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Holy shit⦠AGI finally has a number. 𤯠For the first time, we can actually measure how close we are to real Artificial General Intelligence thanks to a new paper from Yoshua Bengio, Dawn Song, Max Tegmark, Eric Schmidt, and others. For years, everyoneās been throwing around the term āAGIā like itās some mystical milestone. Something vague. Something out there in the future. But this paper just pinned it down with a definition that finally makes sense. They describe AGI as: āAn AI that can match the cognitive versatility and proficiency of a well-educated adult.ā No hype. No āhuman-levelā buzzwords. Just a measurable benchmark based on real cognitive science. The framework is built on the CattellāHornāCarroll (CHC) model ā the same system psychologists use to measure human intelligence. It breaks cognition into ten abilities: reasoning, memory, math, language, perception, processing speed, and more. Then the researchers did something wild ā they tested actual AI models against those same human benchmarks. And hereās what they found: GPT-4 ā 27% toward AGI GPT-5 ā 58% toward AGI In other words, the latest model now operates at over half the cognitive range of an average human adult. But itās not there yet. Both models still scored 0% in long-term memory the ability to store and recall new information over time. Thatās the missing piece. Continuous learning. Lifelong memory. The foundation of real general intelligence. What makes this paper groundbreaking isnāt just the data itās the clarity. For the first time, we have a framework that turns āAGIā from a buzzword into a measurable spectrum. We can finally track progress. We can finally quantify intelligence. And right now, humanityās best AI is sitting at 58% of the way to AGI. The countdown has officially begun.


The paper starts by calling out the elephant in the room: nobody actually agrees on what AGI is. Every year the definition shifts from ābetter than humans at chessā to ābetter than humans at everything.ā They argue this ambiguity has slowed progress. So they built a quantifiable definition.


Their definition is refreshingly simple: āAGI is an AI that matches the cognitive versatility and proficiency of a well-educated adult.ā Not superhuman. Not godlike. Just human-level cognition across domains. This focus on versatility (breadth) and proficiency (depth) is what separates AGI from narrow AI.


To measure that, they used the CattellāHornāCarroll (CHC) theory the same psychometric model used in human IQ tests. It breaks intelligence into 10 ābroad abilitiesā reasoning, memory, math, perception, etc. Then they built AI test batteries modeled after human cognition.

When they ran GPT-4 and GPT-5 through the test batteries, the results were wildly uneven. GPT-5 crushed it in reasoning and math. But both models completely failed at long-term memory scoring 0%. Basically, they can think fast⦠but forget everything.


That long-term memory gap is the missing piece of AGI. Humans learn through retention and recall. AIs? They just remix whatās already encoded. Without persistent memory, true understanding canāt emerge.


The paper also distinguishes between speed and intelligence. GPT-5 performs simple tasks quickly, but raw speed ā smarter thinking it just means faster prediction. Humans trade speed for reflection. AIs donāt yet.


Whatās powerful here is that this framework lets us track AGI like a scoreboard. Instead of arguing āare we close to AGI?ā we can now ask: āHow much cognitive ground remains before parity?ā Next frontier: memory + multimodal reasoning.

The takeawayās clear: AGI isnāt a magic switch itās a spectrum of cognition. And for the first time, we can measure it. Right now, humanityās smartest model stands at 58% human. The other 42% will define the next decade. Read the full paper: <a target="_blank" href="https://www.agidefinition.ai/" color="blue">agidefinition.ai</a>