Tesla owners are starting to use their cars to maintain their vehicles; is the era of making money with smart devices here?

Author: Zhang Feng

A former xAI engineer revealed that Musk is advancing the "Macro Hard" plan, which involves renting the idle onboard computers of approximately 4 million Tesla vehicles in North America to conduct distributed training of "human simulator" AI capable of replacing white-collar jobs. The model iterates daily, and the cost is far lower than building a self-built data center. This is not only a disruptive attempt at AI computing power supply models, but also opens a door for ordinary users to participate in value creation through smart terminals.

From another perspective, Musk's plan is "using cars to support cars," where car owners obtain economic benefits by allowing their vehicles to provide computing power, storage, or data support during idle periods, thereby partially or even completely covering the vehicle's usage costs.

From another perspective, Musk's plan is "using cars to support cars," where car owners obtain economic benefits by allowing their vehicles to provide computing power, storage, or data support during idle periods, thereby partially or even completely covering the vehicle's usage costs.

I. Main Characteristics of Tesla Owners' "Car-for-Car Maintenance" Model

The core of the "car-for-car maintenance" model lies in transforming intelligent vehicles from simple transportation tools into "mobile computing assets" that can generate continuous income. Tesla, with its massive fleet of intelligent vehicles and advanced hardware architecture, has become a pioneer in this model.

