ASSAI

The AI industry today is largely centralized and often doesn’t provide fair compensation to data contributors. Most systems rely on Large Language Models (LLMs), which are general-purpose tools for diverse tasks. While LLMs work for broad responses, they struggle with specialized business needs that require deeper analysis and domain-specific problem solving. Additionally, running LLMs is becoming more complex and expensive, with unsatisfactory returns on investment. The future of AI is moving toward verticalized systems that focus on specific tasks and deliver precise solutions. This shift challenges LLMs by offering cost-effective and efficient alternatives. Small Language Models (SLMs) are emerging as a solution, offering domain-specific, customizable, and high-performance options. Modern SLMs use Mixture of Experts (MoE) and Mixture of Agents (MoA) architectures, blending the benefits of specialized systems with the versatility of LLMs while maintaining flexibility and adaptability.