AGI EXPLAINED
What counts as AGI?
Artificial general intelligence has no single agreed finish line. OpenAI, Google DeepMind, and researchers looking at how AI learns ask different questions about the same systems.
Compare their frameworks with our own definition, based on how much you trust AI to do knowledge work. These are ways to think about progress, not a live ranking of current models.
AGI usually means AI with abilities that extend beyond a narrow specialty. What remains disputed is how broad those abilities must be, how strong they must be, and whether the system must work or learn independently.
COMPARE THE FRAMEWORKS
Compare AGI definitions and stages
Choose a framework, then scroll through its stages or key questions. The visual follows what you are reading.
AGI is when you trust it with the work
At AGITools, we define AGI through human intuition: how much responsibility would you feel comfortable giving an AI? Our starting point is the trust you place in it to do knowledge work, rather than a mathematical score or a calculated benchmark. For us, AGI arrives when that trust extends across fields, beyond the confidence you place in yourself or a leading human expert.
These are our proposed stages of trust. The 99.9% figure expresses near-complete confidence; it is not a measured success rate or a medical safety standard. Trust has to be earned through dependable results, honest uncertainty, and knowing when to ask for help. Sounding confident is not enough.
AGITools · Our proposed definition of AGI
From conversation to whole organizations
OpenAI has described five levels of AI capability. Its Charter separately defines AGI around highly autonomous systems that outperform humans at most economically valuable work. The levels are a way to discuss progress, not a public score for today’s models.
OpenAI discussed these levels in a 2025 OpenAI Forum presentation. They are capability descriptions, not a claim that each level has been achieved.
OpenAI Forum · “Deep Research in the OpenAI Forum” (2025); OpenAI Charter
How broad, and how capable?
Google DeepMind’s “Levels of AGI” is a matrix, not simply a ladder. It compares performance with generality: how well a system works, and across how many different tasks. The levels below describe performance across a broad range of cognitive tasks.
The paper’s 2025 revision renamed level 4 “Exceptional”; older summaries may still say “Virtuoso.” The authors also discuss autonomy separately from this capability scale.
Google DeepMind · “Levels of AGI for Operationalizing Progress on the Path to AGI” (2025 revision)
Can it learn what it has not seen?
Researcher François Chollet approaches intelligence through how efficiently a system learns new skills. This is a way to evaluate generalization, not a numbered set of AGI stages.
These three questions summarize Chollet’s measurement lens. They are our reading aid, not stage names from the paper or a claim that a particular system is AGI.
François Chollet · “On the Measure of Intelligence” (2019)
Scroll to explore
Why the definition changes the answer
OpenAI’s levels describe a progression from conversation to independent action and organization-scale work. Its Charter uses a separate threshold based on most economically valuable work.
Google DeepMind asks whether strong performance extends across most cognitive tasks. The learning-focused view asks how efficiently a system adapts to tasks it has not practiced. A system can look advanced through one lens while leaving another question open.
Our definition asks how much responsibility you would hand over. The turning point is when you trust AI with work across fields more than you trust yourself or a top expert. That is a judgment about earned trust, rather than a benchmark score.