What’s on our plate today…

• Nvidia becomes the most valuable company on Earth—how a 3D graphics chip bet reshaped the future of AI 💸

• Quiz: Who helped Nvidia become AI royalty? 🧠

• Quick hits: Google bets $2.4B on code-gen, xAI eyes Tesla cash, Perplexity vs Chrome 🗞️

• Brain Snack: From gamer chip to AI crown—how GPUs became global power plays 💡

• Meme: “Me compiling CUDA: 99% fan noise, 1% code” 😆

Let’s dive in. No floaties needed…

The Laboratory

How Nvidia dominated AI to become the most valuable company in history?

Nvidia has managed to remain the central figure in the global struggle for AI dominance. While countries and companies battle it out to prove their dominance in AI, Nvidia surpassed Microsoft and Apple in market cap, once again becoming the most valuable publicly traded company in the world. To put things into perspective, Nvidia’s current value surpasses the combined value of all publicly listed companies in the UK.

Nvidia’s central role has also attracted undue attention from nation-states trying to ensure they have access to the latest breakthroughs in AI chip technology. The company has had to contend with export restrictions imposed by the U.S., which is trying to keep AI chip tech from China. But it was not always the case. Nvidia was not always the center of attention for powerful nations and Big Tech companies, and it took decades for this niche startup focused on bringing advanced 3D graphics to PCs and multimedia applications to become the most valuable company in history.

Nvidia’s unlikely rise

Nvidia was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem. At the time of inception, the company focused on advanced 3D graphics. In simple terms, their goal was to design a chip that would enable realistic 3D graphics on personal computers. To achieve this aim, the company soon pivoted to designing and building chips that used an electronic circuit known as a graphics processing unit (GPU).

The difference between traditional CPUs and GPUs is that while CPUs are capable of handling a large number of calculations, they can be overwhelmed by the number of calculations needed to render complex graphics. GPUs, on the other hand, are specifically designed to handle these processes. Nvidia was one of the first companies to bet on GPUs, which would eventually evolve for use in non-graphical applications and paved the way for accelerated computing.

By the early 2000s, Nvidia GPUs were in high demand among gamers for their ability to accelerate graphics and image processing. In 2006, Nvidia developed the CUDA (Compute Unified Device Architecture), a platform that allowed GPUs to be used for non-graphical computing tasks. And by 2010, the company had shifted to developing hardware and software for next-level computing, which would be focused on AI.

CUDA: Nvidia’s secret weapon in AI

Nvidia GPUs played a central role in training AI models. When combined by the thousands, GPUs form data centers serving as repositories and homes for AI models.

However, while headlines focused on the company’s hardware, its GPUs, the real strength of the company lay in its CUDA platform, which helped turn its hardware into a fortress. The platform plays a central role in Nvidia’s dominance in AI, both technically and commercially.

CUDA was developed as a proprietary programming framework that allows developers to write software that runs efficiently on Nvidia’s GPUs, not just for graphics, but for general-purpose computing tasks like training AI models, running simulations, and scientific computing. Think of it as the operating system for a GPU. The importance of the platform is underscored by Nvidia CEO Jensen Huang’s remark: “CUDA is the reason Nvidia has a 15-year head start in AI.”

The long road to overnight success

Before Nvidia became a household name, the very products that have helped the company achieve its trillion-dollar status were making it difficult to survive.

When CUDA was released, Wall Street was not enthusiastic about the platform. According to the New Yorker, at the time, the investors did not agree with Huang’s argument that the existence of CUDA would enlarge the supercomputing sector, and by the end of 2008, Nvidia’s stock price had declined by 70%.

Nvidia sought a diverse customer base, including stock traders, oil prospectors, and molecular biologists. At one point, the company reportedly signed a deal with General Mills to simulate the thermal physics of cooking frozen pizza.

Nvidia’s breakthrough came unexpectedly through collaboration with key AI researchers. The biggest leg up for Nvidia’s GPUs in AI research circles came from Geoffrey Hinton, a professor at the University of Toronto. Hinton was so impressed with Nvidia GPUs' performance in training neural networks that he recommended it to everyone, including his protégé Alex Krizhevsky, and his research partner Ilya Sutskever.

In  2012, Krizhevsky and Sutskever utilized Nvidia GPUs to train AlexNet, demonstrating that specialized GPUs could train neural networks up to a hundred times faster than general-purpose CPUs. Hinton told The New Yorker, “To do machine learning without CUDA would have just been too much trouble.” This deep neural network dramatically improved performance in a key vision challenge, ushering in the modern deep learning revolution and cementing GPUs as essential for AI workloads.

The rapid development of artificial intelligence and machine learning propelled Nvidia’s growth, allowing it to capture 80% of the GPU market by 2023.

Currently, Nvidia is walking a tightrope, trying to balance its success with global geopolitics. Recently, the company announced it is filing applications to restart sales of its H20 AI chips to China. The U.S. had banned the sale of these chips to China, only to reverse course after Nvidia CEO Huang attended a dinner at the current President Donald Trump’s Mar-a-Lago Club.

A chipmaker at the heart of global power plays

Nvidia’s transformation from a niche graphics company to the world’s most valuable public firm is more than a business success story; it’s a case study in foresight, resilience, and technological supremacy. What makes this ascent so remarkable is how deliberately long it took.

Jensen Huang and his team placed a bet on accelerated computing before the world knew it needed it. CUDA, once dismissed as a failed science experiment, became the foundation upon which the future of AI is now being built. And while GPUs are the visible product, Nvidia’s real power lies in its lock on the software ecosystem that fuels nearly every major AI breakthrough of the last decade.

The company didn’t stumble into the AI boom, it helped create the conditions for it. By continuously investing in both hardware and software, Nvidia made sure that when deep learning exploded in popularity, it would be impossible to train or run the largest models without their chips. The impact of this dominance has gone well beyond Silicon Valley. Today, Nvidia’s hardware is considered so strategic that it’s the subject of U.S. export controls, trade negotiations, and global industrial policy. AI may be a war of algorithms, but the battleground is silicon and Nvidia holds the high ground.

That commanding position, however, is not without its pressures. Nvidia now faces the weight of expectations from investors, governments, and partners alike. While it enjoys staggering profit margins and a near-monopoly in AI infrastructure, it must also navigate a geopolitical minefield.

Despite these challenges, Nvidia is not resting on its laurels. The company is expanding beyond chipmaking into cloud services, AI-as-a-service, robotics, and even digital twin simulation through platforms like Omniverse. It has successfully repositioned itself from a hardware company into a full-stack AI infrastructure provider. Its ability to scale from GPUs in laptops to data centers powering frontier AI models gives it a versatility few can match.

Wednesday Trivia

Quick Hits, No Fluff

• Musk seeks Tesla shareholder approval to invest in xAI after SpaceX committed $2 b to its funding round—no merger, vote planned at November's meeting.

• Bengaluru‑based Gibran raises $2.6 M seed to build adaptive, nature‑inspired AI systems with a focus on drug discovery.

• OpenAI indefinitely delays its open‑weight model to run more safety tests—again—to address high‑risk areas before release.

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