--- type: "Topics" locale: "zh-CN" url: "https://longbridge.com/zh-CN/topics/39569587.md" description: "šŸŽÆ Jensen Huang's Latest Interview: NVDA is No Longer Just a Chip CompanyIf you're still viewing NVIDIA through the old lens, you might have completely missed the main storyline.Jensen Huang's latest interview reveals the true ambition of this market cap giantā€”it's not making chips, it's building AI factories; it's not managing a company, it's designing a precision machine.Here are the 5 most thought-provoking core insights from this interview šŸ§µšŸ‘‡1/ From Chips to Data Centers: A Complete Strategic RebuildHuang stated plainly that the scale of modern AI problems is no longer solvable by a single GPU. NVIDIA's positioning has shifted from "chip supplier" to "data center-level system designer."Their goal is not linear expansion, but non-linear acceleration. From algorithms, software, hardware, networking to cooling, they provide a complete, optimized "AI factory" solution.In other words, NVIDIA is no longer selling parts, but the production lines for the AI era.2/ Management as Design: He's Building the Company Like a MachineTo achieve this level of extreme co-design, the management approach must change completely.Huang directly manages over 60 top experts from different fieldsā€”from memory, CPU, optics to algorithms. He doesn't hold one-on-one meetings; instead, he throws problems directly to the entire team.The benefit is simple: break down departmental silos and make systems thinking the default mode.3/ The Craziest Bet: Making CUDA UbiquitousHe mentioned again in the interview that NVIDIA's riskiest decision was embedding CUDA into every consumer GeForce GPU.This move once nearly wiped out profits and caused the market cap to plummet. But Huang's logic was clear: the success of a computing platform depends on the developer ecosystem and user base.By putting CUDA into millions of gamers' PCs, NVIDIA unknowingly laid the deepest moat for the AI revolution.4/ AI Scaling: After One Bottleneck, Comes AnotherHow does Huang view the next step for AI? He sees it as a cycle of breaking through bottlenecks:Ā· Data bottleneck ā†’ solved by synthetic dataĀ· Inference bottleneck ā†’ complexity far exceeds trainingĀ· Agent bottleneck ā†’ AI will move towards collaborative teamsThe ultimate constraint remains computing power, and behind that, energy. The only solution: extreme co-design to maximize performance per watt.5/ His Decision-Making Style: Build Belief First, Then ConsensusMany wonder how Huang drives such bold transformations.His method: first paint the future using first principles, then gradually build consensus through rounds of conversations, meetings, and presentations.By the time major decisions like acquiring Mellanox or going all-in on deep learning are officially announced, everyone already feels "it was inevitable."SummaryThe true core logic of NVIDIA is laid bare in this interview:Ā· Not making chips, but building AI factoriesĀ· Not just managing a company, but designing it as a machineĀ· Not chasing short-term profits, but betting on the ecosystem to win the futureIf you're still looking at NVIDIA through the old framework, perhaps it's time to think again:They aren't participating in the AI eraā€”they are building it.$NVIDIA(NVDA.US) $C3.AI(AI.US)" datetime: "2026-03-27T06:12:33.000Z" locales: - [en](https://longbridge.com/en/topics/39569587.md) - [zh-CN](https://longbridge.com/zh-CN/topics/39569587.md) - [zh-HK](https://longbridge.com/zh-HK/topics/39569587.md) author: "[č¾°é€ø](https://longbridge.com/zh-CN/profiles/16318663.md)" --- > ę”Æꌁēš„čÆ­č؀: [English](https://longbridge.com/en/topics/39569587.md) | [ē¹é«”äø­ę–‡](https://longbridge.com/zh-HK/topics/39569587.md) # šŸŽÆ Jensen Huang's Latest Interview: NVDA is No Lonā€¦ ### ē›øå…³č‚”ē„Ø - [NVIDIA (NVDA.US)](https://longbridge.com/zh-CN/quote/NVDA.US.md)