{"slug": "nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips", "title": "NVIDIA CTO Says AI is Now About Software, Networking And Power – Not Just Chips", "summary": "NVIDIA CTO Michael Kagan said at WeAreDevelopers World Congress in Berlin that modern AI is no longer just about chips but about software, networking, and power, describing AI infrastructure as an \"AI factory\" that converts energy and data into intelligence. Kagan emphasized that the networking layer, gained through NVIDIA's 2020 acquisition of Mellanox, is critical for connecting thousands of GPUs to train larger AI models at scale, and that AI performance is now limited by software and power, not just hardware.", "body_md": "# NVIDIA CTO Says AI is Now About Software, Networking And Power – Not Just Chips\n\nFor most people, NVIDIA means GPUs. But at WeAreDevelopers World Congress in Berlin, CTO **Michael Kagan** barely mentioned them.\n\nInstead, he focused on the infrastructure behind AI: networking, energy, software, data centers, robotics, and the massive engineering challenge of scaling it all. NVIDIA’s framing was clear: **modern AI is no longer just hardware – it’s an “AI factory.”**\n\n## AI is changing how people interact with computers\n\nKagan compared AI to past tech revolutions. Electricity came through a wall socket. Cloud computing put infrastructure on demand. Now AI is changing the interface again **making computers accessible through natural language instead of code**.\n\nBefore AI, only a few million people on Earth could operate computers by programming them. Now everybody can program the computer and run the AI, and if you don’t know how to do it, just ask AI. It will tell you.\n\nIn other words, **users no longer need to understand the technical details behind computing**. If they’re unsure how to do something, AI can often explain it or do it for them.\n\nKagan framed AI less as a software category and more as a **new computing layer **that expands access to technology. The computer stays the same – the interface between people and machines is what’s getting much simpler.\n\n## How NVIDIA linked thousands of GPUs\n\nOne of the most interesting parts of the discussion focused on Mellanox, the networking company Kagan co-founded in 1999 before NVIDIA acquired it in 2020.\n\nMellanox was originally built for large-scale cloud computing, helping connect servers across massive data cent**ers**. But as AI workloads grew, that networking layer became even more important because the challenge was no longer just building individual processors, but **making thousands of them work together efficiently**.\n\nThat’s why Kagan says modern AI systems should be viewed as single computers made up of huge numbers of GPUs spread across racks and data centers:\n\nThe computer is not a box under the table anymore.\n\nThat made the Mellanox acquisition strategically important: **it gave NVIDIA the networking needed to connect GPUs and train larger AI models at scale**.\n\n## AI factories turn energy and data into intelligence\n\nKagan repeatedly used the term **AI factory** because, in his opinion, it better describes the role modern AI infrastructure plays.\n\nTraditional data centers store data and process requests. AI infrastructure does something different: it **uses huge amounts of data and power to train models and run inference across applications**. “AI factories take the energy and the data and convert it to intelligence”, says Kagan.\n\nHe compared the process to a power plant generating electricity. It’s a metaphor, but it captures how NVIDIA sees AI infrastructure: as a production system where intelligence is the output.\n\nWhen asked what an AI factory looks like, Kagan said:\n\nThe first thing you notice when you go to the AI factory is cables.\n\n**Those cables connect hundreds, thousands, and eventually millions of GPUs into one computing environment**. NVIDIA breaks that challenge into two parts: scale-up, which links GPUs with NVLink, and scale-out, which connects those systems into massive clusters.\n\nAs those systems grow, networking becomes essential. Every processor has to stay in sync, and NVIDIA says lowering the cost of generating AI tokens remains one of its key goals.\n\n## AI is now limited by software and power, not just chips\n\nAI performance used to improve mainly as hardware improved, but Kagan says the challenge is now more complex.\n\nA big reason is the rapid **growth of inference**. Traditional computing follows a simple pattern: a user sends a request, the computer processes it, returns a result, and waits for the next instruction. Agentic AI works differently, with models constantly exchanging information with software tools and other services while completing tasks. That creates far more communication inside the data center.\n\nThose interactions happen much faster than humans can issue requests, while Moore’s Law is slowing down. That means smaller and faster transistors alone are no longer enough to meet demand.\n\nThat is why building larger processors alone is no longer enough to keep up with growing AI workloads. According to Kagan, **CUDA has become one of NVIDIA’s biggest long-term advantages** because it allows developers to fully exploit the company’s hardware:\n\nChips without software are just expensive sand.\n\nHe explains that NVIDIA wants to give developers a stable platform while the hardware keeps evolving. Too much general hardware can be inefficient, but** too much specialization can quickly become outdated**. CUDA gives developers the flexibility to support new workloads without starting from scratch.\n\n**Power** is another major limit. Kagan said electricity is now one of the biggest constraints on new AI data centers. NVIDIA is working on ways to connect multiple sites over long distances so they can act like one system, with training done where power is available and inference closer to users.\n\n## Progress in AI depends on understanding complex systems\n\nThe session concluded with advice for young engineers: rather than recommending a specific programming language or AI framework, Kagan encouraged students to develop **strong foundations in mathematics, physics and chemistry before specializing.**\n\nHe also reflected about his childhood curiosity, saying he used to take new toys apart just to see how they worked. If he were starting school today, he said he’d seriously consider studying digital biology, since understanding the human body is still one of the most fascinating engineering challenges:\n\nThe most complicated machine that is out there is a human.\n\nEven though the conversation touched on GPUs, networking and AI infrastructure, **Kagan kept coming back to one idea**: progress in AI depends on understanding complex systems. Whether that’s millions of processors, distributed data centers or the human body, engineering starts with curiosity and a desire to figure out how things work.", "url": "https://wpnews.pro/news/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips", "canonical_source": "https://shiftmag.dev/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips-10784/", "published_at": "2026-07-30 13:12:09+00:00", "updated_at": "2026-07-30 13:31:50.326339+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-chips", "ai-research", "ai-policy"], "entities": ["NVIDIA", "Michael Kagan", "Mellanox", "WeAreDevelopers World Congress", "NVLink"], "alternates": {"html": "https://wpnews.pro/news/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips", "markdown": "https://wpnews.pro/news/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips.md", "text": "https://wpnews.pro/news/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips.txt", "jsonld": "https://wpnews.pro/news/nvidia-cto-says-ai-is-now-about-software-networking-and-power-not-just-chips.jsonld"}}