When DeepSeek unveiled its open‑source R1 reasoning model earlier this year, it sent shockwaves not just through Silicon Valley, but across global markets. Nvidia’s stock dropped nearly 17 percent in a single session—wiping out some 600 billion dollars in value—and even raising whispers that AI was evolving past the need for massive computing infrastructure. In interviews and public statements since then, Nvidia’s founder and CEO Jensen Huang has insisted the market got it wrong, arguing that the advent of DeepSeek R1 isn’t a threat to computing power—it’s a catalyst. He’s been clear: rather than signaling the end of GPU demand, DeepSeek’s efficiency breakthrough underscores how AI reasoning demands even more compute.
It’s hard to overstate how unsettling that stock plunge was. From one day to the next, Nvidia lost nearly a trillion dollars in market cap alongside other AI‑focused firms. I remember the morning it happened—my cousin, a software engineer at a cloud‑services startup, messaged me in disbelief, saying his entire equity plan took a serious hit. He wasn’t cheering or jeering. He was just confused: how could a more efficient model threaten hardware? Huang’s narrative—that reasoning and post‑training stages tax GPUs just as much, if not more—isn’t just theoretical. It’s grounded in how companies and researchers build AI systems in real life.
One evening, a few weeks after the R1 event, I sat across the table from a university professor friend over coffee. She’d been running experiments combining smaller LLMs with reasoning modules to reduce energy and computing costs. Initially, her team saw dramatic savings. But when they grew more complex—adding memory layers, chain‑of‑thought prompting, retrieval augmentations—the computing appetite jumped. She sighed and said, “We saved GPU hours in training, but inference costs exploded.” Her experience echoed Jensen Huang’s point: reducing the cost of pre-training doesn’t eliminate the need for compute at reasoning or inference steps.
What made the market reaction even stranger was the underlying assumption: that AI had three phases—pre‑training, post‑training, and inference—and that cheaper pre‑training implied dirt‑cheap everything. Huang corrected that misconception: reasoning and fine‑tuning require intensive compute. Each layer of logic the AI applies consumes GPU cycles. Instead of replacing hardware needs, DeepSeek’s R1 model is raising awareness of the next frontier—models that think more, faster, deeper. Nvidia’s GPUs are central to that.
I spoke with a startup founder last month who builds AI for medical image analysis. She uses large open‑source models like DeepSeek’s R1 as starting points, fine‑tuning them with patient data. What surprised her team was that once they added doctor‑validated reasoning steps and iterative diagnostics loops, GPU costs skyrocketed. They needed clusters for inference at clinical scale. One nurse told her that automated flagging had improved diagnoses—but it required dozens of GPUs running 24/7 behind the scenes. She paused and noted, “We didn’t want costly infrastructure. Then we realized it's worth it.” She was echoing Huang: stronger reasoning = stronger need for GPUs.
That real‑world pattern shows why Huang responded with optimism instead of fear. On CNBC and TechCrunch interviews, he described DeepSeek R1 as “incredibly exciting” and “a gift to the world’s AI industry.” That heat isn’t threatening—it's fueling adoption of GPU‑powered computing infrastructure. He reminds listeners that R1 is open‑source: it invites experimentation, ecosystem growth, and the kind of model‑infrastructure feedback loop Nvidia thrives on. More developers, more use‑cases, more demand for AI infrastructure.
He’s not alone in seeing the silver lining. At Computex Taipei, Huang called R1 “a gift to the world’s AI industry,” praising it for igniting global discourse on reasoning and inference. The open‑source nature encouraged experimentation. Companies began implementing R1 variants in robotics, edge AI, finance, biotech. Everywhere, developers realized something they already suspected: efficient does not mean cheap at scale. Gavin, a robotics engineer, told me how R1‑like models took user interactions to another level—BUT only when paired with powerful onboard GPUs. For chat, R1 felt accessible—but for automated decision‑making on drones, it demanded parallel streams of compute.
In his keynote at GTC, Huang drove the point home. He introduced the Vera Rubin successor chips, showing they can cluster hundreds of GPUs for reasoning‑scale loads. He emphasized that the market overreacted. If anything, NVIDIA’s path forward is clearer: build chips that make reasoning fast, cost‑effective, and scalable for enterprise. That’s where AI is headed: from cold training runs toward dynamic, reasoning‑driven model deployment in real‑world use‑cases.
Underneath all this is a broader conversation about the future of AI and computational power. We’re seeing efficiency improvements, but AI isn’t moving toward smaller hardware trends. Instead, we’re seeing compute diversifying across training, reasoning, serving, robotics, and autonomous systems. Huang underscored how Nvidia has stopped thinking of itself solely as a chip company. Now it positions itself as an AI infrastructure provider—a platform company bridging silicon, software, systems, and ecosystem. The DeepSeek moment didn’t weaken that proposition; it strengthened it.
Personal stories bring the trend into sharper focus. A healthcare data scientist told me about running DeepSeek-derived reasoning on small clinics. The upfront results were promising: better diagnostic suggestions, fewer false positives. But then she pushed the model live, linking it to hospital workflows. Real‑time inference, patient‑specific reasoning, clinician feedback loops—GPU demands soared. She said, half-laughing: “We thought we were saving money. Then we realized we were just shifting costs.” That subtle shift matters: scalable reasoning demands industrial‑scale infrastructure.
All of which explains Nvidia’s recovery. After losing $600 billion in market value, the stock bounced back—crossing $140, then surging past previous highs. Investors noticed that infrastructure spend didn’t dry up—it accelerated. Cloud providers doubled down on GPU orders. Enterprises announced partnerships. OEMs released AI compute servers. Even beyond Nvidia, companies across the chip supply chain benefited. DeepSeek didn’t reduce demand. It expanded awareness of AI reasoning's complexity—and the need for hardware.
Back at Nvidia’s Shanghai robotics labs, engineers used R1‑style reasoning to improve autonomous navigation—but only when paired with onboard GPUs tailored for inference rather than training. That required ruggedization, thermal design, redundancy—all infrastructure built around compute demands. It wasn’t the loss of GPU relevance; it was the grounding of GPU in real-world systems. And Nvidia is positioned for that future.
There are nuances though. China’s DeepSeek did remind the world that open‑source models can compete with closed‑source offerings. It forced Western firms to consider licensing, collaboration, and distributed compute models. That’s healthy. And reflection of Huang’s vision: a broad ecosystem benefits Nvidia. Efficiency creates opportunity, not obsolescence.
The whole episode is a reminder: markets react to headlines and assumptions. A drop in share price doesn’t always reflect long-term fundamentals. In Nvidia’s case, what looked like disruption was more like discovery. DeepSeek revealed new layers of compute demand—not fewer. And Jensen Huang turned survivor’s remorse into a rallying cry: compute is alive and growing deeper, wider, more reasoning‑centric.
So when the next efficiency breakthrough happens—on‑device LLMs, quantized models, edge AI chips—keep an ear out for who’s building infrastructure too. Because foundational compute always finds new paths to grow. And when Nvidia’s next generation of hardware—Rubin, Blackwell, Grace—targets reasoning, inference, robotics, it’s continuing a conversation DeepSeek helped start.
In homes, in hospitals, in factories, from start‑ups to cloud giants, AI models will grow smarter, faster, more context-aware. And behind each of them is, quietly but powerfully, GPU-driven compute. That’s what Jensen Huang meant when he said the market got it wrong about DeepSeek. It didn’t set AI free from computing. It invited more of it 😊