Reflecting on the early days of the AI boom, Huang said Nvidia didn’t move quickly enough to back foundational AI labs like Anthropic \u2014 even as the need for massive compute funding was becoming evident.<\/p>\n
The $10 Billion Reality Check<\/p>\n
Responding to Dwarkesh Patel on the podcast<\/a>, Huang pointed to a structural shift that Nvidia initially underestimated: frontier AI labs weren’t venture-scale bets.<\/p>\n “What they were trying to do couldn’t have been done through VCs,” he said, emphasizing that firms like Anthropic required multi-billion-dollar \u2014 often $5 billion to $10 billion-scale \u2014 backing. That\u2019s far beyond traditional startup funding models.<\/p>\n At the time, Nvidia lacked both the precedent and the “sensibility” to deploy capital at that scale outside its core business, Huang admitted.<\/p>\n Course Correction In Real Time<\/p>\n Nvidia has since pivoted.<\/p>\n Huang added that Nvidia is now “delighted to invest” in players like OpenAI and Anthropic, signaling a more aggressive capital strategy as AI scales into a trillion-dollar opportunity.<\/p>\n The shift reflects a broader realization: in AI, supplying chips isn’t enough \u2014 owning exposure to the labs driving demand may matter just as much.<\/p>\n What It Means For Investors<\/p>\n The takeaway is less about a past miss and more about Nvidia’s evolving playbook.<\/p>\n While earlier phases of AI were defined by hardware dominance, the next phase hinges on capital allocation and ecosystem positioning \u2014 where Nvidia is now playing catch-up.<\/p>\n As Huang put it, the company won’t make the same mistake again.<\/p>\n Image via Shutterstock<\/p>\n