I came across some interesting information about how OpenAI defines artificial general intelligence internally. According to these company documents that got leaked, they have a pretty specific way of measuring when AGI is actually reached.
Basically, their definition isn’t based on technical capabilities or passing certain tests like most people think. Instead, they consider AGI achieved when they create an AI system that can bring in at least $100 billion in profits for the company.
This seems like a really business-focused approach rather than focusing on the actual intelligence or capabilities of the AI. I’m curious what everyone thinks about this definition. Does it make sense to measure AGI success through financial metrics? Or should we be looking at other factors like reasoning ability, creativity, or general problem-solving skills?
Has anyone else seen these documents or heard about this approach to defining AGI? I thought the tech community would find this pretty surprising since most discussions about AGI focus on technical benchmarks rather than revenue targets.
I have to say this definition feels fundamentally flawed to me. Revenue generation has very little correlation with actual intelligence capabilities. Consider how many brilliant researchers or inventors never made substantial profits from their work, while marketing-driven products rake in billions without requiring sophisticated intelligence. An AI could theoretically achieve this $100B threshold by being extremely good at a few commercially viable tasks like content generation or data analysis, while completely lacking general intelligence in other domains. True AGI should demonstrate flexible reasoning across unprecedented situations, not just excel at monetizable applications. The fact that OpenAI would tie their AGI milestone to profit margins rather than cognitive benchmarks suggests they are more focused on justifying their massive investments to stakeholders than on measuring genuine intelligence breakthroughs. This approach could actually incentivize building narrow but profitable AI rather than pursuing the broad, adaptable intelligence that AGI is supposed to represent.
This revenue-based definition actually makes more strategic sense than it initially appears. From a business perspective, $100B in profits would indicate that an AI system has become so universally valuable and capable that it can generate massive economic impact across multiple industries. You cannot reach that level of revenue without solving real-world problems at an unprecedented scale. The technical benchmarks we typically discuss in AGI conversations are often academic exercises that do not translate to practical utility. A system that generates $100B in profits would necessarily need to demonstrate advanced reasoning, creativity, and problem-solving across diverse domains to create that much value. In essence, the financial metric serves as a proxy for comprehensive real-world performance rather than narrow test performance. However, this approach does raise questions about accessibility and whether such a definition prioritizes commercial success over genuine intelligence advancement. The concern is that it might lead to optimizing for profitable applications rather than advancing our understanding of intelligence itself.
honestly this sounds like typical corporate bs to me. defining agi by how much money it makes is kinda missing the point entirely - we could have a really dumb system that just happens to exploit some market really well and hit 100b. meanwhile actual intelligent systems might not be profitable at all initally. feels like they’re just trying to justify their valuations rather than focusing on real intelligence breakthroughs.