As AI models achieve previously defined benchmarks for intelligence (e.g., reasoning), their failure to generate transformative economic value reveals those benchmarks were insufficient. This justifies 'shifting the goalposts' for AGI. It is a rational response to realizing our understanding of intelligence was too narrow. Progress in impressiveness doesn't equate to progress in usefulness.

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.

The pursuit of AGI may mirror the history of the Turing Test. Once ChatGPT clearly passed the test, the milestone was dismissed as unimportant. Similarly, as AI achieves what we now call AGI, society will likely move the goalposts and decide our original definition was never the true measure of intelligence.

The definition of AGI is a moving goalpost. Scott Wu argues that today's AI meets the standards that would have been considered AGI a decade ago. As technology automates tasks, human work simply moves to a higher level of abstraction, making percentage-based definitions of AGI flawed.

The most sophisticated benchmarks, like Arc AGI, are not meant to be a permanent 'final exam' for AI. They are designed as moving targets that are expected to become saturated and obsolete. This forces researchers to constantly focus on the next most important unsolved problem at the AI frontier.

As AI achieves impressive milestones, like assisting in creating a cancer vaccine, the public conversation immediately discounts the achievement. The goalposts shift from "AI helped solve a problem" to demanding a fully autonomous, one-shot solution. This pattern of escalating expectations obscures the real, incremental progress being made.

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.

Even as AI models surpass technical AGI benchmarks, the host argues people will keep moving the goalposts. The true, socially accepted definition of AGI will be its "feel"—its ability to generalize and execute complex, nuanced tasks with minimal instruction, like a human.

David Weisberg observes that "recursive self-improvement"—an AI's ability to improve itself—was considered the definition of AGI just two years ago. As models approach this capability, the goalposts for AGI are moving, suggesting we are entering an era that was recently defined as the technological singularity.