Nvidia's AI dominance faces test without Jensen Huang

Nvidia confronts a difficult scenario regarding the maintenance of its supremacy in the artificial intelligence chip sector should founder and Chief Executive Officer Jensen Huang leave his position. The impact of such a departure might differ from that experienced by other major technology firms after their founders exited, given Huang’s pivotal role in shaping Nvidia’s strategy and expanding its influence within the global technology industry.

Huang’s central role in Silicon Valley history

The list of prominent technology founders who left a mark on Silicon Valley includes Steve Jobs, Gordon Moore, Bill Gates, Jeff Bezos, Mark Zuckerberg, Larry Page, and Sergey Brin. Huang stands out among the first generation of tech founders for transforming Nvidia from a small graphics chip specialist into one of the most important companies in the AI economy.

Large corporations typically retain the capacity to continue growing after their founders step away from executive management. Twitter, now known as X, continued operations after Jack Dorsey’s departure, while Google and Amazon sustained growth following their founders’ transitions to different roles. However, Nvidia’s situation may prove more complex due to the close association between its trajectory and Huang’s vision and decisions.

From near bankruptcy to tech giant

Nvidia was established in 1992 by Huang and other engineers, initially focusing on designing chips specialized in three-dimensional graphics processing. The company spent its early years facing financial and commercial difficulties before the computer gaming sector experienced a boom in the late 1990s. This surge coincided with the popularity of games like "Doom" and increased demand for the graphics processors the firm produced.

During the challenging period spanning 1992 to 2000, which saw the launch of "GeForce" processors turn Nvidia into a prominent name in the technology industry, Huang was the individual who kept the company alive and directed its course. He relied on persistence and the ability to overcome obstacles. Throughout the 1990s, Huang frequently repeated a phrase that became a corporate motto: "Our company is 30 days away from going out of business." Nvidia continued to adopt this approach even after becoming a giant, treating competition and innovation as continuous challenges that do not permit complacency.

Three factors behind Nvidia’s AI edge

Nvidia’s advantage in the AI market did not stem solely from possessing chip technology that competitors could not replicate. The company does not manufacture chips itself but designs them, and other firms can develop similar processors. Its success is linked to three main factors that Huang helped build and manage.

The first factor was the company’s early entry into the AI race. Before large language models and applications like "GPT" became widespread, Huang began shifting Nvidia’s focus from traditional graphics cards to meeting the needs of enterprises and data center operators seeking processors capable of accelerating machine learning tasks. Huang recognized that the graphics cards developed by the company could be adapted for these uses, granting Nvidia an early lead in the AI market. Competitors like AMD failed to seize the opportunity at the same speed despite possessing powerful graphics processors.

The second factor involved the development of the "CUDA" ecosystem, a software package owned by Nvidia that has become fundamental to the AI industry. CUDA provides the tools developers need to leverage the capabilities of the company’s processors and run their applications on them. The importance of CUDA in this field resembles the status of programs like Microsoft Word and Adobe Photoshop, where users find it difficult to switch to alternatives even when competing tools emerge, due to habituation to the existing system. Furthermore, CUDA works exclusively with Nvidia processors, leading companies such as OpenAI and Anthropic to prefer this environment for developing AI technologies, thereby purchasing the company’s chips.

The third factor relates to the network of commercial relationships Huang built with technology companies and semiconductor suppliers worldwide. These connections assisted Nvidia over the past decade in securing long-term agreements on both the supply and demand sides of the AI market.

Supply chain and demand networks

At a time when major firms like Apple and Google faced difficulties securing memory chips, Nvidia managed to obtain the supplies it needed from companies such as Samsung and SK Hynix. The company also maintained access to advanced manufacturing technologies at Taiwan Semiconductor Manufacturing Company (TSMC), which produces its chips. This relationship dates back to the 1990s, when Huang began a close collaboration with TSMC founder Morris Chang, at a time when both companies were still in their early growth stages.

On the demand side, Huang’s relationships facilitated agreements with AI companies and major data centers. Among these was OpenAI, to which Huang personally delivered AI chips approximately ten years ago, when it was still a startup and Elon Musk was part of its team. Nvidia’s strategy extended to investing in AI companies, pumping funds into several of them in recent years. These firms used part of the financing to purchase more Nvidia chips, contributing to the construction of an interconnected ecosystem where AI companies rely on the firm’s processors to develop and operate their technologies.

While some analysts view this ecosystem as a potential indicator of a bubble in the AI market, Nvidia sees it as a success of its commercial strategy and an expansion of its influence within the sector.

Rising competition threatens dominance

Conversely, Nvidia faces increasing challenges that could affect its ability to maintain its advanced position. The advantage of the CUDA ecosystem is coming under pressure as companies like AMD and Apple move to reduce developers’ reliance on the firm’s technologies. Major technology companies, including Google, Microsoft, and Amazon, are developing their own AI chips in an attempt to decrease dependence on external processor suppliers. OpenAI is also seeking to hedge its bets by developing its own capabilities in this area.

Apple’s moves constitute an additional challenge, given its extensive experience in chip design and its direction toward entering the enterprise-specific AI market. Meanwhile, AMD, Nvidia’s traditional competitor since the 1990s, is attempting to narrow the technical and commercial gap between the two firms. The challenges are not limited to technological competition but extend to Nvidia’s ability to maintain its relationships with suppliers in Asia and technology companies in Silicon Valley, connections in which Huang played a fundamental role in building and developing over the years.

Although modern companies generally possess significant capacity to overcome the absence of their founders and CEOs, Nvidia may be among the cases most susceptible to the impact of such a departure. This is because its success in the AI market has depended not only on chip design but also on Huang’s vision, his ability to anticipate technological shifts, build partnerships, and secure supply chains.