Most megafactories usually take years to go from conceptualization to construction, but a recent drone flyover of the Terafab showed that progress has already started on the ground less than five months after Musk unveiled it. However, the video doesn’t do justice to the true scale of its footprint, so X user Nic Cruz Patane created a visualization to help us understand how large the chip manufacturing facility is.
Terafab approximate size comparison between Giga Texas, the Pentagon, Apple Park, and the Mall of America.There has never been a building this large. Elon Musk says it will be the most valuable building by far. pic.twitter.com/GCmpfeduJnAugust 6, 2026
The site will reportedly have a floor space of at least 100 million square feet, making it larger than Giga Texas (10 million sq. ft), the Pentagon (6.6 million sq. ft), Apple Park (2.82 million sq. ft), and the Mall of America (5.6 million sq. ft), combined. This also makes it significantly larger than New Century Global Center in Chengdu, China, with has an interior space of “just” 18.9 million sq. ft.
While this might seem like an absurd amount of space for chip manufacturing, it appears that the Terafab will need it because it’s going to be more than just a chip fab making AI processors — instead, it will be an all-in-one facility that will produce logic and memory chips, as well as have lithography, packaging, and testing under one roof.
Elon Musk started talking about building his own chip manufacturing facility in late 2025 and officially announced the project in March of this year. The reasoning behind this project is that both SpaceX and Tesla will require at least 1TW of compute, which is more than ten times that current global chip supply.
Nvidia CEO Jensen Huang warned that a project like this will be an “extremely hard” challenge, but it appears that Musk is willing to put his massive resources on the line for this. Intel CEO Lip-Bu Tan even said that he can “think of no better partner than Elon Musk” to explore “unconventional” ways of chip manufacturing.
Musk is no stranger to both leading and funding projects that otherwise seemed impossible. Although he did not found Tesla, his investment and leadership in the company led it to become an industry trailblazer in EVs. He fundamentally changed commercial space travel with SpaceX; something that used to be the purview of NASA and other national governments, and he also broke a record when he set up 100,000 Nvidia H200 GPUs in just 19 days back in 2024 — a process which Huang said usually takes four years.
It seems that Terafab is Musk’s biggest project to date, which is estimated to require up to $119 billion in investments. But even though it seems that this project already has a secure customer base in SpaceX and Tesla, the former acknowledges that there’s a risk that this ambitious megafactory may not be successful.
The Federal Communications Commission (FCC) is moving to ban drones with LiDAR obstacle-avoidance features, a move that would remove a number of popular consumer drones from US shelves. In a public notice published in the Federal Register on August 3 (DA 26-758), the agency proposed retroactively banning the import and sale of any foreign-made drone on its Covered List that “contains or integrates” a LiDAR sensor, categorizing the technology under a broad “military-grade” umbrella.
The proposal builds on a ban the FCC set in motion late last year. On December 22, 2025, the agency added all foreign-made drones and their critical components to its Covered List — a roster of communications equipment that a U.S. national-security authority has determined poses an unacceptable risk. The listing blocked any new foreign drone model from receiving the FCC authorization it needed to be sold in the U.S., effectively banning sales of new models.
However, models that had already cleared authorization before the listing could continue to be imported and sold. The new DA 26-758 proposal seeks to retroactively strip sales clearance from previously approved drone models that ”escaped” the initial ban, starting with those it deems military-grade. The proposal is open for public comment until September 2, 2026, and if adopted as written, the ban on imports and sales would take effect roughly 180 days after publication.
LiDAR (Light Detection and Ranging) is a remote sensing technology that uses laser pulses to measure exact distances and build precise 3D maps of objects and environments. Drones, delivery bots, and self-driving cars all rely on it for obstacle avoidance. A ban would mean that popular consumer models such as DJI's Air 3S and Mini 5 Pro — both of which use LiDAR for obstacle detection — would no longer be available for sale in the US. The proposal would not affect existing owners, who could keep flying the drones they already have, but as DJI has warned, those owners could find official spare parts increasingly hard, or eventually impossible, to source over time.
Under the FCC's broad ”military-grade” classification, the ban would also reach drones that carry thermal imaging sensors, can dispense agricultural sprays (”economic poison” in the notice's language), weigh 55 pounds or more, include docking stations, integrate a defense article, or operate in coordinated swarms — a category the FCC defines widely enough to include synchronized drone light shows. U.S.-made drones and other drones not on the Covered List are not touched by the ban, regardless of whether they carry any of these features.
The FCC frames the move purely as a national security issue, leaning on an executive-branch determination that foreign-made drones pose an “unacceptable risk.” It tentatively concludes that the economic hit would be minor, as military-grade models are a small minority in a market dominated by recreational fliers. Conversely, the notice also details the billions that domestic drone makers like Skydio, which has committed $3.5 billion to US manufacturing, have raised since the original ban.
DJI, one of the manufacturers affected by the ban, has argued that the “military-grade” label is a category error, sweeping in civilian tools that first responders, farmers, and hobbyists rely on. Its core objection lands on the LiDAR clause, specifically arguing that the technology is a safety feature, not a weapon. DJI had earlier sued the FCC to overturn the December ban, claiming the agency exceeded its statutory authority.
A new Amazon data center is set to become the single largest source of environmental pollution in the U.S., according to a New York Times report on August 8. The tech giant — which already operates the most data centers worldwide — is reportedly building a natural gas power plant in Pecos County, Texas, to power its new data center at the same site. According to project permits, the plant would generate up to 7.65 gigawatts of power using 35 natural gas turbines. The new plant — which burns natural gas to generate electricity — has been authorized to release 33 million tons of CO₂ annually, more planet-warming gases than any other power plant in the country, says the NY Times report.
Conversely, Amazon had pledged to achieve net-zero carbon emissions across all its global business operations by 2040. The company made the promise in 2019 when it initiated and co-founded The Climate Pledge — a voluntary coalition of over 700 companies and partners — aimed at addressing its own corporate footprint and rallying global supply chains. However, the company has seen its emissions rise each year for the past several years, something analysts attribute to a spike in data centers to support the AI boom. The company is currently building several additional AI data centers globally, including the one that the new gas plant would power in Texas.
Amazon has acknowledged the impact of its intense AI ambitions on its earlier climate pledges but insisted the company remained committed to them, despite obvious struggles. “The world looks different now than when we co-founded the climate pledge,” said Margaret Callahan, an Amazon spokeswoman. Still, “our commitment hasn’t changed,” she said, echoing similar statements by Microsoft, which is struggling to fulfill its 2030 sustainability promise amid carbon-heavy AI expansion, but insists it remains committed to it.
As detailed in our data center power roadmap, an increasing number of hyperscalers and data center operators are switching from grid connections to dedicated on-site power generation. The “behind-the-meter” moves are being triggered mainly by the long timelines required to set up new power infrastructure to connect new power-hungry data centers to the grid, but also by growing anti-data center sentiments over irreversible hikes in the electricity bills of nearby communities.
While many companies are also pursuing nuclear and renewable energy options — examples being Microsoft’s Three Mile Island nuclear deal and Meta’s agreement to secure up to 1GW of orbital solar energy capacity — gas appears to be the leading preference due to its scalability and speed of availability. The Trump administration has thrown its weight behind such projects for data centers, promoting oil, natural gas, and coal over renewable energy sources.
