Why an LLM (Large Language Model) is Not Merely Predicting the Next Word

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WHY IS AN LLM ABLE TO GENERATE OUTPUT THAT SEEMS TO BE MORE THAN JUST PREDICTING THE NEXT WORD? 
(LLMs as Complex Adaptive Systems with properties of Emergence).
If an LLM (Large Language Model) is all about predicting the next word via a probabilistic function, (as explained in all AI textbooks) how is it able to generate such creative, seemingly intelligent answers to our questions? My hypothesis is that LLMs are Complex Adaptive Systems (CAS) such as those found in Nature, the Physical Sciences, and Human Society. (weather, insect colonies, economies and financial markets, flocks of birds and shoals of fish as Superorganisms, stampedes, viruses, city traffic flows, fluid dynamics, all manner of networks with feedback loops).  One of the attributes of a CAS is that it exhibits the phenomenon of Emergence.  Emergence is when something complex and unexpected arises from the interactions of its individual components (Agents). Emergence happens when (1) there are interconnections between parts of the system: networks (2) when there are feedback loops in the system (3) when actions and reactions between its components  are non-linear (exponential) (4) The system is VLS (Very Large Scale) large scale and scalable. 
Therefore, LLMs like ChatGPT with its billions of parameters (Neurons) is a CAS that in its output is able to go beyond what it was taught, to do self-supervised learning and to evolve in its capabilities.
 


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