Description:
Tesla FSD autonomous driving: Explore the latest changes in Tesla FSD Beta v12, the Dojo vs. NVIDIA competition, camera vs. LiDAR approaches, Level 5 outlook, market size, and revenue opportunities.
Focus Keyword: Tesla FSD
Table of Contents
- Introduction: What is Tesla FSD?
- Original Content (Unaltered)
- Additional Section: Tesla FSD Future Guide
- Internal & External Links & Image (Tesla FSD alt)
Introduction: What is FSD?
FSD (Full Self-Driving) is Tesla’s ambitious autonomous driving software aiming for full driverless operation (Level 5). In this article, we explore the latest upgrades in Tesla FSD Beta v12, the competition between Tesla’s Dojo supercomputer and NVIDIA’s solutions, the debate over camera versus LiDAR approaches, the outlook for Level 5 autonomy, and the market and revenue opportunities in autonomous driving.
Original Content
1. Latest Version of FSD and Upgrade Status
Tesla has been implementing significant changes in its autonomous driving software with FSD Beta v12 throughout 2023–2024. Starting from FSD Beta 12, a major portion of vehicle control has been handed over to an end-to-end neural network, greatly reducing the dependency on human-written code. In fact, the latest FSD v12.312.4 versions have removed over 300,000 lines of code that previously managed steering and acceleration, transitioning to a system where a neural network trained on vast amounts of real-world driving video data controls the vehicle. This change has improved the vehicle’s ability to understand and react like a human driver, greatly reducing issues such as phantom braking, unnecessary lane changes, and instability during left and right turns. For instance, FSD Beta 12.4.2 has been observed to maintain the vehicle at the center of the lane while navigating curves and to respond smoothly to pedestrians or sudden vehicles. Additionally, U-turns or left turns in limited-visibility situations are executed more adeptly and comfortably, reducing the need for driver intervention. Many testers provided positive feedback during the early 2024 rollout of FSD Beta 12.2.1.
Thus, the FSD 12 series is hailed as a “game changer” in Tesla autonomous driving, offering smoother decision-making and a more human-like driving experience compared to previous generations. Notably, in March 2024, FSD Beta v12.3 was deployed to thousands of vehicles across the United States, evolving into a true single-stack autonomous driving system by fully integrating highway and urban driving software stacks (previously separated in v11). Elon Musk stated that Tesla aims to drop the “Beta” tag from FSD v12, and indeed, starting with the late March 2024 update, FSD (Beta) was rebranded as “Supervised”, demonstrating confidence in its maturity. However, since driver supervision is still required, it is not yet considered fully autonomous, and further software stabilization and testing are ongoing.
2. Tesla’s Hardware Stack (Dojo vs. NVIDIA, etc.)
Tesla is not only advancing its software but is also pursuing an independent path in AI hardware infrastructure. Key examples include the Dojo supercomputer and the D1 chip. Tesla’s self-designed first-generation Dojo D1 chip, built with approximately 5 billion transistors on a 645㎟ die, delivers 362 TFLOPS in BF16 low-precision operations. Each D1 chip consumes about 400W, showing a density comparable to NVIDIA’s A100 GPU. Tesla has developed an AI compute tile by connecting 354 D1 chips in a mesh configuration, and a single tray (weighing 135kg) comprising 6 such tiles achieves 54 petaflops (PF) under BF16/CFP8, consuming over 100 kW per tray. One Dojo cabinet consists of two such trays plus interface equipment, and connecting 10 cabinets into an ExaPod configuration yields a total compute performance of 1.1 exaflops. Tesla began installing its first Dojo cabinet at the end of 2023 and is currently operating a Dojo cluster with several petaflops as of 2024, with plans to install 7 ExaPods (totaling 8.8 exaflops) in Palo Alto. To this end, Tesla plans to invest over $1 billion in Dojo development from 2024 to 2025, and it may even offer Dojo as a Service to external companies if needed. Interestingly, one catalyst for developing Dojo was Elon Musk’s remark about the insufficient supply of training GPUs, hinting at Tesla’s strategy to develop its own chip alternative. Until Dojo is fully operational, Tesla has also expanded its existing GPU-based HPC, and in 2024, it built a cluster of 10,000 of the latest NVIDIA H100 GPUs, achieving more than double its previous training capacity. This setup, with FP64 precision at 340 PF, rivals some of the world’s top supercomputers and is used to train Tesla’s extensive driving video dataset (200PB+ Hot tier).
