Artificial intelligence has been a game-changer for Nvidia, to say the least. The company enjoyed a historic year in 2025, raking in record revenue in the third quarter on its way to becoming the first-ever business to reach a $5 trillion valuation. As the dust settles, Nvidia sits atop a very high totem pole of tech giants, overtaking Google’s parent company, Alphabet, as the company with the highest market capitalization in the world.
What Is Nvidia?
Nvidia is a tech company that’s best known for inventing the graphics processing unit — a computer chip that excels at producing realistic 3D graphics. Although this chip was initially intended for video games, its advanced computing capabilities have made it a coveted piece of hardware for powering the models and infrastructure behind artificial intelligence.
But it took decades for Nvidia to achieve this kind of success and recognition. So, how did an aspiring chipmaker evolve into a tech titan that now underpins much of the modern AI industry? We’re taking a deep dive into Nvidia’s origins, its role in the tech sector and what it means for artificial intelligence in the years ahead.
What to Know About Nvidia
Nvidia’s ascent from scrappy startup to tech royalty began in 1993 in a Silicon Valley Denny’s, where Jensen Huang and his friends Chris Malachowsky and Curtis Priem sketched out a vision for a computer chip that could render realistic 3D graphics. This idea distinguished Nvidia from incumbents like Intel and Advanced Micro Devices (AMD), which made their name on computer processing units (CPUs) — a type of semiconductor that acts as the “brain” of a computer, managing its operating system and applications.
In 1999, Nvidia forever changed the chip landscape with the release of its first graphics processing unit (GPU), a computer chip known for accelerating graphics rendering and image processing to produce high-quality 3D graphics. Though the chip was intended for gaming applications, its ability to process multiple computations in parallel made it well-suited for high-performance computing and, eventually, artificial intelligence — particularly generative AI.
Sensing an opportunity, Nvidia began shifting its focus to AI in 2006 when it launched its Compute Unified Device Architecture (CUDA) platform, enabling GPUs to be used for non-graphics tasks. This paved the way for the company’s AlexNet, a convolutional neural network that proved neural networks could outperform other machine learning methods in classifying images.
Having ushered in the modern era of deep learning, Nvidia went on to enhance its GPUs with AI features through products like its RTX GPU, Maxwell architecture, Volta architecture and Turing architecture. It also contributed to the rise of the metaverse with its Omniverse platform, which has evolved into a tool that supports a range of physical AI applications from robotics to digital twins.
Here’s a more detailed timeline of Nvidia’s major milestones:
- 1993: Jensen Huang, Chris Malachowsky and Curtis Priem found Nvidia.
- 1995: Nvidia releases its first graphics processor, NV1.
- 1997: Nvidia launches its RIVA series of graphics processors, earning widespread customer praise, receiving awards and cementing itself in the gaming industry.
- 1998: Nvidia reinforces its reputation with the RIVA TNT, a chip that accelerates graphics on personal computers.
- 1999: Nvidia launches GeForce 256, the industry’s first GPU.
- 2001: Nvidia provides its GPUs for Apple’s Power Mac G4 supercomputers, beginning its expansion beyond the gaming industry.
- 2006: Nvidia releases CUDA, allowing scientists, researchers and software developers to use Nvidia’s GPUs for non-graphics tasks.
- 2012: Nvidia unveils AlexNet, a neural network that effectively marks the modern age of deep learning.
- 2016: Nvidia donates its DGX-1 supercomputer to the fledgling startup OpenAI, making ChatGPT possible.
- 2020: Nvidia releases the beta version of its Omniverse platform, contributing to the rise of the metaverse.
- 2024: Nvidia overtakes Microsoft to become the world’s most valuable company.
- 2025: Nvidia reaches $5 trillion in value, the first company to achieve such a feat.
Nvidia’s Main Products and Services
While Nvidia broke into mainstream tech with its GPUs, its portfolio now includes products for data centers, cloud computing and networking.
Graphics Processing Units
Nvidia became a household name thanks to its GeForce GPU in 1999, and the company hasn’t looked back since. Although GPUs were initially intended for gaming, Nvidia designs its latest chips for data centers and AI applications. Nvidia’s GeForce cards remain available to this day, but other GPUs include its Blackwell architecture and RTX series.
Cloud Platforms
Nvidia has adapted its cloud platforms for the age of AI. The DGX platform is a popular option that excels at helping businesses with “AI factories” manage their computing workloads. Nvidia Omniverse is also available on DGX, enabling companies to easily run simulations, build digital twins and explore other physical AI services.
Data Center Platforms
America’s data center boom wouldn’t be possible without Nvidia. While the company has developed GPUs and cloud platforms to meet the needs of data centers, it’s also designed data center platforms. For example, its HGX platform can accelerate more demanding tasks like AI inference and high-performance computing in data centers.
Laptops and Workstations
Personal computing remains a core part of Nvidia’s portfolio. Users can purchase laptops powered by Nvidia’s GeForce and RTX series. For professionals who desire personal devices with advanced capabilities, Nvidia provides workstations that can produce 3D visualizations, train large language models and guide AI agent workflows.
Embedded Systems
Nvidia’s embedded systems support edge AI, or AI that’s integrated at the “edge” of a network to handle data closer to its source. For instance, the Jetson platform can complete tasks like running generative AI models, processing real-time data from sensors and operating computer vision — all of which fuel advanced use cases in robotics.
