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.

For investment managers, the message is clear: adapt or risk being left behind. The firms that will emerge as the new leaders in the industry will be those that are fast, cheaper, and better. They will harness the power of AI and other emerging technologies to streamline processes, enhance decision-making, and deliver superior results for their clients.

To fully capitalize on the transformative potential of AI, organizations must approach its implementation not as a singular, monolithic endeavor, but as a parallel-path operator — a multifaceted, iterative process that weaves the technology into the very fabric of the business.