Justin Sullivan
Tesla, Inc. (NASDAQ: TSLA) is one of the dominant electric vehicle companies. During the 2010-2012 period, Tesla had a minimal market share as the company began ramping up production of its Roadster and its first Model S vehicles, as shown in Table 1.
Largest market share between 2013 and 2017 with the arrival of Model S and Model Global and Chinese: Technology, Trends and Market Forecasts.
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This domain is complicated for investors to handle when Elon Musk said that “Tesla is an artificial intelligence company, not a car manufacturer. ” I raise this point because Tesla’s efforts in the robotaxis industry pale in comparison to competition such as Alphabet Inc. ‘s Waymo. (GOOG), Amazon. com, Inc. ‘s Zoox, Inc. (AMZN), and Baidu, Inc. (BIDU). ) Apollo, Cruise from General Motors Company (GM) and Motional from Aptiv PLC (APTV) and Hyundai Motor Company (OTCPK: HYMTF).
The existing prestige of Tesla’s robot taxi competition in terms of actual ride-sharing policy and ridership varies particularly among other regions. But one thing that’s not unusual is that they’re in other stages of the level four autonomous driving feature ring. Tesla, on the other hand, currently operates with Level 2 autonomy. The difference lies basically in the level of autonomous driving features and the need for human intervention, according to BMW:
Tesla’s Full Self-Driving (“FSD”) package, designed to be at range level 2, nowhere near the full diversity required for the L4. But investors deserve Tesla’s strategy that increasingly emphasizes its synthetic intelligence (“AI”) and software capabilities.
The main difference between the competition Tesla and Robotaxi is their strategic resolution to move from LiDAR to an AI-based one in their autonomous vehicles. LIDAR (Light Detection and Ranging) is a generation of remote sensing that uses laser light to measure distances.
Tesla’s strategy, meanwhile, uses a series of cameras, radar, and its FSD computer.
In Table 2, I show the load breakdown between Tesla’s FSD autonomous computer and the competition from Robotaxi. High-end LiDAR sensors vary in price and can cost between $10,000 and $75,000 per unit, and Robotaxis requires one or two sensors. The overall charge is consistent with vehicle levels of $10,000 to $150,000.
Also in Table 2, I show the costs of Tesla’s 3 components: the FSD computer, which costs between $1,500 and $3,000 (1 required), the cameras, priced between $200 and $500 (8-9 required), and the radar systems, priced between $200 and $1,000 (4-6 required).
The total Tesla formula fee ranges from $3900 to $13,500 depending on the vehicle, to a LIDAR fee of between $10,000 and $75,000.
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Tesla uses a combination of cameras, radars, and its FSD computer. The FSD computer costs between $1,500 and $3,000 per unit. Each vehicle requires 8 to nine cameras, with a value ranging from $200 to $500 per camera, as well as radar formulas that add an additional $800 to $6,000 to the total fee per vehicle. formula to a wider range of $3,900 to $13,500 per vehicle, putting it at the center of Tesla’s strategy to expand its autonomous vehicles and enter the mass market.
Tesla’s AI-based formula requires an investment in the use of NVIDIA Corporation’s (NVDA) H100 chips used to exercise Tesla’s neurons. Each H100 chip costs between $30,000 and $40,000, and Tesla allegedly bought 100,000 units, which equates to a total cost of between $3 billion and $4 billion.
These upfront costs, while high, allow Tesla to continually refine its AI models as it transitions from L2 to L4. Table 3 presents my analysis.
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As shown in Table 4, equipping a fleet of 100,000 cars with LiDAR can cost between $1 billion and $15 billion, depending on the rapid generation and sensors used.
By contrast, Tesla’s AI-based FSD approach, as noted above, uses more effective components, such as cameras and radars, and the cost of equipping 100,000 cars costs between $390 million and $1. 35 billion.
This lower rate allows Tesla to deploy giant Robotaxi fleets, as well as greater profit prospects through mass adoption.
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Trade-offs between revenue and charges will need to be considered when comparing LiDAR-based systems to Tesla’s AI-based FSD technology, as shown in Table 5. Offering maximum accuracy and reliability and enabling L4 autonomous driving, LiDAR systems have very high upfront hardware charges, ranging from $10,000 to $150,000 per vehicle. Investments in R
By contrast, Tesla’s AI-based FSD system, as noted above, is priced between $3,900 and $13,500 consistent with the vehicle. However, R&D spending is particularly higher: AI and device learning infrastructure costs between $3 billion and $4 billion, largely due to the value of Nvidia H100 AI processors.
Although R&D prices are high, Tesla’s lower hardware expenses and scalable generation position it for further profit expansion. I anticipate a compound annual expansion rate (“CAGR”) of 30% to 50%, and 10% to 20% for LiDAR-based systems.
This research highlights Tesla’s prospect of achieving greater long-term profitability through increased market penetration as it migrates from L2 to L4.
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I estimate that market prices for LiDAR-equipped vehicles range from $70,000 to $200,000 per vehicle. These cars are in high-end niche markets due to their high prices. The annual sales volume of those cars can reach between 50,000 and 100,000 units, generating between $3. 5 billion and $20 billion in revenue, depending on my earnings.
Tesla’s FSD formula, on the other hand, is designed for the mass market, with existing vehicles lower and ranging from $50,000 to $80,000. With this pricing strategy, Tesla can target a wider audience by adding Robotaxi services.
I estimate potential annual sales volumes to be between 200,000 and 500,000 cars and annual profits between $10 billion and $40 billion. This represents a 2 to 3 times increase in profits for LiDAR-based systems.
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Therefore, Tesla, Inc. is well placed to dominate the mass market, specifically in the Robotaxi sector, especially when it reaches L4 autonomy, which can take anywhere from 3 to five years. This charging advantage, detailed in this article, not only allows Tesla to deploy larger fleets more efficiently, but also allows the company to dominate the mass-market segment, adding future Robotaxis in the long run.
However, the gap between L2 and L4 autonomy is significant, as L4 systems can drive completely autonomously without human intervention in fast environments. This feature is still in progress at Tesla. This delay may have a short-term impact. effect on Tesla’s competitiveness in the autonomous vehicle market.
Among Tesla’s competitors, Waymo uses a complex LiDAR formula that costs between $100,000 and $200,000 per vehicle. The company covers urban spaces with its Robotaxi services, with the aim of achieving a market of 10-20% by 2030. Waymo has expanded its Robotaxi policy to Los Angeles and San Francisco, with a 24/7 advertising service. the city of San Francisco since it received the commission’s approval in August 2023.
Cruise, a subsidiary of General Motors, also uses a suite of sensors and forecasts a market share of 10-15% through 2030.
Zoox, a company owned by Amazon. com, Inc. (AMZN), differentiates itself with its self-driving cars specially designed for urban environments. Its autonomous rideshare service will soon launch its first public assistance in Las Vegas.
Baidu Apollo has been effectively demonstrated in various pilot systems throughout China. Apollo’s open source platform enables collaboration with partners, making it a central player in the Chinese autonomous vehicle market.
Motional, a joint venture between Aptiv and Hyundai, has formed partnerships with ride-sharing corporations such as Lyft to integrate its generation into existing transportation networks. However, it recently halted a self-driving and food delivery program with Uber Eats in Santa Monica, California.
Tesla inventories a warehouse.
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Robert Castellano has 38 years of experience in semiconductor market research.