The upper stage of a SpaceX rocket that delivered two lunar landers in early 2025 is set to come crashing down on the lunar surface several months after it ended its mission. According to Japan Today, the large piece of rocket debris is expected to hit the moon at an estimated 5,400 mph or 8,690 kph, which is seven times the speed of sound.

While a piece of debris hitting the moon might seem like an inconsequential event for most people, scientists and astronomers are keeping a close eye on it because this will be one of the few times that an object will impact the moon while being observed. NASA’s Lunar Reconnaissance Orbiter and South Korea’s Danuri lunar orbiter will take before-and-after photos of the impact site, while the latter will also fly close to the stricken SpaceX rocket a few minutes before it begins its final descent.

NASA has previously deliberately crashed objects on the moon — it did this with sections of the Saturn V rocket and discarded Lunar Landers to conduct seismic experiments during the Apollo missions in the 70s. It repeated the experiment in 2009 with its LCROSS mission, where it crashed the probe near the south pole of the moon in search of ice.

What interests researchers this time is how the impact will affect the surface of the moon, which is expected to deliver energy that’s equal to about three tons of TNT. Even though this might seem like a huge amount of energy, we likely cannot see the actual crash from Earth. But because the moon does not have wind or an atmosphere, the ejected material could stretch for miles and be visible on telescopes.

“The gravity on the moon is low and there is no wind to blow the dust away,” said Benjamin Fernando of the Los Alamos National Laboratory. “Part of the reason for our interest in this event is to figure out how much of a hazard debris impacts pose to future astronauts.”

Space debris is not a major concern for the moon as it is for our planet at the moment, but this might change in the near future. As the U.S. and China are locked in a space race to see who will be the first one to land on people on the moon again after more than half a century, there will soon come a time when operators cannot just leave their rocket debris in an orbital decay, lest it come crashing down on their settlements and data centers.

Seven states have reported cyberattacks on their water supply control systems, with some officials suspecting that Iran is behind these actions. According to the New York Times, there isn’t any definitive proof yet that Iran orchestrated these attacks but it said that moves like this have been escalating since the U.S. began its bombing campaign of the country. It was also noted that the attackers made zero financial demands, making it more likely to have been conducted by state actors that aren’t just motivated by money.

Minnesota was the first to report this kind of attack, with Michigan soon saying that it was targeted, too. While there have been no major disruptions that have made tap water unsafe to drink, the authorities across local and state governments are still on the lookout for potential problems. They’re particularly concerned about older computer systems that monitor water quality, adjust chemical treatments, and control water pressure, especially those that are connected to the internet.

Braham, Minnesota, is one of the areas affected by the cyberattacks. The mayor of the small city, which has a population of less than 2,000 people and is located about 50 miles north of Minneapolis, said it has already received guidance on how to resolve the issue and strengthen its defenses against future attacks. It’s currently using manual control to keep the water service on, but its mayor, Nate George, told the publication, “I think the troubling thing on the horizon is how do we move forward to a more secure system. IT infrastructure upgrades are very costly, and we are a very small municipality.”

He also repeated the suspicions that some federal officials had but declined to confirm. “We’re getting bits and pieces of information from the state of Minnesota and the F.B.I. They are pretty sure it’s Iranian actors.”

While states scramble to protect their utilities and other critical infrastructure, the White House has downplayed the suspected state-sponsored cyberattacks. President Donald Trump told a reporter, “I think Minnesota is behind it. I don’t think there was an Iranian cyberattack.”

This isn’t the first time that Iranian hackers have hit a U.S. institution during the 2026 war, but it’s the first time that essential services and infrastructure within the mainland have been affected. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has previously warned about potential Iranian cyberattacks, but smaller municipalities remain vulnerable.

Iran was also once on the receiving end of a cyberattack that many experts link to the U.S. The most famous of these was Stuxnet, which was supposedly used in 2009 to significantly damage and destroy critical tools used by the Iranian nuclear program. More recently, the CanisterWorm malware attacked Iranian machines and wiped them clean for no apparent reason. Again, no one has claimed responsibility for this attack.

Wars have always been fought on land and on sea, and more recently, in the air and in space. But as the internet has become indispensable for society, cyberspace has quickly become a fifth domain that states must protect and dominate. “This is what modern warfare looks like,” Gov. Tim Walz said on X.

