By | ChanLianShe CLS
In April 2026, the presence of NVIDIA CEO Jensen Huang was frequently seen at global power centers.
From the Davos Forum to the GTC conference, he repeatedly conveyed a core message to the world: AI is not only a technological revolution but also the largest infrastructure construction project in human history.
However, a lengthy conversation with Silicon Valley podcast host Dwarkesh Patel on April 15th tore apart the facade of Huang's controlled narrative.
In nearly two hours of intense exchange, Huang not only publicly challenged Google's TPU, admitted to making a serious strategic mistake, but also proposed the idea of token stratification and issued an unprecedented fierce rebuttal against export controls.
In this unscripted dialogue, Huang was not only defending NVIDIA's market value but also desperately safeguarding the company's dominant position as the global computing power hub.
Redefining NVIDIA: Electrons In, Tokens Out
Many worry that if AI software becomes as common and cheap as water and electricity, NVIDIA's chips might also become less important. Huang countered this concern with an extremely simple model: NVIDIA's job is "input electrons, output tokens."
Here, "electrons" refer to the most primitive electrical energy, the fundamental energy that drives computer operation. The so-called "tokens" can be understood as language fragments or logical units generated by AI.
He believes that making every word and every piece of logic generated by AI more valuable involves an extremely complex process that is difficult to easily replace.
To solidify this position, NVIDIA's strategy is to "do as many necessary things as possible, while doing as little as possible." Any环节 that doesn't require hands-on effort is handed over to ecosystem partners, while NVIDIA itself focuses intensely on the most difficult technological core.
This strategy has given NVIDIA absolute leverage in the supply chain. NVIDIA's purchasing commitments are currently close to $100 billion and may even exceed one trillion in the future.
Huang bluntly stated that partners are willing to invest in building factories for him because NVIDIA possesses the absolute capability to convert this production capacity into global demand.
Interestingly, Huang pointed out that the real bottleneck in computing power expansion is not chip manufacturing. He believes semiconductor technical challenges can be solved within two or three years, but the hardest bottleneck is actually the "plumbers and electricians" in infrastructure. He even suggested inviting plumbers to next year's NVIDIA GTC conference.
Furthermore, more than hardware, he is concerned about energy policy, because without sufficient electricity, even the most advanced computing factories cannot operate.
Criticizing Custom Chips: Without Anthropic, TPU Growth Would Be Zero
When discussing competitors' custom chips (ASICs), Huang was highly aggressive. He directly named Google's TPU and Amazon's Trainium, publicly challenging them to run performance tests. He mocked that the opponents' claimed 40% cost advantage is completely unfounded.
He further exposed the commercial difficulties of his opponents. Huang believes that while these specialized chips are fast for certain fixed computations, they lack flexibility and cannot keep up with the annual几十-fold changes in AI algorithms. More crucially, he believes there is no substantial market opportunity for custom chips. He bluntly stated that without the major client Anthropic propping them up, the growth of TPU and Trainium would actually be zero.
However, Huang also engaged in rare self-reflection. He admitted that he had missed the best window to invest in OpenAI and Anthropic. The reason was that he underestimated the hunger of these model labs for massive computing power investments.
He直言 this was his miscalculation; he thought these labs could survive on venture capital, but the reality is they must seek massive support like that from big tech companies. He emphasized that he would not make the same mistake again.
Affirming the Chinese Market: A Rebuttal to Export Controls
On the issue of export controls to China, Huang's emotions ran highest.
He repeatedly interrupted the host, vehemently stating that comparing AI to nuclear weapons is "crazy." He believes China possesses over 60% of the global chip manufacturing capacity and a vast pool of AI research talent, making the idea of restricting China's access to chips completely unrealistic.
He issued a stern warning to policymakers. Huang pointed out that restrictive policies are instead forcing China to accelerate the development of its domestic chip industry, pushing the entire AI ecosystem towards internal architectures.
His nightmare scenario is: if future global AI models are all optimized for non-US hardware, the US will completely lose its technological leadership.
Huang firmly believes that competition is the guarantee of leadership. China is currently one of the world's largest contributors to open-source models, and most of these成果 currently run on NVIDIA's technology architecture. If the US abandons this huge market out of fear, it will not only hurt the profits of American companies but also cause the US to lose the opportunity to define global technology standards.
The Underlying Logic of Acquiring Groq: Tokens Need Stratification
Towards the end of the conversation, Huang explained the deeper business considerations behind NVIDIA's acquisition of Groq.
Previously, NVIDIA had tested various exotic chip architectures in simulators and found none performed better than the existing architecture. However, Groq was acquired because market demand had changed: tokens began to be stratified.
所谓 "stratification" means different customers have different demands for AI response speed. Huang gave an example: for software engineers requiring extremely high efficiency, if the AI's response could be one second faster, they would be willing to pay a higher price.
Groq's technology, while not high in total throughput, excels in its extremely fast response speed. NVIDIA uses this to enter the "premium token" market, creating different price tiers for services based on response speed.
The entire lengthy conversation revealed one fact: Huang's 30-year collaboration with TSMC doesn't even require legal contracts; this trust-based ecosystem is his foundation. Every decision he makes, whether publicly challenging opponents or arguing vehemently amidst control controversies, has only one purpose:
To ensure that every path globally for converting electrons into intelligent tokens must pass through NVIDIA.
Article Information Source: Jensen Huang – Will Nvidia’s moat persist? Dwarkesh Patel, YouTube
