Tesla's AI Plans and Its Nvidia Ties Affect the EV Giant's Earnings Outlook
AI mastery could cement Tesla's lead in autonomous driving and beyond
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The EV maker's ambitious AI initiatives, particularly those related to autonomous driving, have positioned Tesla at the forefront of automotive innovation and technological advancement. But will this translate into gains in the foreseeable future?
Tesla's journey to build one of the world's most powerful AI infrastructures has been marked by continuous and significant investments, strategic decisions and occasional controversies. From the unveiling of its first in-house supercomputer in 2021 to the recent plans to deploy 85,000 $NVIDIA Corp(NVDA)$
Tesla's quest for AI supremacy began in earnest in 2021 when the company unveiled its groundbreaking in-house supercomputer at the Computer Vision and Pattern Recognition (CVPR) conference. At the time, the setup ranked among the world's top supercomputers.
This supercomputer was designed to tackle one of the most significant challenges in automotive technology: training deep neural networks for autonomous driving. Tesla's approach leveraged its vast fleet of vehicles to collect real-world data.
Tesla continued to expand its AI infrastructure. Last summer the company launched an AI cluster supercomputer running 10,000 Nvidia GPUs. This new supercomputer, called "Dojo," was years in the making and is one of the most powerful machines of its kind.
Turning points
Then this past April, during Tesla's first-quarter earnings call, Chief Executive Elon Musk made a stunning announcement that seemed to mark a turning point in the company's AI capabilities. Musk declared that Tesla was no longer constrained in its ability to train AI, a sharp contrast to the company's previous limitations. The cornerstone of this transformation is an ambitious plan to deploy a staggering 85,000 Nvidia GPUs for AI training by the end of 2024, more than an eightfold increase in GPU capacity in just over a year from the previous expansion.
The move is aligned with Musk's earlier statements about Tesla's commitment to AI development, including plans to spend more than $2 billion on AI training in 2023 and 2024, with a focus on computing for full self-driving (FSD) training.
The narrative seemingly took an unexpected turn in early June, when Musk confirmed that another batch of 12,000 Nvidia GPUs, originally ordered for Tesla, had been redirected to X (formerly Twitter) and xAI, two of his private companies.
The reallocation of these GPUs could have significant implications for Tesla's AI strategy, as these units are integral to the company's plan to build out its AI infrastructure for autonomous driving. This concern was particularly pressing given the rapid advancements in AI technology and the intense competition in the autonomous driving sector.
The optics of reallocating resources from a publicly traded company to private entities owned by its CEO were particularly problematic for some shareholders and industry observers. Critics raised concerns about potential conflicts of interest, suggesting that Musk's decision could reflect a prioritization of his private ventures over Tesla's needs. Such actions, they argue, could undermine investor confidence and raise questions about the governance practices at Tesla.
Tesla is developing a new in-car voice assistant that will work similarly to $Amazon.com(AMZN)$
Most of these concerns have turned out to be without merit. First, Musk has clarified that the reallocation was due to logistical issues, as Tesla's infrastructure was not ready to immediately utilize the GPUs. By reallocating GPUs to X and xAI, where they could be put to immediate use, the resources were not wasted and could contribute to advancing AI projects more quickly.
Second, Musk's broader vision involves integrating AI developments across his various ventures. The expertise and technological advancements in AI and data processing at X and xAI could enhance Tesla's AI features, creating a synergistic effect.
In fact, Tesla is developing a new in-car voice assistant that will work similarly to Amazon's Alexa, or even better, ChatGPT's touted voice-chat feature. Tesla's voice assistant is expected to be highly integrated with a vehicle's systems and continuously improve through machine learning?. There is a good chance that these Nvidia GPUs will be used to train Tesla's in-car computer to respond to commands in a natural, flexible manner. This would enhance the functionality of Tesla vehicles, making them more user-friendly and enhancing their current, somewhat lacking voice recognition features.
While the massive GPU deployment has garnered significant attention, this isn't Tesla's only strategy for advancing its AI capabilities. The company is pursuing a dual-path approach that combines its investment in Nvidia GPUs with the development of its custom-built Dojo supercomputer.
Musk outlined this strategy during Tesla's fourth-quarter 2023 earnings call, describing Dojo as "a long shot worth taking because the payoff is potentially very high" though he acknowledged that it is "not something that is a high probability."
Despite Musk's cautious words, Tesla's commitment to Dojo appears strong. Tesla might upgrade Dojo, with new chips potentially powering a planned $500 million Dojo cluster in New York. Moreover, semiconductor giant $Taiwan Semiconductor Manufacturing(TSM)$
By pursuing both off-the-shelf GPU solutions and custom hardware like Dojo, Tesla is effectively hedging its bets in the rapidly evolving AI field. Its dual strategy not only reduces risks associated with relying on a single technology source but also increases Tesla's chances of breakthrough success. On its second-quarter earnings call later this month, Tesla's updates on Nvidia GPU deployment and Dojo development will be crucial for investors to assess Tesla's AI capabilities and its ability to maintain a competitive edge in the high-stakes race for autonomous driving supremacy.
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