What Is Next-Generation AI and How Will It Change the Way We Build Intelligent Systems?

Next-generation AI moves beyond data-heavy models toward organic, adaptive intelligence that learns continuously, operates efficiently, and mirrors biological cognition, fundamentally changing how intelligent systems are designed, trained, and deployed.

From Humanoid Robotics to the Future of AI

Bruno Maisonnier is best known as the founder of Aldebaran Robotics, a pioneer in humanoid service robots later acquired by SoftBank.

Today, he operates on the global stage as a leading voice in artificial intelligence, shifting focus from robotics hardware to the foundations of intelligence itself.

Understanding Organic, Third-Generation AI

Bruno introduces the concept of organic, third-generation AI, a new approach that departs from traditional, data-intensive AI systems.

This paradigm emphasizes:

• Learning from limited data
• Continuous adaptation
• Energy-efficient intelligence
• Behavior inspired by biological systems

Rather than optimizing static models, organic AI aims to build systems that learn and evolve more like living organisms.

Building Smarter and More Sustainable Intelligence

At AnotherBrain, Bruno is developing AI technologies designed to overcome the limitations of current deep learning approaches.

Next-generation AI systems are intended to:

• Generalize better across environments
• Operate reliably in real-world conditions
• Reduce dependence on massive datasets and computing power

This shift has implications for robotics, embedded systems, and real-time decision-making.

Rethinking How Intelligent Systems Are Designed

Organic AI changes how engineers and researchers approach system design.

Instead of training large models offline, next-generation AI:

• Learns continuously on-device
• Adapts to new situations in real time
• Integrates perception, learning, and action

This approach blurs the line between software and embodied intelligence.

Inspiration and Multiplicity in Deep Innovation

Bruno also shares how curiosity and inspiration fuel his ability to launch and manage dozens of projects simultaneously.

For him, long-term innovation in AI requires:

• Crossing disciplinary boundaries
• Questioning dominant paradigms
• Sustaining creative momentum over decades

This mindset is central to building truly transformative technologies.