Buried deep within Tesla's Q1 2024 earnings call lies a bombshell revelation, a potential game-changer that seems to have slipped past even the most astute Wall Street analysts. While the call, like many before it, heavily emphasized Tesla's commitment to full self-driving (FSD), a seemingly offhand remark from Elon Musk hints at a parallel strategy, one that could position Tesla not just as a leader in electric vehicles and AI, but as a global distributed computing powerhouse.

The clue emerges during a discussion about Tesla's approach to AI and the sheer computational power required to train its neural networks. Musk casually mentions that Tesla is "probably the most efficient company in the world for AI inference," a necessity born from the limitations of its aging Hardware 3. This efficiency, he suggests, will "pay dividends in many ways." But the true scope of these dividends becomes clearer when he pivots to a future scenario where millions of Tesla vehicles are equipped with the significantly more powerful Hardware 4 and 5, and operating as robotaxis.

Even with robotaxis in heavy use, Musk estimates each vehicle could be idle for over 100 hours per week. What, he asks, should be done with all that unused computational capacity? His answer: deploy it. Imagine, he suggests, a fleet of 100 million Teslas, each with a kilowatt of inference compute. That’s 100 gigawatts of distributed compute power, more than any single company, potentially even more than the entire world currently possesses, available to tackle tasks ranging from scientific problems to large language model workloads.

This vision, while audacious, isn't entirely without precedent. Musk explicitly draws a parallel to Amazon Web Services (AWS), which emerged from Amazon's need to monetize its excess computing capacity. What began as a bookstore's internal solution ultimately became a global behemoth, redefining cloud computing and generating billions in revenue. Could Tesla be poised to replicate this success, leveraging its vast and ever-expanding fleet of "smart cars" as a distributed computing network?

The implications are staggering. Imagine a world where your Tesla, while parked and charging, contributes to research breakthroughs, processes complex financial models, or helps train the next generation of AI. The revenue potential, especially as the Tesla fleet grows, could be astronomical.

Potential Revenue from Distributed Computing

Let's crunch some numbers. Assuming Musk's conservative estimate of 100 million Teslas with 1 kilowatt of usable compute each, and just 10 hours per week of idle time, that translates to 1 billion kilowatt-hours of compute time available per week. Even at a modest $0.10 per kilowatt-hour, the potential revenue stream would be $100 million per week, or a staggering $5.2 billion annually.

Advantages of Tesla's Distributed Network

But the true value may lie beyond raw computational power. Tesla's network would offer several unique advantages:

Global Distribution: Unlike centralized data centers, Tesla's network would be inherently distributed, with nodes (vehicles) scattered across the globe, offering low latency and resilience to regional outages. Liquid-Cooled Thermal Management: Tesla vehicles are already equipped with sophisticated liquid-cooled thermal management systems, essential for high-performance computing and a significant cost advantage compared to traditional data centers. Direct Control: Unlike relying on individual users to volunteer their devices, Tesla has full control over its fleet's compute resources, allowing for efficient workload distribution and seamless updates. Shared CapEx: The cost of the compute hardware would be shared by the entire Tesla ecosystem, meaning individual users wouldn't need to bear the full burden of expensive components.

Challenges and Assumptions

This vision, of course, hinges on several key assumptions:

Robotaxi Success: The widespread adoption of robotaxis is essential to free up the necessary compute hours. If robotaxis fail to materialize, the amount of idle time per vehicle would be significantly lower. Software Development: Tesla needs to develop the software infrastructure necessary to manage this distributed computing network, a non-trivial task that will require significant engineering investment. User Adoption: Tesla needs to convince users that lending their vehicles' compute power is beneficial, whether through financial incentives or by highlighting the contributions to societal good.

Despite these challenges, the potential upside is undeniable. While Wall Street may be focused on traditional metrics like vehicle sales and gross margins, Tesla appears to be playing a much longer game, one that could see it transcend the automotive industry and become a major player in the future of distributed computing.

Growth of Compute Power in Tesla Vehicles

"Fun Fact: Did you know that Tesla's original Roadster, released in 2008, contained a fraction of the compute power found in a modern smartphone? Today's Model S Plaid boasts over 10 teraflops of performance, surpassing even the most powerful gaming consoles. Imagine what a fleet of 100 million of these vehicles could achieve."