Beyond One Model: How AGI Will Actually Work

What is AGI?
The Role of System Design in AI Today
Size Do Matter
- Foundational Models: The Giants
- Specialized Models: The Optimized Experts
How Large Systems Mirror AGI's Potential
The Path Forward

The common perception of Artificial General Intelligence (AGI) often suggests a singular, all-powerful model capable of solving problems comparable to those of a superhuman brain. However, this view is overly simplistic and misleading. The reality is much more complex and potentially more fascinating.

AGI will not emerge from a single, monolithic model. Instead, it will develop by integrating specialized components within a carefully designed system. This system-level approach is central to the most successful applications in today's AI and large language model (LLM) ecosystems.

Many features, such as ChatGPT's memory, Claude's artifacts, and innovative tools like Bolt, are not inherent to the models. Instead, they result from a systematic design orchestrating AI alongside various traditional components to fulfill specific business and user needs.

This post will explore why AGI will be achieved at the system level, the limitations of foundational models, and how the collaboration of specialized tools will shape the future of AI. AGI may not be as "general" as we envision, but it will be robust, purpose-driven, and the outcome of intentional design.

What is AGI?

The term AGI has a surprising degree of flexibility. Some might argue that AGI could emerge in just a few years, but it might not represent "true" intelligence. Instead, it could be a label marketing departments use to create hype.

In its most authentic sense, AGI refers to a form of intelligence exhibited by machines that can perform any intellectual task that a human can do. Unlike today's specialized AI systems, which excel in narrow domains, AGI would be versatile enough to reason, learn, and adapt to different tasks without needing specific adjustments for each one.

However, achieving this level of capability is no small feat. We still lack a clear understanding of human or animal intelligence functions. Fundamental questions about how we perceive, reason, and adapt remain unanswered, making it difficult to model these capabilities in machines.

That said, this does not mean AGI is unattainable. While true human-like intelligence is a distant goal, we can make significant progress by shifting some decision-making to the system level. By integrating everything we have learned so far—combining specialized AI models, traditional computing components, and system-level design—we can build a robust foundation for what may one day evolve into the advanced intelligence we often dream about in science fiction.

The Role of System Design in AI Today

At its core, a model—whether GPT, Llama or another large language model (LLM)—is fundamentally a function that takes an input and generates an output. While this function is complex, it is still a computational process. Even when you interact with these models through APIs, a system layer always manages and fulfills your requests.

By their nature, LLMs generate text. However, this raw output often includes provider-specific information, such as system-level instructions, secure details, or base prompts embedded by the model's creators for fine-tuning purposes. To make this output usable, it is usually filtered or processed. Features like function calling, even at the API level, adjust this raw input to streamline development and make it more practical for real-world applications.

Moreover, chatbots built on these models feature numerous additional capabilities. They do not simply pass your input to the model and return the response. Instead, they operate at the system level, running multiple prompts, evaluating outputs, and augmenting your inputs with context or memory gathered by the system (such as ChatGPT's memory feature).

Models like v0 and Bolt take this a step further. They execute generated code in isolated environments to produce reliable outputs and minimize problems stemming from initial prompts. This system-level execution enhances functionality, ensuring that these tools do not just generate text but also effectively solve specific problems.

These examples illustrate a key point: a model is merely a text generator. Without a robust system surrounding it, its capabilities are limited. The system design transforms the raw power of these models into practical, user-friendly tools that deliver meaningful results.

Integrating AI models with system-level components ensures that what you interact with is more than a basic text generator; it is a sophisticated tool designed to address complex problems. In essence, the system design is what elevates models from theoretical capabilities to real-world utility.

Size Do Matter

In AI system design, the model's size is a pivotal factor significantly impacting performance and efficiency. On one end of the spectrum, we have foundational models, typically large-scale neural networks trained on vast amounts of data. These models excel in their ability to understand and generate human-like language, demonstrating a remarkable capacity for generalization across various tasks. However, their sheer size often comes with high computational costs and slower response times.

Conversely, task-optimized specialized models represent the other end of the spectrum. These are typically smaller, tailored, fine-tuned models for specific applications or tasks. While they may lack the broad-ranging capabilities of foundational models, they offer enhanced efficiency and faster processing times, making them particularly effective in targeted environments. Each type of model thus serves a unique purpose, with its strengths and limitations, highlighting the importance of choosing the right approach based on the specific needs and goals of the developed AI system.

Foundational Models: The Giants

Foundational models like GPT and Claude are general-purpose powerhouses. Their large size and extensive training datasets allow them to perform various tasks with minimal fine-tuning. However, this scale comes with challenges.

High Resource Requirements: Training and deploying foundational models demand immense computational power, making them expensive to train and maintain.
Broad but Inefficient: While they can tackle various tasks, they often need more precision for specialized applications.
Diminishing Returns: Increasing model size doesn't necessarily lead to proportionate improvements in performance, particularly for narrowly defined tasks.

