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Nvidia are a tech company based in Silicon Valley – now known most widely for designing and manufacturing computer chips which are used in the development of artificial intelligence (AI) products.
They were founded in California back in the early 90s by Jensen Huang, Chris Malachowsky, and Curtis Priem. Huang remains the CEO and President of Nvidia to this day, while Malachowsky is a senior employee at the company (Priem sold his shares for approximately $30 million in the early 2000s – they would have been worth almost $100 billion today).
The original focus of the company was graphics processing units (GPUs) designed to be used within the gaming industry (another competitor active within this space that also found success was ATI Technologies, which has since become part of AMD). They started on this path with $40,000, though later attracted large amounts of venture capital funding from well-known names such as Sequoia. These early days were relatively rough for the company – key contracts with household names like Sega (Nvidia were due to provide the graphics chip for the Dreamcast console) fell apart due to a lack of technological innovation and experience within an increasingly crowded market.
The real growth came when Nvidia made the decision in the mid 1990s to focus on their ‘RIVA’ chips, which focused on graphics acceleration. This was followed up in 1999 by the GeForce 256, which became highly influential within the industry. These chips were continually refined over the years – in 2006, the CUDA platform made the technology particularly useful in the context of scientific work, and in the 2010s, talk of artificial intelligence inspired Nvidia to again pivot into pitching their chips as being important tools for the growth of a relatively new industry.
When the AI boom (particularly in the area of generative AI chatbots like ChatGPT) did take off from approximately 2022 onwards, the growth for AI hardware meant that Nvidia’s chips became the beating heart of the most talked-about industry on earth.
The most significant factor leading to Nvidia reaching this milestone has undoubtedly been the growth in artificial intelligence as an industry. ChatGPT released in late 2022, and similar AI chatbots have taken the consumer market by storm. Since that release date, Nvidia stock has climbed approximately 871% (comparably large tech stocks might be pleased with 40-60% growth over the same period).
However, a rising tide does not necessarily lift all boats – Nvidia positioned itself particularly well by ensuring that its market share remained extremely dominant – competitors like Intel and AMD hold almost miniscule proportions of the market by comparison, with Nvidia controlling about 77% of AI chip production.
There have been a number of events and trends within the international political landscape that have had a particularly significant impact on Nvidia’s growth.
First, the US government have made a number of decisions that have been highly impactful (both positive and negative) for Nvidia, as a US company. over the last few years. This has been particularly true in light of Donald Trump’s often controversial and highly impactful policy decisions.
For example, the US government has banned Nvidia from selling some of its most advanced hardware to China amidst growing international security tensions (the company responded by producing adapted versions of some products that remain complaint with the law). US policymakers are also looking to encourage more in-house manufacturing – causing tensions with Nvidia’s close links to manufacturing locations like Taiwan (which maintains extremely close ties to China – US allies like South Korea are seen as much more preferable by the current administration).
The US government has simultaneously poured a lot of money into fuelling the growth of its most successful AI-adjacent companies (Nvidia being the best example) via wide-ranging subsidies covering semiconductor manufacturing and research and development grants.
Lawyers contribute massively to the growth of large companies like Nvidia (and more specifically to large companies in the booming AI tech space). There are a number of points here that aspiring lawyers (especially solicitors looking to work at top City law firms, who will advise these types of clients) can take from these stories ahead of application forms and interviews. A useful way to approach these stories can be by splitting the relevant facts and considerations into distinct practice areas (while maintaining an understanding of how they can interact with each other too – cross-selling is common practice between teams at large firms).
Obviously, a large emphasis here falls to Corporate Law. Large companies like Nvidia will need constant help from a corporate perspective – whether that be dealing with reorganising their share capital, dealing with a range of shareholder perspectives, or deciding how best to deal with management disputes (lawyers are expected to be all-round business advisors as well as understanding the legislation nowadays).
Another key team involved in advising firms like Nvidia will be the Antitrust practice area. A lot of Nvidia’s work is market-dominant – to such an extent that the relevant competition law regulators (like the CMA in the UK) are likely to be keeping a watchful eye on these companies in order to ensure that they do not gain a monopoly over the market (thus decreasing competition and driving down prices and innovation). This point also links closely with prevailing political sentiment and policy decisions concerning, which has been discussed above already in detail.
Finally, you might also consider the work that intellectual property (IP) lawyers will be doing for companies like Nvidia. The designs behind their cutting-edge technology are arguably the most valuable assets of the company (as is common with many tech hardware firms), and need to be rigorously protected through the successful registration and ongoing protection (think potential litigation) of IP rights like patents. STEM-qualified lawyers are particularly in demand in areas like these.
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