Beyond AI: How living intelligence reshapes our technological and cultural ecosystems

AI systems have evolved beyond task automation to demonstrate capabilities akin to human social cognition. Kosinski’s work on large language models highlights their emergent ability to track beliefs and intentions, solving tasks comparable to those handled by young children. These developments are not merely academic; they open pathways for practical applications in leadership development, team dynamics, and decision-making—fields where I frequently engage as a busness psychologist and consultant.

However, AI is only one piece of the puzzle. Webb describes living intelligence as a triad of technologies: AI for learning and adapting, advanced sensors for capturing and analyzing data in real time, and biotechnology for creating systems that can simulate, complement, or even augment biological functions. This convergence mirrors how cultures in organizations—whether teams or departments—must constantly adapt to external pressures and internal evolution.

Practical possibilities and emerging applications

Healthcare is already seeing significant impacts from the intersection of AI, sensors, and biotechnology. Advanced sensors, such as wearable or ingestible biosensors, enable continuous health monitoring. These devices not only detect diseases early but also allow for personalized treatments based on real-time data. Nanotechnology has introduced tiny machines capable of monitoring patient recovery or delivering targeted treatments within the bloodstream, providing unprecedented precision and efficiency in medical care. Webb highlights "living systems" like nanobots that interact with biological environments, while Kosinski’s exploration of AI suggests the potential for ToM-equipped virtual assistants to support patient care with empathy, understanding emotional cues or stress markers in patients.

In addition, biotechnology has paved the way for generative biology, a field where algorithms and biological data combine to create new proteins, genes, and even entire organisms. For example, Google DeepMind’s AlphaProteo designs proteins with specific properties, transforming drug development by creating enzymes capable of breaking down pollutants or engineering materials that self-regulate temperature and ventilation. This could revolutionize not only healthcare but also industries like construction and energy, where "smart" biological materials could reduce waste and improve efficiency.

Leadership and organizational development could also benefit from ToM-equipped AI systems. These models, as Kosinski demonstrates, can understand and anticipate human intentions and behaviors, providing actionable insights into team dynamics or potential conflict areas. Imagine leadership coaching tools capable of gauging team morale or detecting subtle shifts in collaboration patterns, enabling leaders to respond proactively. These advancements align with the need for deeper, group-focused understanding of culture, as discussed in my recent article.

In urban planning, sensors embedded in infrastructure could transform how cities function. From water meters that detect leaks to traffic systems that adjust dynamically to congestion patterns, these systems learn and adapt based on environmental data. Webb’s concept of large action models (LAMs) takes this further, with AI-driven systems making real-time decisions to optimize energy use or improve safety. In agriculture, similar technologies could monitor soil health, predict weather patterns, and automate irrigation, creating a more sustainable food supply chain.

Challenges and ethical considerations arise alongside these opportunities. As sensors and AI systems collect increasingly granular data about individuals, maintaining transparency and trust will be essential. Organizations and governments will need to create robust frameworks to ensure data privacy and prevent misuse. Similarly, AI’s emergent capabilities, such as ToM, bring questions about bias and accountability. If systems can predict and influence human behavior, who is responsible for the outcomes—particularly when errors occur? These concerns mirror the challenges of leading cultural change, where transparency, empathy, and inclusion are critical to success.

Leaders and organizations cannot afford to focus solely on AI while neglecting its intersections with sensors and biotechnology. The systems described by Webb and Kosinski are fundamentally interdisciplinary, requiring collaboration across technical, ethical, and strategic dimensions. Leaders who embrace this convergence (in all kinds of ethical ways) might not just drive innovation but also play a role in shaping meaningful societal impacts of these technologies.

So ...

Just as culture cannot be dictated but emerges from shared experiences, the future of technology will depend on its alignment with human needs and values. Whether in the context of a team learning to navigate new dynamics or a healthcare system adopting living intelligence, success lies in co-creation and adaptability.

If leaders understand and embrace these intersections, they might not only shape the future of their socio- and techo-cultures but perhaps also redefine the boundaries of human potential.