Think of technology and society as dance partners in an eternal waltz. Each responds to the other’s movements in an intricate pattern of adaptation and counter-adaptation. Our opposable thumbs didn’t just help us grip tree branches – they enabled tool use, which then drove further physical evolution of our hands. Our socially-evolved brains allowed us to transmit knowledge across generations, creating ever more sophisticated technologies that further shaped our cognitive development.

Back in 1988, computer scientist Hans Moravec made a fascinating observation: While we could program computers to match adult-level performance on intelligence tests or master complex games like chess, giving them the basic sensory and mobility skills of a toddler proved nearly impossible. Over three decades later, this “Moravec’s paradox” still holds true, even as AI continues to make headlines for beating world champions at increasingly complex games.

Consider this: When early farming tools were developed, no central authority directed their evolution. Instead, countless individual farmers experimented with different approaches, gradually discovering what worked through a process of trial and error. The most effective innovations spread naturally through voluntary adoption, not through planned deployment.

In today’s rapidly evolving artificial intelligence landscape, a fundamental question faces technology leaders and business decision-makers: Why do the most advanced AI systems, trained on trillions of words of data, still struggle with tasks that human infants master effortlessly? The answer lies in understanding the profound differences between how human and artificial intelligence develop – differences that have crucial implications for how we approach AI development and deployment in business contexts.

In the rapidly evolving landscape of artificial intelligence (AI), language models have emerged as a powerful tool for understanding and generating human-like text. While large language models (LLMs) have garnered significant attention for their impressive capabilities, a new contender has entered the arena: small language models (SLMs). These compact yet powerful models are challenging the notion that bigger is always better, offering a more efficient and specialized approach to natural language processing (NLP) tasks.