual business at $415 million.

The company seeks to replace legacy software akin to Oracle NetSuite with its offerings that may automate routine tasks and provide predictable insights. The massive round of funding from top VCs signaled that the startup is likely to see phenomenal revenue growth.

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However, one VC investor who declined to invest told TechCrunch that when he reviewed the deal in August, DualEntry’s annual recurring revenue (ARR) was only about $400,000. DualEntry co-founder Santiago Nestares denies this figure. When asked about revenues when the transaction was finalized, Nestares replied that they were “significantly higher.”

Despite this, extremely attractive valuation relative to revenues is becoming an increasingly common investment strategy among leading VC firms. This tactic is generally known as “kingmaking.”

This approach involves committing massive amounts of resources to a single startup in a competitive category, with the goal of overwhelming its rivals by giving the chosen company a checking account advantage significant enough to create the appearance of market dominance.

Kingmaking is nothing recent, but its timing has modified dramatically.

“Venture capitalists have always evaluated a group of competitors and then placed bets on who they thought would be the winner in that category. The difference is that it happens much earlier,” said Jeremy Kaufmann, partner at Scale Venture Partners.

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The early aggressive one financing contrasts with the last investment cycle.

“The 2010 version of this was called simply ‘capital as a weapon,’” said David Peterson, partner at Angular Ventures. He emphasized that the canonical example of this is the massive funding for Uber and Lyft, but the capital arming of ride-sharing firms only began once they reached a Series C or D round.

Similar to Uber vs. Lyft, investors in competitors DualEntry, Rillet and Campfire, are apparently equally eager to see their bets succeed with significant capital. In early August, Rillet raised a Series B for $70 million led by a16z and Iconiq, just two months after the company closed a $25 million Series A led by Sequoia.

Similarly, Campfire AI had two parallel rounds of funding. In October he caught approx Series B for $65 millionjust months after announcing a $35 million Series A round led by Accel.

AI ERP is just one of several categories of AI applications where startups are rapidly raising funding. “There is no new data between rounds. Series B takes place 27-60 days after the series. So regularly,” Jaya Gupta, partner at Foundation Capital, published in October last month. In addition to AI, ERP wrote that it sees this pattern in categories akin to IT service management and SOC compliance.

While some startups like Cursor and Lovable have reportedly grown at a breakneck pace between rounds, several VCs told TechCrunch that this wasn’t the case for everyone. Investors say AI ERP and several other categories of startups that raised multiple rounds in 2025 still have ARRs in the single-digit tens of millions.

While not all VCs agree that kingmaking is a clever investment strategy, there are explanation why offering large amounts of capital may be useful even when a startup maintains a modest burn rate. For example, well-funded startups are seen as more likely to survive by large buyers, making them the preferred supplier for significant software purchases. Investors say this strategy has helped Harvey, an artificial intelligence legal startup, attract clients from large law firms.

However, history shows that mass capitalization does not guarantee success, with notable failures akin to logistics company Convoy and the bankruptcy of scooter company Bird.

But these precedents don’t hassle major VC firms. They’d relatively bet on a category that looks like a good case for AI, and they’d relatively invest early because, as Peterson puts it, “Everyone has fully learned the lesson from energy law. In 2010, companies could grow faster and be bigger than almost anyone realized. You couldn’t overpay if you were an early investor in Uber.”

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