Humans&, a recently launched artificial intelligence startup, has secured $480 million in seed funding to build software designed to improve human collaboration with AI.
The round, one of the largest ever for an early-stage company, signals strong investor interest in human-centric AI despite ongoing debates over automation and job displacement.
Record Seed Round and Valuation
Humans& announced it closed a $480 million seed financing round at a $4.48 billion valuation, positioning the company as an early unicorn in the AI space.
The sheer scale of the investment is highly unusual for a seed-stage startup, with some analysts calling it among the largest such rounds on record.
The funding was led by venture capital firm SV Angel and co-founder Georges Harik, with participation from Nvidia, Jeff Bezos, Google Ventures (GV), Emerson Collective, and other institutional backers.
Investors appear to be betting on the company’s approach and founding team rather than on a commercial product, as the startup has yet to announce a general release or customer deployments.
Founders and Philosophy
Humans& was founded just three months before this funding round, making its capital raise notable for both size and pace.
The founding team includes AI researchers and engineers formerly associated with Google, Anthropic, xAI, OpenAI, and Meta.
Co-founders include Andi Peng (ex-Anthropic), Eric Zelikman (ex-xAI), Georges Harik (early Google employee), Yuchen He, and Noah Goodman, a Stanford professor.
The company’s stated mission is to build “human-centric” AI tools that support collaboration and teamwork rather than replace human labor.
In its public materials, the startup describes AI as connective infrastructure that helps people work together more effectively, rather than as autonomous agents that make independent decisions.
What the Software Is Designed to Do
Detailed product plans have not been disclosed, but the company frames its work as tackling fundamental challenges in AI research and human interaction.
Early descriptions suggest the software will focus on:
- long-horizon reasoning — letting AI understand and plan over extended timelines, not just short questions and answers;
- multi-agent learning — coordinating multiple AI systems and humans in workflow contexts;
- memory and context retention — maintaining knowledge from past interactions to improve outcomes in collaboration.
This human-centric framing implies a shift from generative tools that simply automate tasks toward systems that augment human capabilities and strengthen team performance.
