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Nvidia starts production on first AI supercomputers built in U.S.
One Nvidia factory in the U.S. has already come online, while two others are expected to reach mass production in 12 to 15 months.
Chip manufacturer Nvidia has officially started production of AI supercomputers at multiple U.S. factories operated through partnerships, marking the first such hardware to be built within the country.
On Monday, Nvidia announced that its Blackwell chips have begun production at a factory operated in partnership with TSMC in Phoenix, Arizona, as detailed in a press release. The chip maker also continues to work on the construction of two future factories to be operated in Texas, in partnership with Foxconn in Houston and Wistron in Dallas.
Nvidia says that mass production of the Blackwell GPUs is expected to ramp up at both Texas facilities within the next 12 to 15 months, together with the Arizona factory representing a total of over a million square feet of U.S. manufacturing space.
“The engines of the world’s AI infrastructure are being built in the United States for the first time,” said Jensen Huang, Nvidia Founder and CEO. “Adding American manufacturing helps us better meet the incredible and growing demand for AI chips and supercomputers, strengthens our supply chain and boosts our resiliency.”
The manufacturer also says that it aims to produce up to $500 billion worth of AI infrastructure in the U.S. within the next four years, through these partnerships and others with Amjor and SPIL. Nvidia also expects the facilities to create hundreds of thousands of jobs, along with driving trillions of dollars in economic security in the decades to come.
NVIDIA CEO Jensen Huang talks about Elon Musk’s prowess in AI across his several companies:
— TESLARATI (@Teslarati) January 7, 2025
READ MORE ON NVIDIA: Elon Musk explains reasoning for Nvidia chip re-route from Tesla to X
Blackwell chips were designed for use in high-powered AI data center applications, and the news comes as Elon Musk’s xAI, Tesla, and several other companies are working to expand their infrastructure for supercomputing. It also comes amidst an ongoing tariff war launched by the Trump administration, which is expected to hit a wide range of products, including semiconductor chips.
Musk estimated last year that Nvidia purchases would comprise roughly $3 to $4 billion of Tesla’s $10 billion AI expenditures, and the company spent much of the year constructing a massive supercomputing cluster at its Gigafactory in Texas. The location houses 50,000 Nvidia H100 supercomputing chips, used to help train Tesla’s Full Self-Driving (FSD) system, and Musk also said last June that the facility’s power needs would increase from 130MW to over 500MW in around 18 months.
Additionally, Musk’s xAI began operations at a Memphis, Tennessee facility with 100,000 Nvidia H100 and H200 units last July, and the site is in the process of being expanded to 200,000. Nvidia was also a strategic investor in a $6 billion Series C investment into xAI, alongside fellow GPU manufacturer AMD.
Weeks before the funding was announced in December, it was also reported that xAI had gained a $1.08 billion priority Nvidia order of GB200 AI servers, after Huang was personally approached by Musk.
Just hours after Starship Flight 13 achieved a successful soft splashdown of its upper stage in the Indian Ocean on July 24, Elon Musk announced an ambitious next step for the company’s next launch of the rocket.
“Unless we discover problems after mission data review, SpaceX will attempt to catch the ship with the tower on [the] next flight,” the SpaceX CEO posted on X on Friday.
That “next flight” is expected to be Flight 14. The plan involves returning the Starship upper stage, commonly called the “ship,” to the Starbase launch tower in Texas and catching it mid-air using the same mechanical “chopsticks” arms that have already proven themselves with the Super Heavy booster.
Unless we discover problems after mission data review, SpaceX will attempt to catch the ship with the tower on next flight
— Elon Musk (@elonmusk) July 25, 2026
A successful catch would mark the first time an orbital-class upper stage has been recovered this way, advancing SpaceX’s goal of full and rapid reusability for the entire vehicle.
SpaceX has already demonstrated the tower-catch technique multiple times with Super Heavy. The first successful catch came on Flight 5 in October 2024, when Booster 12 was plucked from the sky by the Mechazilla arms. Subsequent flights, including those involving Boosters 14 and 15, repeated the feat. Several of those recovered boosters were later inspected, refurbished, and flown again, proving the system’s viability for quick turnaround.
Traditional reusable rockets, such as SpaceX’s own Falcon 9 or Blue Origin’s New Shepard, land on legs either on land or droneships. Rocket Lab has recovered its small Electron first stages by helicopter, but those are far lighter vehicles.
SpaceX Starship just nailed something it’s never done before
The China Academy of Launch Vehicle Technology (CALT), a subsidiary of the China Aerospace Science and Technology Corp. (CASC), completed a catch of its booster on July 10. They are the only entity besides SpaceX to attempt and complete the feat.
Flight 13 provided encouraging data. The ship executed a controlled reentry, flipped, and soft-landed intact in the ocean after deploying Starlink satellites, offering the first clear post-splashdown views of an undamaged heat shield. The Super Heavy booster, meanwhile, experienced a harder splashdown in the Gulf of Mexico.
