AI vs AGI: Is ChatGPT Artificial General Intelligence?
How ChatGPT and other current AI tools compare with artificial general intelligence.
Artificial intelligence (AI) describes machine-based software designed to perform specific tasks, whereas artificial general intelligence (AGI) refers to a hypothetical system capable of matching or exceeding human cognitive abilities across every task.
At Layer3Labs, we build and run automation systems for client workflows, so we evaluate machine learning models based on the specific tasks they solve today rather than hypothetical future milestones.
By IBM's standard, ChatGPT is not AGI, though some researchers argue advanced LLMs already qualify. The 2023 Levels of AGI paper rated ChatGPT Level 1, Emerging AGI.
AI (today's systems) vs. AGI (artificial general intelligence): Side-by-Side
| Dimension | AI (today's systems) | AGI (artificial general intelligence) |
|---|---|---|
| Definition | Software systems defined under 15 U.S.C. 9401(3) as machine-based systems that make predictions, recommendations, or decisions for human-defined objectives. | A hypothetical stage in machine learning where systems can match or exceed human cognitive abilities across any task, or outperform humans at most economically valuable work. |
| Scope of tasks | Specialized execution across defined domains such as text generation, computer vision, code writing, or game playing. | Any task a human can do, at human level or better (IBM: "across any task"). |
| Learning new skills | Measured by tests such as ARC-AGI, which score how efficiently a system learns unfamiliar tasks; IBM says current LLMs are not considered AGI. | Efficiently acquires new skills on unfamiliar tasks through fluid intelligence rather than stored knowledge. |
| Exists today? | Yes, widely deployed in consumer software, commercial workflows, and enterprise applications. | No; there is no agreed test or consensus that any system has achieved it, and IBM says current LLMs are not considered AGI. |
| Examples | ChatGPT, Google Gemini, Apple Siri, Grammarly, AlphaFold, and automated enterprise pipelines. | None agreed: the 2023 paper's own general Level 1 examples (ChatGPT, Bard, Llama 2, Gemini) sat below the Competent AGI bar it says best matches prior conceptions of AGI. |
| Position on the Levels of AGI scale | In the 2023 paper's examples: ChatGPT at general Level 1 (Emerging AGI), AlphaGo at narrow Level 4 (Exceptional) and AlphaFold at narrow Level 5 (Superhuman). | Level 1 Emerging AGI through Level 4 Exceptional AGI on the general track; the paper says Level 2 Competent AGI best matches prior conceptions of AGI and no public system had reached it at the time of writing. |
| How it is measured | Domain-specific benchmarks, language understanding tests, classification accuracy, and task-specific completion metrics. | ARC-AGI fluid-intelligence tests, the OpenAI Charter yardstick of outperforming humans at most economically valuable work, and, for OpenAI, an independent expert panel under its Microsoft agreement. |
| What it means for a business using AI now | Immediate operational value through targeted automation of intake, document extraction, customer routing, and data entry. | No production software to purchase or deploy, requiring teams to focus on current narrow tools rather than speculative capabilities. |
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AI vs AGI: Is ChatGPT AGI?
ChatGPT is not AGI by IBM's standard, and the 2023 Levels of AGI paper rated it below Competent AGI.
According to IBM's analysis of artificial general intelligence, current large language models are not considered AGI. Some researchers argue advanced LLMs already qualify and many disagree, and there is no agreed test that would settle it.
Morris and colleagues at Google DeepMind wrote the Levels of AGI paper. It rated ChatGPT, Bard, Llama 2 and Gemini as Level 1 "Emerging AGI", the lowest rung of its general track. In the paper's terms, those models performed equal to or somewhat better than an unskilled human at the time of writing. Level 2 Competent AGI, the next rung, requires at least the 50th percentile of skilled adults. The paper gives no rating for models released since.
Under the OpenAI Charter, AGI represents highly autonomous systems that outperform humans at most economically valuable work. For analysis of model-specific releases, our guide on whether GPT-6 Astra is AGI covers OpenAI's latest AGI claims.
Core Differences Between Narrow AI and General Intelligence
The primary difference between artificial intelligence and artificial general intelligence centers on operational scope and autonomous adaptability across novel domains.
