VCs’ ‘Kingmaking’ Strategy: Massive Early AI Rounds to Forge Market Winners
Venture capital firms are increasingly using a ‘kingmaking’ strategy—deploying very large Series A/B rounds into early-stage AI startups to secure category dominance. Recent examples include DualEntry’s $90M Series A at a $415M valuation despite reported ARR near $400k, and rapid follow-on rounds for AI ERP and related categories (Rillet, Campfire AI: multiple $25M–$70M rounds in months). Key VC names tied to this trend include Lightspeed, Khosla, a16z and Sequoia; observers quoted include Jeremy Kaufmann (Scale Venture Partners), Jaya Gupta (Foundation Capital) and David Peterson (Angular Ventures). Motivations cited: create enterprise credibility, hire top talent, deter competitors and capitalize on winner-take-most dynamics in AI. Risks: steep valuation-to-revenue disconnects, potential bubbles, pressure to scale revenue quickly, and historical precedents of overcapitalized failures (Convoy, Bird). For crypto traders, the trend signals that traditional VCs are intensifying capital concentration in AI, which may shift investor attention and liquidity toward AI startups and related tokens, while also underscoring systemic risk from overvaluation. Primary keywords: kingmaking, venture capital, AI startups, early-stage funding, DualEntry. Secondary/semantic keywords: Series A, valuation, ARR, winner-take-most, enterprise software.
Neutral
The article describes a financing trend in traditional venture capital—large early rounds for AI startups—that reallocates private capital but does not directly alter crypto protocols or token fundamentals. Short-term, the news may draw investor attention and liquidity away from some crypto assets into AI equities or tokenized AI projects, creating rotational flows that can cause volatility in specific tokens tied to AI or venture sentiment. It may also boost investor appetite for blockchain projects intersecting with AI. Long-term, concentrated VC capital can produce market leaders that attract institutional partnerships with crypto firms, potentially bullish for tokenized infrastructure that complements AI. Conversely, overvaluation and possible downstream failures (if funded startups underperform) could trigger risk-off moves across venture and correlated digital-asset markets. Past parallels: late-stage capital weaponization (Uber/Lyft) created dominance but also eventual market corrections; failures like Convoy and Bird show overcapitalization risk. Overall impact is neutral for broad crypto market direction because effects are indirect and dependent on subsequent funding outcomes and investor rotations.