Bootstrapping Life-Inspired Machine Intelligence: The Biological Route from Chemistry to Cognition and Creativity

Abstract: Achieving advanced machine intelligence remains a central challenge in AI research, often approached through scaling neural architectures and generative models. However, biological systems offer a broader repertoire of strategies for adaptive, goal-directed behavior - strategies that emerged long before nervous systems evolved. This paper advocates a genuinely life-inspired approach to machine intelligence, drawing on principles from biology that enable robustness, autonomy, and open-ended problem-solving across scales. We frame intelligence as flexible problem-solving, following William James, and develop the concept of "cognitive light cones" to characterize the continuum of intelligence in living systems and machines. We argue that biological evolution has discovered a scalable recipe for intelligence - and the progressive expansion of organisms' "cognitive light cone", predictive and control capacities. To explain how this is possible, we distill five design principles - multiscale autonomy, growth through self-assemblage of active components, continuous reconstruction of capabilities, exploitation of physical and embodied constraints, and pervasive signaling enabling self-organization and top-down control from goals - that underpin life's ability to navigate creatively diverse problem spaces. We discuss how these principles contrast with current AI paradigms and outline pathways for integrating them into future autonomous, embodied, and resilient artificial systems.

• The paper reveals that leveraging biological design principles, like autonomous goal-setting and multiscale self-assemblage, can foster scalable machine intelligence.
• It introduces the 'cognitive light cone' to quantify an agent’s spatiotemporal scope for goals, predictions, and creative problem-solving.
• The study contrasts biological adaptability with current AI rigidity, advocating for modular, self-reconstructing architectures inspired by evolutionary processes.

Life-Inspired Machine Intelligence: From Biology to Scalable Cognition

Biological Intelligence as a Template for Machine Flexibility

This paper, "Bootstrapping Life-Inspired Machine Intelligence: The Biological Route from Chemistry to Cognition and Creativity" (2602.08079), contends that progress in artificial intelligence will be catalyzed by moving beyond brain-centric inspiration and instead appropriating the broad, multi-scale problem-solving mechanisms ubiquitous in the biological world. The authors emphasize a definition of intelligence as flexible, goal-directed problem-solving and introduce the concept of the "cognitive light cone" as a formalism to describe the spatiotemporal scale of an agent's goals and predictive capacity. This vector of inquiry is grounded in an overview across evolutionary biology, developmental science, cognitive neuroscience, robotics, and AI, supporting the assertion that biological evolution offers a readily interrogable "recipe" for autonomy, scalability, and open-ended innovation in intelligent systems.

The bulk of the argument is that contemporary machine learning architectures—particularly large, passive neural networks—underutilize the full spectrum of strategies for adaptive behavior evidenced even in organisms without nervous systems. The authors posit five design principles, isolated from the study of living systems, that underpin biological robustness and flexible adaptation: autonomy, multiscale self-assemblage, continuous capability reconstruction, nuanced exploitation of embodiment, and pervasive signaling-driven coordination.

The Cognitive Light Cone: Quantifying Intelligence

A central theoretical construct in the paper is the "cognitive light cone," a boundary that delineates the scope and granularity of an agent's goals, predictive modeling, memory, and control across space and time. Unlike traditional views mapping intelligence to a static level (e.g., species or architecture), this formalism enables comparative measurement of diverse agents—cells, collectives, organisms, machines—by analyzing the maximal extents of problems they can independently set, represent, pursue, and solve.

The cognitive light cone grows as organisms evolve, mature, or cooperate: a bacterium's light cone is temporally and spatially local, whereas a human or a social collective may instantiate goals and predictions that extend well beyond the individual’s immediate context. In biological evolution, the ratcheting up of cognitive light cone size, and its associated predictive and control infrastructure, provides a mechanistic substrate for genuine creativity and innovation—capacities conspicuously lacking in most engineered AI systems.

Five Biological Design Principles for Scalable Machine Intelligence

Autonomy and Self-Generated Goals

Autonomy in biological organisms is manifested via continuous self-maintenance, spontaneous goal setting, action selection, and active learning. From the perspective of information theory and dynamical systems, this is formalized through the concept of the Markov blanket, encapsulating the agent-environment boundary and the conditions for predictive, goal-directed agency. Machines, by contrast, are rarely designed to generate or protect their own goals; instead, their objectives are typically extrinsic, static, and externally curated.

Multiscale Self-Assemblage and Nested Agency

Scaling in biology is achieved by compositional hierarchies: agentic components (e.g., cells) coordinate within collectives (tissues, organs) via nested Markov blankets, preserving local autonomy yet achieving global goals through dynamic alignment mechanisms (e.g., signaling networks, bioelectric fields). This distributed, modular architecture yields resilience to local failures, continual competence building, and the possibility of emergent behavior. Most current AI systems, built around monolithic architectures or rigidly centralized control, are ill-equipped to benefit from these properties.