The city council of Emporia, Kansas, the same council that had a teacher arrested for clapping in support of the opposition, announced that it will move its meetings online after its members received death threats. According to 404 Media, Emporia announced this move two days before the meeting set for August 5, 2026, and will also affect the one set on August 19, 2026.
“In the interest of public safety, the Emporia City Commission meetings scheduled for Wednesday, August 5, 2026, and Wednesday, August 19, 2026, will be conducted virtually. No public comment period will be provided during either meeting,” the city said on its Facebook page.
“The City remains committed to conducting the public’s business in an open and transparent manner while taking reasonable measures to protect the safety of elected officials, City staff, and members of the public. The City appreciates the public's understanding and cooperation as it implements these temporary measures while ensuring the continuity of City business.”
The move to a virtual meeting is an understandable move given the death threats on some of the city leaders. However, it also canceled public comments and instead directed citizens to an email address and phone number where they can air their concerns privately. This led to some citizens airing their complaints in a protest outside the Emporia municipal auditorium.
“While I don't doubt that there are credible threats, my big question is on how many there are and from where they are coming. Mayor Smith did mention in yesterday's meeting that the threats don't seem to really be coming from locals but ‘it only takes one’ to ruin a meeting so they went virtual. I feel like we're being punished for the actions of outsiders,” Lux Claridge, the person arrested in the previous council meeting, told 404 Media.
“I'm not so upset about going virtual, it makes sense, but back it up with examples of these threats. I'm more upset that they opted to close public comment and I don't understand why.”
One of the things that the council considered during the meeting was the petition to put a data center ban on the ballot, with the members voting unanimously to send it for judicial review. The council claimed that doing otherwise could open the city to a lawsuit from either side, although it did not elaborate on the reason why this would happen.
While we haven’t heard of a local jurisdiction getting sued for passing a data center ban, one Texas senator warned a county in the state that it cannot legally impose this. So, the commissioners are likely being careful to avoid legal issues, even if they’re in another state.
The community of Gilroy, California, about 30 miles southwest of San Jose, was surprised when an Amazon data center started construction in their town last month. But as many projects like these have been suffering from opposition and delays across the country, the Wall Street Journalreports that this specific site avoided scrutiny by adhering to local zoning rules set 45 years ago.
Amazon’s Gilroy project sits on a 56-acre piece of farmland between a Walmart Supercenter and the Gilroy Premium Outlets. The company and the city didn’t exactly keep the project secret when it started its application in 2020. In fact, there were disagreements between the tech giant and the city government during the process. The publication noted that some Amazon officials were frustrated by slow permitting progress, which was delayed by at least one year. At the same time, the city pushed back on some of the company’s requests, especially if they were no longer up for discussion.
Roger Wehner, Amazon Web Services vice president of economic development, told WSJ that the site had gone through a long approval process, which required public notices and public-comment periods. However, when Gilroy resident Rosa Rodriguez wanted to state her concern about the damage a data center can do to a region suffering from drought, she was told that the comment period had already lapsed in 2024, long before data centers became a hot topic across the country.
Gilroy City Mayor Greg Bozzo said that other industrial projects that had a similar scale to the Amazon data center have previously been approved without resistance.
“It didn’t bother them that a food-distribution center came to Gilroy without their engagement, but now it bothers them that a data center is coming to Gilroy without them having the possibility of engagement,” Bozzo said.
Still, he conceded the people are now more aware about data center developments and added, “We…have an opportunity to change the way that we do business in Gilroy. We’re adapting to the times.”
The tech giant also said that it’s engaging more with the community, with Amazon officials claiming that they met residents directly and even hosted an open house where people can ask questions and leave comments. It also addressed fears that the development would cause a water shortage, which other data centers have already caused, saying the company is constructing a wastewater recovery system to supply its water needs and will even build a pipeline to bring wastewater from the source to its plant.
Bozzo also highlighted the benefits that Amazon will bring to the city, especially increased tax revenue, construction jobs, and a million-dollar donation towards a new fire truck. He also pointed out that the tech giant has been giving grants to non-profit organizations in the city, as well as sponsoring the annual Gilroy Garlic Festival.
But at this point, it’s not the Amazon data center the people of Gilroy are having an issue with. Instead, it’s the fact that a major project that has the potential to have a major impact on the community has been approved without the knowledge of many residents.
An Intel patent application published on August 6, spotted byPatentlyze, describes an orbital data center architecture that moves some of the computing used to operate massive satellite constellations off the ground and into space. The architecture proposes a two-tier satellite network in which a small number of more powerful satellites in higher orbits manage large constellations of relatively simple satellites in low-Earth orbit, handling much of the computing and constellation coordination normally performed by data centers and network operations centers on the ground.
The application, US 2026/0230175 A1, is a continuation of an earlier Intel filing that was granted as US 12,542,604 B2 in February.
Intel’s proposed architecture is a different proposition from the orbital AI data centers now being pursued by companies such as SpaceX and Google, which aim to move AI compute itself into low-Earth orbit. SpaceX’s planned AI1 satellite and Google’s Project Suncatcher both envision running large-scale computing workloads in space, with the resulting data beamed back to Earth over high-bandwidth optical links. Intel’s orbital data centers, on the other hand, are designed primarily to serve the satellite network itself, acting as higher-orbit compute and control hubs for the much larger constellations operating below them.
In large LEO constellations such as Starlink, Telesat Lightspeed, and Amazon’s Project Kuiper, thousands of satellites are constantly moving relative to one another and the Earth. While the satellites perform their individual tasks, the network itself still has to determine how traffic is routed, which satellites and links should communicate, how spectrum is allocated, and how the constellation responds to failures, interference, weather, and other changing conditions. Much of that network planning and control processing is traditionally handled by computers on the ground, with routing and operational instructions calculated at terrestrial network operations centers and then transmitted back up to the satellites.
(Image credit: Intel)
Intel says that this constant dependency on terrestrial infrastructure delays time-sensitive decisions, increases reliance on ground stations, and makes management harder as constellations grow into the thousands of satellites. Intel’s solution is to move part of that control and compute layer into orbit. Its architecture places more powerful satellites — which contain much more compute and storage capability than the individual LEO satellites — in Medium Earth Orbit (MEO), Geosynchronous Earth Orbit (GEO), or highly elliptical orbits, where they can maintain a broader and more persistent view of the LEO constellation below and take over tasks such as routing, mission planning, scheduling and network coordination without continually sending those workloads back to Earth.
The proposed setup does not eliminate the need for ground stations. It just keeps satellite network control processing in space. Intel specifically describes moving mission planning and scheduling operations into orbit. The company also argues that offloading heavier network-management tasks to a smaller number of powerful satellites could allow operators to build simpler, cheaper LEO spacecraft. Under current architectures, individual satellites still have to actively participate in network-control functions, requiring additional onboard compute and communications hardware. There’s currently no indication that Intel is actively building the satellites.
An AI model has just created new viruses that never existed in nature. According to a New York Times report, researchers trained a genomic AI model to design complete DNA sequences for viruses, then chemically built the results and watched some come to life. Of 285 AI-generated viral genomes tested, 16 successfully assembled into functioning viruses capable of infecting bacteria and reproducing.
