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SpaceX and Google mull massive partnership on Musk’s orbital data dream: report
The two companies are currently in talks for a rocket launch deal to support the placement of data centers in orbit as part of their push into space-based computing.
SpaceX and Google are in the process of ironing out the details of a potential partnership, a new report from the Wall Street Journal says. The two companies are currently in talks for a rocket launch deal to support the placement of data centers in orbit as part of their push into space-based computing.
In a move that blends cutting-edge AI demands with the final frontier of space exploration, Google is in exclusive talks with Elon Musk’s SpaceX for a rocket launch deal to deploy data centers in orbit. The Wall Street Journal is now reporting today, May 12, that the discussions mark Google’s aggressive expansion into space-based computing, addressing the exploding energy needs of artificial intelligence that terrestrial infrastructure can no longer sustain.
Exclusive: Google is in talks with SpaceX for a rocket launch deal as the search giant expands its own efforts to put orbital data centers in space https://t.co/QUCD3cPjxi
— The Wall Street Journal (@WSJ) May 12, 2026
SpaceX, nor Google, have commented on the report.
The catalyst for a potential deal is clear: AI’s voracious appetite for electricity. Global data centers consumed about 415 terawatt-hours (TWh) of electricity in 2024—roughly 1.5 percent of worldwide usage—according to the International Energy Agency. That figure is projected to more than double to around 945 TWh by 2030, with AI-focused servers growing at 30 percent annually, outpacing overall electricity demand growth by more than four times.
Some forecasts peg data center consumption exceeding 1,000 TWh by 2026, equivalent to Japan’s entire national electricity use. A single large AI training facility can draw as much power as 100,000 homes. On Earth, this translates to grid overloads, skyrocketing costs, land shortages, and massive water demands for cooling—constraints that threaten to throttle AI progress.
Orbital data centers promise a radical workaround. In space, satellites can harness constant, unobstructed sunlight for power—solar panels generate roughly five times more energy in orbit than on the ground, with no night cycle or atmospheric interference.
Excess heat radiates harmlessly into the vacuum of space, eliminating energy-intensive cooling systems and water usage. No terrestrial land or power grid is required, freeing operations from regulatory and environmental bottlenecks.
Musk has long championed the concept, framing it as inevitable. “Space-based AI is obviously the only way to scale,” he wrote on SpaceX’s site following the xAI merger. “Global electricity demand for AI simply cannot be met with terrestrial solutions… In the long term, space-based AI is obviously the only way to scale.”
He has repeatedly highlighted solar advantages: “Space has the advantage that it’s always sunny,” and “any given solar panel is going to give you about five times more power in space than on the ground.”
Musk predicted in early 2026 that “in 36 months but probably closer to 30 months, the most economically compelling place to put AI will be space,” adding that within five years, annual space-launched AI compute could surpass Earth’s cumulative total. “SpaceX will be doing this,” he declared when discussing scaled-up Starlink satellites with high-speed laser links for orbital data transfer.
Meanwhile, Google has been quietly advancing a similar vision under Project Suncatcher, its internal “moonshot” initiative. CEO Sundar Pichai has described plans to launch two prototype satellites equipped with Tensor Processing Units (TPUs) by early 2027 for testing thermal management and reliability in orbit. In interviews, Pichai has called orbital computing a potential “normal way to build data centers” within a decade, enabled by launch cost reductions.
SpaceX is uniquely positioned to make this reality. The company recently filed with the FCC to launch up to one million satellites dedicated to orbital data centers at altitudes between 500 and 2,000 kilometers, projecting capacity for 100 gigawatts of AI compute.
These talks align with SpaceX’s broader ambitions, including a potential IPO where orbital infrastructure features prominently in investor pitches.
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Challenges remain formidable, as is expected with a project with expectations so lofty. Radiation-hardened hardware, laser-based inter-satellite and Earth-downlink communications, launch economics, and orbital debris management are key hurdles.
Yet early movers like Starcloud (which trained the first large language model in orbit in late 2025) and Google’s prototypes signal accelerating momentum. Rivals, including Amazon and Blue Origin, are exploring similar paths, but SpaceX’s Starship and Starlink heritage give it a launch cadence edge.
This partnership could redefine AI infrastructure, turning the skies into the next data center frontier. As Earth’s power limits loom, Musk’s vision, combined with Google’s ambition, could position space not as sci-fi, but as the scalable solution for humanity’s computational future.
SpaceX confirmed today that it has officially signed its third massive compute deal, providing compute at its Colossus data center in Southaven, Tennessee.