Networking Solutions
With companies building AI factories that contain thousands of GPUs, Nvidia has created networking solutions for handling an immense amount of calculations. Nvidia’s Spectrum-X platform is an Ethernet solution built to manage intense AI workloads, performing parallel computations while reducing latency.
What Is Nvidia’s Role in the AI Race?
Nvidia represented 92 percent of the GPU market at the end of 2025, making it a vital partner for any company needing more chips to power its AI ambitions — including some of its fiercest competitors. Cloud provider Oracle has already teamed up with Nvidia on initiatives involving enterprise AI, agentic AI and sovereign AI, and the two recently joined forces to build the U.S. Department of Energy’s largest AI supercomputer. Meanwhile, AI startup OpenAI secured $100 billion and software from Nvidia to boost its AI models and infrastructure.
This web of circular deals has inextricably tied Oracle, OpenAI and several other companies to Nvidia, while further concentrating resources in the hands of a few players. As a result, the entire industry has become increasingly dependent on Nvidia’s hardware and software to meet its computational demands and help sustain its rapid expansion.
And it seems that the U.S. federal government could be Nvidia’s next big customer. President Trump has taken note of the company’s strong standing, and tapped it to take on a greater role in leading America’s AI sector. In January 2025, Trump recruited Nvidia, Microsoft, OpenAI and Oracle to spearhead the Stargate Project, which dedicates $500 billion over four years to expanding AI infrastructure in the U.S. In addition, Nvidia was among a host of companies that made commitments to supporting AI education for K-12 students in the United States at Trump’s urging.
With its dominance in the chip sector, Nvidia has become a lifeline to American AI companies and the Trump administration as the country enters a crucial phase in the AI race. Huang seems to recognize this new reality, taking steps to capitalize on Trump’s pro-tech approach and sway the political climate in Nvidia’s favor.
How Has Nvidia Impacted U.S. Tech Policy?
While Trump still tries to enforce his “America First” agenda, Nvidia’s prominence has pressured him to make some exceptions. For example, Trump initially implemented strict tariffs and export rules early in his second term, only to reverse course after Huang claimed these rules hurt Nvidia’s international business and called them a “failure.” Given that Huang recently affirmed the growing demand for Nvidia’s chips in China, Trump could pass even friendlier trade policies that benefit Nvidia — regardless of the security risks involved.
Nvidia has also flexed its influence in AI regulation. Huang is an outspoken critic of state AI regulations, arguing that they could stifle American tech innovation. He even went so far as to blame state laws if the U.S. loses the AI race during a November 2025 meeting at the White House. It should then come as no surprise that Trump signed an executive order in December 2025 seeking to limit state regulations on AI.
Combining its chip prowess with its political presence, Nvidia is poised to exert its authority in the years ahead. The company may not have Google’s name recognition or OpenAI’s hype, but it has essentially built the backbone of the AI industry — and that could give it the power to dictate the direction that artificial intelligence takes moving forward.
Why the Future of AI Runs on Nvidia
In recent years, companies have ramped up efforts to integrate AI into physical forms that facilitate direct interactions with the real world. The belief is that this approach will allow AI to gather more real-time data by engaging with its surroundings, potentially leading to artificial general intelligence — AI that can think and learn like humans. While companies like OpenAI and Tesla have attempted to catch up with this trend through robotics initiatives, Nvidia is way ahead of the curve.
Nvidia’s hardware and software have already become mainstays in robotics and manufacturing, and the company has positioned itself for the long term with the release of new AI server systems, world and vision language models and a suite of AI resources designed for autonomous vehicles — another form expected to grant AI access to massive volumes of real-time data. Given these developments, Nvidia could be betting on itself to become the brains of the AI industry, not just the backbone.
Still, the path to AI supremacy will probably be anything but smooth for Nvidia. Although the company remains one of the industry’s chief chipmakers, more competitors are investing in their own chips, slowly eroding Nvidia’s market share. Intel and AMD have also made product announcements to ring in 2026, with Intel riding the wave of excitement generated by its deal with the Trump administration last year. Feeling the pressure from other players, Nvidia may have decided to speed up the timeline for some of its product releases.
It’s hard to blame Nvidia for looking over its shoulder, considering all the hype around the AI industry that continues to drive record spending. Don’t expect the company to go anywhere anytime soon, though. There’s no guarantee of who will win this seemingly endless AI race, but count on Nvidia to continue steering the technology’s course for the foreseeable future.
Frequently Asked Questions
What is a graphics processing unit?
A graphics processing unit is a computer chip that specializes in generating high-quality 3D graphics. Nvidia designed the chip to support gaming applications, but its computational capabilities have made it essential for building artificial intelligence models and the accompanying infrastructure, including data centers.
Why is Nvidia considered the “backbone” of the AI industry?
Nvidia has become known as the “backbone” of AI because of its dominance in the chip sector, particularly graphics processing units. Alongside its GPUs, Nvidia has also designed software, hardware and platforms that enable companies to build, train and deploy AI solutions, making the chipmaker vital to the artificial intelligence industry.
How has Nvidia influenced U.S. tech policy?
Nvidia CEO Jensen Huang has criticized the United States’ sweeping tariffs and export rules, leading Trump to reconsider his stance on the matter. In addition, Huang has pushed back against state AI regulations, contributing to Trump’s decision to sign an executive order banning all state regulations on AI.