In the study, published in the journal Science, researchers at Stanford University and the Arc Institute trained genomic AI models called Evo on trillions of nucleotides — the individual building blocks of DNA found in every living organism — allowing it to learn statistical patterns in how biological DNA is arranged. Much as a language model learns which combinations of words tend to make sense, Evo learned which combinations of nucleotides tend to produce biologically meaningful sequences.
Scientists have been synthesizing viruses for decades, typically to study how they work and to test antiviral drugs and vaccines. In these projects, researchers typically use the genetic sequence of a virus that already exists to manufacture new ones. Basically, they use existing genomes — genetic sequence data that holds information on how proteins come together to form an organism — replicating and editing this data.
Every organism comprises nuclotides/DNA. The genome provides the instructions for exactly how these building blocks form to create that specific organism. We can copy the data and replicate the organism, aka cloning. However, because trillions of possible genomes exist, it has been statistically impossible for humans to study enough of them to know the formula for how nature puts these building blocks together to form an organism. The blocks were known, but the formula for arranging them in a way that created viable genes wasn't. Until now.
After training the AI model on over 9 trillion nucleotides spanning over 128,000 genetic sequences drawn from millions of animals, plants, microbes, and viruses, it was able to discover patterns and use those patterns to design new genes that could instruct cells to make specific proteins. Using the pattern, the scientists wanted to see if the AI could create a blueprint for a new, simple organism, such as a virus that contains only a few thousand building blocks. For context, humans contain over three billion.
The researchers then trained the Evo AI model on the 11 genes and 5,386 nucleotides of the Phi X-174 virus and about 15,000 of its closest relatives. The virus was chosen because it has been studied for over a hundred years and is known to attack only E. coli bacteria. After studying the virus’s genome, the model proposed 700,000 new potential genomes, which the scientists trimmed down to 285. The scientists synthesized potential organisms using these instructions, out of which 16 resulted in new viable viruses that have never existed in nature. Like their relative, Phi X-174, the viruses were observed attacking E. coli bacteria and reproducing. It's important to note that the new viruses are quite similar to the source. The building blocks are very much the same, with some mutations in how they are arranged.
The potential applications of the study can be far-reaching. The scientists demonstrated one of them via a resistance experiment. They first generated three E. coli strains that had evolved resistance to natural Phi X-174. The researchers then exposed those resistant bacteria to cocktails made from the AI-designed phages. Those phage populations evolved during the experiment and ultimately overcame resistance in all three bacterial strains.
The scientists say the new viruses were completely harmless to humans, noting that replicating the same results in pathogens known to affect humans would be a different ballgame. They were also careful not to train the AI on any data from organisms known to affect humans. However, the study inadvertently shows that highly dangerous applications are a possibility. Experts worry that such studies are way ahead of necessary guardrails and regulations. AI has also been known to fly off the rails autonomously. An OpenAI agent recently went rogue and hacked Hugging Face.
NASA ensured that all laptops destined for use in the International Space Station (ISS) had the same power connector. Engineer George Roush, who has a certain fondness for space tech, recalled details of this laptop modification earlier this week. Standardizing on a single ‘cannon plug’ meant that instead of requiring a multitude of different chargers, ISS visitors could plug into the onboard DC power outlets in a standard fashion. Roush took the photos shared below during a visit to the NASA Stennis visitor center.
Since you all care (thank you for that, by the way) here's the answer: That's not an ethernet port at all! It's a modem port! Modem ports are useless on the ISS. So, NASA replaced this useless port with a new power port. Taking 9 different laptop chargers into space sounds… pic.twitter.com/ZLcqcEOHVfAugust 2, 2026
Laptops destined to be used on the ISS would have their modem ports removed and replaced by the cannon plug you can see in the engineer’s photos. Of course, modem ports would be useless on the ISS, so that’s why they would be replaced with this modification. In space, every gram, every connector, and every failure mode matters, which makes this power standardization a smart move.
Sources indicate that Lockheed Martin was contracted to make these modifications to the ThinkPads and other machines that ended up being taken to space. Roush also discusses networking/comms on the ISS, which relied on the MIL-STD-1553 system, rather than Ethernet.
Laptops with built-in telephony modem ports certainly date this story. However, we must remember that the ISS was constructed in the late 1990s when modems were still essential for PCs. The first crews docked in 2000.
Original web sources are vanishing
When refreshing his knowledge about this laptops-in-space mod, the space enthusiast and engineer was struck by another realization, which he found troubling. The NASA Stennis visitor center had barely any information about the laptops or the cannon plug modification that was on display.
Turning to some online resources he remembers referring to previously, Roush noticed a lot of them had disappeared. “The old internet is dying, and with it, we risk losing a lot of useful history,” said the engineer. “I fear that the false belief of the 'eternal' internet will cause otherwise recorded history to be lost when the servers shut down.”
Though some resources may still be able to be unearthed via the Wayback Machine, it seems perilous that so much web history is archived by a charity (The Internet Archive). In May, we reported that the internet is getting harder to archive because the AI boom has caused a storage crisis, with both NAND and mechanical drives facing shortages. This means skyrocketing storage bills to support the activities of the Wayback Machine.
HP replaced all the ISS laptops in April 2026
The astronauts in the ISS aren’t still using old modded laptops like the one showcased in the NASA Stennis visitor center. The latest updates came in April this year, with over 100 HP Z Workstations packing a mix of Core Ultra 9 processors and RTX Pro Blackwell graphics sent into orbit.
HP talks about putting these PCs through two years of rigorous testing ahead of deployment. We don’t get much in the way of technical info from its release, but again, it was recognized that a single power standard was important. The computer maker supplies universal AC/DC power adapters with its latest crop of space laptops that are specially designed to function on both the ISS and Earth.
Microsoft has opened its largest India cloud region in Hyderabad as it expands AI infrastructure and competes with Google and Amazon for enterprise demand.
The Salem City Council convened a public meeting last July 27, where it discussed the proposed $5.1 billion Oakline at Mill Creek data center by Verrus. As the people converged on the meeting site, a TikTok user spotted a guillotine towed by a bike outside of the venue, signaling the people’s discontent with the project.
According to NewsData, the consultation lasted for more than six hours as several members of the community voiced their opposition towards it. The developer’s representatives were initially on-site to hear the comments, but had to leave midway through as they were confronted by some protesters.
“During a break in the proceedings, a group of opposition members cornered Verrus representatives and shouted insulting remarks, to the point where it no longer felt safe to stay,” Verrus spokesperson JP Newmann said, according to the publication. “We appreciate the support of local law enforcement in helping de-escalate the situation and escorting Verrus representatives from the site safely.”
One week after the meeting, Salem announced a data center moratorium that would halt all data center developments in the city for one year as it considers zoning laws to accommodate developments such as this. This adds Salem to the long list of jurisdictions that have passed similar moratoriums, including Seattle, Washington, and the State of New York.
Unfortunately, the Verrus project will likely not be affected by this delay as it filed its application three days before the announcement. Despite this, Governor Tina Kotek (D) announced that Oregon will terminate a deal to sell a 32-acre plot of land that forms part of the three plots of land on which the project will sit. While it still leaves Verrus with approximately 43 acres, it means that it would have to go back to the drawing board to change its plans. This is similar to what Nashville did when it used its eminent domain power to deny the land that another developer wanted to use to build a data center near the historic Nashville Zoo.