This model has several key characteristics. First, resource reuse and monetization of idle resources. Generally, Tesla vehicles are driven for less than 4 hours a day, with the remaining time spent parked or charging. Even while driving, its Autopilot system only utilizes about 30% of the hardware computing power. By dynamically scheduling this idle computing power through technical means, vehicles can be transformed into distributed computing nodes during off-peak hours, supporting AI training and inference tasks. Car owners can earn income such as rental fees, points, or car loan deductions by sharing idle resources without additional investment, achieving "passive asset appreciation." Second, low barriers to entry and high convenience. Tesla's HW4.0 and above hardware platforms already possess powerful computing capabilities (720 TOPS main computing power) and integrate battery, cooling, and network modules, requiring no hardware modifications from car owners. Participating in computing power sharing only requires authorization through the in-vehicle system and setting up the revenue model, with virtually zero operational costs. This "plug-and-play" participation method significantly lowers the user threshold and facilitates rapid scaling. Furthermore, there is mutual empowerment and ecosystem enhancement. For car owners, the revenue can be directly used to offset vehicle usage costs, improving the economic efficiency of vehicle use; for Tesla and its affiliated AI companies (such as xAI), it provides a low-cost, highly flexible supply of computing power, supporting the large-scale deployment of its AI applications. This model enhances user loyalty to the Tesla brand and deeply embeds vehicles into a broader AI and digital service ecosystem, forming a closed loop of "hardware sales—software services—ecosystem benefits." Finally, dynamic scheduling and intelligent support. The system can automatically adjust its computing power output strategy based on the vehicle's status (driving, charging, parking) to ensure that it does not affect the owner's normal use of the vehicle. For example, it operates at full capacity when charging, participates with low power consumption when parking, and pauses contribution when driving. This intelligent resource scheduling mechanism not only ensures the owner's experience but also maximizes the utilization of computing power resources. II. Decentralized Applications Usher in a New Era of User Empowerment Tesla's computing power sharing program is essentially a deep integration of AI and Web3 concepts. It marks the official beginning of an era of "user-empowered" applications driven by data, AI, and Web3. The core of Web3 is decentralization and the return of ownership. In the traditional internet era, data and computing resources were concentrated in the hands of a few technology platforms. While users contributed data and attention, they found it difficult to share the value they created. Web3 utilizes technologies such as blockchain and smart contracts to build a decentralized network architecture, enabling individuals to truly own and control their digital assets, including data and computing power, and directly participate in value distribution. Smart contracts provide a trusted and automated mechanism for distributed resource transactions. In Tesla's computing power network, the processes of computing power leasing and revenue settlement between car owners and xAI can be automated through smart contracts. The contract code is open, transparent, and tamper-proof, ensuring fair transactions without intermediaries and significantly reducing trust costs. After car owners contribute computing power, their earnings are automatically distributed to their digital wallets according to predetermined rules, forming a smooth value loop. Distributed computing networks are a crucial infrastructure for the democratization of AI. AI development has long been constrained by computing power bottlenecks, especially for SMEs and developers, where high computing costs hinder innovation. The Tesla model demonstrates a possibility: by integrating the idle computing power of millions of smart devices globally, a decentralized, publicly accessible computing power market can be built. Anyone or organization can rent these decentralized yet powerful computing resources by paying with tokens or fiat currency, thereby lowering the barriers to AI research and deployment. Data privacy and security are ensured through technologies such as federated learning. In distributed AI training, data does not need to leave the local device; only model updates are aggregated using encryption, thus achieving "the model moves as the data remains stationary." Tesla's plan to combine federated learning with a Trusted Execution Environment (TEE) ensures complete isolation of vehicle owner data, avoiding privacy risks. This clears the way for more fields involving sensitive data (such as healthcare and finance) to access distributed computing networks. Users are transforming from consumers into "productive consumers." In this new paradigm, ordinary users are no longer merely passive recipients of digital services, but rather active contributors and value co-creators within the network. By providing computing power, storage, or data, users directly participate in the training and optimization of AI models and reap economic rewards. This shift in identity will profoundly change the value flow and ecological structure of the digital economy. III. Three Fundamental Conditions for the Era of Earning Money with Smart Terminals The feasibility of the "car-for-car" model reveals the possibility of a broader "era of earning money with smart terminals." However, the large-scale implementation of such decentralized, resource-sharing applications depends on the maturity of three key conditions. Condition 1: A sufficient number of smart terminals with idle capacity. This is the material basis for achieving economies of scale. Terminals need to have certain computing, storage, or data acquisition capabilities, and have significant idle periods. Tesla's fleet of over 4 million (and continuously growing) smart terminals in North America is a typical example. In addition, billions of smartphones, smart home devices, IoT sensors, and even personal computers worldwide represent a huge pool of idle resources. The higher the standardization and intelligence of the terminals, the easier it is to achieve unified resource scheduling and management. Condition 2: A communication network with sufficient capacity, low latency, and high reliability. One of the core challenges of distributed computing networks is how to efficiently and stably connect dispersed nodes. Tesla plans to build dual-link redundancy based on Starlink satellite internet and terrestrial 5G networks, aiming to control network latency to below 60 milliseconds to meet the needs of most AI inference tasks. In the future, with the further development of 6G and low-Earth orbit satellite constellations, a globally seamless, ultra-high bandwidth, and ultra-low latency network environment will become the lifeblood of distributed applications, supporting massive numbers of terminals to work collaboratively in real time. Condition 3: A sufficiently powerful and universal AI capability and scheduling platform. This includes two aspects: first, AI frameworks and algorithms that can effectively utilize heterogeneous and distributed computing power for training and inference; and second, a central scheduling system capable of real-time task scheduling, load balancing, status monitoring, and revenue settlement for millions or even hundreds of millions of