AGI Is Already Here. The Definition Doesn't Matter.
If you traveled back in time to 2015 and told a room full of computer scientists that in a decade a computer would pass the Bar Exam, diagnose MRIs, and fix complex code bugs without human intervention, they would have had a unanimous name for it. Artificial General Intelligence.
They would have told you that if a machine could do all of that, the world would be unrecognizable. Well, here we are in 2026. The machines can do all of that, and yet the debate rages on. We are witnessing something called the "AI Effect." As soon as AI solves a problem, we stop calling it "intelligence" and start calling it "computation." We have collectively decided to define AGI as "whatever the machine can't do yet." To understand where we are going, we first have to understand where this term came from.
The term "Artificial General Intelligence" isn't as old as the field itself. While the concept of "thinking machines" goes back to Alan Turing in the 1950s, the specific term AGI was popularized around 2002 by researchers Ben Goertzel and Shane Legg. They coined it to distinguish their ambitious goal, a flexible, adaptive intelligence like a human, from the "Narrow AI" of the time, which was merely good at playing chess or filtering spam.
Their original definition was simple. A system that can solve a variety of complex problems in a variety of environments, just like a human. If you strictly applied that 2002 definition to the technology of 2026, we arguably reached the finish line years ago. Anthropic's latest models can solve problems in Python, debug in C++, and explain the logic in French. That is variety and complexity. So why don't we call it AGI? The answer is simple. The goalposts didn't just move. Everyone brought their own.
The reason the current landscape feels so confusing is that the "Godfathers" of the industry are fighting a philosophical war over the definition. On one side, you have the "Scientific Faction" led by groups like Google DeepMind. Shane Legg, the man who helped coin the term, has shifted toward a nuanced "Levels of AGI" framework. He views intelligence like a video game leveling system. While our current models are "Competent" (better than 50% of skilled adults), they haven't yet reached "Superhuman" status across the board. For this faction, AGI is a scientific milestone of perfection.
On the other side, you have the "Physical Skeptics," most notably represented by Yann LeCun, formerly the Chief AI Scientist at Meta. LeCun argues that the term AGI is meaningless until a machine possesses a "World Model," an understanding of cause and effect in physical reality. He contends that an LLM knows "if I drop a glass, it breaks" only because it read it in a book, not because it understands gravity. In his view, until an AI has that physical grounding, it is less intelligent than a house cat.
Then there is the "Economic Faction," led by Sam Altman and OpenAI. In their leaked internal documents from late 2024, the definition of AGI appeared to shift from a philosophical breakthrough to a starkly capitalist metric. They defined it as a system that can autonomously generate $100 billion in profit. They don't care if the machine has a "soul" or if it understands physics. They care if it can replace labor at scale.
While these three factions argue over definitions, the ground has shifted beneath our feet. It doesn't matter if the machine has a "World Model" or if it hits "Level 5" on a DeepMind chart. The only definition that matters to you, your career, and your family is "Economic AGI." This asks a much simpler, colder question. Can this system replace the economic output of a human being?
If a "narrow" model can analyze a contract faster than a lawyer, code better than a junior developer, and manage logistics better than a supply chain manager, then for all economic intents and purposes, AGI is here. While speaking at the World Economic Forum, Anthropic's CEO Dario Amodei revealed that some of his engineers "don't write any code anymore" and predicted AI would handle "most, maybe all" of software engineering within six to twelve months. A senior Google engineer recently said Claude Code recreated a year's worth of work in a matter of hours. We are waiting for a sci-fi moment where the robot wakes up and announces it has a soul. But the revolution isn't about consciousness. It is about competence and productivity.
You don't need a machine to be alive to take your job. You just need it to have context. Thanks to the new wave of "Cognitive Twins" and agentic workflows, it finally does. While the Godfathers argue over whether we've crossed some philosophical threshold, the tools are already reshaping how work gets done. The professionals who thrive in the next decade won't be the ones who waited for a consensus on what to call it. They will be the ones who learned how to work alongside it. The era of debating the definition is over. The era of living with this reality has begun.