To achieve real autonomous driving, three things must come together:
- Software
- Hardware capable of running that software
- The ability to mass-produce that hardware at scale
If even one of these is missing, it becomes impossible to create an autonomous driving product that can be sold massively in the market. Even if a company has great software and capable hardware, without a scalable manufacturing system the cost structure collapses, and the market simply won’t accept the price.
Cars are consumer industrial products, and industrial products are brutally sensitive to cost. You cannot ignore this.
1. Software: The Core of Autonomous Driving
Software is the heart of autonomous driving. Nothing comes close.
For years, many companies pursued rule-based approaches to self-driving. But over the last few years, the entire industry has rapidly shifted toward end-to-end (E2E) neural networks. As of today, companies still insisting on rule-based stacks are either already gone or on their way out.
Tesla currently operates FSD at a Level 2+ capability. Traditional OEMs often argue that “Level 2+ isn’t real autonomy” and claim they will jump straight to Level 4.
That is simply not realistic.
Highways may allow for such ambitions due to fewer edge cases. But in real urban driving, such as city streets, alleys, and parking lots, the long-tail scenarios are effectively infinite. No fleet of a few dozen test vehicles will ever gather the required data for Level 4 in a reasonable timeframe.
Only large-scale real-world data, continuously collected from a massive active fleet, can enable real autonomous driving systems to mature.
2. Hardware: Onboard Chips and Sensors
Autonomous driving hardware falls into two main categories:
- Onboard compute (the “brain” of the vehicle)
- Sensors to perceive the environment (cameras, radar, lidar, etc.)
Tesla uses a vision-first approach relying solely on cameras. This system has already received regulatory approval in multiple countries. Most other manufacturers employ a multi-sensor stack: lidar, radar, cameras, ultrasonic sensors, and more.
But the most important factor in hardware is how simple the hardware stack can be while still achieving performance.
Cars are mass-produced industrial goods, so reducing part count is one of the most powerful ways to improve cost efficiency.
Recently, the cost of autonomous driving chips has become a major issue across the industry. Tesla shifting some AI5/AI6 production from TSMC to Samsung is widely interpreted as a response to TSMC’s rising costs. Samsung’s next-gen node still faces yield challenges, which is why Elon Musk visits the Samsung fab near Giga Texas to personally review progress.
Tesla’s AI5 chip is estimated to be roughly 600 mm², comparable to Nvidia’s RTX 5090 GPU. With Nvidia’s gross margin at around 75%, this means OEMs buying Nvidia hardware pay nearly 4× more than Tesla does for its in-house solution. This cost difference directly affects the final price of the vehicle.
3. Large-Scale Manufacturing Capability
Companies like Waymo operate autonomous services using off-the-shelf chips, and their tech is impressive.
But integrating autonomous-driving hardware into a mass-produced consumer vehicle is a completely different challenge. It cannot be solved with good software alone.
True hardware mass production requires meeting all of the following simultaneously:
- Simplified and highly automated vehicle production processes
- Low-cost autonomous-driving chips and sensors
- Factories and production lines capable of assembling them efficiently
- A robust global supply chain that supports scale
If even one of these is missing, the product remains a prototype.
A tech demo, not a consumer product.
Waymo’s hardware stack may be advanced, but:
- The per-vehicle cost is extremely high
- They lack independent vehicle manufacturing
- Their stack cannot scale to hundreds of thousands or millions of units
This inherently caps their deployment at a few hundred or a few thousand vehicles.
Tesla, in contrast, designs its autonomous hardware into the vehicle from the very beginning, optimizing manufacturing processes around it. This results in production speed and cost efficiency that competitors cannot match. For example, the Cybercab is targeting one vehicle every 5 seconds, supported by gigacasting, structural battery packs, and highly integrated assembly lines.
No other company today can mass-produce both the vehicle and its autonomous compute at this level of integration and speed. In autonomous driving, success ultimately comes down to one key question: “Who can achieve a cost structure that scales to millions of vehicles?”
Conclusion
The conclusion is the same as every other well-reasoned analysis:
Buy Tesla stock.
Tesla is the only company that simultaneously fulfills all three requirements for successful autonomous driving:
- World-leading real-world driving data
- Rapidly improving E2E autonomous software
- In-house autonomous-driving chips
- Mass production of those chips across millions of vehicles
- Complete control of factories, logistics, and the supply chain
This isn’t just a technological advantage.
It is a structural advantage that widens automatically with time.
Traditional OEMs and Big Tech can pour billions into the problem and still fall behind, because autonomous driving isn’t a market where “being good at one thing” is enough.
You need the entire stack, from silicon to software to manufacturing.
And Tesla is the only company that has it.
So the final message is simple, and many others have said the same.
Because it’s true.
Buy Tesla stock.