Skild AI Secures $300M Funding to Build ‘Robot Brains’

As an experienced technology analyst, I find Skild AI’s Series A funding round of $300 million at a valuation of $1.5 billion quite impressive. The involvement of prominent investors like Lightspeed Venture Partners, Coatue, SoftBank, and Jeff Bezos adds credibility to the company’s mission to develop practical artificial general intelligence (AGI) for various machines and robotic devices.


As a crypto investor, I’m excited to share that last week, Skild AI, an innovative startup hailing from Carnegie Mellon University, successfully closed a Series A funding round, raking in a impressive $300 million. With this new investment, the company’s valuation has been set at a staggering $1.5 billion.

Lightspeed Venture Partners and Coatue spearheaded the investment round with notable participation from SoftBank and Jeff Bezos.

Among the investors are Felicis Ventures, Sequoia, Menlo Ventures, General Catalyst, CRV, Amazon, SV Angel, and Carnegie Mellon University.

Skild AI focuses on developing “universal artificial intelligence” (UAI) – a type of AI system engineered to function effectively across diverse machines and robotic units. Our objective is to construct an adaptable AI that can effortlessly blend into devices, given they possess adequate power and appropriate interfaces.

Skild AI holds its own among businesses working on sophisticated AI technology, joining ranks with OpenAI and Anthropic AI. Yet, what sets Skild AI apart is its dedication to implementing AI solutions in tangible robots and machinery.

With few specifics shared by the company regarding their strategies or alliances, the presence of prominent tech investors suggests a significant level of intrigue towards their methods. The involvement of Jeff Bezos and Amazon has ignited buzz about potential future collaborations, particularly due to Amazon’s extensive capabilities in cloud computing and advanced AI technology.

Moving ahead with Skild AI’s development of a modular artificial “brain,” the increasing demands for computational power are expected. To meet these requirements, potential collaborations with chip manufacturers or leveraging sophisticated cloud services may become necessary solutions.

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2024-07-15 01:25