Jensen Huang doesn't hold one-on-one meetings with his 60 direct reports because solving Nvidia's most complex engineering challenges requires everyone in the room attacking the problem simultaneously. In a recent conversation with Lex Fridman, the Nvidia CEO explained how his unconventional leadership structure and "extreme co-design" philosophy have transformed the company from a GPU chip designer into an architect of entire data center ecosystems. Why Did Nvidia Stop Thinking About Individual Chips? For decades, Nvidia's competitive advantage came from building the fastest graphics processing units (GPUs) on the market. But the AI revolution changed the game entirely. Modern artificial intelligence problems no longer fit inside a single computer, no matter how powerful. When researchers want to train massive language models or run complex AI workloads, they need thousands of computers working in perfect synchronization. This created a problem that no amount of incremental chip improvement could solve. The core challenge is what computer scientists call Amdahl's law. If computation represents only 50% of your total workload, and you somehow make computation infinitely faster, you've only sped up the entire system by a factor of two. Everything else becomes the bottleneck. Huang explained this constraint forces engineers to think about the entire system, not just individual components. What Does "Extreme Co-Design" Actually Mean in Practice? Extreme co-design means optimizing across every layer of the technology stack simultaneously. This includes hardware components like GPUs, CPUs, memory systems, and networking chips, but also extends to power delivery, cooling systems, software architecture, and even the physical design of the data center racks themselves. Rather than having separate teams optimize each component independently, Huang's approach demands constant collaboration across all disciplines. "When you're designing a computer, you have to have an operating system of computers. When you're designing a company, you should first think about what is it that you want the company to produce," explained Jensen Huang, CEO of Nvidia. Jensen Huang, CEO of Nvidia The practical implication is that Nvidia's organizational structure mirrors the technical problem it's trying to solve. Rather than following the typical "hamburger" organizational chart that most tech companies use, Huang built his leadership team around the actual engineering dependencies that exist in distributed AI systems. How to Manage Extreme Complexity Across Competing Specialties - Assemble World Experts in Each Domain: Huang's 60-person staff includes specialists in high-bandwidth memory, optical networking, power delivery, cooling systems, CPU architecture, GPU design, and algorithms. Each person brings deep expertise in a specific area that impacts the overall system. - Create Transparent Problem-Solving Sessions: Rather than siloed one-on-one meetings, Huang presents problems to the entire group. Engineers from different specialties listen in and contribute insights about how solutions in one domain affect their own area, catching conflicts before they become expensive design mistakes. - Empower Specialists to Self-Regulate Attention: Not every engineer needs to weigh in on every decision. The key is that specialists know when their domain is affected and can jump in with critical constraints or opportunities that others might miss. This approach works because the people on Huang's staff understand the interconnected nature of the problem. When someone discusses cooling solutions, the memory expert might realize it affects thermal constraints on high-bandwidth memory. The networking specialist might see implications for power consumption. The CPU architect might identify bottlenecks in data movement. Huang emphasized that no conversation involves just one person attacking a problem. "We present a problem and all of us attack it, because we're doing extreme co-design. And literally the company is doing extreme co-design all the time," he stated. Huang The reason this structure exists is fundamentally about physics and mathematics. When you're trying to make 10,000 computers work together and achieve a million-fold speedup, you can't rely on Moore's Law anymore. Dennard's scaling, which historically allowed chips to get faster while staying cool, has largely stopped. The only way forward is to optimize every single component in relation to every other component, which is why Huang's war room approach has become essential to Nvidia's competitive advantage in the AI era.
