A new artificial intelligence venture, Humans&, has rapidly emerged as one of the most heavily capitalized startups in the sector, securing an extraordinary $480 million in seed funding. The three-month-old company, still without a public product, is making a bold bet that the future of AI lies in coordination and social intelligence, rather than merely sophisticated conversation or information retrieval for single users.
This substantial initial investment underscores the industry's keen interest in the firm's ambitious vision. Humans& boasts a founding team with deep roots in top-tier AI organizations, including former researchers and engineers from Anthropic, Meta, OpenAI, xAI, and Google DeepMind.
The Shift from Solitary to Social AI
Current AI models, particularly chatbots, excel at processing requests and summarizing information for an individual user. However, these systems are not designed to navigate the intricate dynamics of group decision-making, long-term project management, or balancing diverse stakeholder priorities. Humans& aims to address this gap by creating foundational AI models specifically engineered for multi-user interaction and collective problem-solving.
Andi Peng, a co-founder and former Anthropic employee, reportedly indicated a transition from an initial phase of AI focused on specialized question-answering. Peng suggested the industry is now entering a subsequent era where users seek more integrated and collaborative AI tools, a space Humans& intends to occupy.
CEO Eric Zelikman, previously a researcher at xAI, highlighted the limitations of existing AI through a common organizational challenge: selecting a company logo. He noted that such group decisions often involve arduous discussions to align various viewpoints, a process current AI tools cannot effectively facilitate. Zelikman explained that today's AI prioritizes immediate user satisfaction and factual accuracy, objectives that do not foster the ability to ask probing questions, retain long-term context, or guide a team towards consensus.
Rethinking Foundation Model Training
To realize its vision, Humans& is innovating in how foundation models are trained. Yuchen He, a co-founder and former OpenAI researcher, revealed that the company is employing long-horizon and multi-agent reinforcement learning. These advanced techniques are tailored to train models for complex environments where both humans and AI agents interact over extended periods, fostering sustained collaboration.
- Long-horizon reinforcement learning: Enables models to plan, execute, refine, and follow through on actions over time, rather than merely generating isolated responses.
- Multi-agent reinforcement learning: Prepares models for scenarios involving simultaneous input and interaction from multiple AIs or human participants.
He emphasized the importance of a model's memory, stating that a robust understanding of itself and its users, maintained over time, is crucial for effective collaboration. This contrasts sharply with many contemporary models that treat each interaction as largely distinct.
Ambitious Market Vision and Looming Competition
Humans& is developing its product and underlying model in parallel, allowing their capabilities and interface to evolve symbiotically. While specifics remain under wraps, the team has hinted at a future where its offerings could potentially supplant established collaboration platforms like Slack, Google Docs, or Notion, catering to both enterprise and consumer needs.
The company’s ambition is to become the central nervous system for organizations, serving as the connective tissue that comprehends individual skills and motivations, then harmonizes them for collective benefit. This strategy positions Humans& in direct competition with tech giants already active in the AI collaboration arena. Anthropic is advancing its Claude Cowork for team optimization, Google is embedding Gemini within Workspace, and OpenAI is actively promoting multi-agent orchestration for workflow automation.
Despite the intense competitive landscape, the timing might prove advantageous for Humans&. Noted investor Reid Hoffman recently advocated for integrating AI at the workflow level, emphasizing that true leverage comes from optimizing how teams share knowledge and make decisions, rather than isolated AI projects.
Challenges for Humans& are significant. Developing and scaling a new foundation model demands immense capital and compute resources. The company will vie for talent and GPUs not only with its peers but also with the very giants whose alumni constitute its founding team. Furthermore, the speed at which major AI labs are developing their own collaboration features presents an existential question regarding Humans&'s ability to maintain its unique advantage.
However, CEO Zelikman maintains a resolute stance against acquisition, stating that the team has declined previous offers. He expressed confidence in the assembled talent and the potential to build a generational company that fundamentally transforms human-AI interaction.
Humans& is making a profound bet on the future direction of artificial intelligence. Its success hinges on whether AI’s next significant leap will be defined by models capable of navigating human social complexity. The substantial $480 million backing and a formidable founding team equip the startup for a powerful attempt. The industry awaits to see if this focus on social intelligence truly marks the next frontier, or if collaboration features will ultimately be absorbed by existing tech powerhouses.
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Source: The Tech Buzz - Latest Articles
