Nvidia (NVDA) has swiftly risen to prominence as one of the leading chip companies in the world within just a few years. The company’s revenues have surged from $27 billion in fiscal 2023 to a staggering $130.5 billion in fiscal 2025, with share prices experiencing a remarkable 680% increase since January 2023. While not as widely recognized as some Big Tech giants, Nvidia is at the forefront of the global AI movement, propelled by its cutting-edge chips like the Blackwell Ultra showcased at its recent GTC event.
Many of the breakthrough technologies underpinning Nvidia’s processors, which power gaming PCs worldwide, as well as the software driving them, originated from Nvidia Research, the company’s modest yet innovative research and development department. Established in 2006, this group is credited with pivotal advancements such as Nvidia’s ray-tracing technology for realistic lighting in gaming and professional design, NVLink, and NVSwitch facilitating high-speed communication between graphics chips and CPUs essential for AI systems.
Currently, Nvidia Research is engaged in developing new chip architectures, quantum computing, and software simulators to train robots and autonomous vehicles for real-world navigation. Despite Nvidia’s current success, the research team remains committed to embracing failure as a stepping stone to success, allowing promising projects the time and resources needed to flourish.
Bill Dally, Nvidia’s senior vice president of research and chief scientist, highlights the importance of acknowledging that not all initiatives will succeed, underscoring the value of taking risks and pushing boundaries. While Nvidia’s research team is relatively small compared to counterparts in Silicon Valley, their impact on bringing innovations to market is substantial.
Dally emphasizes the significance of swiftly discarding failed ideas while nurturing those with potential until they mature into viable products or technologies. Nvidia’s ray tracing technology, a decade-long endeavor, now features in numerous popular games and design software, illustrating the company’s commitment to long-term vision and persistence.
Bryan Catanzaro, vice president of applied deep learning research at Nvidia, points to AI as a prime example of persistence paying off, transforming a once-dismissed concept into a game-changing technology. Despite initial skepticism surrounding AI’s feasibility, Nvidia’s dedication to innovation has proven its transformative power over the years, showcasing the company’s unwavering commitment to pushing boundaries and driving progress.
Now? However, there were a few of us who saw this as a real opportunity, and so the company allowed us the space to keep experimenting and gradually producing better results, leading to more incremental investments,” added Catanzaro. Nvidia’s DLSS, short for deep learning super sampling, is a prime example of a product the company persisted with despite initial challenges. Launched in 2019, the first version of DLSS enhances a game’s graphics quality and performance using artificial intelligence. However, the software initially fell short of expectations. I recall testing it on my own computer and not noticing much improvement while gaming. Fast forward to the present day, and the company now introduces DLSS 4, significantly enhancing game visuals for even the most demanding titles like “Cyberpunk 2077.” “DLSS 1.0 had its flaws, and many doubted its potential, considering it a poor technology. But we believed in it,” Catanzaro stated. “I believe Nvidia possesses an unwavering faith in its vision of the future, persistently pursuing it.”Research Fueling Chip SalesNot all successful research projects translate into revenue-generating products directly. Nevertheless, they can indirectly boost sales by driving GPU demand. “I’m content with people developing applications for GPUs that expand the market,” Dally explained. “Recently, our team developed Sana, a text-to-image generative network. Although it hasn’t become a product, it’s a significant success as external users utilize it, stimulating GPU demand.” This is the ultimate objective. However, Nvidia’s newly revealed Blackwell Ultra and Vera Rubin superchips arrive at a time when the company faces heightened competition. AMD presents its own AI chips as rivals to Nvidia, and Nvidia’s clients are creating or deploying specialized AI processors. Additionally, disruptive events like the launch of DeepSeek’s R1 AI model, causing Nvidia’s market value to plummet by nearly $600 billion in January, and uncertainties surrounding governmental interventions such as tariffs and export controls, continue to impact the company’s stock performance. As tech giants like Amazon, Google, Meta, and Microsoft plan to invest billions in AI infrastructure in the coming years, Nvidia’s research endeavors become increasingly crucial in securing a share of that reward. The key is to fail fast, learn from it, and progress.
