NVIDIA just hit $2 trillion - but here's why there might be more room for growth

George Lawton Profile picture for user George Lawton February 26, 2024
An early lead on neuro-symbolic AI platforms and a more equitable model that #acceleratetrust with employees, partners, and users suggest room for future growth.

Business growth concept picture for business growth abstract background © ChristianChan - Shutterstock

In an event that may complicate Sam Altman’s $7 trillion plan to corner the AI chip market, NVIDIA’s market cap just eclipsed $2 trillion. This 5,500 % rise since 2022 was buoyed by incredible earnings growth and the white-hot market for AI.

Many suggest this rapid growth may soon dwindle owing to increased AI chip competition from Intel, AMD, cloud vendors, and chip startups. Others have commented on the business fundamentals and market psychology behind this, including sky-high margins and insatiable demand leading to a secondary market. 

The deeper and more significant pattern has been NVIDIA's early lead and long-term road map for neuro-symbolic computing and equitable culture. A highlight is NVIDIA’s digital twins lead, which is the future of symbolic computing. This is something glossed over by the other AI chip vendors and their platforms today. 

Second, even though NVIDIA is currently fourth by market cap, its CEO is only the 21st richest person according to the Forbes billionaires list, behind Elon Musk, Jeff Bezos, Mark Zuckerberg, Larry Ellison, Larry Page, Sergey Brin, and Michael Dell, whose companies are currently lower in market cap. That and the fact it hasn’t announced major layoffs since 2008 when it cut only 360 jobs, says a lot about the merits of an equitable culture.

Statistical symbolic synergy

In the long run, the enthusiasm fueled by the generative AI boom is best appreciated as the spark that ignited the much larger pile of kindling already in place around cloud, data infrastructure, and AI innovations. NVIDIA is an early leader in supporting not just better AI but also the supporting infrastructure for digital twins required for scaling a more sustainable economy. 

On their own, the transformer models underpinning ChatGPT are not the fire since they tend to hallucinate. Hallucinations don’t just occur in transformers. They are inherent in all statistical approaches. Hence the adage, “There are lies, damn lies, and statistics.” All statistical methods must be grounded in more symbolic approaches for building systems we trust. It’s this combination that will drive long-term adoption and progress. 

Transformers are incredibly good at translating, particularly when applied to a much smaller domain. The original Google paper, Attention Is All You Need, led to a better translator between English and French. In a business context, this means translating the data and its context across multiple systems to join up disparate views of processes, operations, infrastructure, partners, products/services, employees’ customers, and other stakeholders. Symbolic methods help ground the translations to build trust. 

The AI community has had a long and simmering debate dating back to the mid-50s about what it called neural networks and symbolic methods, hence the term neurosymbolic AI. One camp imagined that progress could be made by discovering patterns in a way that mirrored the statistical nature of interconnected neurons. Although there was some early success in optical character recognition, it fell out of favor until more robust algorithms were developed in the 1990s, and then better scaling mechanisms emerged in the last decade.

Another camp tried to engineer decision-making by modeling the logical processes. This resulted in the creation of expert systems capable of mirroring the decision trees experts like doctors might make in diagnosing a disease. But these required complicated manual efforts to encode knowledge into structured formats, and they fell out of favor in the 1990’s 'AI winter'.

But it’s important to appreciate that both neural and symbolic approaches are subsets of a much larger pie. Neural networks are just one machine learning type, yielding more efficient statistical analysis and processing techniques. Symbolic AI is just a subset of symbolic processing, which underpins most programming logic embedded in traditional applications, including transaction processing, ERP, CRM, computer, and mobile apps. 

The problem with symbolic processing is it is not very malleable. Once you set up an ERP or CRM system, much integration work is required to contextualize its data for use across views not offered by the vendors. This is where digital twin architectures come in. They need a digital thread with a semantic translation layer that maps data into the format best suited for different symbolic and statistical processing types. This translates to better AI models and more efficient enterprise processes.

Open onramps

NVIDIA’s special sauce and room for growth is its early lead in developing an integrated platform spanning statistical and symbolic approaches. Today, most AI chip vendors only focus on supporting better AI since that is the shiniest opportunity. 

Meanwhile, NVIDIA has a rich history of rendering better physics models in the gaming community. Over the last several years, it’s been extending these core competencies across its Omniverse platform. It currently supports digital twins for robots, self-driving cars, medical research, and physical environments. These lower the bars to simulate and visualize products, factories, and infrastructure for different stakeholders. 

Crucially, NVIDIA is also paving an open onramp for integrating data across the much larger supporting ecosystem of enterprise vendors and innovative startups. NVIDIA has played a leading role in developing standards like OpenUSD for the interoperability of 3D content, glTF for 3D scenes and models, and OpenXR for augmented reality and spatial computing. It has also built long-standing partnerships with the leading product lifecycle management (PLM), cloud, construction, design, simulation, and enterprise IT vendors. 

These open onramps are significant because they help bridge the integration challenges driven by proprietary business models and technical differences across existing platforms. The deep integration across NVIDIA AI and Omniverse tooling is growing into the Zurich of competing ecosystems. 

Equitable structure

At a time when other CEOs are making headlines for their insane riches fueled by their obscene levels of ownership, it’s heartening to show it’s still possible to make money while sharing the wealth with employees. The fact that NVIDIA could grow to be such a large company without CEO Jensen Huang taking home the lion's share says a lot about his ethos. He was humble enough to honor the Denny’s Diner, where the whole thing started twenty years ago amongst a group of friends. 

It’s also telling that, unlike other Silicon Valley giants, NVIDIA has not imposed any major layoffs in recent years. Its last big round of layoffs was in 2008, when it cut 360 jobs, representing 6.5% of its team. Since then, the company has continued to invest in its team while pushing out innovations that have taken years to pay off, and some still have not. 

For example, in 2021, Huang announced an ambitious effort to build an Earth-2 supercomputer for improving climate simulations and a Cambridge-1 supercomputer for digital biology research. Earth-2 pioneered progress in combining faster GPUs, deep learning and breakthroughs in physics-informed neural networks, AI supercomputers, and vast quantities of observed and model data to learn from. Huang estimated this combination could speed simulations millions of times. He predicted further progress could lead to billion-times leaps in progress. He said:

All the technologies we’ve invented up to this moment are needed to make Earth-2 possible. I can’t imagine a greater or more important use.

My take

It is heartening to see a Silicon Valley company succeed by promoting equity, openness, and long-term vision. Maybe it will encourage the growth of more businesses inspired by the potential of #acceleratetrust to drive profits and sustainability. 

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