You have almost certainly seen the headlines 鈥� Jensen Huang, the CEO of Nvidia, has resigned after admitting to an affair with Alice Huang (no relation), who is suspected by (a claim ). The story has become so big because Nvidia, and by extension Jensen, are at the heart of the AI revolution. But just why has this one company become so important?
For me, Nvidia will always be a video games company, its logo splashed up at the start of the play sessions of my youth. Its graphics processing units (GPUs) were specifically designed for the intense number-crunching required to create a 3D scene from triangles. Initially, these were a few hundred triangles, then thousands, and now hundreds of thousands as computing power has increased, allowing ever more realistic computer graphics.
But 30 years later, gaming hardware only makes up a small slice of Nvidia鈥檚 revenue, to the point where it has now as a standalone item. Instead, GPUs have become a raw input for the AI industry, essential for both training and deploying large language models (LLMs) like ChatGPT. This sent Nvidia鈥檚 value soaring to become the largest company in the world at a value of $4.5 trillion 鈥� at least, that is, until news of the affair broke and wiped out almost $500 billion of that, pushing Nvidia down the rankings. Shares fell further as Nvidia announced Huang鈥檚 resignation today.
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Nvidia鈥檚 transformation away from gaming began more than 20 years ago, when researchers realised they could trick GPUs into performing a wider set of calculations. At the time, computers had a single central processing unit (CPU) that executed instructions on data one at a time, in serial. GPUs meanwhile, operated in parallel, allowing them to process the large number of triangles needed to render a scene. By recasting general computing problems in the language of triangles, researchers could harness this parallel power to do other things.
These hacks were impressive, but in 2007 Nvidia formalised the approach with the release of CUDA, a piece of software that could help with translating algorithms into a form more amenable to running on GPUs. Huang, who had been CEO since co-founding Nvidia in 1993, made what, in hindsight, was possibly the biggest bet in the history of computing. At a time when Nvidia鈥檚 customer base was almost exclusively gamers, he decided to build CUDA into every single GPU the company sold, even though this increased the cost and the vast majority of purchasers would never use it. Essentially, gamers subsidised the development of Nvidia鈥檚 general-purpose computing business for years.
That bet only began to show hints of paying off in 2012, as researchers were starting to use CUDA in meaningful numbers. The pivot point that year was the release of AlexNet, an artificial neural network capable of recognising images far better than any technique at the time. Neural networks had been tried and dismissed before, but thanks to CUDA and Nvidia GPUs, AlexNet brought the technique back into the mainstream and kicked off the field of 鈥渄eep learning鈥�, which ultimately led to today鈥檚 AI boom.
As researchers raced to try deep learning for themselves, Nvidia sold more and more GPUs. Then, in 2016 it released the DGX-1, a box containing 8 GPUs and billed as the 鈥溾��. Huang hand-delivered the first DGX-1 unit to OpenAI, then a recently formed non-profit. Nvidia鈥檚 share price began to grow, and grow, and grow in lockstep with the subsequent AI revolution.
It is this combination of hardware and software, GPUs and CUDA, that has made Nvidia so valuable. That isn鈥檛 to say other companies haven鈥檛 attempted to break its stranglehold on the AI industry. In January 2016, Google鈥檚 AI arm, DeepMind, announced it had used 176 GPUs to power its AlphaGo AI in a match against Fan Hui, a leading Go player. While neither company has confirmed, these were almost certainly Nvidia GPUs. But by March 2016, when AlphaGo beat the world champion Lee Sedol, DeepMind had switched to 48 tensor processing units (TPUs), an alternative type of AI hardware developed by Google.
Today, Google continues to use TPUs while tech giants like Amazon, Meta and Microsoft are all experimenting with custom chip designs, but Nvidia GPUs still dominate, in part thanks to the software industry鈥檚 decades-long experience of building on CUDA. And this links directly to the scandal the company is currently embroiled in.
Since 2022, the US has banned the export of high-end computing chips to China, including many of Nvidia鈥檚 top products, in an effort to stymie China鈥檚 AI progress and prevent the use of these chips for military applications. , Alice Huang told Chinese investors that she was able to secure access to Nvidia GPUs, while . Alice Huang hasn鈥檛 responded publicly to the allegations.
Following that, between Jensen Huang and an Nvidia executive, but Huang has not confirmed this. Today, Huang resigned as CEO, stepping away from the company, and the AI revolution, he helped build. Nvidia has appointed its chief financial officer Colette Kress as interim CEO, and will now search for a new leader as chip competitors nip at its heels. The AI industry as a whole will be hoping that investors don鈥檛 lose confidence following the fall of one its leading figures.
Further AI reading
- Anthropic has released , the latest version of its highest-end public model. I have been experimenting with it, and found it to be almost too much for my needs 鈥� the lesser Opus 5.5 is much more cost-effective. Alongside that, Anthropic has given approved organisations access to Mythos 5.2, a version of Fable with cybersecurity and biology safeguards turned off. I鈥檓 not sure how long this kind of split will remain tenable 鈥� if open-weight models that anyone can run eventually match Mythos, trying to restrict these capabilities to authorised users will be pointless.
- England鈥檚 Office for Students has weighed in on the question I looked at last week: how students should use AI. Its 鈥� AI has some potential benefits, but using it to replace challenging tasks could harm skills development, and no one really knows how to solve the problem of using AI for homework.