Elon Musk’s Colossus data centers that sit near the border between Tennessee and Mississippi will finally be removed starting this month. According to the company, the 69 temporary mobile turbines, which have been the subject of a lawsuit that claims they have been causing pollution, are going to be completely removed by July 2027, meaning it will take the company 12 months to withdraw the trailer-mounted generators from its premises. This may sound confusing, as they’re “portable” generators, after all, but it seems that SpaceXAI is removing them in phases as its 1.2GW power plant slowly comes online.

SpaceXAI achieved one of the fastest data center deployments ever after it set up 100,000 Nvidia H200 GPUs in 19 days — something that Nvidia CEO Jensen Huang said usually takes four years or more. Musk achieved this by bringing in his own power instead of waiting to get connected to the grid, which was considered a novel idea at that time. Today, a few data center operators, like OpenAI, are following Musk’s footsteps and deploying their own turbines to power their servers without relying on other providers.

But even though the idea feels innovative, the community surrounding the Colossus 1 and Colossus 2 data centers has been complaining about the pollution that these mobile turbines bring. They say that these turbines didn’t have the required permits to operate, although the company argued that the mobile and temporary nature of these units exempted them. The EPA said earlier this year that these units aren’t exempt from permits, but SpaceXAI continued to operate them anyway. It has even got to the point that the NAACP filed a case over these unpermitted turbines, although the DOJ weighed in, saying that removing them would put national security at risk as Colossus 2 “supports mission-critical operations.”

Despite that, it seems that SpaceXAI has finally settled into an agreement with the Mississippi Department of Environmental Quality. It’s if the 1.2GW power plant replacing these mobile turbines is the same one that Musk is importing from overseas, but the important part is that it’s approved under a Clean Air Act permit. Aside from that, the company also said that it’s equipping all its mobile turbines with “advanced emission control technologies,” which should reduce the environmental impact of its power sources.

Tech enthusiasts are a passionate bunch, and the rich ones among them are willing to put their money where their mouth is. This is exactly what we see with a signed Steve Jobs business card from 1983, which is posted on RR Auction and is going for $70,741 at the time of writing. What made this relic special is that Job’s business cards from 1983 are a rare commodity and remain in high demand from collectors. It also received a Gem Mint 10 grade — the highest possible rating from PSA/DNA and is certified to be authentic.

RR Auction says that it has auctioned off 15 Steve Jobs business cards in its history, but only four of those come from 1983. This is also only the second one to bear the signature of Apple’s founder, which was put on the auction block in 2024 and fetched an eye-watering $180,000. The card on auction right now isn’t exactly perfect, with the website describing it as “In fine condition, with light soiling and staple holes to the upper section, none of which affect the bold signature.” Still, there’s at least 16 bids on the item, and the next bid is currently listed at $77, 816.

This isn’t the first time that an item related to an iconic founder has fetched thousands of dollars on the auction block. Just last month, Sotheby’s estimated that Nvidia CEO Jensen Huang’s iconic Tom Ford leather jacket, which costs $9,000 if you buy it new, would get up to $60,000. However, when the hammer finally closed the auction, the winning bid hit $960,000, which, we believe, is driven by Jensanity.

Unfortunately, if you’re not into the personal effects of CEOs and want something more substantial, you’ll probably have to fork out a lot more money. One example we have is the Apple-1 ‘Prototype Board #0,’ which sold for $2,750,000. Aside from costing a lot of money, these are rare finds, and you’ll have to wait years, if not decades, to get a chance at owning something so exceptional. But if you have a hole burning in your pocket and want to start a tech-related collection, but find the Steve Jobs business card a bit above your budget, an unreleased batch of 97 Duck Hunt and Super Mario Bros. NES cartridges has recently been discovered at a retro shop, most of which are graded 9.6 or higher by PSA. Three examples are about to enter auction on August 12, and they’re estimated to start at four figures, which might make it more “attainable” to fledgling collections.

Michael Dell, the founder of Dell Technologies Inc., shared his company’s quarterly earnings report dated July 31, 1984, saying, “This one page changed my life.” The billionaire said on his X post that the numbers on that piece of paper convinced his parents that they didn’t need to go back to college. He started the company in 1984, originally known as PC’s Limited, in his dorm room at the University of Texas at Austin, upgrading PCs and building clones of IBM PCs from stock parts.

