NVIDIA and Tesla, the first $10 Trillion-Dollar Companies?

The Rise of AI and the Implications for Enterprise Innovation

Could NVIDIA and Tesla be the first companies to surpass a $10 trillion valuation by revolutionizing AI and autonomous driving?
As NVIDIA’s groundbreaking chips power Tesla’s Full Self-Driving technology, these companies are setting the stage for a massive leap in enterprise innovation, redefining AI, autonomous systems, and the global market, positioning themselves as leaders in the next wave of technological transformation.

A few weeks ago NVIDIA dominated business headlines by announcing an 88% increase in revenue from the prior quarter. This was due to demand for NVIDIA’s A100 chips that enable computing and calculations for data-driven AI applications. One of NVIDIA’s largest buyers of these chips is Tesla. Just last week, Tesla announced that they can process 160 billion frames of video per day to train their Full Self-Driving (FSD) software.

Going Beyond the Headline

Over the past few years, Tesla has been developing its own AI platform called Dojo. On September 11, Morgan Stanley announced that Dojo could be responsible for a $600 billion increase of Tesla revenue. A few weeks earlier, Elon Musk wrote on X “Training compute should soon not be so much of a limiting factor. Very difficult bringing the 10k H100 cluster online btw. Similar experience to bringing our now 16k A100 cluster online. Uptime & performance are low at first, then improve with lots of work by Tesla & Nvidia.”  Tesla is leveraging 10,000 H100 and 16,000 A100 NVIDIA chips to perform these functions.

Bridging Hardware and Software

Tesla's use of NVIDIA's SuperCluster to process computations for its Full Self-Driving (FSD) mode is a major signal to the market that two companies can come together to create something that has never been done before. For an excellent explanation of the Use Case that Tesla is addressing, read this breakdown from Farzad Mesbahi (@farzynness) and reposted here: https://x.com/farzyness/status/1695542990792523818

“This is how Tesla's FSD v12 learns. We've been taught that a red sign with the word "STOP" means that once we arrive at the sign, we need to stop and check for cross traffic. We've also been taught that the white lines on either side of the car are "barriers'' that tell us where the car should be as we approach the perpendicular line. We've also learned about crosswalks, right of way rules, speed bumps, rain, snow, cyclists, not to smash into oncoming traffic, navigating through crap (or missing) lane markings, etc. Some of this we've learned by reading a book and then practicing said things on the road, and other things we've learned by driving around and experiencing the environments on our own.

With Tesla's latest v12 FSD update, Tesla's vehicles learn in a similar way, but it's actually much broader than you think. Tesla isn't telling the AI that trains the FSD system what a stop sign is. It isn't telling it what white lines are. What pedestrian sidewalks are. What other cars look like. What red brake lights mean. etc. Instead, for the specific example below, Tesla is feeding the AI a ton of video depicting what proper driving looks like at a stop sign, with drivers coming to a stop while slowing down at a reasonable speed while centered between the lines. With this footage, the AI says to itself "OK - one thing I'm noticing is that every time the car comes to a stop, the surrounding areas have these "STOP" sign things on either side every single time, and the car is always centered between white lines on the road when it approaches these signs". The AI then "writes code" for the car to behave correctly at every stop sign it encounters.

This is how Tesla's system learns.

This means that for Tesla to reach self-driving under any condition, it needs to collect all driving conditions that a human encounters with many examples of each. It needs to see stop signs that are just on one side. Stop signs that are partially covered by a tree. Stop signs that have been vandalized. Etc. Etc. Etc. Luckily, Tesla is able to do this because it has a fleet of ~4 million cars driving around the world today, and this fleet is growing exponentially.

This means that every condition a Tesla finds itself in, that footage can be used by the AI system to learn the proper behavior based on how Tesla drivers navigate that scenario. This means that the Tesla @elonmusk was driving on FSD v12 learned from all other Tesla drivers in the world driving their own cars. With this data, the AI system was able to generate commands for the steering wheel, accelerator, and pedal to navigate around its own environment as good as a human could, and possibly significantly better than a human as Tesla collects and processes more data.

Imagine having a car with this AI system that never gets tired, never makes a mistake, is always paying attention, is constantly monitoring every angle around the car, etc.

This is what Tesla has achieved with v12.

As we finish out the decade, Tesla has plans to reach an annual goal of 20 million car sold per year by 2030. All these cars will be outfitted with the camera systems used to collect the data that the AI system uses to train itself. Tesla is also investing billions of dollars in training compute to dramatically increase how much data the AI can process at once, which will allow the company to make improvements quicker and be able to process every conceivable scenario that a driver could face on the road. If Tesla's AI successfully learns how to drive under any condition, this will mark one of the greatest technological achievements of our time. The age of the self-driving car is finally here.”

Use Case + Data + Compute = Real World AI Solutions for Enterprise Innovation

If you’re reading this and asking yourself how to identify a use case, big data, AI processing and modeling, you're not alone. Enterprises worldwide are looking at these examples between Tesla and NVIDIA as a roadmap on how to harness these new technical capabilities. We remain committed to supporting enterprise clients in Use Case Discovery, Data Architecture and AI Modeling to generate powerful new ideas, products and services across the enterprise.