Author: Jesse
This is the latest thinking from independent analyst @farzyness, who has 360,000 followers. He started investing in Tesla in 2012 and was part of Tesla's leadership team from 2017 to 2021.
A person simultaneously owns a battery company, an AI company, and a rocket company, and they all nourish each other.
I have been thinking about this for several months, and to be honest, I really can't see how Musk could lose. This is not from the perspective of a "fanboy," but from a structural standpoint. The triangular relationship between Tesla, xAI, and SpaceX is evolving into an unprecedented existence: an industrial-grade, synergistic, cash-flow-generating flywheel behemoth. While this sounds convoluted, it is described very accurately.
Let me break down what is actually happening here, because I feel that most people are observing these companies in isolation, while the real focus is on the connections between them.
1. The Starting Point of the Flywheel: Energy
Tesla manufactures batteries, and they do so at a massive scale. They deployed 46.7 gigawatt-hours (GWh) of energy storage systems in 2025, a year-on-year increase of 48.7%. Their 50 GWh factory being built in Houston will start production this year. The total planned capacity reaches 133 GWh per year. The gross margin for this business is 31.4%, while the automotive business is only 16.1%. This "boring" energy storage business generates nearly double the profit per dollar of revenue compared to cars.
Why is this important? Because xAI just purchased $375 million worth of Tesla Megapacks (energy storage) to power the world's largest AI training facility, Colossus. Currently, 336 Megapacks have been deployed. These batteries provide backup power and demand response capabilities for a system with 555,000 GPUs, consuming over 1 gigawatt (enough to power 750,000 households).
2. Breaking Free from Nvidia: Chip Independence
Tesla not only sells batteries but is also developing its own AI chips.
Currently, Nvidia monopolizes AI training hardware, controlling about 80% of the market. All major AI labs (OpenAI, Google, Anthropic, Meta) are competing for Nvidia's quotas. The H100 and now the Blackwell chips are bottlenecks for the entire industry. Jensen Huang's pricing power is what most monopolists dream of.
If you are Musk and want to build the world's largest AI system, what do you do? You can't rely on Nvidia forever. That is a lifeline, a lever held by others, especially when you plan to drive hundreds of millions of robots in the next 10 to 20 years.
By the way, Musk's plan for Tesla is to produce as many robots as there are humans.
Tesla's AI5 chip is set to launch from the end of this year to 2027. Musk claims it will be the world's most powerful inference chip, especially in terms of unit computing cost. In other words, it will have extremely high efficiency.
The AI6 chip has already signed a $16.5 billion contract with Samsung for manufacturing. The key point is: Musk stated that the AI6 is designed for "Optimus robots and data centers." This means that Tesla products and xAI products will share the same set of chips.
Nvidia currently wins in "training," but "inference" is the long-term profit point. Training happens once, but every time someone uses the model, inference occurs. If you are running millions of Tesla cars, millions of Optimus robots, and billions of Grok queries, inference is where the real demand for computing power lies.
By building its own inference chips, Tesla and xAI have achieved "decoupling" while Nvidia focuses on training. It's like avoiding a fortified front and executing a flanking maneuver.
3. Space-Based AI Computing
Musk mentioned "space-based AI computing" in Tesla's Dojo 3 roadmap. They are rebooting the Dojo 3 project for this vision. Doing the math, this seemingly crazy idea becomes reasonable.
If you want to deploy 1 terawatt of AI computing power in space each year (which is the scale of global AI infrastructure), according to Musk, you would need more funding than the total existing money supply, based on current chip costs. Nvidia's H100 sells for $25,000 to $40,000, which is economically unfeasible.
But if you have low-cost, inference-designed chips that are mass-produced and extremely energy-efficient, the math changes. Tesla's goal is to manufacture AI chips at "the lowest cost per silicon." This is key to achieving large-scale space computing.
Without cheap chips, space AI is a fantasy; with cheap chips, it becomes inevitable.
Competitor StarCloud, supported by Nvidia, trained the first AI model in space last December. This proves the concept is feasible. Therefore, the current focus is not on validating hypotheses but on creating an environment that can be deployed at scale.
Imagine this: SpaceX sends orbital data centers into low Earth orbit via Starship, with each rocket carrying 100 to 150 tons. These data centers run models developed by xAI, using chips designed by Tesla, powered by solar energy and Tesla batteries. Free solar energy, zero-cost cooling. Inference results are transmitted directly to Tesla cars and Optimus robots on Earth via Starlink.
4. The Closed Loop of Data and Connectivity
SpaceX already has nearly 10,000 Starlink satellites in orbit and has been authorized to launch another 7,500. They have 6 million direct mobile customers. The V3 satellites launched this year have a downlink capacity of 1 terabit per second (1Tbps), which is ten times that of contemporary systems.
The flywheel is spinning wildly here:
xAI builds models (Grok 3 has 3 trillion parameters, Grok 4 won in global testing, and Grok 5 with 6 trillion parameters will be released in Q1 2026).
These models enter Tesla cars. Grok has been online in cars since July 2025, providing conversation and navigation, while the cars' autonomous driving also uses the same Tesla chips.
Grok will become the "brain" of Optimus robots. Optimus plans to produce 50,000 to 100,000 units this year, reaching 1 million units by 2027.
This means: xAI models, Tesla makes chips, Tesla builds robots for execution, Tesla produces batteries for power, SpaceX provides global connectivity and access to space, xAI trains using the full data from Tesla and X, and issues commands from space via solar satellites.
5. An Unassailable Moat
This moat is inevitable.
Tesla has 7.1 billion miles of FSD driving data, over 50 times that of Waymo. Real-world data trains better models, better models improve vehicle performance, and better vehicles collect more data.
X (formerly Twitter): xAI has exclusive access to real-time human data generated by about 600 million monthly active users. This is different from YouTube or search data; it is raw, unstructured, real-time human thought. When Grok hallucinates, they can correct it against real-time consensus faster than anyone else.
What do competitors have to catch up?
Google has vertical integration (TPU chips, Gemini, YouTube), but Waymo's scale is too small and lacks rockets and real-time social data streams.
Microsoft has Copilot and Azure, but relies on OpenAI, has no hardware entities, no space infrastructure, and no autonomous driving data.
Amazon has AWS, custom chips, and logistics robots, but lacks consumer AI products for large-scale adoption, has no car fleet, and no launch capabilities.
Nvidia, while monopolizing training, lacks a "physical layer." They do not have cars or robots in factories to collect data, nor a global satellite network. They sell chips but do not control application endpoints.
To compete with Musk, you need to simultaneously found or acquire five different top companies, while he is consolidating his advantages every day.
Conclusion
Most analysts view Tesla, xAI, and SpaceX as independent investments, but this is completely wrong. The value lies not in the individual parts but in how they nourish each other.
xAI is valued at $250 billion, SpaceX is valued at about $800 billion and is seeking a $1.5 trillion IPO, and Tesla is valued at $1.2 trillion. The total enterprise value exceeds $2 trillion, and the premium for synergies has not even been accounted for.
Each link enhances another:
Tesla's success means xAI gets more training data.
xAI's success means Tesla cars and robots become smarter.
SpaceX's success means the entire system has global coverage.
Energy business success lowers the power costs for all facilities.
Chip strategy success frees them from dependence on Nvidia.
Optimus's success means the total addressable market (TAM) for the labor market exceeds $40 trillion annually.
Did I miss anything? If you can see a flaw that I haven't noticed, I would love to hear it. Because after observing for so many years, I really can't find one.
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