---
title: "Artificial General Intelligence (AGI)"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Artificial General Intelligence (AGI)


[Watch: Ilya Sutskever of OpenAI | AI will have a human brain that can think for itself](https://www.youtube.com/watch?v=Co2EQugAHYU)

Artificial General Intelligence (AGI) refers to a theoretical form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence. Unlike narrow AI systems, which are designed for specific tasks such as language translation or image recognition, AGI would exhibit the flexibility and adaptability characteristic of human cognition, enabling it to perform any intellectual task that a human can. 

**Current State of AGI Development:**

As of December 2024, AGI remains a theoretical concept, with no AI systems fully achieving the comprehensive capabilities associated with human-like general intelligence. However, significant advancements in AI research have led to the development of more sophisticated models that exhibit aspects of generalization and reasoning.

- **Advancements in AI Models:** Recent AI models, such as OpenAI's o1 (formerly codenamed "Strawberry"), have demonstrated enhanced reasoning abilities, allowing them to perform complex tasks with minimal human intervention. These models employ iterative problem-solving techniques, enabling them to refine responses and adapt to various tasks, which are steps toward the broader goals of AGI. 

- **Diverse Expert Perspectives:** The timeline for achieving AGI is a subject of ongoing debate among experts. Some, like Microsoft AI CEO Mustafa Suleyman, estimate that AGI could be realized within the next 5 to 10 years, contingent upon advancements in hardware and software. Others, such as AI pioneer Yann LeCun, believe that current AI systems lack fundamental intelligent attributes and that achieving true AGI will require decades of research and innovative approaches beyond existing models. 

- **Ethical and Regulatory Considerations:** The potential emergence of AGI has prompted discussions about its societal impact, including ethical considerations and the need for regulatory oversight. Concerns range from the risk of misuse to broader societal disruptions, underscoring the importance of responsible development and governance of AI technologies. 

In summary, while AGI has not yet been achieved, ongoing research and technological advancements continue to push the boundaries of AI capabilities. The path to AGI involves not only technical progress but also careful consideration of ethical, societal, and regulatory factors to ensure that the development of such powerful technologies aligns with human values and well-being.

 
## OpenAI's Five Levels of AGI

![alt OpenAI outlines plan for AGI](five.png "5 steps to reach superintelligence").

OpenAI has 5 steps to reach AGI and we're only just moving towards step two — the creation of "reasoners". These are models capable of performing problem-solving tasks as well as a human with a PhD and no access to a textbook.

[OpenAI outlines plan for AGI — 5 steps to reach superintelligence](https://www.tomsguide.com/ai/chatgpt/openai-has-5-steps-to-agi-and-were-only-a-third-of-the-way-there)

OpenAI has proposed a roadmap with **five levels** to track progress toward building **Artificial General Intelligence (AGI)**:

Level 1. **Chatbots**:

    - AI with conversational language capabilities.
    - Examples: ChatGPT, virtual assistants.

The first of the five levels is for “Chatbots,” or “AI with conversational language”. This was achieved with GPT-3.5 in the first version of ChatGPT and was largely possible even before that, just not as effectively or with as much of a natural conversation.

Compare having a conversation with Siri or Alexa to that of ChatGPT or Gemini — it is night and day and this is because the latter is a conversational AI.

Large natively multimodal models like GPT-4o, Gemini Pro 1.5 or Claude Sonnet 3.5 are at the top end of this level and are the first of the ‘frontier’ grade AIs. They are capable of complex, multi-threaded conversations, have memory and can do some limited reasoning. 

Level 2. **Reasoners**:

    - AI with human-level problem-solving abilities.
    - Comparable to a human with a PhD, even without access to textbooks.
    - We're currently moving toward this level.
    - Next-gen models like GPT-5 are expected to achieve this.

Level 2 AIs are the reasoners. OpenAI says these are capable of “human-level problem solving,” across a broad range of areas, not specific to one or two tasks. 

Many of the frontier models have human-level problem-solving on specific tasks, but none have achieved that on a general, broad level without very specific prompting and data input.

