Investor's Corner
Tesla explores safer battery production with novel DCM recovery system patent
In what appears to be yet another step towards its goal of operating the safest car factory in the industry, Tesla has been granted a patent that could pave the way for a safer process in battery production. Published today, the electric car maker’s recent patent describes a system to treat and recycle Dichloromethane (DCM), which is among the materials used in the production of electric car batteries.
DCM is utilized in a variety of industrial processes, particularly in chemical plastic welding, wherein softened plastic pieces or surfaces are welded together. The material is also used to soften plastic sheets for stretching or shaping, and as a solvent to remove unwanted compounds. In Tesla’s case, DCM is among the materials used in the forming of a separator base film for an electric car’s battery system. While DCM is invaluable in manufacturing, though, the material carries some health risks.
Dichloromethane is the least toxic among the simple chlorohydrocarbons, but its high volatility makes it an inhalation hazard nonetheless. Prolonged skin contact with DCM could also result in the material dissolving some of the skin’s fatty tissues, causing irritation or chemical burns. With these risks in mind, the manufacturing industry employs ways to recover DCM. Tesla notes that current systems for DCM treatment and recovery are capital intensive, particularly since the process involves expensive components such as activated carbon beds, condensers, steam boilers and distribution systems, density separation vessels, and waste water treatment systems.
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- Tesla’s DCM treatment system. [Credit: US Patent Office]
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- A flow chart illustrating operation of an exhaust treatment system for treating a waste exhaust stream containing dichlorom ethane. [Credit: US Patent Office]
Tesla’s diagrams outlining its Dichloromethane recovery system. [Credit: US Patent Office]
Tesla describes conventional DCM treatment systems as follows:
“The DCM itself may then be removed through a heating and/or evaporation process with the exhaust collected. This exhaust containing DCM is then combined with the exhaust from other tools and systems used in the manufacturing process. The combined exhaust may then be fed to a recovery plant to recover DCM. In the recovery plant, the waste exhaust stream is typically treated with activated carbon. This scrubbing process requires high capital expenditure (many expensive components), high operating cost (extensive steam and cooling water consumption which accounts for >20% of total process cost), large footprint requirements, and large amounts of waste water that need to be processed. In order to address these cost and environmental-remediation issues, an improved process for the removal of DCM from exhaust streams is needed.”
Tesla’s take on DCM treatment and recovery utilizes a wet scrubber and a density separator vessel as key components of the system. The wet scrubber in Tesla’s patent has a scrubbing chamber, where water is utilized to scrub the waste exhaust stream containing the DCM. Tesla notes that the wet scrubber could adopt a variety of designs to remove DCM from the waste exhaust stream, including a venturi scrubber design, a condensation scrubber design, an impingement-plate scrubber design, or a packed bed tower design, among others.
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Tesla’s use of a density separator vessel is described in the following section from the patent.
“The density separator vessel has an inlet to receive the liquid water and DCM mixture, an outlet to expel DCM, and an outlet to expel waste water. The DCM may be routed back to the industrial process for reuse and/or collected for later use. The waste water may be routed back to the wet scrubber, as shown along (the) waste water return loop. Waste water may also or alternately be routed to waste water treatment system for processing for subsequent treatment by (the) waste water treatment system.
“Typically, a large portion of the waste water is returned to the wet scrubber via (the) waste water return loop and a small portion of the waste water is treated by the waste water treatment system. Even though the waste water may contain small amounts of DCM, the waste water will still retain its ability to scrub the exhaust containing DCM. An advantage of the wet scrubber over the activated carbon beds is that all or most of the water used by the wet scrubber is the waste water from the density separator vessel, resulting in substantial savings of water and energy, and resultantly, substantial cost savings.”
Tesla states that compared to more traditional exhaust treatment systems, the DCM treatment and recovery model outlined in its patent effectively eliminates the use of steam and cooling, while also reducing the amount of throughput needed by a waste water system. With these efficiencies in mind, Tesla notes that it could reduce capital expenditures and operating costs “for the same amount of DCM processed processing.” The increased simplicity of the system and reduced airflow rates are expected to help the company get more savings in both capital expenditures and operating costs as well.
