Gibran Secures $2.6M Seed Funding from Together Fund to Build Evolutionary AI
Gibran, a rising new AI research company, has secured $2.6 million in seed funding from Together Fund to develop a new generation of adaptive, scale-free AI systems based on ideas from nature and evolutionary biology. The investment is an important early validation of a fundamentally new approach to AI — one which attempts to break away from big static models and build systems that can learn and adapt constantly and evolve with human guidance over time.
The seed round is the latest strategic bet for Together Fund, an early-stage venture capital firm founded by Girish Mathrubootham of Freshworks and Manav Garg of Eka Software. Together Fund has built a reputation for backing disruptive ideas in foundational AI, and the firm described Gibran’s vision — which combines large language models (LLMs) with biologically inspired, self-organizing architectures — as “one of the most original ideas” it has encountered in the field to date.
Gibran’s founding team possesses a unique blend of background in AI research, evolutionary biology, and real-world product development. Govind Balakrishnan and Srikant Chakravarti, both serial founders, co-founded Curio before, a human-curated audio news platform powered by AI that lasted for eight years and was familiar for smart recommendations and its human-curated journalism. They are accompanied by Suzanne Sadedin, a renowned evolutionary biologist known for her research on harnessing AI and agent-based simulations to explore cognitive evolution, as well as Edgar Duéñez-Guzmán, a specialist in generative systems and cooperative multi-agent dynamics. Collectively, the group seeks to reimagine the potential of machines in augmenting human creativity and discovery in areas where conventional data-driven AI tends to struggle.
“Our central thesis is that AI should not replace humans, but rather evolve with them,” said Balakrishnan in a statement. “That means building systems that learn not just from data, but from people — continuously and contextually.”
At the heart of Gibran’s research is the concept of “scale-free” AI systems. In nature, many biological systems — from neural networks in the brain to ecosystems — exhibit scale-free properties, meaning they maintain functional resilience and adaptability regardless of size or complexity. Gibran’s vision is to translate these ideas into artificial systems that grow in capability and nuance through interaction with users and their environments, instead of being frozen at the point of deployment like conventional LLMs.
This method may be particularly useful in areas such as drug discovery, where access to large labeled datasets is limited and the set of potential solutions is huge. By creating AI agents that have the capacity to recombine existing biological or chemical patterns to create new hypotheses, Gibran hopes to become an indispensable co-pilot to scientists tackling complicated issues where human instincts and machine search need to complement each other. In schooling, such adaptive agents would be able to learn from every student’s experience and feedback continuously, developing customized paths that change and evolve over a learner’s lifetime.
Together Fund’s Manav Garg emphasized the potential transformative impact of these systems across industries. “This is not just about productivity. It’s about human flourishing,” Garg said. He envisions Gibran’s AI agents extending far beyond labs and classrooms, potentially reinventing how creators in filmmaking, game design, or industrial design collaborate with intelligent tools that adapt to their unique vision and workflow over time.
In contrast to more conventional AI based on static parameters and huge static datasets, Gibran’s research agenda involves looking into root architectures for “autonomous alignment,” in which ethical and contextual learning goes on throughout the lifespan of the system. This implies that the AI doesn’t just recite what it learned — it learns new approaches and behaviors influenced by its context and by the individuals around it, more like an ecosystem than a mechanism.
The funding will help Gibran recruit a world-class team of researchers in generative systems, evolutionary algorithms, and cognitive science. The startup also plans to collaborate with academic partners and domain experts in fields like molecular biology, theoretical physics, and computational neuroscience to test its early prototypes in complex, real-world environments where open-ended exploration is critical. Initial R&D outputs are expected to surface by December 2025, with pilot collaborations likely in sectors where creativity, discovery, and human–AI synergy are essential.
By taking inspiration from how intelligence emerges and adapts in natural systems, Gibran hopes to push the AI field beyond the current plateau of training bigger and bigger static models — towards systems that are not only more resilient and resource-efficient, but fundamentally more human-aligned.
To Balakrishnan and his co-founders, this is the future of AI research: a world in which machines don’t merely process information faster, but learn to evolve along with us, adapt to us, and facilitate us to reach into the unknown and make human beings greater, more inquiring, and more able.
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