These two companies have had a relationship that goes back almost a decade and we've been fortunate to have a ringside seat to follow the twists and turns. One of the reasons we ended yesterday's post, "Tesla’s in China – It’s just a question of how long" (TSLA) with:
....Mr. Musk has his blind spots but China sneaking up on Tesla probably isn't one of them. He knows that Western companies will eventually lose the battle for electric vehicle dominance and something that he saw sometime in the last couple years seems to have scared him into action on the fronts where Tesla has a competitive advantage: access to some truly brilliant people; artificial intelligence facilitated by a long history with Nvidia and autonomous vehicles.
So again, we wish him luck, and think he'll succeed but this stuff is serious business.
More on TSLA - NVDA after the jump, including the time Tesla fired Nvidia.
And from Yahoo Finance May 23:
Nvidia's (NVDA) first quarter results beat analyst expectations, with revenue rising 262% to $26.0 billion. The company also announced a 10-for-1 stock split and that it is raising its dividend.
In a Yahoo Finance exclusive interview, Nvidia founder and CEO Jensen Huang spoke about the results and how the demand for his company's products is "just so strong." He also weighed in on how companies like Meta (META) and Tesla (TSLA) are pushing AI technology forward.
Jensen says Meta's Llama large language models are "really, really important" given how they are "activating large language models and generative AI work all over the world."
On Tesla, Jensen describes how the company's latest Full Self-Driving technology is "an end-to-end generative model," saying it "learns from watching videos, surround video, and it learns about how to drive... using generative AI [to] predict the path... how to understand and how to steer the car. And so the technology is really revolutionary."....*****....Video Transcript
.....Uh You also saw uh uh Elon talking about uh the incredible infrastructure that he's building and, and um one of the things that's, that's really revolutionary about, about the, the version 12 of, of Tesla's uh full self driving is that it's an end to end generative model.
And it learns from watching videos, surround video and it, it learns about how to drive uh end to end and generate using generative A I uh uh predict the next, the path and the and the uh how to steer the uh how to understand and how to steer the car.
And so the the technology is really revolutionary and the work that they're doing is incredible.
So I gave you two examples, a start up company that we work with called recursion has built up a supercomputer for generating molecules, understanding proteins and generating molecules, molecules for drug discovery.
The list goes on, I mean, we can go on all afternoon and, and just so many different areas of people who are, who are now recognizing that we now have a software and A I model that can understand and be learned, learn almost any language, the language of English of course, but the language of images and video and chemicals and protein and even physics and to be able to generate almost anything.
And so it's basically like machine translation and uh that capability is now being deployed at scale in so many different industries, Jensen.
Just one more quick.
Last question.
I'm glad you talked about um the auto business and, and what you're seeing there, you mentioned that automotive is now the largest vertical enterprise vertical within data center.
You talked about the Tesla business.
But what is that all about?
Is it, is it self driving among other automakers too?
Are there other functions that automakers are using um within data center?
Help us understand that a little bit better.
Well, Tesla is far ahead in self driving cars.
Um but every single car someday will have to have autonomous capability.
Uh It's, it's safer, it's more convenient, it's more, more fun to drive and in order to do that, uh it is now very well known, very well understood that learning from video directly is the most effective way to train these models.
We used to train based on images that are labeled.
We would say this is a, this is a car, you know, this is a car, this is a sign, this is a road and we would label that manually.
It's incredible.
And now we just put video right into the car and let the car figure it out by itself.
And and this technology is very similar to the technology of large language models, but it requires just an enormous training facility.
And the reason for that is because there's videos, the data rate of video, the amount of data of video is so so high.
Well, the, the same approach that's used for learning physics, the physical world um from videos that is used for self driving cars is essentially the same um A I technology used for grounding large language models to understand the world of physics.....
....MUCH MORE, the transcript doesn't really do justice to what Mr. Huang is saying, if interested follow the link and take a look at the video, something of a coup for Yahoo Finance
And TSLA - NVDA. From August 2023's Elon Got Himself A Supercomputer: "Tesla's $300 Million AI Cluster Is Going Live Today" (TSLA):
....Before we get to the headline story, some background. Tesla and Nvidia have a history.
In 2015 - 2016 when everyone thought that autonomous driving was just around the corner, the challenge was seen as both a sensor issue, for example: LIDAR vs cameras, and a machine learning/artificial intelligence problem which boils down to training the AI 'puters with as much data as you can so that out in the real world the autonomous vehicle can say to itself: "Yeah, I've seen this situation before, here's the response that worked best. Both the training and the on-the-road-recall, if they are to be anywhere near efficient, require the fastest chips you can find. Tesla had a whole bunch of data from a few billion miles of actual driving for computers to train on, and, combined with Nvidia's fastest-in-the-world GPU chips, it was a match made in heaven.
Except it wasn't.
The challenge of autonomous driving on open roads alongside non-autonomous vehicles was bigger than anyone in that simple, optimistic time ever envisioned, even in their nightmares. Here's one example about Waymo from a 2017 post:
"When Google was training its self-driving car on the streets of Mountain View, California, the car rounded a corner and encountered a woman in a wheelchair, waving a broom, chasing a duck. The car hadn’t encountered this before so it stopped and waited."
In May 2015 we were posting " Nvidia Wants to Be the Brains Of Your Autonomous Car (NVDA)" and seven months later the more declarative "Class Act: Nvidia Will Be The Brains Of Your Autonomous Car (NVDA)"
Then in October 2016, what was probably the high-water mark for the relationship "Nvidia Could Make $1B From Tesla's Self-Driving Decree: Analyst (TSLA, NVDA)"
Sadly, the task was just too difficult but Mr. Musk thought it was doable if only he could get even faster chips than Nvidia had on offer:
NVIDIA Partner Tesla Reportedly Developing Chip With AMD (TSLA; NVDA; AMD)
Today in leveraged WTFs....
The only reason for Tesla to do this is that NVIDIA's chips are general purpose whereas specialized chips are making inroads in stuff like crypto mining (ASICs), Google's Tensor Processing Units (TPUs) for machine learning and Facebook's hardware efforts.
We've said NVIDIA probably has a couple year head start but this bears watching, so to speak....
Culminating in August 2018's
"Nvidia CEO is 'more than happy to help' if Tesla's A.I. chip doesn't pan out" (NVDA; TSLA)
And possibly related July 13:
Elon Musk's x.AI Launches
The company was formed in March so it's valuation is probably around a hundred billion or so.
Just
kidding. I have no idea what sort of valuation it has been assigned.
x.AI is a Nevada corporation which, as our corporate attorney readers
well know, is handy as hell for a privately-held stealth company. As
part of the company's coming-out I think they dropped the period in the
name on the original incorporation papers.
Mr. Musk was one of the founder/funders ($100 million gift not equity) of ChatGPT parent OpenAI when it was a .org (non-profit) and seemed a bit miffed when Sam Alman hooked up with Microsoft to the tune of $10 billion.
So Elon went out and bought a garage-full of GPUs....
I just love that 2018 story ""Nvidia CEO is 'more than happy to help' if Tesla's A.I. chip doesn't pan out" (NVDA; TSLA)".
