The belief that a future Artificial General Intelligence (AGI) will solve all problems acts as a rationalization for inaction. This "messiah" view is dangerous because the AI revolution is continuous and happening now. Deferring action sacrifices the opportunity to build crucial, immediate capabilities and expertise.

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.

Instead of a single "AGI" event, AI progress is better understood in three stages. We're in the "powerful tools" era. The next is "powerful agents" that act autonomously. The final stage, "autonomous organizations" that outcompete human-led ones, is much further off due to capability "spikiness."

The hype around an imminent Artificial General Intelligence (AGI) event is fading among top AI practitioners. The consensus is shifting to a "Goldilocks scenario" where AI provides massive productivity gains as a synergistic tool, with true AGI still at least a decade away.

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 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 discourse around AGI is caught in a paradox. Either it is already emerging, in which case it's less a cataclysmic event and more an incremental software improvement, or it remains a perpetually receding future goal. This captures the tension between the hype of superhuman intelligence and the reality of software development.

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.

While AI is capable of disrupting most knowledge work now, large enterprises move too slowly to implement it. Widespread job disruption will be delayed by organizational friction and slow adoption, not technological limitations, even if AGI were achieved today.