What he meant, however, was not that HE now thinks like humans. His point was simpler and more immediate: AI is reaching a stage where it can create real economic value, not just generate text, images or code. That shift—from answering questions to making money—is what makes claiming different now.
The distinction matters because most people today experience AI as a tool to help with tasks. Huang is pointing to something else emerging: systems that can build products, launch services and potentially generate revenue with far less human input than before.
Chatbots for value creation
To understand the difference, it helps to look at how AGI is usually defined. In simple terms, it refers to AI systems that can perform a wide range of human-level tasks, including reasoning, adapting and solving unfamiliar problems.
Huang’s definition moves away from this idea and replaces it with something more concrete. In the podcast, Lex Fridman defined AGI as an AI system capable of launching and running a company worth more than $1 billion, essentially asking if AI can now create real-world economic results.
Huang agreed, but with a key clarification. “You a billion and you didn’t say forever,” he said, suggesting that even short-lived success would reach that threshold.
This framing changes the conversation. Instead of asking if AI thinks like a person, she asks if AI can produce something valuable enough that people will pay for it.
The tools make this believable
Huang pointed to emerging systems such as OpenClaw, an open-source platform where networks of AI agents can work together to build digital applications and products. These agents can handle tasks such as writing code, generating content, and managing workflows that previously required teams of people.
The idea is not that AI runs companies independently today, but that the building blocks are already in place. A group of AI agents, guided by humans, can create a product that scales quickly enough to generate significant revenue or even reach a billion-dollar valuation, at least briefly.
This is a different kind of history. It’s less about intelligence in the abstract and more about whether AI can participate directly in the economy.
What HE still can’t do
Huang did not claim that AI has solved every problem related to general intelligence. He acknowledged that even large-scale artificial intelligence systems would struggle to build and support a complex company like Nvidia, which requires long-term planning, real-world awareness and judgment shaped by experience.
These limitations remain central to skepticism from researchers. Current AI systems can pass exams, write production-grade code, and process large amounts of data, but they still produce errors, struggle with unfamiliar situations, and lack the kind of contextual understanding that humans rely on.
The gap, then, is clear. AI may be getting better at generating value, but it is still unable to fully replace human decision-making in all areas.
What counts as AGI
Huang’s claim has drawn immediate skepticism from researchers and industry experts, many of whom argue that the dispute is not about progress but about definitions. In academic and technical circles, artificial general intelligence is still widely understood as a system that can match human-level performance in a wide range of tasks, including reasoning, adapting to unfamiliar situations, and applying common sense.
By this standard, current artificial intelligence systems are weak. They can write code, pass professional exams, and generate content at scale, but they still produce factual errors, struggle with new scenarios, and lack the kind of contextual understanding that people build through experience.
Critics say Huang’s framing shifts targets by focusing on economic output rather than cognitive ability. A system that helps create a valuable product or that briefly achieves a high rating, they argue, is not the same as one that can reason, plan, and operate independently across domains for long periods.
Even Huang acknowledged some of those limitations in the same conversation, noting that AI systems would struggle to build and support a complex organization like Nvidia. This distinction—between generating short-term value and demonstrating broad, human-like intelligence—lies at the heart of the push.
“Making money” matters
The shift from capacity to economic output has wider ramifications than the debate over definitions might suggest. The term AGI appears in contracts and strategic agreements at companies such as OpenAI and Microsoft, where certain conditions are tied to whether AGI is achieved.
If AGI is interpreted as the ability to generate significant economic value, those thresholds may be reached sooner than expected. This would affect partnerships, competition and access to advanced AI systems.
It also changes the way businesses think about adopting AI. A tool that can generate revenue is treated differently than one that simply improves productivity.
Changes stock for Nvidia
For Nvidia, the shift reinforces its central role in the AI economy. The company’s chips power the systems used to train and run advanced AI models, meaning any increase in AI-driven economic activity translates into demand for its hardware.
The figures reflect this expectation. Shares of Nvidia were trading around $176 on Monday, down about 0.3% in early trading on Tuesday, even as long-term growth forecasts remain strong.
At his GTC conference in early March, Huang predicted at least $1 trillion in chip revenue from his Blackwell and Vera Rubin platforms by 2027, adding roughly $500 billion in additional order visibility as of October 2025.
He also outlined ambitions to reach $3 trillion in revenue, compared to fiscal 2026 revenue of $215.9 billion, highlighting how closely Nvidia’s future is tied to expanding AI-driven economic activity.
Subtle but important shift
Huang’s statement does not resolve the issue of whether AGI has indeed arrived. What it does is shift the focus of the conversation to something more tangible: whether AI can create value that people are willing to pay for.
For most people, this distinction is easier to understand. AI is no longer just a tool that helps you write emails or generate images. It is moving towards systems that can directly contribute to how money is made.
This shift, more than the label itself, is what could shape how companies build, how work is done, and how the next phase of the AI economy unfolds.
Justin is a seasoned personal finance author and business journalist with over a decade of experience. He makes it his mission to break down complex financial topics and make them clear, relatable, and relevant—helping everyday readers confidently navigate today’s economy. Before returning to his Middle Eastern roots, where he was born and raised, Justin worked as a business correspondent at Reuters, reporting on stocks and economic trends in both the Middle East and Asia-Pacific regions.
