Stock Markets May 22, 2026 11:03 AM

ARK Venture Joins $700M Series A for Hark Labs at $6 Billion Valuation

Oversubscribed round led by Parkway Venture Capital backs Hark’s vertically integrated personal AI and hardware roadmap

By Ajmal Hussain NVDA AMD INTC QCOM CRM

Hark Labs raised in excess of $700 million in a Series A round that values the company at $6 billion post-money. The oversubscribed financing was led by Parkway Venture Capital and included participation from ARK Venture Fund alongside major strategic backers from the semiconductor and enterprise software ecosystems. Hark says the capital will accelerate development of foundation models, software, and hardware designed to deliver personalized AI for enterprise workflows and human-machine interaction.

ARK Venture Joins $700M Series A for Hark Labs at $6 Billion Valuation
NVDA AMD INTC QCOM CRM

Key Points

  • Hark Labs raised over $700 million in an oversubscribed Series A at a $6 billion post-money valuation; led by Parkway Venture Capital with participation from ARK Venture Fund and several strategic investors.
  • The company is pursuing a vertically integrated product strategy that combines foundation models, software, hardware, and user interfaces to build personalized AI for enterprise deployment - impacting the AI, enterprise software, and hardware sectors.
  • Hark plans to deliver its AI models later this summer and subsequently introduce hardware devices designed to host and interface with those systems, indicating a staged product rollout from software to hardware.

Hark Labs announced Thursday that it secured more than $700 million in a Series A financing round, which places the company at a $6 billion post-money valuation. The round was oversubscribed and led by Parkway Venture Capital, the company said.

Among the investors who took part in the financing were ARK Venture Fund, NVIDIA, Align Ventures, AMD Ventures, Brookfield, Greycroft, Intel Capital, Prime Movers Lab, Qualcomm Ventures, Salesforce Ventures, and Tamarack Global.

Hark is developing artificial intelligence systems intended for enterprise deployment and to support operational workflows. The company described its approach as vertically integrated, encompassing foundation models, software systems, hardware, and user interfaces to produce personal intelligence products.

The funding is earmarked to advance work on personalized intelligence and to build hardware intended to act as an interface between humans and machines. Hark said the capital will help accelerate getting those products into users’ hands quickly and at scale.

"We’re building the AI that everyone deserves but no one has built yet — one that actually knows you, speaks your language, is highly personalized, and lives on hardware made for you. This funding helps us put that in people’s hands quickly and at scale," said Brett Adcock, Founder and CEO of Hark.

Hark has indicated plans to release its AI models later this summer, offering access to its personal AI platform initially via software. The company then intends to roll out hardware devices designed specifically for those systems.


Below is a concise account of what the financing and Hark’s roadmap mean in practical terms:

  • Scale of financing - A significant Series A that signals sizable investor support for Hark’s integrated software-hardware strategy.
  • Strategic investor mix - Involvement from chipmakers and enterprise software venture arms suggests alignment between model development and the hardware and infrastructure needed to run those systems.
  • Product timetable - Hark has set a near-term target to release models this summer, followed by hardware introductions.

Risks

  • Timing uncertainty - Hark has stated plans to release AI models later this summer and to follow with hardware, but the announcement reflects intentions rather than completed deliveries, leaving schedule risk for both software and device launches. This affects enterprise adoption and hardware manufacturing timelines.
  • Execution complexity - Building and integrating foundation models, software stacks, and bespoke hardware is operationally demanding; successful deployment at scale across enterprise workflows is not guaranteed. This impacts AI infrastructure providers, chipmakers, and enterprise IT buyers.

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