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Chronicles

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Samsung announces Q Fund, aimed at investing in early stage AI startups; the fund has already invested in Covariant AI and robotics startup Vicarious AI

Samsung's team designed to seek out innovative, forward-thinking technology today announced the launch of the Q Fund …

VentureBeat Khari Johnson

Context & Ripple Effects

Samsung is adding another vehicle to a fast-growing stack of corporate investment programs: after the $1.2B US IoT commitment split between startups and R&D, the $150M NEXT Fund for pre-seed-to-Series-B companies, and the $300M Automotive Innovation Fund that put $90M into autonomous-safety firm TTTech, the Q Fund now points that machinery specifically at early-stage AI.

The move follows the template Google set with Gradient Ventures, which paired seed checks with access to Google engineers — signaling that big tech firms see dedicated AI funds as a way to buy early visibility into technology their own labs may not produce fast enough. The Q Fund's first two checks went to Covariant AI and robotics startup Vicarious AI.

First-order effects

  • Covariant AI and Vicarious AI gain a strategic backer whose core businesses — chips, displays, devices, appliances — are natural landing spots for robotics and machine-learning technology, beyond what the capital alone provides.

Second-order effects

  • Samsung's device-and-component rivals face pressure to match the structure: Qualcomm Ventures answered within months with up to $100M earmarked for on-device AI startups at $1M–$10M per deal, confirming that AI deal flow is becoming a competitive arena among corporate investors, not just traditional VCs.

Third-order effects

  • If the pattern holds, dedicated AI venture arms become a standard layer of startup financing, with founders weighing strategic fit and platform access alongside valuation — and hardware giants effectively outsourcing parts of their R&D scouting to a portfolio.

The trend: Hardware giants are institutionalizing dedicated AI venture funds to secure early access to machine learning and robotics talent they cannot reliably develop in-house.