Market Analysis

How Nvidia’s Research Thrives on Quick Failures and Bold Ideas

Amos Simanungkalit · 20 Mar14.3K Views

Image Credit: Nvidia

In just a few years, Nvidia (NVDA) has become a leading chip company, with revenues soaring from $27 billion in fiscal 2023 to $130.5 billion in fiscal 2025. Its stock price has surged over 680% since January 2023. While Nvidia may not be as widely known as other tech giants, it plays a central role in the global AI boom, thanks to its powerful chips, like the Blackwell Ultra, unveiled at its annual GTC event.

The company's research and development department, Nvidia Research, established in 2006, has been the backbone behind many of its groundbreaking technologies, including ray-tracing for realistic gaming graphics, and NVLink and NVSwitch for enabling communication between GPUs and CPUs at speeds required for AI systems. Currently, Nvidia's research efforts focus on new chip architectures, quantum computing, and software that helps robots and self-driving cars navigate the real world.

Despite its success, Nvidia’s research team is relatively small compared to other Silicon Valley firms. According to Bill Dally, Senior VP of Research at Nvidia, the team consists of just 300 people, but it’s known for making a significant impact with its small size. Dally emphasizes the importance of failing often as part of the research process, noting that if every project succeeds, they aren’t pushing boundaries enough.

A prime example of this is Nvidia's ray-tracing technology, which took over a decade to develop but is now widely used in major games and design software. Similarly, Nvidia's Deep Learning Super Sampling (DLSS) technology had a rocky start but has evolved to dramatically improve gaming visuals, with the latest version (DLSS 4) significantly enhancing even demanding games like "Cyberpunk 2077."

While not all research projects directly contribute to revenue, they help drive sales indirectly by expanding the market for GPUs. One such example is the Sana project, a text-to-image generative network that, although not a commercial product, still fuels demand for GPUs.

As Nvidia faces increased competition from companies like AMD, which is developing its own AI chips, and from customers creating specialized processors, its research efforts are critical. The company’s newly launched Blackwell Ultra and Vera Rubin superchips come at a time when its market cap experienced a significant drop due to DeepSeek’s R1 AI model.

With major tech companies such as Amazon, Google, Meta, and Microsoft set to invest heavily in AI infrastructure, Nvidia’s research will be vital to maintaining its position in the rapidly evolving market. Nvidia’s approach is clear: fail fast, learn, and continue moving forward.