Current artificial intelligence operates as narrow or applied intelligence. Under 15 U.S.C. 9401(3), the United States legal code defines artificial intelligence as a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. These systems excel at designated tasks, such as transcribing speech, detecting image patterns, or generating responses based on training patterns.
Artificial general intelligence represents an unbuilt category of computing. IBM defines AGI as a hypothetical stage in the development of machine learning in which an AI system can match or exceed the cognitive abilities of human beings across any task. IBM's definition sets no task limit, so one AGI system would be expected to handle any task a person can.
As documented on Wikipedia, whether AGI exists remains a subject of intense debate within the AI community, with no agreed test or consensus that any system has achieved it. Teams seeking foundational concepts can read our overview on what AGI is.
- Narrow AI: Solves defined, task-specific problems using specialized architectures or pattern matching within its training distribution.
- Artificial General Intelligence: Would match or exceed human cognitive abilities across any intellectual task; no system is agreed to have done so.
The DeepMind Levels of AGI Framework
The Levels of AGI framework created by Google DeepMind evaluates artificial intelligence across two separate axes: performance depth and operational generality.
Morris and colleagues (including Shane Legg) first published the Levels of AGI paper in November 2023 and revised it in September 2025. It sets six performance tiers, from Level 0 to Level 5. The authors divide each level into Narrow and General categories to differentiate between specialized systems and universal cognitive architectures.
Level 0 represents no AI, where narrow examples include basic calculators and compilers, while general examples include human-in-the-loop computing like Mechanical Turk. Level 1 Emerging means equal to or somewhat better than an unskilled human. The paper's narrow Level 1 examples are classic symbolic AI (GOFAI) and SHRDLU, and its general Level 1 examples at the time of writing were ChatGPT, Bard, Llama 2 and Gemini.
The paper said no public system had reached the higher general tiers at the time of writing. Level 2 Competent requires performance at or above the 50th percentile of skilled adults, and the paper's narrow examples are Apple Siri, Alexa and IBM Watson. Level 3 Expert requires matching the 90th percentile of skilled adults, seen in narrow systems like Grammarly and DALL-E 2. Level 4 Exceptional requires matching the 99th percentile, seen in narrow systems like Deep Blue and AlphaGo. Level 5 Superhuman outperforms 100 percent of humans, achieved in narrow domains by AlphaFold, AlphaZero, and Stockfish, while General Level 5 constitutes artificial superintelligence.
Benchmarks and Verification Standards for AGI
An AGI test has to measure how efficiently a model learns unfamiliar tasks, rather than what it already knows.
The ARC-AGI benchmark, developed through the ARC Prize from François Chollet's 2019 research, measures skill-acquisition efficiency on unfamiliar tasks. The benchmark exists in three versions: ARC-AGI-1, ARC-AGI-2 and ARC-AGI-3.
Under an agreement announced on October 28, 2025, documented by Microsoft, any future declaration of AGI by OpenAI must be verified by an independent expert panel.
Origins of the Term and Alternative Meanings
According to Wikipedia, Mark Gubrud used the term in 1997, and researchers Shane Legg and Ben Goertzel revived and popularized it in the early 2000s.
In non-technical contexts, the acronym AGI carries an entirely different meaning within United States law. The Internal Revenue Service (IRS) defines AGI as adjusted gross income, calculated as "your total (gross) income from all sources" minus certain adjustments. Tax filers and financial professionals reviewing tax forms encounter this accounting term, which shares no connection with machine learning.
Superintelligence and Federal Terminology Distinctions
Artificial superintelligence (ASI) describes an unbuilt machine intellect that exceeds human cognitive abilities across virtually every discipline.
For how ASI differs from AGI, including Nick Bostrom's definition and the Future of Life Institute's call for a prohibition, see AGI vs ASI and what superintelligence means.
Federal policy introduces an additional naming distinction. On September 29, 2026, Executive Order 14434 was signed, instructing executive-branch agencies to use Super Intelligence or SI in non-statutory documents instead of AI. However, the order binds this label directly to the definition of artificial intelligence in 15 U.S.C. 9401(3). Federal SI refers to today's AI as defined in that statute. It does not mean artificial superintelligence or AGI, and the AI vs SI comparison covers the label in detail.