Examining the Dojo roadmap, Musk announced in September 2024 that the next-generation Dojo 2 chip is being prepared with production expected to begin by the end of 2025. Dojo 2 is intended to match NVIDIA’s next-generation AI training chip, the Blackwell B200 series, and Musk stated, “To truly prove our superiority, we must go through a third generation,” expressing expectations for Dojo 3 to be released by the end of 2026. In other words, while the first-generation D1 chip is already in operation, Tesla plans to boost performance with Dojo 2 and finally achieve ultimate competitiveness with Dojo 3. The first-generation Dojo employed TSMC InFO_SoW packaging technology to integrate a 5×5 array of D1 dies into a single wafer module and used a network interface with a V1 chip to ensure scalability. Thanks to this innovative design, Tesla Dojo is regarded as a breakthrough AI supercomputer that eliminates high-bandwidth bottlenecks. Morgan Stanley and others have projected that Dojo could increase Tesla’s enterprise value by up to $500 billion by opening new markets such as mobility services and network services (SaaS) beyond just vehicle sales.
Meanwhile, NVIDIA’s latest GPU architecture, Blackwell, also merits attention. Unveiled at the 2024 GTC, the Blackwell Series (B200) delivers up to 7–30 times the speed of its predecessor, Hopper (H100), while consuming only 1/25 of the energy, dramatically boosting AI performance. The flagship chip, GB200, connects two GPU dies via a 10TB/s interposer and features a record 208 billion transistors—the largest chip ever made. This powerhouse achieves up to 30 times the performance of the H100 in large-scale language model (LLM) inference while reducing cost and power consumption by up to 25 times. For example, training a model with roughly 1.8 trillion parameters that previously required 8,000 H100 GPUs and 15MW can now be done with just 2,000 Blackwell GPUs consuming 4MW, representing a significant leap in efficiency. The Blackwell architecture incorporates high-bandwidth memory (HBM3) and next-generation interconnects to deliver up to 20 PF of compute power per chip under FP4 precision, surpassing competing AI inference solutions. NVIDIA is expected to release a range of products based on Blackwell from late 2024 through 2025, including a mid-cycle upgrade known as the Blackwell Ultra (B300 Series) slated for the second half of 2025. Blackwell Ultra will maintain the existing architecture while integrating 12-Hi HBM3E memory and enhanced networking, yielding approximately a 50% performance boost. According to NVIDIA, Blackwell Ultra will officially launch in H2 2025, followed by the “Vera Rubin” platform in 2026. The Rubin generation will combine GPUs with an Arm-based “Vera” CPU and next-generation NVLink interconnects in a superchip configuration, offering a highly integrated data center AI solution. NVIDIA CEO Jensen Huang stated that Rubin will deliver a “big, huge step up” for its partners, and the first-generation data center GPU in the Rubin series will feature 288GB of HBM4E memory (8 stacks) and a new X1600 switch. Furthermore, NVIDIA plans to roll out Rubin Ultra and subsequent generations around 2027 as part of a “one-year cycle” strategy to maintain its AI chip dominance.
Tesla vs. NVIDIA Performance Evaluation: Tesla leverages its Dojo supercomputer to build a large-scale, distributed AI training infrastructure, whereas NVIDIA follows a strategy focused on the strongest single-chip and modular supercomputer approach. A single Tesla Dojo D1 chip provides hundreds of TFLOPS in BF16 performance, and when scaled with thousands of chips in parallel, it achieves exaflop-level performance. In contrast, a single NVIDIA Blackwell GPU module offers tens of petaflops on its own, and multiple GPUs linked via NVLink further expand its capability. Tesla’s advantage lies in its specialized design for vision-based autonomous driving that minimizes memory bandwidth and latency, while NVIDIA’s strength is its proven software ecosystem and high-performance chips for general AI workloads. Currently, Tesla continues to enhance its FSD model using its HW4 vehicle computer (a custom SoC delivering about 72 TOPS ×2) and a training cluster built on H100 GPUs, and it plans to reduce GPU dependency with Dojo 2/3. Ultimately, when Dojo 3 matures around 2026–2027, a next-generation AI compute showdown between Tesla Dojo and NVIDIA Rubin is anticipated.