terminals. The "zero-baseline distributed inference network" developed by xAI for the Macrohard project is a prototype of such a platform. It needs to solve a series of complex engineering problems such as dynamic node joining and leaving, network instability, task migration, and security isolation. The strength of AI capabilities directly determines the upper limit of the value that these distributed resources can create. Only when these three conditions are simultaneously met and mutually reinforcing can a truly global, decentralized digital infrastructure supported by user smart terminals be built, ushering in a new era where "everything can contribute, and everything that contributes can benefit." IV. Application Prospects: Taking the Medical Device Field as an Example The "making money with smart terminals" paradigm has strong scalability and also has broad application prospects in the medical device field, which has high demands for computing and data processing and involves sensitive data. Application Scenario 1: Distributed Medical Data Analysis and Model Training. Modern medical equipment (such as high-end CT, MRI, gene sequencers, and wearable health monitoring devices) generates massive amounts of data, and the devices themselves have strong local computing capabilities. Under the premise of ensuring absolute security of patient privacy (through technologies such as federated learning, homomorphic encryption, and TEE), the idle computing power of multiple hospitals can be combined to jointly train AI models for disease diagnosis, new drug development, and epidemic prediction. Participating medical institutions can obtain model usage rights, research collaboration opportunities, or direct economic benefits. Application Scenario Two: Real-time Edge Health Monitoring and Early Warning Network. Hundreds of millions of personal wearable devices (smartwatches, health patches, etc.) constitute a vast distributed biosignal sensing network. By incentivizing users to share anonymized and desensitized real-time physiological data (such as heart rate, blood pressure, and blood sugar trends), a dynamic health map covering the entire population can be constructed. AI systems operating on this network can provide services such as early disease warnings, real-time monitoring of public health events, and personalized health guidance. Data contributors can receive discounts on health management services, insurance discounts, or token rewards. Application Scenario 3: Sharing and Optimization of Scarce Medical Computing Power. Certain specialized medical computing tasks (such as protein folding simulation and radiotherapy dose planning) require extremely powerful computing capabilities, but not all hospitals have the resources to equip themselves with supercomputers. Through distributed networks, tasks can be broken down and distributed to the idle time slots of numerous research institutions, universities, and even personal high-performance computers, accelerating research processes or clinical protocol development through crowdsourcing. Of course, we must also recognize that applications in the medical field face the most stringent privacy, security, and regulatory requirements. Cutting-edge privacy-preserving computing technologies must be adopted to establish an absolutely trustworthy data isolation mechanism. Simultaneously, close collaboration with healthcare regulatory agencies is needed to establish a compliance framework that conforms to regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR. The design of revenue models also requires greater caution to avoid inappropriate inducement or ethical controversies. V. The Future Development Path from Pilot Exploration to Ecosystem Prosperity Based on current technological trends and industry dynamics, we can make the following predictions regarding the future implementation roadmap of "making money with smart terminals" applications: Phase 1: Vertical Industry Pilots and Model Validation. Represented by the Tesla-xAI collaboration, small-scale closed testing will be conducted in specific regions (such as some cities in North America). The focus is on verifying technical feasibility (stability of computing power scheduling, task completion efficiency, and network latency control), economic models (attractiveness to car owners, and cost savings for enterprises), and legal compliance (data privacy and computing power leasing agreements). Meanwhile, other tech giants and startups may launch similar pilot projects in areas such as video rendering, scientific computing, and edge CDN. The second phase: Cross-terminal expansion and platform development. With successful pilots and the initial establishment of standards, the model will expand from specific scenarios such as smart cars to a wider range of terminal types, including smartphones, PCs, and smart home appliances. Third-party platforms focusing on connecting various terminal resources with AI computing power needs may emerge, providing unified SDKs, scheduling middleware, and clearing systems. A blockchain-based computing power token trading market will begin to take shape, enabling standardized pricing and instant trading of computing power resources. The regulatory framework will begin to improve, clarifying the rights and responsibilities of digital resource contributors. Phase Three: Ecosystem Maturity and Large-Scale Commercialization. Distributed computing networks have become an important component of AI infrastructure, complementing centralized cloud data centers. Many small and medium-sized AI enterprises, research institutions, and individual developers are accustomed to acquiring computing power through decentralized markets. Smart terminals are pre-installed with resource contribution modules, and "contributing equals earning" has become one of the factors considered in consumer purchasing decisions. Innovative applications surrounding distributed computing power, storage, and data are exploding, such as distributed AI model crowdsourcing training, real-time urban sensing networks, and personal data markets. Phase Four: Deep Integration and Paradigm Restructuring. The deep integration of Web3, AI, and the Internet of Things (IoT) is forming a global "machine economy" ecosystem. Smart terminals are not merely computing nodes, but autonomous economic agents capable of making independent decisions and engaging in transactions (through embedded AI agents). Collective intelligence based on massive amounts of real-time terminal data will propel scientific research, social governance, and business innovation to new heights. The fundamental logic of the digital economy is being restructured, making value creation and distribution more democratic and granular. A new era may be emerging: In this era, smart terminals will no longer be passive tools, but active value-creating nodes; ordinary users will no longer be just consumers, but co-builders and sharers of the digital economy; computing resources will no longer be centrally monopolized, but will move towards democratization and networked distribution. Behind this lies the convergence of three major trends: data, AI, and Web3. Data is the fuel, AI is the engine, and Web3 is the blueprint for reshaping production relations. This integration is giving rise to a more open, collaborative, and incentive-compatible new digital economy ecosystem. For everyone who owns a smart device, perhaps in the not-too-distant future, allowing our devices to earn income while "resting" will be as commonplace as sharing Wi-Fi today. A more inclusive and efficient digital world, supported by billions of smart devices worldwide, is slowly unfolding.