Operational Implications for Businesses Deploying AI
Today's AI fits bounded, repetitive tasks better than open-ended work that would need general problem-solving.
Across the workflows we have automated for SMB teams, machine learning delivers dependable value when connected to structured data pipelines. Intake document extraction, customer relationship management (CRM) record updates and routine client-update drafts work because people set the rules and review the outputs.
Do not let a model act on open-ended inputs without a human check; keep a review step on anything that reaches a client.
Operational leaders should evaluate software vendors by the concrete integrations they support today. Reliable tools provide structured API access, role-based permissions, and transparent error handling. Put AI budget into automations that cut hours in current work; no product on sale offers AGI.
- Deploy for defined workflows: Connect AI to intake pipelines, document extraction, and CRM record updates with explicit validation rules.
- Retain human oversight: Maintain human review on exceptions, sensitive communications, and compliance-sensitive operations.
- Disregard speculative roadmaps: Evaluate software platforms exclusively on documented capabilities and API reliability available today.
Evaluation Framework for Commercial Deployment
Judge AI tools on what they do today, and keep a lab's AGI research out of the buying decision.
Organizations with high-volume, repeatable tasks should implement narrow AI tools immediately. Modern models process text, analyze documents, and extract structured fields when guided by clear prompt engineering and strict integration rules.
This recommendation does not serve organizations seeking fully autonomous systems that operate without human oversight, compliance checks, or structured rules. A business expecting software to handle autonomous legal reasoning, unguided financial auditing, or unsupervised client advisory work will find no commercial tool capable of fulfilling those expectations. Those teams should maintain established manual review procedures.
Our assessment would change if an independent expert panel verified that a public system reached Level 2 Competent AGI on the Levels of AGI scale. It would also change if a system showed sustained fluid reasoning on ARC-AGI without retraining for each domain.
The Verdict
AI is commercial software that works today, and AGI is a research goal no system is agreed to have met. Today's AI, including ChatGPT, is not AGI by IBM's standard, and the 2023 Levels of AGI paper rated ChatGPT only Level 1 Emerging AGI. No public system had reached the Competent AGI bar at the time of writing. Artificial general intelligence remains an unachieved threshold where software would match skilled humans across all cognitive tasks.
For businesses and technology operators, current artificial intelligence delivers immediate value across targeted workflows such as document parsing, CRM updates, and customer communications. Do not wait for AGI, and do not assume current models can handle open-ended work without a person checking the output.
To build reliable operations today, audit your team's repetitive administrative bottlenecks and test narrow machine learning tools against measurable task completion metrics.
Researched from primary vendor documentation and public regulator sources. Pricing and availability are accurate as of Oct 9, 2026 and can change — confirm current terms with each vendor before you buy.
Frequently Asked Questions
- No, ChatGPT is not AGI. The 2023 Levels of AGI paper rated ChatGPT Level 1, Emerging AGI, equal to or somewhat better than an unskilled human. It said no public system had reached Level 2 Competent AGI at the time of writing. IBM says current LLMs are not considered AGI, while noting some researchers argue advanced LLMs already qualify.
- The difference between AI and AGI centers on the breadth of cognitive capability. Today's artificial intelligence (AI) consists of narrow systems designed to execute specific tasks, such as generating text, transcribing speech, or playing games. Artificial general intelligence (AGI) is a hypothetical system that matches or exceeds human cognitive abilities across any task. The OpenAI Charter adds the test of outperforming humans at most economically valuable work.
- No, AGI does not exist yet. IBM says current LLMs are not considered AGI, and Wikipedia records no agreed test or consensus that any system has achieved it. The Levels of AGI paper said no public system had reached Competent AGI at the time of writing.
- There is no agreed test yet. ARC-AGI measures how efficiently a system acquires new skills on unfamiliar tasks rather than testing stored knowledge. Under an agreement with Microsoft announced in October 2025, any AGI declaration by OpenAI will be verified by an independent expert panel.
- Under the OpenAI Charter, AGI is defined as highly autonomous systems that outperform humans at most economically valuable work. OpenAI's agreement with Microsoft adds that any AGI declaration by OpenAI will be verified by an independent expert panel.
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