The artificial intelligence industry has been chasing "Artificial General Intelligence" (AGI) for years, but OpenAI's own CEO recently admitted the term is essentially meaningless. In August 2025, Sam Altman publicly described AGI as "not super useful," acknowledging what researchers have quietly known for some time: the industry's most celebrated milestone lacks any universal definition, scientific consensus, or even a shared operational framework. What Exactly Is AGI, and Why Can't Anyone Agree? OpenAI itself proposed a definition in 2023, describing AGI as "a highly autonomous system that surpasses humans in most economically valuable tasks." But here's the problem: this definition focuses on economic value rather than actual cognitive abilities, and it's vague enough to mean almost anything. OpenAI even added an informal contractual threshold, suggesting AGI would be achieved when its systems generated more than $100 billion in profits for early investors. Other voices in the field disagree entirely. Mark Gubrud, who claims to have coined the term in 1997, and Jensen Huang, CEO of NVIDIA, argue that "we have achieved AGI" already, since current large language models (LLMs) can perform tasks better than most humans. Meanwhile, Yann LeCun and colleagues prefer the term "Super Human Intelligence" and have attempted to provide a more rigorous scientific definition in peer-reviewed research. Other The lack of consensus reflects a deeper problem: different companies propose divergent scales entirely. OpenAI distinguishes five levels of increasing autonomy, while Google DeepMind favors a taxonomy organized by application domain. This creates what researchers call a "rhetorical horizon" that serves fundraising and captures public anxiety without anyone actually knowing what they are discussing. Why Does This Vagueness Matter Beyond Academic Debate? The ambiguity around AGI is not merely a technical quibble. It reflects the absence of universal metrics, making it impossible to measure progress toward a goal that remains undefined. This becomes particularly problematic in a tense geopolitical context where AI development is intertwined with national security and economic dominance. The United States, under the Trump administration, has placed AI and robotics at the heart of its industrial and military strategy, with massive investments in autonomous systems and humanoid robots for defense applications. Simultaneously, China is accelerating massively in this field, backed by a major structural advantage: access to massive amounts of data from its population of 1.4 billion people, combined with privacy laws far less restrictive than those in Western democracies. Beijing is exploiting this data advantage to train predictive surveillance systems capable of identifying potential protests, analyzing the emotions of prisoners, and implementing widespread social scoring. The country now operates approximately 600 million AI-equipped cameras, or roughly one camera for every two citizens. How to Navigate the AGI Confusion as a Business or Investor - Demand Specific Metrics: When companies claim progress toward AGI, ask for measurable benchmarks and timelines rather than accepting vague promises about achieving "general intelligence." - Focus on Current Capabilities: Rather than waiting for AGI, evaluate what today's AI systems actually do well: writing, coding, analysis, and service production where they regularly outperform individual humans. - Assess Geopolitical Risk: Understand that AI development is now entangled with national security concerns, data access, and industrial espionage, which may affect supply chains and technology availability in your industry. The real issue is that the AI industry has built enormous expectations around a concept nobody can define. OpenAI's own acknowledgment that AGI is "not super useful" as a term suggests the field may need to move beyond this rhetorical framework and focus instead on measurable, domain-specific capabilities. What makes this moment significant is that AI systems in 2026 already have remarkable capabilities that are profoundly transforming intellectual work and service production, regardless of whether they constitute "AGI." The question may not be whether we have achieved general intelligence, but rather whether we are prepared for the economic and geopolitical consequences of increasingly powerful, narrowly focused AI systems that excel in specific domains.