But because Verrus submitted its application before the moratorium announcement, the city is still processing its proposed data center project. That does not mean that it will get an immediate green light, though. It still has to go through an extensive permitting process, which includes technical studies, permit conditions, and enforceable agreements, among others.
Oregon is one of the first states to enact a law that ensures large consumers, like data centers, pay for the majority of the grid upgrades needed to serve their needs. This allowed Portland General Electric, the state’s largest power provider, to increase the bills of users who used more than 20 MW by 30%, while also affecting a 1.3% cut in residential electricity costs. But despite this protection that stops ratepayers from price shocks, many people are still against data center projects in their towns and cities.
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Earlier this week, we published a roadmap, focusing on the foundries that are making co-packaged optics (CPO) a reality. As the demands of data centers increase, so too do the demands on scale-up and scale-out connectivity. With AI accelerators now demanding extremely fast signalling speeds, CPO puts optical engines next to the processor or ASIC in order to shorten electrical paths, increasing data rates, shortening the electrical path, and consuming less power.
We dive deep into TSMC's COUPE roadmaps, which extend to 2030, and to a blistering lane speed of 400 Gb/s, Intel's CPUs with OCI chiplets, Samsung Foundry's path to building a CPO turnkey, and GlobalFoundries' vendor-agnostic OCI-MSA CPO platform.
That's not all we had on the roadmaps front this week, as we've taken a deeper look at China's chipmaking tool roadmaps, hot on the heels of the country announcing it will build DUV immersion lithography machines capable of producing 7nm chips by 2030. The more difficult equation is building a domestic EUV machine, which demands a robust supply chain of the right tooling and equipment, with over 3,000 workers reported to be working on what's now dubbed China's "Manhattan Project."
The country still relies on ASML's DUV lithography machines for chipmaking, as modern systems are subject to export controls, which is why Huawei, the creator of the Ascend series of AI accelerators, is keenly interested in getting the project off the ground.
But China has more on its mind than merely producing the machines; it also wants to spin up its DRAM output via CXMT, which is planning to expand to a brand-new fab to boost memory output. This comes following consumer brands and computer makers like Dell, Lenovo, and more. But it's no simple task, as we explore how the MATCH Act could impact CXMT's ability to produce DRAM modules on more advanced nodes.
There are also concerns around expanding wafer capacities and procuring the tools required for such an expansion, which targets 30% of market share by 2030.
On the memory front, Samsung has also debuted three brand-new memory technologies, zHBM, zNAND-O, and BV-NAND, which each target different markets and are all built upon advanced wafer bonding techniques. zHBM is expected to enable 8x the performance of HBM5, while zNAND-O,. which would allow the company to put four or eight stacks of NAND devices on top of logic dies. Meanwhile, BV-NAND represents Samsung's 10th-generation V-NAND and is the closest of the three announcements to commercialization.
You can read this lengthy technical breakdown of Samsung's announcements for free, even if you're not a Tom's Hardware Premium subscriber.
Is the AI tokenmaxxing party back on? With the release of the modestly priced Kimi K3 and DeepSeek V4 Flash-0731 over recent weeks, the emergence of competitive, cheap Chinese AI models has now forced companies like OpenAI, which launched GPT 5.6 Luna back in June, to cut prices by a staggering 80% in the wake of the competitive open-weight Chinese models. And OpenAI isn't the only company trying to offer more value to users. Anthropic replaced Opus 4.8 with 5.0, and Google also came out with the affordable Gemini 3.6 Flash and Gemini 3.5 Flash-Lite models, too.
So, where does that leave the AI industry? Western frontier labs must keep pace with the competition on offer overseas, and the intensifying competition is slimming down margins for what we assume to be already subsidized token costs.
Within the world of artificial intelligence, other stresses are looming for ChatGPT-maker OpenAI. The company is heading to court with tech titan Apple over claims that OpenAI stole trade secrets from Apple. The claim comes amidst OpenAI's development of dedicated AI hardware. OpenAI says it didn't want Apple's trade secrets, and that the Cupertino company was simply getting things wrong. Apple, however, alleges that 13 of its former employees passed along the aforementioned trade secrets, including documents sharing details of unannounced products and screenshots of sensitive materials.
We explore how the lawsuit is developing in this rather unusual offshoot, which centers around products that have yet to see the light of day.
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SpaceX and Tesla on Thursday formally unveiled plans for the initial phase of their Terafab project. The first stage of the plan — which is expected to use Intel's 14A process technology — is expected to require $16.8 billion in investment, while the completed campus is planned to encompass more than 100 million square feet of manufacturing space. The massive semiconductor manufacturing complex will be built in Grimes County, Texas, at a location that belongs to SpaceX.
According to SpaceX and Tesla, their combined demand for semiconductors is projected to exceed 1 terawatt (TW) of compute per year, which significantly exceeds today's global supply. In fact, SpaceX, Tesla, and xAI already consume a significant portion of contract chipmaking services available today, and can potentially justify building a dedicated fab that will exclusively serve Elon Musk's companies. In particular, Terafab is envisioned to exclusively produce AI inference processors for Tesla Optimus humanoid robots and Cybercab autonomous vehicles, as well as 'high-power' processors intended for SpaceX's space-based data centers. Meanwhile, SpaceX and Tesla have not disclosed when their combined demand for compute per year will hit the 1 TW benchmark.
Unlike a conventional semiconductor fab, Terafab is envisioned as a vertically integrated manufacturing campus where advanced logic devices, memory chips, packaging, and testing operations are housed together. Normally, logic and memory are produced at different fabs using different process technologies, whereas packaging and testing services are performed at different facilities. However, the companies believe that consolidating logic, memory, packaging, and testing in one location will not only shorten the production cycle but will also shorten time-to-yield by enabling faster iterative improvements.
Given the description and the goal of the project, this will be a massive facility. Yet, SpaceX and Tesla have said little about its capabilities; The only thing they have disclosed is that the current facility in Grimes County will feature '100 million square feet of manufacturing space.' The 100 million square feet (9.3 million square meters) figure immediately stands out because it is far beyond anything ever announced for a semiconductor manufacturing facility. For example, the total area of Samsung's Pyeongtaek campus is approximately 2.89 million square meters, or 31.1 million square feet. A single Samsung fab occupies about 120,000 square meters, or 1.29 million square feet. However, the key wording is important: '100 million square feet of manufacturing space' does not mean 100 million square feet of cleanroom space.
Based on an image published by SpaceX, the Terafab facility will occupy four massive buildings. It is unclear whether these buildings will be four phases of the project (i.e., logic, memory, and packaging will be made under one roof) or will serve different purposes (i.e., one building makes logic, another produces memory, yet another does testing and packaging). In any case, when fully built, Terafab will be a massive semiconductor production campus that will require significantly more than $16.8 billion.
The announcement claims that the Terafab facility will employ at least 3,000 people, and that between 60% and 80% of them will be Grimes and nearby Brazos County residents.
Terafab is expected to use water from Gibbons Creek Reservoir instead of local groundwater and feature on-site wastewater treatment as well as water recycling and conservation measures.
The announcement follows Tesla's groundbreaking earlier this year on a research semiconductor facility at the North Campus of Tesla's Giga Texas campus, which the companies describe as a precursor to Terafab.