3. Camera-Based vs. LiDAR-Based Autonomous Driving (Tesla vs. Waymo, etc.)
Tesla’s vision-based approach is considered one of the boldest in the industry. Tesla vehicles use 8–9 cameras to achieve 360-degree awareness, and since 2021, they have eliminated ultrasonic sensors and radars, relying 100% on cameras + AI for FSD development. Elon Musk has famously stated, “LiDAR is a fool’s errand,” arguing that using cameras to see the world like humans is more cost-effective and scalable. He asserts, “The world was designed to be seen by vision,” emphasizing that just as human eyes are sufficient for driving, a camera-only system can suffice for vehicles. This Tesla Vision approach simplifies the sensor suite, reducing vehicle costs and aiding mass adoption. By focusing on software and neural networks rather than hardware sensors, Tesla can increase the software portion of its revenue and improve profitability over time. Additionally, Tesla collects camera data from millions of vehicles worldwide to train its machine learning models for various road conditions. This vast data pool and high mileage help Tesla outperform competitors, with OTA updates providing immediate feedback for system improvements.
However, a camera-only approach has clear limitations and challenges. The lack of sensor redundancy raises reliability concerns; for example, if camera lenses are obscured by severe weather or misaligned during assembly, there is no alternative sensor to correct errors. Many experts warn that “a vision-only system may not guarantee safety at a full autonomous (Level 4-5) level,” underscoring the need for sensor fusion to ensure redundancy. Although Tesla has implemented measures like camera self-cleaning and FSD disengagement in case of faults, abandoning the precise distance and speed data provided by LiDAR and radar inevitably compromises performance under adverse conditions. In contrast, Waymo and Cruise use a multi-sensor approach combined with high-definition mapping to maximize safety. For instance, Google Waymo’s 5th-generation autonomous kit includes at least 13 sensors (one long-range rotating LiDAR, three surround LiDARs, three radars, and five or more cameras), ensuring triple or quadruple redundancy in all directions. Waymo vehicles can detect 3D objects up to 300 meters with LiDAR, recognize traffic signals 500 meters ahead with high-resolution cameras, and use radar to monitor object speed in harsh weather. This multi-sensor data, when combined with pre-built HD maps, allows for centimeter-level accuracy in vehicle positioning and environmental perception. Consequently, Waymo and GM Cruise have already launched driverless taxi (Level 4) services in cities like Phoenix and San Francisco. Although occasional stops or traffic disruptions may occur by design for safety, these systems have accumulated millions of kilometers without major incidents.
Technology and Market Position Comparison: Currently, the companies closest to full autonomous driving are Waymo and Cruise, which have demonstrated Level 4 capabilities in limited areas. In contrast, Tesla FSD still requires driver supervision and is classified as Level 2+. Critics argue that “a vision-only approach cannot achieve more than Level 2,” while Elon Musk contends that Waymo and Cruise’s reliance on LiDAR is limited by cost and scalability. Although both approaches have their merits, it is evident that Tesla has yet to launch a truly driverless service, whereas competitors have started generating revenue. Market analysts generally position Waymo, Mobileye, and Cruise as technology leaders, with Tesla seen more as a follower. However, Tesla’s alternative AI approach may lead to exponential improvements once a critical threshold is reached. Mobileye’s CEO, Ammann Shashua, has noted that “no single method can meet all requirements for autonomous driving (cost, modularity, geographic scalability, high reliability), and each approach has its strengths and weaknesses.” For instance, while LiDAR offers high accuracy and reliability, its cost hinders mass adoption; camera-based systems are cost-effective but face challenges in ensuring reliability. Mobileye has therefore adopted a dual sensor system (“True Redundancy”) that uses forward cameras for full driving capability while simultaneously employing a LiDAR+radar set to cross-check results. This hybrid approach represents a middle ground between Tesla and Waymo, and Mobileye supplies ADAS kits to various automakers while also piloting its own robo-taxi projects.
4. Prospects for Achieving Level 5 Autonomous Driving
SAE Level 5 refers to full autonomy—vehicles operating without any driver intervention, eliminating the need for a steering wheel or pedals. So far, no company has completely implemented Level 5. Based on current technology, many challenges remain before Level 5 can be achieved. Although Tesla FSD and other systems show Level 4-like performance on highways and urban roads, issues such as severe weather (heavy rain/snow), unpaved roads, and unpredictable scenarios persist. Without perfect handling of these “long-tail” issues, safety cannot be guaranteed, and full commercialization of autonomous vehicles requires overcoming both technical hurdles and regulatory approval, as well as gaining public acceptance. Elon Musk has repeatedly predicted “full self-driving will be completed next year” since the mid-2010s, yet driver intervention remains necessary in 2023, highlighting the difficulties of reaching Level 5.
