Justin Sullivan
Tesla, Inc. (NASDAQ: TSLA) is one of the dominant electric vehicle companies. During the 2010-2012 era, Tesla maintained a minimal percentage of the market as the company began ramping up production of its Roadster cars and early Model S cars, 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 dominance is tricky for car investors when Elon Musk said that “Tesla is an AI company, not a car company. “I mention this because Tesla’s efforts in the robotaxis industry are discounted compared to competitors like Alphabet Inc. ‘s Waymo. (GOOG), Zoox de Amazon. com, Inc. (AMZN) and Baidu, Inc. (BIDU) Apollo. , General Motors Company (GM) Cruise and Aptiv PLC (APTV) and Hyundai Motor Company (OTCPK:HYMTF).
The existing prestige of robo-taxis that compete with Tesla in terms of actual ride-sharing policy and passenger numbers varies greatly between other regions. But one thing that is not unusual is that they are in other stages of achieving level four autonomous driving functions. Tesla, on the other hand, currently operates in the Level 2 range. The difference basically lies 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 Tesla’s competition and Robotaxi is their strategic decision to move from LiDAR to 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, on the other hand, uses a combination 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. overhead charge consistent with vehicle levels of $10,000 to $150,000.
Also in Table 2, I show the charges for Tesla’s 3 components: the FSD computer, which costs between $1,500 and $3,000 (1 required), the cameras, which charge 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, ranging in value from $200 to $500 per camera, as well as radar formulas adding an additional $800 to $6,000 to the total fee per vehicle. formula to a broader 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 calls for an investment in the use of NVIDIA Corporation’s (NVDA) H100 chips used to power Tesla’s neurons. Each H100 chip costs between $30,000 and $40,000, and Tesla reportedly purchased 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 Table 4 shows, 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.
In 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, the expenditure on R
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 greater market penetration as it migrates from L2 to L4.
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I estimate market prices for LiDAR-equipped cars to be between $70,000 and $200,000 per vehicle. These cars are in high-end niche markets due to their higher prices. The annual sales volume of these cars can only reach 50,000 to 100,000 units. , generating between $3. 5 and $20 billion in revenue, depending on my income.
On the other hand, Tesla’s FSD formula is designed for the mass market, with a decrease in existing vehicles 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 between 200,000 and 500,000 cars and annual profits between $10 billion and $40 billion. This represents a gain 2 to 3 times that of LiDAR-based systems.
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Therefore, Tesla, Inc. is well positioned to dominate the mass market, specifically in the Robotaxi sector, especially when it reaches L4 autonomy, which can take between 3 and 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 term.
However, the gap between L2 and L4 autonomy is significant, with L4 systems capable of driving fully autonomously without human intervention in express environments. This feature is still in progress at Tesla. This delay could have a short-term 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 is invading urban spaces with its Robotaxi services, targeting a 10-20% market share through 2030. Waymo has expanded its Robotaxi policy to Los Angeles and San Francisco, consistent with a 24/7 advertising service. per week, in the city of San Francisco since it obtained approval from the commission in August 2023.
Cruise, a subsidiary of General Motors, also uses a suite of sensors and is expected to succeed in a market share of 10-15% through 2030.
Zoox, a Amazon. com, Inc. (AMZN) company, differentiates itself with its self-driving cars designed especially for urban environments. Its autonomous ride-hailing service will soon launch its first public ride in Las Vegas.
Baidu Apollo has successfully demonstrated pilot systems throughout China. Apollo’s open source platform enables collaboration with many 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-hailing corporations like Lyft to integrate its generation into existing transportation networks. However, it recently stopped a freelance 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.