Reflection AI will gain immediate access to NVIDIA GB300 chips at SpaceX’s Colossus 2 data center. In return, Reflection will pay SpaceX $150 million per month starting on July 1, with total payments reaching approximately $6.3 billion if the contract runs through its duration, which is until 2029. Either party can terminate the agreement with 90 days’ notice after the initial three-month period.
CNBC first reported the deal.
🚨 SpaceXAI has agreed to a new compute deal with Reflection AI.
Reflection gets access to NIVIDIA GB300s, and will pay $150M per month to SpaceXAI for the compute. pic.twitter.com/bNPare8U5u
— TESLARATI (@Teslarati) June 22, 2026
This latest partnership highlights SpaceX’s strategy of commercializing its massive Colossus supercomputing infrastructure, originally developed to power Elon Musk’s Grok AI models. The company has rapidly expanded its customer base in the AI sector following its February 2026 merger with xAI, a transaction that valued the combined entity at $1.25 trillion.
SpaceX has previously signed significant compute deals with other major players.
It granted Anthropic exclusive access to the full capacity of its Colossus 1 data center, which exceeds 300 megawatts and includes over 220,000 NVIDIA GPUs. Details from SpaceX’s IPO filings indicate Anthropic will pay $1.25 billion per month through May 2029, potentially generating around $45 billion over the term of the deal.
Additionally, Google agreed to pay SpaceX $920 million per month for compute capacity from October 2026 through June 2029. This 32-month period will provide Google access to roughly 110,000 NVIDIA GPUs, along with supporting processors and memory. Capacity ramps up through September at a reduced fee, with termination options after the first year.
SpaceXA also established arrangements for computing power with Cursor, an AI coding startup. SpaceX acquired them in a $60 billion all-stock deal.
These arrangements position SpaceX’s collective position as an AI infrastructure powerhouse with high-margin revenue potential. The Google deal alone could generate nearly $29.5 billion over its term, while the Reflection contract adds another $6.3 billion.
Combined with the Anthropic arrangement, SpaceX stands to realize tens of billions in revenue from compute leasing in the coming years, which diversifies beyond SpaceX’s traditional rocket launches and Starlink operation.
The deals underscore growing demand for advanced AI training and inference capacity amid chip shortages and surging model development needs. Reflection, valued at $25 billion and focused on “American open intelligence” with government and national security ties, cited recent restrictions on closed models as validation for open-source approaches.
For SpaceX, the partnerships transform capital-intensive data centers into flexible revenue sources while supporting its broader AI ambitions after the company has gone public.
It is safe to say SpaceX won’t be going for electric rockets anytime soon.
In a characteristically blunt reply on X, SpaceX frontman Elon Musk stated, “Unfortunately, electric rockets are impossible,” following reports that MSCI had assigned SpaceX its lowest possible ESG rating of CCC.
The assessment, issued just this past week, coinciding closely with SpaceX’s public market debut, placed the company on par with nations like Russia in sustainability scoring and cited significant risks in environmental, social, and governance areas.
MSCI flagged SpaceX’s exposure to rocket emissions and other operational impacts, alongside governance concerns such as concentrated control by Musk and limited shareholder protections. Musk’s terse comment directly addressed the environmental pillar, underscoring a core physical constraint that ESG frameworks often overlook when evaluating high-thrust industries.
Unfortunately, electric rockets are impossible
— Elon Musk (@elonmusk) June 21, 2026
Electric propulsion systems do exist and are widely used in space. Ion thrusters and Hall-effect thrusters accelerate ionized propellant, typically xenon or krypton, using electric fields, achieving very high specific impulse, often exceeding 3,000 seconds compared to roughly 300–450 seconds for chemical rockets.
This efficiency makes them ideal for satellite station-keeping, orbit raising, and deep-space missions where low thrust over long durations is sufficient. SpaceX’s own Starlink satellites employ electric propulsion for these purposes.
However, launching from Earth’s surface demands something entirely different: enormous thrust delivered rapidly to overcome gravity and atmospheric drag. A typical orbital-class booster must generate thrust far exceeding its weight, often in the millions of Newtons within seconds.
Chemical rockets achieve this through exothermic combustion of dense propellants, producing high-mass-flow, high-velocity exhaust. Electric systems, by contrast, expel very small amounts of mass at extremely high speeds. Generating equivalent thrust would require impractical onboard power levels, massive energy storage or generation systems, and prohibitive added mass, rendering the approach infeasible with current or near-term technology.
Musk has previously expressed a similar sentiment, noting a desire for electric orbital rockets while acknowledging the inescapable requirements of Newton’s third law and energy delivery. The distinction is clear: electric propulsion excels once a vehicle is already in space; it cannot replace the high-thrust chemical phase required to reach orbit from the ground.