Experts generally believe that Level 5 vehicles will not become mainstream before 2030. Reports from Berg Insight suggest that “vehicles with Level 5 capabilities are not expected to appear before 2030, and even if they do, it may be much later,” while Goldman Sachs forecasts that only about 10% of new vehicles may feature Level 3–4 autonomy by 2030. Even Level 4 may be confined to limited conditions, and Level 5 might only be achievable by the mid-to-late 2030s. On the other hand, some tech optimists argue that exponential advances in AI could lead to faster breakthroughs; for example, former executives from Meta have wagered that “Level 5 vehicles could appear in urban areas by 2030.” However, these optimistic views are often based on limited conditions or specific regions. In short, current technology alone is insufficient for Level 5, and additional advances—such as more powerful computing hardware, smarter AI algorithms, and enhanced sensor/data integration—are required. Tesla’s planned Dojo 3 supercomputer, 4th-generation FSD computer, and NVIDIA’s next-generation Rubin GPU are expected to power even more sophisticated neural networks in real time, training on several times more data than today’s systems to deliver superior driving decisions. For example, if the current FSD neural network has learned from hundreds of millions of kilometers, Dojo 3 and Rubin hardware could be trained on tens of billions to trillions of kilometers of simulation and real-world data, achieving performance even under extreme conditions. Even with such advancements, regulatory approvals and public trust will dictate the timeline for full Level 5 deployment. In summary, while further technological evolution may bring some Level 5 features by the late 2020s to early 2030s, truly full autonomous driving (with no steering wheel or pedals) is expected to be commercially viable only by the mid-2030s. Ultimately, the question “When will fully autonomous vehicles emerge?” remains speculative, as we must continue monitoring technological trends and pilot outcomes.
5. Autonomous Driving Market Size and Tesla’s Revenue Outlook
Autonomous driving is set to create vast opportunities that extend far beyond traditional car sales, impacting a range of mobility sectors. Key areas include robo-taxis, autonomous trucks, self-driving buses/shuttles, and autonomous delivery by drones or robots.
• Robo-Taxi Market: Robo-taxi services, which transport passengers without a human driver, are predicted to be the most revolutionary mobility innovation. Although currently in its early stages (with a market size in the hundreds of millions of dollars), the market is expected to grow at an annual rate of over 90%, reaching around $45.7 billion by 2030. Some optimistic estimates even suggest that the market could explode to several hundred billion dollars in the early 2030s. For instance, Market.us analysis forecasts that by 2033, the potential robo-taxi market could be as high as $450 billion. Despite variances in forecasts, robo-taxis are set to disrupt ride-sharing, the taxi industry, and the overall paradigm of car ownership.
• Autonomous Freight Transport: Autonomous trucks in long-haul and logistics sectors are expected to dramatically improve efficiency by operating 24 hours a day without driver shifts. The global autonomous truck market is projected to start growing in the late 2020s and reach around $67 billion by 2030. In markets such as the United States and China, early adoption is expected to be rapid; McKinsey estimates that cumulative economic benefits in China could exceed $600 billion by 2035.
• Autonomous Buses and Shuttles: In public transportation, the introduction of self-driving buses and shuttles can reduce labor costs and enable round-the-clock operation. Several cities are already testing limited autonomous shuttle services, and the market is expected to grow to approximately $800–900 million by 2030. Although smaller than the passenger car market, this sector is bolstered by urban transit innovation and government projects.
• Autonomous Delivery Services: The market for last-mile delivery using drones or autonomous delivery robots is also expected to grow rapidly. Pilot projects for electric delivery robots and autonomous delivery vehicles started in the early 2020s, and the global autonomous delivery market, valued at about $18.7 billion in 2023, is projected to grow at over 20% per year, reaching between $50 billion and $100 billion by 2030. Estimates vary, with Allied Market Research projecting $144.2 billion by 2033 and Fortune BI forecasting $134.9 billion by 2032, indicating a new market scale in the order of hundreds of billions of dollars. This sector is creating various service models, as demonstrated by companies such as Uber Eats and Domino’s Pizza testing autonomous delivery.
Furthermore, niche markets such as autonomous driving for agriculture, construction, mining, military unmanned vehicles, and personal mobility (e.g., wheelchairs, carts) are expected to contribute to a total autonomous driving economy worth trillions of dollars by 2040. For example, Allied predicts that by 2040, the global autonomous vehicle market (including vehicles with autonomous features) could reach approximately $980.7 billion (around 1.3 quadrillion KRW).