Continuous Rebuilding and Adaptive Reconstruction

Living organisms routinely rebuild themselves at multiple hierarchical levels and timescales, ranging from tissue regeneration and plastic neural circuit remodeling to behavioral and cultural learning. Rather than solely preserving optimal solutions, biological entities exploit environmental and internal variability to enhance anti-fragility, meta-learning, and continual re-optimization. In contrast, leading AI systems struggle with stability-plasticity trade-offs, catastrophic forgetting, and out-of-distribution robustness.

Embodiment and Constraint Exploitation

Rather than viewing embodiment and energetic/physical constraints as mere limitations, biological systems exploit the computational affordances of their substrate: from biomechanics and reaction-diffusion systems to externalization of cognition (e.g., stigmergy, tool use, symbolic artifacts). This enables substantial amortization of computational effort, robust behavioral regularization, and rapid adaptation to changing ecologies, including collective and social interfaces. The AI field has only recently begun to appreciate the power of physics-based priors (e.g., neuromorphic hardware, physics-informed learning), but embodiment remains mostly underutilized.

Pervasive Signaling and Collective Top-Down Control

Hierarchical and distributed coordination in biology is established through pervasive, multi-modal signaling: bioelectrical, chemical, mechanical, and neural communication enabling flexible self-organization, top-down constraint propagation, and emergent field-like goal setting. These signaling architectures enable specification of abstract, high-level objectives that are realized at the micro level via adaptive component behavior—without global centralization. Analogous mechanisms are largely absent in today's machine learning systems, which typically enforce fixed update and signaling dynamics.

Contrasts with Contemporary AI Architectures

The analysis in the paper highlights several key divergences between biological intelligence and mainstream AI paradigms. Most notably:

• Goal origination and protection: Biological intelligence relies on internally generated, hierarchically protected goals, whereas AI systems simply optimize externally imposed objectives.
• Compositional growth: Living systems accrue capabilities over evolutionary/developmental time via layering and repurposing, while AI systems are trained in rigid, one-shot supervised or reinforcement learning regimes.
• Plasticity and anti-fragility: Biological structures adapt constructively to perturbation, leveraging instability as a resource; current AIs exhibit brittle adaptation and are prone to catastrophic failure modes under distributional shift or component degradation.
• Physical and social constraint utilization: Organisms fully employ the affordances of their bodies, environments, and social structures for cognitive outsourcing; digital AI rarely exploits its substrate to a comparable degree.

The authors argue that integrating these principles into artificial systems—autonomous exploration, continual reconstruction, modular and embodied architectures, and goal-driven signaling—could lead to systems with generalizable, adaptive, and scalable intelligence exceeding current benchmarks.

Implications and Prospects for Life-Inspired AI

The theoretical and practical implications of this perspective extend beyond AI engineering to foundational questions in theoretical biology, neuroscience, and complex systems. The formalization of intelligence in terms of control scope and goal generativity, rather than task-specific benchmarks, enables a substrate-agnostic framework for both synthetic and natural systems.

Future developments in AI—especially in embodied robotics, adaptive collective systems, and hybrid bio-digital intelligences—could exploit this paradigm. Key research directions include instantiating multi-level autonomy via agentic modules, dynamic Markov blanket architectures for compositional adaptation, and the use of pervasive, flexible signaling fields for coordination and control. The prospect of "cognitive light cone engineering" opens new avenues for measuring, expanding, and comparing intelligence across artificial and biological substrates.

The paper also highlights the duality that increased cognitive scope—while enabling richer problem-solving—also enlarges the failure modes (e.g., dissociation, loss of coherence, emergence of internal defection like cancer), emphasizing that robust failsafes and ethical design will be crucial as machine intelligences internalize more autonomy and reconstruction.

Conclusion

This work (2602.08079) provides a comprehensive and formal blueprint for life-inspired machine intelligence, arguing that scalable, robust, and creative artificial agents will emerge only by integrating design principles that have been selected in biology for navigating open-ended, uncertain, and multi-scale problem spaces. By shifting the focus from isolated neural mechanisms to embodied, hierarchical, signaling-rich architectures capable of autonomous goal-setting and continual reconstruction, AI research can potentially bridge the gap with the flexibility and resilience observed in nature. The challenge is not to replicate biological systems in detail, but to abstract and reimplement their fundamental strategies, continuing the evolutionary arc of intelligence in new substrates.