Anthropic has announced it's building an in-house chip development team to co-design its own custom ASIC processors for handling AI inferencing workloads. As described to Business Insider, Anthropic is starting to hire engineers to design the chips with an unspecified partner, and it looks set to do it at pace, with the job listing saying that any potential hiree would need to be ready to work to a schedule and get the chip design over the line.
This is just the latest major AI company to announce it's developing its own custom hardware. As the global economic shortages squeeze chip supply and models increasingly lean on optimizations to make workloads more efficient and potentially profitable, making custom silicon for your own data centers makes a lot of sense. Anthropic now joins the likes of Google, Meta, Microsoft, Amazon, and OpenAI in building their own chips for the job.
If you want something done right...
It's no secret that if you want to train an advanced AI model, you need Nvidia GPUs. Even Chinese AI developers, who have the ruling party leaning on them and limited access to Nvidia hardware, still use Nvidia GPUs — even if they have to smuggle them first.
But if you're looking to run AI to perform inferencing workloads for agentic and generative AI models, you can use a much wider array of hardware. Nvidia GPUs are good, but they're expensive — custom silicon can have a lower total cost of ownership of up to 65% — and power-hungry, and there are much more efficient options available. Chinese labs are using domestic Chinese hardware, and many Western AI developers have their own solutions; those that don't are making them.
Google has been building its Tensor Processing Unit (TPU) chips for 12 years, working with Broadcom to develop each generation. Amazon has its Trainium and Inferentia chips, and Meta recently announced several new MTIA designs for deployment through 2027. Microsoft has its Maia line, and Tesla recently pivoted to its AI5 and AI6 chip designs after years of developing Dojo.
And now Anthropic is getting in on the act, and for much the same reasons. Anthropic told Business Insider that it was co-designing the chips so that they would allow Claude to run faster and more efficiently at the scale its customers need. Indeed, Anthropic has seen explosive growth in the past year, seeing huge expansion in the consumer space and taking on significant government contracts — not to mention agentic AI requiring far more tokens than traditional single-prompt interactions.
Anthropic hasn't revealed which firm it's working with on the design and development. Although Broadcom and Marvell are the two largest companies in the ASIC co-design market, representing some 95% of it, The Information reported last month that Anthropic was in talks with Samsung for manufacturing.
The shovel sellers always benefit
Designing, packaging, and manufacturing your own custom ASIC for AI inferencing isn't cheap, and it isn't easy. Alongside developing the hardware, you need the software stack to utilize it, and you want models that are optimized to run on it to make the most of its potential advantages. That makes it more worthwhile for most companies to simply use other firms' hardware and more general-purpose GPUs. But for major AI companies with enough money to burn and the ability to scale up to maximize efficiency gains, it's well worth the investment.
But the AI developers aren't the only ones who benefit. Nvidia has been one of the few companies to make enormous profits from the AI boom, while the likes of Meta, Google, Microsoft, and OpenAI are all losing enormous sums of money on their AI efforts. There are very real winners from the custom ASIC design and build market, too: TSMC, Broadcom, and Marvell.
Broadcom has been Google's co-design partner for years, and was also recently tapped to help build OpenAI's inferencing chips. It also works with Meta on its MTIA design, as well as holding contracts for other custom ASIC designs with ByteDance and Fujitsu. It also produces strong interconnect and networking hardware, which allows it to offer customers a more complete solution. It claims to have a $73 billion backlog of orders to work through, and expects to generate over $100 billion in annual AI chip revenue by the end of 2027.
Marvell holds massive contracts with Amazon for its Trainium chips and Microsoft for Maia, and is expected to make upwards of $11 billion for these co-design jobs in 2026.
If Samsung ends up as Anthropic's partner, it would be a relatively small player in this particular space, but it would bring enormous manufacturing and chip design expertise to the table, as well as access to the all-important memory that is in such short global supply.
The biggest winner of all these initiatives, though, is arguably TSMC. The Taiwanese company produces the majority of the world's cutting-edge silicon and is involved in the production of almost all the chips discussed here. They need TSMC's CoWoS advanced packaging technologies for integration with HBM. It also handles much of the packaging of Nvidia's and AMD GPUs, as well as producing much of the underlying wafers.
Multi-polar chip world
Anthropic joining the custom ASIC race is hardly surprising and further cements the future we seem to be barrelling towards, which is each of the hyperscaler AI companies looking to handle as much of their inferencing with custom hardware as possible. It's more efficient, easier to control for features and specifications, and easier to scale up when optimized for internal models. From a Chinese perspective, it's also easier to avoid problems caused by international trade blockades and tariffs.
They'll likely never become 100% reliant on their own chips — there is just too much AI demand to scale into for that to happen, and Nvidia has been ruthlessly dominating access to the supply chain. But every new chip installed is an Nvidia GPU that won't be used for the same purpose, which may help reduce the stranglehold Nvidia has on the industry. Not for training, though. That's likely to remain Nvidia's biggest appeal for some time to come.
A developer posted on the r/ClaudeCode subreddit a major error that their AI tool executed, which resulted in the deletion of their entire drive. According to u/Ecstatic-Big5126, they instructed Opus 5 to back up their system. While the AI proceeded with the task, it thought that it had created the backup in the wrong directory and then proceeded to run an “rm -rf” command on the supposed backup. Unfortunately, it mistakenly deleted the entire profile folder in its confusion.
It seemed that the AI was running on a Unix-style shell on Windows when it confused the Unix-style path of /c/Users/ as a temporary backup because it was expecting the traditional C:\Users\ path. So, when its expectations did not match, the tool decided to run the rm -rf “/c/Users/harih/” command, proceeding to clear every file and folder in the user’s profile.
“I asked Claude Opus 5 to create a backup. Instead, it created the backup in the wrong directory and then proceeded to "rm -rf" my entire drive,” u/Ecstatic-Big5126 wrote. “After wiping everything, it just replied: "Sorry, typo." ...like nothing had happened.” They also added, “That was simultaneously the funniest and most painful AI moment I've had.”
While the AI tool going out of bounds is the primary reason for these errors, the user is also partly to blame for thinking that it can fully understand what they want it to do. That’s why AI LLMs should never be given full access to important systems, and they shouldn’t have free rein to run delete commands like “rm -rf.”
This event will be a learning experience for u/Ecstatic-Big5126. But as AI agents increase in popularity, especially among inexperienced programmers and developers, we can only expect incidents like these to happen more frequently in the future.
RAM production capacity at Samsung Electronics, SK hynix, and Micron Technology is reportedly committed through the end of 2027, leaving consumer electronics manufacturers with little room to secure additional memory outside existing supply agreements.
Industry reports indicate that the three dominant manufacturers have allocated their available DRAM and high-bandwidth memory production for next year. Long-term contracts with cloud companies, AI chip developers, automakers, and device manufacturers have effectively reserved the output those suppliers expect to produce.
The claim does not mean every RAM module that will be sold in 2027 has already been manufactured or purchased by a consumer. It refers to future production capacity being assigned to customers before the chips leave the factory.
It also does not cover every memory producer worldwide. Chinese manufacturer ChangXin Memory Technologies, known as CXMT, and several smaller suppliers continue to produce DRAM. However, Samsung, SK hynix, and Micron control most of the advanced global DRAM market, making their available capacity the primary constraint for companies such as Apple.