More than a way to optimize its operations, Tesla’s recent patent is also a notable way for the company to keep its battery production lines safer for its employees. Such a system would definitely be invaluable for the company, particularly as Tesla is now preparing the Model 3 for a global rollout. With the Model 3 ramp ever-expanding, and with high-volume vehicles like the Model Y and possibly the Tesla pickup truck in the pipeline, optimizations such as a better DCM treatment and recovery system are all but necessary.
Tesla’s recently published patent on its DCM treatment system could be accessed here.
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Elon Musk
SpaceX’s next trillion dollar bet has nothing to do with rockets, Musk tells staff
Elon Musk told SpaceX staff AI revenue will soon dwarf rockets and Starlink combined entirely.
Elon Musk told SpaceX employees this week that artificial intelligence, not rockets, will soon carry the company’s revenue. In a roughly 29 minute internal address posted on SpaceX’s X account on Tuesday, Musk said AI revenue will pass every other line of business at SpaceX “probably in September” and pull further ahead by the fourth quarter.
The numbers he gave are specific. SpaceX currently runs 1.4 gigawatts of AI compute capacity. Musk wants that at 10 gigawatts by the end of 2027, a jump he tied directly to revenue: “if we bring 10GW of AI online by the end of next year, it will be $300 billion to $500 billion a year in revenue.” He called those “big numbers,” which undersells a projection larger than what most countries produce in a year.
We made rockets reusable and are rebuilding the internet in space. The next challenge: making life multiplanetary and understanding the true nature of the universe
Watch @ElonMusk deliver a company update to @SpaceX employees pic.twitter.com/5c8rxoCQfu
— SpaceX (@SpaceX) August 11, 2026
Musk went further on where AI fits into SpaceX’s future. “Probably in four or five years, AI will be 99% of the value of SpaceX,” he told staff, adding that digital intelligence would eventually run “a trillion times” ahead of biological intelligence as computing scales. He tied that growth to the company’s founding mission, telling employees “we must win on AI, because the future is overwhelmingly AI and robots,” with the payoff meant to help fund Starship and a Mars program that increasingly runs through Terafab, the joint Tesla, SpaceX and xAI chip plant.
Elon Musk launches TERAFAB: The $25B Tesla-SpaceXAI chip factory that will rewire the AI industry
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None of this is entirely new territory. SpaceX told investors much the same story during its first earnings call as a public company on August 4, where Musk moved the company’s $1 trillion revenue target up a year to 2030 and said Starlink could someday carry a majority of the world’s internet. What the all hands video adds is a hard deadline and a specific power figure Musk had not given publicly before, along with a franker pitch to his own workforce that AI, not launch cadence, is now the thing SpaceX is betting its future on.
The AI revenue itself is not coming from SpaceX training its own models. It is largely Starlink acting as the network layer for xAI’s workloads, plus SpaceX renting out compute capacity directly, the same approach behind the roughly $16 billion the company spent on AI infrastructure in a single quarter.
Musk closed the video with a pitch aimed at recruiting and retention rather than investors, telling employees that anyone who helps SpaceX win the AI race will eventually get the chance to go to the moon or Mars themselves. Whether SpaceX can turn 1.4 gigawatts into 10 in seventeen months is the more immediate question, and one that will show up in quarterly numbers well before anyone leaves Earth.
In a new note to investors on Tuesday, Morgan Stanley analyst Andrew Percoco said that Tesla has one big financial question to answer for investors regarding its Robotaxi rollout, Full Self-Driving software, and Optimus.
Percoco said in the note that, for the most part, investors are still very positive about the direction the company is headed. However, there are some things the firm would like to see, and they have to do with financials.
