AI systems are powerful because they exhibit “emergent intelligence” — capabilities that arise not from any single component but from the complex interactions of many subsystems working together. These systems — including popular models like ChatGPT, Google Gemini, Anthropic’s Claude, and others — combine neural networks, advanced training methods, attention mechanisms, and specialized computing hardware to create networks of cause and effect that behave in intelligent ways. The result is AI that can understand language, solve problems, and even generate creative outputs far beyond earlier generations of software.
A key part of this power comes from what Thagard calls “mechanisms” — the foundational elements inside AI architectures. For instance, vector-based neural networks simulate huge numbers of artificial neurons interacting at high speed, and training techniques like backpropagation and reinforcement learning enable these networks to improve based on vast amounts of data and human feedback. Meanwhile, hardware innovations — especially graphics processing units (GPUs) — provide the real-time computational power necessary for these interactions to happen at scale.
Thagard also highlights how these mechanisms form causal networks, where the combined effect of interacting components produces outcomes that are more than the sum of their parts. For example, learning and attention mechanisms work together to help AI models grasp context and relevance in language tasks, while the synergy of neural networks with specialized chips makes large-scale processing feasible for global use. These layered interactions are what give AI its ability to perform complex reasoning across diverse domains.
Finally, the article discusses emergent properties — behaviors and abilities that arise from these networks but aren’t found in the individual parts alone. These include sophisticated language understanding across many languages and impressive problem-solving in fields ranging from law and medicine to creative writing and mathematics. Thagard emphasizes that while these systems approximate aspects of human thought, they still lack consciousness, emotions, and other deeply human experiences — reminding readers that AI’s power is a product of engineered complexity, not lived experience.
