Will AGI Emerge from LLMs?

A Reflection on the Scope of Large Language Models and the Need for an Integrated Ecosystem

1. The Hype Around LLMs

In recent years, Large Language Models (LLMs) have captured the attention of the tech world and the public for their ability to answer questions, generate coherent text, and simulate human-like conversations. This has led some to wonder whether simply scaling and refining these models could lead to the long-sought Artificial General Intelligence (AGI).

However, reducing AGI to an LLM is like expecting a microprocessor to handle all the functions of a computer (memory management, peripheral coordination, voltage control, graphics rendering, etc.). A single computing block cannot take on all the necessary tasks to develop true general intelligence. This raises the question: What are the limits of an LLM, and what additional components are required to achieve AGI?

2. LLM as Part of a System, Not the Whole System

Language models such as GPT and its variants, trained on vast amounts of text, learn statistical relationships between words and generate responses based on these correlations. However, on their own, they lack mechanisms to:

Interact with the real world: They do not have sensors or the ability to perform external operations.
Make complex decisions: Multi-disciplinary symbolic reasoning and long-term planning exceed their capabilities.

Just as a computer requires not only a CPU but also a chipset, GPU, network modules, and memory controllers, a complete AGI system must integrate and coordinate various components, including:

High-level reasoning models: Combining LLMs with other specialized architectures.
Perception modules: To process visual, auditory, or sensor-based information.
Orchestration and memory layers: Managing storage and the relevance of information.
Interaction with the environment: Using external APIs, engines, and devices for actions beyond text.

3. The Case of Chain of Thought (CoT): More Than Just Direct Answers

To improve response quality, the Chain of Thought (CoT) method has been adopted, which involves developing a series of reasoning steps leading to the final answer.

Advantages of CoT:

Coherence: A step-by-step process reduces inconsistencies.
Explainability: Helps understand how a conclusion was reached.
Error reduction: Breaking problems down decreases the likelihood of errors in a single inference jump.

However, while CoT improves responses, it does not imply AGI. “Thinking out loud” does not equate to full comprehension or adaptability across diverse domains.

4. LLM: The “Linguistic Brain,” Not the Complete Nervous System

While an LLM is effective at processing and generating language and even performing basic reasoning, true AGI must cover several key aspects:

Access to Multiple Modalities

AGI should be capable of:

Analyzing images.
Understanding sounds.
Processing structured data.
Managing diverse information sources efficiently.

A text-only trained LLM must be complemented to “perceive” and “act” in other domains.

Long-Term Planning and Learning

A general intelligence must:

Learn and adapt dynamically.
Reconfigure its own architecture.
Apply planning strategies to solve complex problems.

Memory and Continuous Context

Expanding context windows or using retrieval-augmented generation (RAG) solutions is insufficient since an LLM’s memory is limited and ephemeral. AGI requires deep, persistent memory that evolves over time.

Interaction and Agency

Beyond answering questions, AGI must:

Make autonomous decisions.
Act in real-world environments.
Set and pursue its own goals.

An LLM alone lacks mechanisms for autonomous action, making orchestration systems essential.

5. Toward an Integrated Architecture: Beyond LLMs

The idea that AGI will emerge simply by scaling an LLM is shifting toward the vision of an integrated architecture or ecosystem composed of specialized subsystems. This approach includes:

Symbolic and rule-based modules: Essential for tasks requiring precision and deterministic logic.
Probabilistic reasoning tools: Complementing natural language handling and statistical processes.
Operational agents: Managing processes, interfaces, and leveraging an AI Operating System (AI OS) to delegate tasks.
Distributed memory systems: Storing history, learnings, and references persistently.

In this ecosystem, the LLM functions as the “linguistic brain”, but global coordination depends on the integration of all these components.

6. Conclusion: AGI Will Not Be Just an LLM

The notion that AGI will emerge simply by scaling an LLM ignores the inherent complexity of intelligence. While LLMs are powerful language-processing tools, they are only one part of a broader system.

To achieve true general intelligence, we need:

Orchestrated ecosystems: Integrating multiple specialized subsystems.
Robust reasoning and memory layers: Surpassing the limitations of an isolated language model.
Full real-world interaction: Through sensory modalities and adaptable learning.
AI OS and autonomous agents: Transforming the LLM’s role into a coordinated component of a “living system” capable of making decisions and acting autonomously.

The future of artificial intelligence points toward hybrid architectures where the power of language is combined with diverse modules and information sources, achieving the integration necessary to reach AGI.

7. The Need for a Holistic Approach to Prepare for AGI

If we continue to focus solely on the evolution of LLMs or multimodal models, we risk being unprepared for the arrival of AGI. Concentrating exclusively on the success of models like DeepSeek R1 or o3-mini can lead to critical mistakes when exploring new business opportunities or launching innovative startups.

It is essential to broaden our perspective and evaluate the complete landscape of artificial intelligence. Instead of disproportionately celebrating isolated advances, we must value and support companies that invest in developing holistic systems. These systems integrate various capabilities—such as reasoning, perception, persistent memory, and agency—and are better positioned to address real-world problems comprehensively. Only through this multidimensional approach can we lay the groundwork for a robust and functional AGI capable of positively transforming the future of technology and business.