Tesla’s Opportunity and Revenue Outlook: As this massive market emerges, if Tesla secures a leadership position in autonomous driving, it could generate tremendous revenue beyond traditional vehicle sales. Consider robo-taxis: Tesla plans to launch its own robo-taxi platform in August 2024 and has already surpassed 1 billion miles in FSD driving, accelerating its vision of a shared vehicle network. Tesla’s philosophy, “Make money while you’re not driving the car,” implies converting its fleet into a robo-taxi network to earn revenue even when idle. If Tesla can convert millions of vehicles into robo-taxis once FSD is complete, it could quickly become the world’s largest ride-sharing operator. According to ARK Invest scenarios, by around 2029, Tesla’s robo-taxi business could contribute nearly 90% of its overall enterprise value and profits. During the same period, electric vehicle sales might account for only one-fourth of Tesla’s revenue and 10% of its profit, with the remaining 90% coming from high-margin service businesses such as robo-taxis. Robo-taxi operations are expected to be significantly more profitable per vehicle than car sales.
In the freight sector, applying FSD to Tesla Semi electric trucks could allow Tesla to provide autonomous truck networks for logistics companies, generating substantial added value. If Tesla captures 20% of the projected $67 billion autonomous truck market by 2030, it could secure about $13 billion in revenue. Furthermore, if Tesla operates its own autonomous truck network or develops logistics SaaS, it could generate recurring revenue far beyond traditional vehicle sales. Similarly, leveraging robo-taxi technology to expand into logistics and bus services could unlock new revenue streams across industries.
The appeal of this software-based business model lies in its high margins. Traditional car sales typically have low margins per vehicle, while software incurs minimal cost once developed. Tesla’s FSD software option (priced at approximately $12,000–$15,000 or available via a $199 monthly subscription) has nearly zero manufacturing cost, so nearly the entire sale price translates into profit. Even though current FSD uptake is estimated at about 5%, this software revenue already contributes hundreds of basis points to Tesla’s overall vehicle margin. Analysts suggest that lowering FSD prices to boost adoption could be the easiest way to significantly increase margins. Elon Musk has repeatedly emphasized that if FSD and robo-taxi services are fully realized, the profit per vehicle could surge by up to 80%, multiplying overall revenue several times. In a robo-taxi scenario, one vehicle can serve multiple passengers, generating ongoing revenue from a single sale. Investors regard this as a game changer for the transportation sector, forecasting “unprecedented value uplift.” Additionally, Tesla is considering licensing its FSD software to other automakers, potentially evolving into a platform akin to an “Android for cars” that generates substantial recurring software revenue.
In summary, Tesla’s revenue model in the autonomous driving era is expected to shift from primarily hardware-based (vehicle sales) to a combination of hardware and high-margin software/services. While vehicle sales may decrease relatively, the share of revenue from mobility services (such as robo-taxis) and software (FSD upgrades, subscriptions) is anticipated to increase dramatically. This transformation will not only boost revenue scale but also improve profitability, enabling Tesla to achieve significantly higher operating margins compared to the traditional automotive industry. If Tesla secures autonomous driving leadership, it could transition from a conventional car manufacturer to a full-fledged mobility services company, realizing a blueprint of rising revenue and profit.
In conclusion, the article has comprehensively reviewed the latest technological trends in Tesla FSD—from hardware competition and sensor philosophies to the outlook for Level 5 autonomy and the vast future market and revenue models. Although many challenges remain, Tesla’s vision-based autonomous driving is already making a significant impact in the industry. The coming years will be crucial in determining the competitive landscape, with the success of FSD improvements, Dojo supercomputer advancements, and the launch of robo-taxi services all playing pivotal roles in Tesla’s future position. Ultimately, while the leader in the autonomous driving era remains uncertain, there is no doubt that this revolutionary shift will fundamentally transform the automotive industry and our way of moving.
Additional Section: Tesla FSD Future Guide
Tesla FSD is not merely an advanced driver-assistance system; it is set to become the cornerstone of full autonomy (Level 5) in the near future. By combining a camera-based vision system with its Dojo supercomputer, Tesla FSD is driving major changes in the autonomous driving landscape.
As Tesla FSD technology evolves, it is expected to significantly boost profitability in mobility services such as robo-taxis, freight transport, and delivery services. This shift from traditional car sales to recurring software and service revenue will redefine the automotive industry’s business model.
Internal & External Links & Image (Tesla FSD alt)
Internal Link (DoFollow)
Autonomous Driving Technology Analysis
External Link (DoFollow)
Tesla FSD Official Page | NVIDIA Official Site
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