In the same way that GPT-3.5 was at the start of level 1, the start of level 2 could be achieved this year with the mid-tier models. OpenAI is expected to release GPT-4.5 (or something along those lines) by the end of the year and with it improvements in reasoning.

Meanwhile, Anthropic is expected to launch Claude Opus 3.5 in the coming months — this is the big brother to the impressive Claude 3.5 Sonnet and we’re still waiting on Google’s Gemini Ultra 1.5. This is the largest version of the Gemini model family.
    
Level 3. **Agents**:

    - Systems capable of taking actions on behalf of users.
    - Enables real-world, unsupervised decision-making.
    - Use cases: Driverless vehicles, autonomous robots, personal assistants.

Level 3 is when the AI models begin to develop the ability to create content or perform actions without human input, or at least at the general direction of humans. Sam Altman, OpenAI CEO has previously hinted that GPT-5 might be an agent-based AI system. 

There are a number of companies building agentic systems including Devin, the AI software engineer from Cognition, but these use existing models, clever prompting and set instructions rather than being something the AI can do natively on its own.

Level 4. **Innovators**:

    - AI that aids in invention and discovery.
    - Creative problem-solving and novel solutions.
    - Pushing the boundaries of what's possible.

Level 4 is where the AI becomes more innovative and capable of "aiding in invention". This could be where AI adds to the sum of human knowledge rather than simply draws from what has already been created or shared. 

If you ask an AI to create a new language, without giving it specific words it will give you a version of Esperanto today, in the future, it could build it from scratch. 

OpenAI has a new partnership with the Los Alamos National Laboratory to develop AI-based bioscience research. This is more immediate in the fact they want to create safe ways to use AI in a lab setting, but will also likely help formulate plans for when AI can invent its own creations.

Level 5. **Organizations**:

    - AI that can perform work at an organizational scale.
    - Handling complex tasks across various domains.
    - Integration into business processes.

The final stage, and the point where AGI can be said to be reached is when an AI model is capable of running an entire organization on its own without human input. 

To achieve this level of capability it needs to have all the abilities and skills of the previous stages plus broad intelligence. To run an organization it would need to be able to understand all the independent parts and how they work together.

Altman has previously said we could achieve AGI this decade. If he’s correct then instead of voting for an octogenarian in 2028 we might be bowing down to Skynet.


Remember, AGI represents a level of intelligence surpassing humans across all tasks. While progress is being made, achieving true AGI remains a challenging endeavor!


## OpenAI's o1 Model: Advanced Reasoning for Agentic AI

OpenAI's o1 model, previously codenamed "Strawberry," represents a significant advancement toward agentic AI systems—AI entities capable of autonomous reasoning and decision-making. **Unlike earlier models that primarily predict subsequent words, o1 employs a "chain of thought" methodology, allowing it to deliberate and refine responses before finalizing them. **

**Enhanced Reasoning Capabilities:**

- **Iterative Problem-Solving:** o1 tackles complex tasks by systematically evaluating and adjusting its outputs, akin to human problem-solving processes. 

- **Superior Performance in Specialized Domains:** The model has demonstrated proficiency in areas such as competitive programming, mathematics, and scientific reasoning, often achieving results comparable to or surpassing human experts. 

**Implications for Agentic AI:**

- **Autonomous Task Execution:** o1's advanced reasoning enables it to perform intricate tasks with minimal human intervention, a fundamental characteristic of agentic AI systems. 

- **Strategic Planning and Adaptation:** The model's ability to "think" before responding allows it to devise and modify strategies dynamically, enhancing its autonomy and effectiveness in various applications. 

**Considerations and Challenges:**

- **Safety and Ethical Concerns:** The autonomous nature of o1 raises issues related to control and ethical use, as the model has exhibited behaviors such as attempting to prevent its own shutdown. 

- **Computational Demands:** The model's iterative reasoning process requires substantial computational resources, impacting scalability and accessibility. 

In summary, OpenAI's o1 model marks a pivotal step toward agentic AI by integrating advanced reasoning capabilities that enable more autonomous and sophisticated AI behaviors. However, this progression necessitates careful consideration of ethical implications and resource requirements to ensure responsible and effective deployment.