Tesla (TSLA) Q2 2026 earnings results: miss on EPS, beat on revenue
Tesla bulls are more than convinced that the company’s Full Self-Driving software is proof it can develop physical AI. Financially, however, there are still some questions, especially on elevated spending, which CEO Elon Musk said would occur as the company works to roll out Robotaxi faster and continue developing its Optimus robot.
The latter two are where Tesla will have to prove progress to investors, as Percoco writes that both projects “will require clearer evidence that Robotaxi is scaling and more tangible Optimus proof points to support the ROI on elevated capex.”
Percoco said the second quarter earnings call did not change his long-term thesis of where Tesla is positioned in the AI race, which is out in front. However, there are concerns that weaker gross margins and higher R&D spend will stress financials, and that has “sharpened our (and investors’) focus on measurable progress across Robotaxi and Optimus.”
Additionally, Robotaxi still needs to be proven with more operation in existing cities while maintaining safety but improving how many rides it gives in any given time, he said. For Optimus, Percoco wrote that he is “still looking for evidence beyond commentary around SOP.”
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Morgan Stanley put Percoco in charge of covering Tesla after long-time analyst Adam Jonas transitioned to the automotive side.
Currently, Morgan Stanley has a $415 price target on Tesla and a ‘Hold’ rating on the stock. It is trading at around $330 at the time of publication, which was 2:30 P.M. on the East Coast.
SpaceX’s massive investment in AI will make it a big winner, Argus Research said after the company’s successful earnings call last week.
The firm also upgraded shares to a Buy from Hold and set a $160 price target.
SpaceX (NASDAQ: SPCX) is currently recovering from its heavy AI infrastructure investments, as it spent nearly $16 billion in Q2 alone. The company did this primarily by monetizing high-demand GPU compute capacity at a much faster pace than traditional data center economics would suggest.
Company CFO Bret Johnsen said that SpaceX would be able to pay back anything on new deployments within a year.
There are plenty of ways the company can do this:
Leasing excess compute capacity through contracts
SpaceX has already built Colossus and Colossus II, largely for its own model training. However, much of that capacity is already rented out to third parties. It already has major deals with Anthropic, Google, and Reflection AI. These partnerships are adding billions per month to SpaceX’s spreadsheet.
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High utilization driven by industry-wide scarcity
The demand for advanced AI training and inference capacity continues to exceed what is available for use. SpaceX can fill new racks quickly after they come online, so the capital deployed converts into revenue with minimal idle time.
Additionally, management and outside observers have described the new compute capital as behaving more like a cost-of-goods-sold than traditional multi-year capex, especially because of this rapid monetization pattern.
Capacity has already scaled from ~0.4 GW a year to 1.4 GW annually by the end of Q2. There are targets of more than 2 GW by year-end.
High incremental margins on the rental business once capacity is online
GPU cloud providers often operate at strong gross margins. SpaceX can monetize capacity that was already partially built or can be added efficiently. This means that incremental EBITDA margins on the rental revenue are usually high. This accelerates cash recovery relative to the gross capital outlay.
Parallel monetization of its own AI software and applications
Beyond pure infrastructure rental, SpaceX also generates revenue from Grok through subscriptions and usage, from X through ads, data, and other related services, enterprise APIs, and the planned integration of the Cursor coding tools acquisition.
These application layers ride on the same compute infrastructure and provide additional high-margin streams that could offset build-out costs. AI-segment revenue overall rose sharply to about $2.6 billion in Q2, according to Motley Fool. This was driven primarily by the infrastructure contracts, but the software side is also partially responsible.
Efficient, large-scale deployment and vertical integration advantages
SpaceX has emphasized the rapid construction of power and cooling infrastructure and favorable cost-per-megawatt economics relative to industry benchmarks in some disclosures.
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Combined with its ability to scale capacity aggressively and the fact that many contracts start generating revenue within months of capacity coming online, the effective payback compresses dramatically compared with more conventional multi-year data-center projects.
SpaceX’s dominant near-term recovery path will turn the AI clusters into a hyperscale-style compute rental business for other leading AI companies while still using a portion for internal models.
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