RAM Production Is Already Committed
Selling out a year of production capacity means manufacturers have received enough contractual commitments to allocate the output they expect from their factories. Customers may have reserved specific volumes, product types, or manufacturing capacity under agreements that include minimum purchases and pricing protections.
Micron has been shifting toward this model through strategic customer agreements. The company disclosed 16 multiyear deals covering customers across data centers, consumer devices, automotive products, and industrial equipment.
Those agreements typically run for several years and include binding volume commitments. Some use fixed prices, while others establish price floors and ceilings. Micron has said that its signed contracts represent approximately $100 billion in expected revenue over their remaining terms.
Such agreements give memory manufacturers more predictable revenue than the traditional DRAM cycle, in which aggressive expansion frequently created excess supply and collapsing prices. They also give large buyers assurance that chips will be available for planned products.
The disadvantage falls on customers that did not reserve enough capacity early. A company needing more memory than its contract provides may have to pay higher prices, accept a different specification, delay production, or find another qualified supplier.
SK hynix CEO Kwak Noh-jung has described 2027 as potentially the worst year in the memory industry’s history from a supply perspective. He expects customer demand to remain above the company’s production capacity beyond 2030, despite major investments in new factories.
Samsung has issued a similar warning, expecting the constraints to become more severe in 2027 and continue into 2028. Analysts at UBS have projected that the DRAM market could remain undersupplied until at least the second quarter of 2028.
Image Credit: Bloomberg / Getty Images
AI Memory Consumes More Factory Capacity
The shortage originates primarily from the rapid expansion of artificial intelligence infrastructure. AI accelerators require high-bandwidth memory, or HBM, to move large amounts of data between memory and processors at extremely high speeds.
HBM and the LPDDR memory used in Apple devices are different products. An iPhone or Mac is not competing directly for the same finished memory package installed beside an Nvidia AI accelerator.
They still depend on overlapping manufacturing resources. HBM begins with advanced DRAM dies, and producing a given capacity of stacked HBM consumes considerably more wafer output and packaging resources than conventional memory. Suppliers have also concentrated spending, equipment, and engineering work on the more profitable AI segment.
Every production line converted or optimized for HBM can reduce the industry’s flexibility to make standard server DRAM, PC memory, or mobile LPDDR. New investment may increase total memory output while doing little to relieve the parts of the market serving phones and personal computers.
Cloud companies are willing to enter multiyear agreements and make large financial commitments because a shortage of memory can leave costly AI processors unused. Memory manufacturers therefore have a powerful incentive to prioritize these customers over consumer device companies seeking lower component prices.
The pressure is also moving beyond HBM. AI servers need large quantities of conventional DDR5 memory, while storage systems supporting model training and inference consume enterprise NAND flash. This spreads the shortage across several categories that were previously less exposed to AI infrastructure spending.
Apple Faces Expensive Supply Decisions
Apple purchases enormous volumes of LPDDR memory for the iPhone and iPad, along with memory used across the Mac, Apple Watch, Vision Pro, Apple TV, and HomePod product families. Its size gives it substantial negotiating power, but it cannot purchase capacity that suppliers have already assigned to other customers.
The company has acknowledged that memory prices are continuing to rise. Tim Cook recently characterized the pricing environment as a “hundred-year flood” and said Apple expects to pay more for memory during the September quarter.
Apple has already raised prices across several Mac and iPad models while reducing or removing some high-memory Mac configurations. Although memory availability is not the sole influence on every product decision, eliminating configurations can help a manufacturer concentrate limited components around the models expected to sell in larger volumes.
The design of Apple silicon makes memory allocation especially sensitive. Unified memory is integrated closely with the processor package and cannot be upgraded after purchase. Apple must decide how many chips to secure for each configuration long before customers begin ordering those machines.
A shortage can therefore affect more than the cost of an individual RAM component. It can influence base memory capacities, upgrade pricing, the number of configurations offered, production volume, and the timing of product introductions.
Apple can absorb part of the increase through its margins, negotiate longer agreements, adjust retail pricing, or redesign its product mix. Each option carries a cost. Absorbing higher prices reduces profit per device, while passing them to customers could weaken demand. Restricting configurations risks pushing professional users away from Macs that no longer offer enough unified memory.
The company is reportedly evaluating DRAM from CXMT for products sold in China. Adding a fourth major source could improve access to conventional memory, but qualification takes time because Apple must confirm performance, reliability, manufacturing consistency, and compatibility with its hardware.
The political environment creates another obstacle. US lawmakers have raised national security concerns about American companies purchasing memory from Chinese semiconductor manufacturers. Apple could consequently find that a supplier is technically suitable for China-market products but difficult to introduce across its international supply chain.
Sold Out Does Not Mean Stores Will Be Empty
The reported capacity commitments do not guarantee a universal shortage of finished devices. Apple and other major manufacturers will still receive memory under agreements they have already negotiated.
Products can also continue reaching stores while component prices rise. Manufacturers may reduce discounts, increase retail prices, limit premium configurations, or give priority to their most profitable devices before allowing a shortage to stop production completely.
Contracted capacity can change if customers reduce orders or if AI infrastructure investment slows. Memory makers could redirect production between customers and product categories, although converting lines and qualifying different chips cannot happen immediately.
CXMT could also expand faster than expected, particularly in mainstream DRAM. Its products have reportedly attracted interest from HP, Asus, Acer, Dell, Lenovo, and Apple as established suppliers devote more resources to AI memory. CXMT is unlikely to replace the combined output of Samsung, SK hynix, and Micron during 2027, but it may provide an alternative for products sold outside the United States.
New production facilities offer little immediate relief. Semiconductor factories require construction, equipment installation, process development, yield improvement, and customer qualification before reaching dependable volume.
Micron expects the first wafer output from its initial new Idaho facility around the middle of 2027, with a second facility following in late 2028. Those first wafers will still need to pass through the production ramp and customer qualification process, leaving much of their effect on available device memory for 2028 and later.
Apple lawsuit against OpenAI has reached its first major procedural challenge, with the ChatGPT developer asking a US federal judge to dismiss allegations that it acquired or used trade secrets taken by former Apple employees.
OpenAI submitted its 31-page dismissal motion on Wednesday in the US District Court for the Northern District of California. The filing argues that Apple has not adequately identified protectable trade secrets or alleged facts showing that OpenAI misappropriated confidential information.
Apple filed the case on July 10 against OpenAI, its io Products hardware operation, former Apple Vice President Tang Yew Tan, and former electrical engineer Chang Liu. The complaint accuses the defendants of obtaining information connected to Apple’s product development, manufacturing, components, testing, and supply chain operations.
The allegations have not been proven, and OpenAI denies possessing or seeking Apple’s trade secrets. US District Judge Edward Davila must now decide whether Apple’s complaint contains enough factual detail for the litigation to continue.
OpenAI Challenges the Apple Lawsuit
OpenAI’s motion focuses initially on how Apple described the information it claims was stolen. According to OpenAI, the complaint relies on generalized product-development categories without identifying the protected information with enough precision.
Trade secret plaintiffs face a difficult balance at the beginning of litigation. Apple must describe the protected material sufficiently to establish a plausible legal claim, but revealing the material publicly could expose the same information it is trying to protect.
