Deep Dive into the Technology and Financials of BYD, Other China Companies and LIDAR Cost and Supplies

$25K Tesla and unsupervised robotaxis are starting this quarter—and BYD, Xiaomi, and legacy auto won’t keep up. With BYD’s 4% margins a sham (thanks to 3% dodged interest and other non-GAAP accounting), Tesla’s cheaper EV and 5,000+ robotaxi fleet by 2025 and one million Tesla robotaxi network in 2026 in will dominate. We break down the numbers, the supply chain, the tech, and the tariffs hitting China’s carmakers hard.

Only 1% of LIDAR are robotaxi grade lidar. 5 are needed for every LIDAR using robotaxi. 1.5 million total LIDAR in 2024 are mostly inferior ADAS grade LIDAR.

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3-5 Years to scale to 100,000 LIDAR base robotaxi if the LIDAR based robotaxi solution was solved first. Billions in investment. 2-3 billion per year R&D by dozens of companies. Still tele-operated mainly. Scaling lidar factories and scaling retrofit kit processes.

Technical Differences- ADAS LIdar vs Robotaxi Lidar
1. Resolution and Range

ADAS-Grade Lidar (Huawei, BYD): Designed for tasks like adaptive cruise control, lane-keeping, and emergency braking, ADAS lidar typically features lower resolution (e.g., 16 or 32 laser channels) and shorter ranges (100-150 meters). This is sufficient for detecting nearby vehicles or obstacles in controlled scenarios where a human driver remains in the loop.
Robotaxi Lidar (Waymo, Apollo Go): Built for Level 4 or 5 autonomy, robotaxi lidar offers higher resolution (e.g., 128 or 256 channels) and longer ranges (200-300 meters or more). This enables detection of smaller objects—like pedestrians or debris—at greater distances, critical for safe navigation without human intervention in complex environments.

2. Field of View (FoV)

ADAS-Grade Lidar: Features a narrower FoV, often focused on specific zones (e.g., front or sides), as it supports targeted assistance functions rather than full situational awareness.
Robotaxi Lidar: Provides a wider FoV, often 360 degrees, to eliminate blind spots and ensure comprehensive coverage of the vehicle’s surroundings, essential for unpredictable urban settings.

3. Accuracy and Reliability

ADAS-Grade Lidar: Offers reliable performance but assumes a human driver can intervene, reducing the need for redundancy or extreme precision.
Robotaxi Lidar: Demands higher accuracy and reliability due to its safety-critical role in full autonomy. It often includes redundant sensors and fail-operational designs to maintain functionality if a component fails.

4. Integration and Processing

ADAS-Grade Lidar: Integrates with cameras and radar for specific tasks, requiring moderate computational power since data processing is less complex.
Robotaxi Lidar: Relies on advanced sensor fusion—combining multiple lidars, cameras, radars, and sometimes ultrasonics—with powerful onboard computers to process vast data volumes in real-time for autonomous decision-making.

Feature and Product Differences
1. Cost and Size

ADAS-Grade Lidar: Engineered for mass-market vehicles, it’s cost-effective (often under $1,000 per unit) and compact, integrating seamlessly into consumer car designs without significant cost or aesthetic impact.

Robotaxi Lidar: Prioritizes performance over cost, with prices ranging from $5,000 to $10,000 or more per unit. It’s larger and requires specific mounting, often using multiple units per vehicle for full coverage.

2. Technology

ADAS-Grade Lidar: Typically employs solid-state or hybrid lidar, which is cheaper, more durable, and suited to ADAS needs, even if it sacrifices some performance.
Robotaxi Lidar: Uses advanced technologies like mechanical spinning lidar (e.g., Waymo’s custom designs) or high-channel-count solid-state lidar, delivering superior resolution and range for full autonomy.

3. Design Purpose

ADAS-Grade Lidar: A supplementary tool enhancing driver safety and convenience, optimized for scalability in millions of consumer vehicles.
Robotaxi Lidar: A core component of a fully autonomous system, designed for maximum reliability and performance in fleets of robotaxis operating in diverse conditions.

Scaling Production if there was a Surge in Robotaxi Demand from fully solved and scaling solution

If demand for robotaxis surged by 10X or 100X, adjusting the product mix and reconfiguring factories would face several hurdles:
Key Challenges

Manufacturing Capacity: Current production lines for high-end robotaxi lidar are limited. Expanding factories or building new ones typically takes 1-2 years for a 10X increase and 3-5 years for a 100X increase.

Supply Chain: Components like laser diodes, photodetectors, and precision optics have long lead times and limited suppliers, risking bottlenecks. For instance, even major suppliers like Hesai struggle to meet demand for millions of units.

Technology Development: A surge might necessitate newer lidar designs, requiring additional R&D and testing, further delaying scaling.
Regulatory Hurdles: New or modified lidar units may need recertification, adding months to years depending on regional standards.

Timelines

10X Surge: Achievable in 1-2 years with existing technology, assuming rapid capacity expansion and supply chain coordination.
100X Surge: Likely requires 3-5 years or more, factoring in new factories, supplier scaling, and potential technological upgrades.

Retrofitting and Scaling from 2,000 to 100,000 Robotaxis
Retrofitting lidar onto OEM cars for robotaxi use (e.g., by Apollo Go) and scaling from 2,000 to 100,000 units involves multiple steps:
Process for Retrofitting

Design and Integration: Create a retrofit kit with lidar, computing hardware, and software, compatible with various car models. This complexity increases design time.
Testing and Validation: Each retrofitted vehicle must meet safety and performance standards, requiring extensive testing per unit.
Installation: A labor-intensive process, potentially taking days to a week per car, depending on the kit and vehicle.
Supply Chain: Retrofit kits demand the same high-end components as new lidar, constrained by availability.

Scaling Challenges

Initial Estimate: If one team retrofits one car per week, 100 teams could handle 100 cars weekly. Scaling to 100,000 cars would take ~19 years without optimization—clearly impractical.
Optimization Needs:
Automation: Streamline installation with automated tools or processes.
Workforce Expansion: Train thousands of technicians and establish multiple retrofit facilities.
Supply Chain: Ramp up kit production, potentially requiring new supplier contracts or in-house manufacturing.
Realistic Timeline
With significant investment (e.g., hundreds of teams, automated processes, and a robust supply chain), scaling from 2,000 to 100,000 retrofits could take 3-5 years. This assumes parallel efforts in kit production, technician training, and facility expansion.
Conclusion

The differences between ADAS-grade lidar (Huawei, BYD) and robotaxi lidar (Waymo, Apollo Go) reflect their roles: cost-effective assistance versus high-performance autonomy. ADAS lidar is cheaper, compact, and simpler, while robotaxi lidar excels in resolution, range, and reliability. Scaling production for a 10X or 100X robotaxi demand surge would take 1-2 or 3-5 years, respectively, due to manufacturing and supply chain limits. Retrofitting 2,000 cars and scaling to 100,000 would also require 3-5 years, driven by labor, testing, and component availability. Both scenarios demand substantial investment and coordination to meet such ambitious goals.