Today's AI models have surpassed the definition of Artificial General Intelligence (AGI) that was commonly accepted by AI researchers just over a decade ago. The debate continues because the goalposts for what constitutes "true" AGI have been moved.

Dr. Vijoy Pandey defines ASI with two concrete benchmarks: 1) an AI system performing 100% of a human task autonomously (economic viability), and 2) an AI inventing novel ideas beyond its training data without human help (technical viability).

Framing AGI as reaching human-level intelligence is a limiting concept. Unconstrained by biology, AI will rapidly surpass the best human experts in every field. The focus should be on harnessing this superhuman capability, not just achieving parity.

Mustafa Suleiman offers clear definitions: AGI is human parity on most tasks. Superintelligence exceeds human performance and discovers new knowledge. The Singularity is the sci-fi point where a superintelligence can recursively self-improve. This clarifies the ladder of AI progression beyond generic terms.

OpenAI's CEO believes the term "AGI" is ill-defined and its milestone may have passed without fanfare. He proposes focusing on "superintelligence" instead, defining it as an AI that can outperform the best human at complex roles like CEO or president, creating a clearer, more impactful threshold.

Benchmarks like GDPVal show models like GPT-4 consistently outperform human experts on professional tasks, meeting the practical definition of AGI for knowledge work. The public discourse, however, has prematurely shifted the goalposts to sci-fi concepts of Artificial Superintelligence (ASI), obscuring the revolution already underway.

The debate over AGI is skewed because the goalposts have continuously moved. According to Cerebras CEO Andrew Feldman, if we apply any standard definition of Artificial General Intelligence from a decade or two ago, such as the Turing Test, current AI models have already blown past it. The achievement is historical; our expectations are what keep changing.

The debate over AGI is reframed: we have already achieved AI that is better than humans at over 50% of individual skills. The bottleneck is not technological capability but the massive cost and effort required to implement and integrate these systems fully, similar to how we have sustainable energy tech but haven't fully transitioned.

Defining AGI as 'human-equivalent' is too limiting because human intelligence is capped by biology (e.g., an IQ of ~160). The truly transformative moment is when AI systems surpass these biological limits, providing access to problem-solving capabilities that are fundamentally greater than any human's.