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.

A consortium including leaders from Google and DeepMind has defined AGI as matching the cognitive versatility of a "well-educated adult" across 10 domains. This new framework moves beyond abstract debate, showing a concrete 30-point leap in AGI score from GPT-4 (27%) to a projected GPT-5 (57%).

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 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 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.

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.

Dan Siroker argues AGI has already been achieved, but we're reluctant to admit it. He claims major AI labs have 'perverse incentives' to keep moving the goalposts, such as avoiding contractual triggers (like OpenAI with Microsoft) or to continue the lucrative AI funding race.