The episode illustrates broader critiques of ESG ratings. Proponents argue they incentivize better risk management and long-term sustainability. Detractors, including Musk—who has previously called ESG a “scam”—contend that such metrics can penalize essential activities when no practical alternative exists, potentially discouraging innovation in sectors like space access.
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SpaceX has sought to mitigate launch-related impacts through reusability: Falcon 9 boosters have flown more than 30 times in some cases, dramatically lowering the manufacturing and emissions burden per kilogram delivered to orbit. Starship’s design further emphasizes rapid reusability and methane propellant, which can theoretically be produced via sustainable pathways.
Ultimately, Musk’s remark serves as a reminder that certain engineering realities persist regardless of scoring systems. As humanity expands its presence in space for communications, science, and exploration, balancing genuine environmental progress with technological necessity remains a central challenge.
ESG frameworks may evolve, but the fundamental limits of electric launch propulsion are unlikely to change soon.
Tesla just trademarked ‘MEGAPOD’ with the United States Patent and Trademark Office (USPTO), its latest move in what seems to be a hint that the company is incredibly focused on its AI efforts and storage needs as compute increases.
The application carries serial number 99893717 and lists the applicant as Tesla, Inc., located at 1 Tesla Road, Austin, Texas 78725.
The filing remains in ‘live pending’ status, and it is a new application waiting for assignment to an examining attorney. It has not yet been published or registered.
Tesla just trademarked MEGAPOD
Summary:
“Modular data center hardware systems for artificial intelligence computing, comprised of computer servers, computer hardware for artificial intelligence processing, computer networking hardware, electrical power distribution units, and… pic.twitter.com/3l85DsKadl— Robin (@xdNiBoR) June 19, 2026
According to the official goods and services description in the application, Tesla describes ‘MEGAPOD’ as:
“Modular data center hardware systems for artificial intelligence computing, comprised of computer servers, computer hardware for artificial intelligence processing, computer networking hardware, electrical power distribution units, and cooling systems, sold as a unit; self-contained modular computing hardware systems for artificial intelligence workloads; integrated computer hardware platforms for artificial intelligence computing, namely, enclosures containing computer hardware, power distribution hardware, and cooling hardware, sold as a unit; downloadable software for monitoring, managing, optimizing, and regulating modular artificial intelligence computing hardware systems.”
This description specifies complete, self-contained modular units that integrate servers and specialized AI processing hardware with networking components, power distribution, and cooling systems. It also includes associated downloadable software for oversight and optimization of these systems. The language emphasizes hardware sold “as a unit” and enclosures that combine the necessary elements for AI computing workloads.
Tesla has an established history of developing and commercializing modular hardware systems. Its Megapack product line, for example, consists of utility-scale battery energy storage systems designed as containerized units for grid applications. The MEGAPOD filing follows a similar pattern of protecting a name for modular, integrated hardware platforms, this time focused on artificial intelligence computing infrastructure.
This could be an early move, especially as Tesla did not have trademark rights to the word ‘Cybercab,’ the name of its self-driving, ride-hailing-focused vehicle.
Trademark applications of this type allow companies to secure priority rights to a name for defined categories of goods and services. The USPTO examines applications for compliance with legal requirements, including distinctiveness and absence of conflicts with prior marks. If the application proceeds successfully through examination, publication, and any opposition period, it could result in a federal trademark registration providing nationwide protection. This is what Tesla’s obvious intention is with ‘MEGAPOD.’
Public reports and analysis suggest MEGAPOD could represent modular, container-style AI computing pods designed for easy deployment. These would bundle servers, AI accelerators, power systems, and cooling into self-contained units suitable for distributed AI workloads. This approach aligns with Tesla’s announced AI compute strategy.
In March 2026, Elon Musk outlined plans for “Digital Optimus” (also referred to as Macrohard), a joint Tesla-xAI project for AI agents capable of handling complex digital tasks. The plans include running these agents on Tesla’s AI4 hardware in parked vehicles as well as dedicated compute units installed at Supercharger stations, which collectively offer substantial unused electrical capacity.
What is Digital Optimus? The new Tesla and xAI project explained
A modular hardware platform like the one described in the ‘MEGAPOD’ filing would support scalable, rapid deployment of such distributed compute resources. It could complement Tesla’s other AI infrastructure efforts, including the Dojo supercomputer used for training models and the development of AI systems for autonomous driving and robotics, by enabling edge or regional AI inference without reliance on traditional centralized data centers.
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