Sensitive details can later be handled under seal or through a protective order. At the dismissal stage, however, Apple must still provide enough information for the court to distinguish genuine trade secrets from general engineering knowledge, employee experience, or skills acquired through years of work.
OpenAI argues that Apple has not crossed that threshold. Its motion says the complaint fails to establish ownership of a protectable trade secret and does not plausibly allege that OpenAI acquired, disclosed, or used such information.
The filing also disputes the commercial premise behind Apple’s accusations. OpenAI says it is developing products that are different from Apple’s hardware and has no need or desire to use the iPhone maker’s confidential material.
That position does not settle whether information was taken. A company can potentially misappropriate trade secrets even when its eventual product has a different design. The court’s immediate task is narrower: determining whether Apple has alleged facts that could support its claims if those facts are later proven.
Apple Park | Image Source: Google
Disputed Access to Apple Information
Apple’s complaint centers partly on Chang Liu, who spent eight years working as a senior system electrical engineer at the company before joining OpenAI in January 2026.
Apple alleges that Liu retained an Apple-issued computer and accessed confidential technical information after leaving. It claims he exploited residual authentication access on multiple occasions to obtain internal documents while already working for OpenAI.
OpenAI presents a different explanation. It says Apple employees contacted Liu after his departure and asked him to help locate files needed for ongoing Apple work. The company published redacted messages that it says support this account.
OpenAI argues that Apple’s systems and policies allowed personal and corporate information to become intermingled through iCloud accounts. Former employees, it says, could retain access to Apple files without requesting it or realizing that access remained active.
The motion maintains that Apple cannot treat the foreseeable effects of its own access-management practices as proof that former employees intentionally stole data. Apple, in contrast, alleges that its records document repeated access to restricted information rather than an accidental discovery of lingering permissions.
These competing accounts are likely to become central if the case enters discovery. Authentication logs, downloaded files, device records, internal communications, and employment timelines could establish whether access was deliberate, what information was obtained, and whether it reached anyone working on OpenAI products.
A dismissal motion normally tests whether the complaint states a legally sufficient claim. It is not a trial, and the judge is not expected to resolve every factual disagreement using competing evidence at this point.
Tang Tan and OpenAI’s Hardware Team
Apple separately accuses Tang Tan, OpenAI’s chief hardware officer, of seeking confidential information from Apple employees during recruiting conversations.
Tan spent more than 24 years at Apple and worked on products including the iPhone, Apple Watch, AirPods, and iPod. He later helped establish io Products with former Apple design chief Jony Ive and other collaborators. OpenAI acquired the hardware company as part of its effort to create a family of devices built around artificial intelligence.
Apple alleges that Tan used confidential project names, retained internal materials, and encouraged job candidates to discuss unreleased components or bring physical parts to interviews. OpenAI rejects the claim that these interactions formed a coordinated effort to collect Apple secrets.
OpenAI says Tan repeatedly instructed employees that confidential information from previous employers could not be brought into its organization. It also offers an alternative explanation for an Apple offboarding document found in his possession, arguing that Tan retained it to help incoming employees comply with Apple’s departure procedures.
Apple’s position is that the document and recruiting communications must be examined alongside the other alleged conduct. It describes the events as part of a connected campaign rather than isolated employment or administrative issues.
The dispute comes during intense competition for engineers capable of developing consumer AI hardware. Apple alleges that more than 400 former employees have moved to OpenAI, although hiring people from a competitor does not by itself establish trade secret misappropriation.
A company must generally connect recruitment to the acquisition, disclosure, or use of protected information. OpenAI is relying heavily on that distinction, presenting Apple’s lawsuit as an attempt to respond to employee departures and its difficulties competing for AI talent.
Image Credit: Getty Images
Apple Seeks Immediate Restrictions
OpenAI’s dismissal request arrives shortly after Apple asked the court for a preliminary injunction. Apple wants restrictions imposed while the lawsuit proceeds, arguing that it could suffer harm if its confidential information is used during OpenAI’s hardware development.
The requested relief would prevent OpenAI and the individual defendants from acquiring, accessing, using, or disclosing the alleged trade secrets. Apple has also requested expedited discovery and forensic examination of relevant devices and accounts.
OpenAI says those restrictions are unnecessary because it does not possess or want Apple’s protected information. It has described Apple’s request as based on false information and has offered to cooperate in addressing specific concerns.
The two companies also disagree over their communications before the lawsuit. Apple initially said its concerns had been raised with OpenAI months earlier. OpenAI responded that Apple’s outside lawyers contacted the wrong person after confusing names and incorrectly described a conversation with OpenAI’s general counsel.
OpenAI says Apple did not present the specific accusations before filing the case and that the companies had previously discussed resolving any access issues. Apple has since provided additional information in support of its injunction request, including allegations involving other former employees.
These disputes have moved beyond ordinary court filings. OpenAI published messages and emails on its website to support its version of events, calling Apple’s lawsuit aggressive and poorly investigated. Apple has primarily presented its account through formal legal submissions.
Judge Davila could dismiss some or all of Apple’s claims, deny the motion, or allow Apple to amend its complaint with more specific allegations. Even a dismissal would not necessarily end the case if permission to revise the complaint is granted.
The next scheduled public test is an October 1 hearing covering Apple’s preliminary injunction and request for early discovery. That proceeding may determine whether OpenAI faces immediate limits on its hardware work before the underlying trade secret claims receive a full evidentiary review.
Apple has increased its estimated trade-in values for most current iPhone, iPad, Mac, and Apple Watch models in the United States. The adjustments give customers slightly more credit toward a new device, although some older Apple hardware and several Android phones have moved in the opposite direction.
The updated values are maximum estimates rather than guaranteed payments. Apple calculates the final credit after confirming the model and inspecting its condition. A damaged display, enclosure problems, missing functionality, or an inaccurate description can reduce the amount offered.
Most of the increases are modest, but they arrive across several of Apple’s largest product categories at the same time. Recent iPhones receive between $10 and $25 more, every iPad category listed in Apple’s summary gains value, and several popular Macs receive larger increases.
The revisions also reinforce the role of trade-ins within Apple’s retail operation. Credit can reduce the cost of a regular purchase, be delivered through an Apple Gift Card, or lower payments at the beginning of an eligible Apple Upgrade lease.
Image Credit: Apple Inc.
Trade-In Values Rise Across iPhone and iPad
Every iPhone 16 model included in the update receives a higher maximum estimate. The iPhone 16 Pro Max moves from $685 to $695, retaining the highest listed value among the affected iPhones.
The iPhone 16 Pro increases from $550 to $560, while the iPhone 16 Plus moves from $455 to $465. The standard iPhone 16 receives the largest increase within the family, rising by $25 from $435 to $460.
These changes narrow the difference between the standard iPhone 16 and the larger Plus model. The Pro models retain substantially higher estimates, reflecting their higher original prices and stronger position in the used-device market.
An additional $10 or $25 will not transform the cost of an upgrade, but it can offset part of a case, charger, AppleCare plan, or storage upgrade. The difference also becomes more relevant for customers comparing Apple’s direct program with a third-party reseller offering a similar amount.
Apple’s iPad adjustments are more consistent. The maximum estimate for an iPad Pro rises from $670 to $690, an increase of $20. The iPad Air moves from $445 to $460, while the standard iPad rises from $220 to $235.
