Are compact models the key to achieving AGI?

Are compact models the key to achieving AGI?

A common assumption in the AI field is that scaling up Large Language Models (LLMs) is the key to unlocking Artificial General Intelligence (AGI). Many believe that increasing the number of parameters in these models will lead to intelligence that rivals or surpasses human cognition. With GPT-4 rumored to contain around 1 trillion parameters and GPT-5 expected to be even larger, the industry is largely following this trajectory. However, comparing these massive models to biological brains suggests that size alone is not the ultimate factor in intelligence.

Understanding the Relationship Between Model Parameters and Brain Neurons

Consider these comparisons:

  • GPT-4
  • Human Brain
  • Elephant Brain
  • Dolphin Brain
  • Chimpanzee Brain

At first glance, it might seem that GPT-4, with its massive parameter count, should surpass animals in intelligence. However, despite its size, GPT-4 lacks the fundamental capabilities of even a chimpanzee. It cannot interact with the physical world, recognize emotions, or engage in strategic decision-making the way biological brains do. This discrepancy suggests that intelligence is about more than just the number of parameters.

The Limits of Scaling Large Language Models

The gap between parameter size and true intelligence highlights the fundamental limitations of LLMs. While these models excel at language generation and text-based reasoning, their understanding is superficial. They can produce text that appears coherent, but they do not truly grasp context, goals, or real-world consequences. Unlike human cognition, LLMs lack mechanisms for long-term planning, prioritization, and adaptation to changing environments. They operate purely based on the data they were trained on, without a deeper sense of cause and effect.

Moving Beyond LLMs: The Need for New AI Architectures

LLMs have demonstrated impressive capabilities, but they are only part of the equation. Emerging frameworks such as Joint Embedding Predictive Architecture (JEPA) offer a more structured world model by creating coherent representations of the environment. However, even advanced architectures like JEPA do not inherently support planning, goal-setting, or autonomous learning. While they provide a better foundation for world understanding, they must be combined with other systems to move towards general intelligence.

The Role of Active Learning in AGI Development

Human intelligence thrives on continuous learning. Our brains constantly update knowledge and adjust strategies based on new sensory inputs. In contrast, LLMs lack this adaptability. Once trained, they remain static and do not refine their understanding in real-time. Active learning, a process where models continuously learn and improve based on real-world data, is largely absent in current AI systems. This limitation prevents LLMs from dynamically evolving in response to their environments.

The Path to True General Intelligence

At smallest.ai, we believe that AGI will not be achieved by simply scaling LLMs. Instead, intelligence will emerge from systems that think and learn more like humans—small, efficient models that can run on edge devices, process real-time sensory data, and continuously update their knowledge. These models will incorporate automated active learning loops, allowing them to refine their understanding and adjust their behavior dynamically.

The Future: Small Models with Big Capabilities

The next evolution in AI will not come from ever-larger models but from smaller, specialized systems that integrate planning, prioritization, and real-world interaction. These models will be:

  • More efficient
  • Continuously learning
  • Embedded in real-world applications

Rather than a single massive AGI model, the future will belong to billions of smaller, hyper-intelligent models, each optimized for specific tasks. This approach mirrors human evolution, where intelligence is distributed across a network of specialized experts rather than centralized in a single entity.

Conclusion

Human intelligence is shaped by continuous learning from sensory experiences. AI models must follow the same path—learning should be an ongoing process, even after deployment. The fixation on larger models is a distraction from the real goal: developing AI that can actively learn and evolve in real-world scenarios.

AGI will not emerge from sheer scale but from on-edge learning systems that refine their intelligence through experience. The future of AI belongs to small, adaptive models that, like humans, continuously evolve and specialize over time.