Musk has previously stressed that ship catches would only follow multiple successful soft ocean landings to minimize risk of debris over land.
If Flight 14 succeeds, SpaceX would take a major stride toward routine, rapid reuse of both stages—critical for lowering launch costs and supporting ambitious plans for lunar and Mars missions. For now, teams are reviewing the Flight 13 data. Should everything check out, the next Starship flight could deliver one of the most spectacular recoveries in aerospace history.
In a move echoing its earlier commitment to open innovation, Tesla CEO Elon Musk announced recently that the company plans to make the design and software of its Model S and Model X fully open source.
This follows the same approach Tesla took with its original Roadster, releasing all available design, engineering, and diagnostic materials in November 2023 so that “whatever we have, you now have.”
Just as Tesla made the original Roadster design & software open source, we plan to do the same with Model S & X
— Elon Musk (@elonmusk) July 24, 2026
The Model S, introduced in 2012, was Tesla’s first mass-produced vehicle and a groundbreaking luxury electric sedan. It offered impressive range, rapid acceleration, and over-the-air software updates that redefined expectations for electric cars.
The Model X, launched in 2015, built on that foundation as a high-performance electric SUV notable for its distinctive falcon-wing doors, spacious interior, and advanced safety features. Both models served as flagships that helped establish Tesla as a leader in the EV industry and popularized long-range battery-electric vehicles.
Production of the Model S and Model X was wound down earlier in 2026, with manufacturing ending in the second quarter. Tesla redirected the Fremont factory space previously used for these vehicles toward higher-priority projects, including Optimus humanoid robots and the Cybercab autonomous vehicle.
By the time of Musk’s open-source announcement, custom orders had closed and only remaining inventory was available.
Open-sourcing the designs and software offers several clear advantages. Owners of these aging but still capable vehicles gain better access to technical documentation, diagnostic tools, and software resources, making independent repairs and modifications easier and more affordable.
Independent repair shops and third-party specialists can support the large existing fleet without relying solely on Tesla’s service network. Enthusiasts and engineers can study real-world implementations of Tesla’s battery, powertrain, and software systems, potentially accelerating broader industry progress in electric mobility.
The step aligns with Tesla’s 2014 patent pledge and its overall mission to advance sustainable transport by sharing hard-won knowledge rather than locking it behind proprietary walls.
By releasing these materials now that the models have left production, Tesla ensures continued support for its early adopters while freeing internal resources for future technologies. The open-source release of the original Roadster already enabled simulations, community projects, and deeper technical understanding.
Extending that practice to the Model S and Model X should deliver similar benefits on a larger scale, helping keep these influential vehicles relevant and repairable for years to come
Tesla flexed one incredible Robotaxi metric during the Q2 Earnings Call that skeptics have to hate to hear. The company’s platform has already driven more than 380,000 miles of unsupervised ride-hailing across several states with no notable incidents.
During the company’s Q2 Earnings Call on Wednesday, Vice President of AI, Ashok Elluswamy, said:
“First of all, I’d like to state that the Robotaxi program has been operating extremely well. Especially in terms of safety, the program has had an impeccable safety record. We have driven more than 380,000 miles of unsupervised Robotaxi, now across six cities in two different states. We have had zero notable incidents. Any reports have been of other actors impacting us when we were stationary. I like to emphasize how safe the operation has been so far. Zero notable incidents over 380,000 miles.”
Elluswamy’s claim over Robotaxi miles is a significant milestone for Tesla in the grand scheme, especially considering this is a sizeable number of miles without any incident.
0 notable incidents across over 380,000 miles traveled by Robotaxi
— Tesla (@Tesla) July 22, 2026
Tesla’s self-driving approach is much different than that of other companies. Tesla has maintained that vision is the only thing needed to have a solid and effective self-driving suite. Many self-driving companies utilize things like LiDAR, sensors, and other elements to improve performance, but Elluswamy sent a jab at those who believe it’s needed.
“Historically, the so-called experts have always claimed that you need LiDARs, radars, HD maps, and the entire kitchen sink to drive safely. Here we show that such is not true. You can have safe, comfortable, and affordable autonomy with just cameras. This record should be a huge validation of Tesla’s entire AI approach.”
The feat of accumulating this many miles without any driver behind the wheel is impressive. The thing is, Tesla is also doing this across several different locations, with varying traffic rules, pedestrian levels, weather patterns, and other important factors.
While Tesla is not ready to roll out an unsupervised platform completely, it is a slow but steady indication that the company is well on its way to figuring things out.
The company’s attitude toward expansion is slow, safe, and controlled, and despite this huge milestone, it will still be some time until we see Tesla truly unleash unsupervised rides more aggressively.
SpaceX wants to catch Starship for launch 14, Elon Musk says
Tesla to open source Model S and Model X designs and software