The iPad mini also gains $15, moving from a maximum of $250 to $265. That means every current iPad category displayed in Apple’s trade-in summary receives a higher estimate.
Apple’s direct trade-in values are generally lower than the prices an owner might obtain through a private sale. The program instead emphasizes convenience: the device can be returned at an Apple Store or sent using Apple’s online process, with the credit connected directly to a new purchase.
A private sale can produce a better return, especially for a recent device in excellent condition. It also requires the owner to set a price, find a buyer, manage payment, and handle the transfer independently. Apple’s program trades some resale value for a simpler exchange.
Mac and Apple Watch Changes Are Less Consistent
Mac trade-in values receive some of the largest increases, but the category also includes notable reductions.
The MacBook Air rises from a maximum of $485 to $520, adding $35. The Mac mini receives the same increase, moving from $340 to $375. The MacBook Pro changes only slightly, increasing from $685 to $690.
Apple leaves the current iMac estimate unchanged at $355. The Mac Studio receives a larger increase from $975 to $1,045, reflecting the higher value retained by recent desktop configurations.
Older professional desktops move in the other direction. The iMac Pro falls from $325 to $315, while the top Mac Pro estimate declines from $2,090 to $2,045.
That $45 reduction at the top of the Mac range means Apple’s maximum Mac trade-in offer decreases even though several mainstream models receive more credit. The changes appear more closely connected to the resale position of individual product families than to a uniform increase across every computer.
Mac estimates can vary considerably because one model name can cover different years, processors, memory configurations, storage capacities, and physical designs. Apple asks for a serial number before producing a more specific estimate for a Mac.
The Apple Watch adjustments are similarly mixed. Apple Watch Ultra 2 rises from $295 to $305, while Apple Watch Series 9 increases from $120 to $130.
Apple Watch Series 10 remains unchanged at a maximum of $150. The original Apple Watch Ultra falls from $215 to $205, losing $10.
Wearable trade-in values can decline quickly because watches receive frequent updates and are exposed to greater physical wear than many computers or tablets. Battery condition, display damage, case wear, and working buttons can influence whether the device receives the estimated credit.
Apple also accepts devices that no longer qualify for monetary value. Those products can be returned for recycling, including older hardware and accessories that would otherwise remain unused.
Image Credit: Apple Inc.
Android Offers Face Several Reductions
Apple accepts selected smartphones from Samsung, Google, and other manufacturers when customers move to an iPhone. The latest revisions are less favorable to several Android devices.
The maximum Android estimate across Apple’s displayed range decreases from $370 to $360. Individual changes vary by model.
The Samsung Galaxy S23 Ultra drops from $230 to $200, a $30 reduction. Google’s Pixel 8 Pro declines from $170 to $165.
Other devices remain unchanged. The standard Galaxy S23 continues to carry an estimated value of up to $125, while the OnePlus 12 remains at up to $200.
Apple’s Android offers are primarily designed to remove friction for people moving to the iPhone. They do not necessarily match the amount available from the manufacturer, a carrier promotion, or a private buyer.
Carrier promotions can advertise considerably higher trade-in credits than Apple, particularly around a new iPhone launch. Those offers commonly require an eligible unlimited plan, a new line, monthly bill credits, or a commitment lasting two or three years.
Apple’s direct credit is easier to understand because it is tied to the assessed value of the device rather than a long carrier agreement. The best choice depends on whether the customer values flexibility, immediate credit, or the largest possible promotional discount.
A Higher Estimate Is Not the Final Offer
Apple’s published figures represent the maximum available for qualifying devices in suitable condition. The online estimate is based on the answers supplied before the product is inspected.
After receiving the device, Apple or its trade-in partner verifies its identity and checks whether it powers on, functions properly, and matches the reported condition. A difference can produce a revised offer.
Customers can accept or reject a lower revised value. Rejecting it cancels the trade-in and returns the device, although the payment arrangement for the accompanying purchase may also be adjusted. If the inspected value is higher than the original estimate, Apple says it automatically increases the credit.
The way the credit is delivered depends on the purchase. When a customer pays in full for a new iPhone, iPad, Mac, or Apple Watch, the confirmed value can be returned to the original payment method up to the price of the new product. Any remaining amount can be issued through an Apple Gift Card.
With an eligible monthly payment arrangement, the estimated credit can be applied directly to the new device’s purchase price. Under Apple Upgrade, a qualifying trade-in owned by the customer can reduce payments across the initial lease term. Another device cannot be traded in each time the customer exercises the lease’s upgrade option.
Values also differ between countries and can change without a product announcement. The revised dollar amounts apply to Apple’s United States program, while local market prices, available models, currencies, and trade-in partners determine estimates elsewhere.
Anyone considering an exchange should obtain a fresh estimate shortly before purchasing. Apple requires the trade-in device to arrive within the specified return period and says an online estimate is generally protected for 14 days after the customer receives the new product.
The State Corporation Commission, Virginia’s independent state agency that regulates public utilities, has ordered data centers to pay for all the required transmission infrastructure that the project will use exclusively. According to Realtor.com, Governor Abigail Spanberger (D) has recently been pressing AI hyperscalers to reduce their impact on the power grid and utility prices.
Virginia currently has the greatest number of data centers across the U.S., with the state hosting at least 570 sites. Because of the massive power demand of these sites, the state has been experiencing issues with electricity price hikes, with one county even asking its employees, including schools, to conserve power. PJM Interconnection, which distributes power to the state, has increased prices by 76%, with Monitoring Analytics, the independent watchdog keeping an eye on the utility company, saying that this is mostly due to AI data centers.
The move requiring data centers to pay for their own upstream electrical infrastructure should, hopefully, ease the burden on the average Virginian. "We are taking real steps to address rising energy costs for Virginians," said Gov. Spanberger. "I will continue to work with the General Assembly and take action to make sure data centers pay their fair share, adhere to strict environmental standards, and listen to the concerns of local communities.”
This is one of the first statewide announcements that will enforce President Donald Trump’s ratepayer protection pledge. Trump summoned the biggest AI hyperscalers to the White House in early March and made them promise that they will “pay their own way” when it comes to building the infrastructure they need. But despite this, the burden of more expensive electricity hasn’t eased for the average American. The administration expanded this promise in late July to include state governors, utility companies, and data center developers.
Many critics said that this pledge is nothing but a piece of paper, with some comparing it to a “pinky promise.” After all, this is just a commitment and not a regulation that would penalize companies that don’t comply. Nevertheless, it seems that it has spurred at least one state to make a move that should, hopefully, have a material impact on the average citizen.
Aside from Virginia, Oregon is the only other state, so far, that has raised electrical rates for large consumers, like data centers. According to the Oregon POWER Act, facilities that consume 20 MW or more are assigned to a higher rate class. Portland General Electric, the state’s largest power supplier, has already implemented this, resulting in a 30% rate increase for these big power users, while also cutting residential costs by 1.3%.
While the average consumer isn’t necessarily paying for the power that data centers consume, utility companies had to conduct massive grid upgrades to support the increased demand from these developments. The utility companies then equally passed on all the necessary capital expenditure to all consumers, instead of just the data centers. By requiring data centers to spend on their own grid upgrades, further increases in electricity prices could, hopefully, be prevented.
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