Specialized Models: The Optimized Experts

Specialized models are significantly smaller and fine-tuned for specific tasks or domains. They prioritize efficiency and precision over generality, making them highly effective for targeted applications.

Smaller Training Datasets: Specialized models use domain-specific data, which reduces training costs while enhancing task-specific performance.
Efficient and Effective: They deliver better results for specific tasks than their foundational counterparts while being cheaper.
Focused Design: Their smaller size enables faster training and iteration cycles, allowing them to adapt quickly to evolving needs.

Instead of seeing foundational and specialized models as competing methods, they can complement each other within the same system. Foundational models serve as generalists, while specialized models can provide precise solutions.

By leveraging the strengths of both types of models, we can create systems that are efficient, scalable, and better equipped to tackle the diverse challenges of AI deployment. This balanced approach will likely become the standard for developing intelligent and adaptable systems as AI evolves.

How Large Systems Mirror AGI's Potential

The journey toward AGI is less about developing a single, all-encompassing model and more about thoughtfully integrating multiple specialized tools into cohesive systems. Large systems that effectively combine these components demonstrate that AGI-like behavior can emerge from collaboration rather than a singular approach.

System-Level Integration: More Than the Sum of Its Parts

Large systems thrive by employing diverse specialized tools, each meticulously designed to address specific facets of intricate challenges. Every component is honed to excel in its particular function, contributing its unique strengths. Together, these elements collaborate seamlessly, creating a dynamic synergy that mimics the complexity and adaptability of general intelligence, enabling the system to navigate and solve multifaceted problems effectively.

For example:

A financial AI system might integrate specialized fraud detection, market prediction, and customer service models, with a foundational model providing overarching language support.
In healthcare, systems combine models trained for diagnosis, patient record analysis, and treatment recommendation, ensuring a comprehensive approach to patient care.

This layered integration mirrors how humans solve problems by bringing together diverse skills and knowledge to tackle challenges holistically.

Parallels to Human Problem-Solving

Human intelligence isn't a single monolithic capability; it's a collection of specialized skills working in concert. For instance, planning a vacation involves:

Logical reasoning to choose destinations.
Numerical skills to budget expenses.
Social intuition to coordinate with friends or family.

Similarly, large AI systems integrate specialized models to handle tasks that collectively contribute to solving broader problems. This approach aligns closely with how humans delegate and collaborate to achieve complex goals.

Human-Designed Systems: Intentional and Thoughtful

The effectiveness of large systems depends on meticulous and intentional design. Designers must carefully select each component for its specific function, from the smallest element to the most complex machinery. Engineers should thoroughly train the models and integrate them seamlessly, ensuring that they contribute to the overall operation and harmony of the system. This deliberate approach allows each part to work together within the intricate framework, ultimately enhancing the system's performance and reliability.

Key aspects of thoughtful system design include:

Compatibility: Ensuring that models and tools communicate effectively, often requiring custom-built interfaces and protocols.
Coordination: Designing workflows that allow specialized components to interact seamlessly, minimizing bottlenecks or redundancies.
Optimization: Continually refining how individual parts contribute to the system's overall performance, balancing cost, efficiency, and scalability.

This intentional design process highlights the essential role of humans in orchestrating AI capabilities. Just as a symphony requires a conductor to harmonize various instruments, AI systems depend on skilled designers to integrate their components coherently.

Large systems, which are built through the integration of specialized tools and models, embody the collaborative nature of intelligence. Although we may not yet fully understand how to replicate human cognition in machines, these systems offer a practical pathway toward achieving functionality similar to an AGI.

By embracing system-level integration and leveraging the strengths of human-designed frameworks, we can move closer to creating intelligent systems that can tackle the complexities of the real world. In many ways, this deliberate orchestration will shape the next era of AI innovation.

The Path Forward

The journey toward AGI starts with recognizing the strengths of current foundational models while acknowledging their limitations. We should view these impressive models as parts of a more extensive, integrated system. We can create powerful, adaptable, efficient, and purpose-driven systems by leveraging these models alongside specialized tools and traditional computational frameworks.

Interdisciplinary collaboration is crucial to this evolution. The development of AGI cannot rely solely on AI researchers and engineers; it requires contributions from a range of fields, including cognitive science, systems architecture, ethics, domain expertise, and even policy-making. These diverse perspectives will be essential for designing systems that align with human values, address real-world challenges, and operate responsibly. This way, AGI systems can be intelligent, ethical, and sustainable.

AGI will not emerge from a single breakthrough or an all-encompassing model; it will result from a combination of specialized components working together to form cohesive, intelligent systems. This journey will be long and challenging, demanding innovation, thoughtful design, and a strong commitment to collaboration between humans and machines.

While AGI may still seem distant, researchers and developers have already laid the foundations today. Every incremental advance in system design, AI agent development, and interdisciplinary expertise brings us closer to this ambitious goal. The future of AGI will not rely on pursuing a single superintelligent model but will draw on the power of collective systems to tackle humanity's most complex problems.