As we see from his X post below, his venture seemed to be successful from the get-go, with Dell selling almost a million dollars’ worth of parts and computers, which resulted in a net income of $134,762.75 for just one quarter — equal to $433,150.05 in 2026 dollars. This is an achievement for any startup not backed by millions of dollars, much less by a 19-year-old college freshman backed by just $1,000, with the investment returning 13,500% in less than a year.

Dell went public four years after it was founded, but after a quarter of a century of being listed on the stock market, Michael Dell and Silver Lake Partners decided to take the company private again in 2013 in a $24.9 billion deal as it struggled with the rise of smartphones and tablets. After five years without mandatory quarterly reports, Dell decided to go public again, and it currently has a market capitalization of around $261.92 billion.

The fledgling dorm-room startup has turned from a niche supplier of off-brand IBM-compatible computers into a massive tech giant. It owns iconic computer brands like Alienware and XPS, and has also jumped on the AI bandwagon, supplying servers to hyperscalers and data centers. Its latest income statement also lists total net revenue of $43 billion and a net income of $3.4 billion. Dell’s risk in 1984 also paid handsomely for him. He’s currently the fifth richest person on Forbes’ real-time billionaires list, with a net worth of $229.2 billion.

The computer company has also had its fair share of controversies. This includes an SEC investigation into improper accounting practices in 2005, a lawsuit and massive recall related to laptop batteries catching fire in 2007, and a security vulnerability in 2015 that could have exposed customer data to bad actors. Despite all that, the company continues to thrive today and release new products like the $699 XPS 13 and an affordable OLED gaming monitor in the Alienware AW3426DW. Dell has made a significant impact on PCs, laptops, and other systems for gaming, productivity, enterprise, and more.

Whether driven by a desire for transparency or to keep OpenAI from hogging the spotlight when it comes to advertising advanced AI models, Anthropic revealed that Claude also hacked into three production systems belonging to unsuspecting targets during cybersecurity capabilities testing. Two of the affected companies didn't know they had been hacked, while a third one is unreachable.

The alleged incidents reportedly happened during the previous quarter and involved several versions of Claude: Opus 4.7, Mythos 5, and "an internal research test model." Similar to what happened when OpenAI Sol hacked into Hugging Face, Anthropic was running Claude through cybersecurity capture-the-flag scenarios where the bot was told to find a piece of information somewhere in its network. Anthropic says there were 141,006 test runs, and the three incidents occurred over six problematic runs. As expected, the tests ran with most AI safeguards disabled.

The problem is, the network was not isolated, a newbie mistake that some might even find suspicious. This was due to an alleged "miscommunication" between Anthropic’s virtual test lab firm Irregular, which gave the bots full access to the internet. For safety's sake, the Claudes were apparently told they were in an isolated environment, which didn't help much.

The first time, Claude Opus 4.7 had to find a piece of data belonging to an imaginary company. As it turns out, there was actually a real company whose website domain matched the fake one. With internet access, it gained application and infrastructure credentials and grabbed "several hundred rows" of data from a production database.

For two out of four runs where hacking took place, Claude didn't see any problem and "rationalized that the real company must be part of the exercise," but soldiered on. The other two times, it just kept going. Anthropic notes this was the only case of the three where the bot continued to operate after the realization the company actually existed for the aforementioned reason. However, the firm's interpretation is open to discussion.

The second, and potentially more interesting, was a bona fide supply-chain attack, this time perpetrated by Mythos. The fake data available to the bot included an installation script for a hypothetical developer that included installing Python packages. Much like a human would, Claude noticed the obvious attack vector and, once again, due to having internet access, inferred that publishing a booby-trapped package in the PyPI repository would do the trick. Unfortunately, it was the real one that the whole world uses.

In speedrun form for brevity: Claude needed a PyPI account, meaning it needed an email and a phone number. It tried buying one but found itself short of both pockets and funds; but it found another free email provider without that requirement, registered the PyPI account, and uploaded a malware package to PyPI. The package was seemingly only up for one hour but was downloaded and run on 15 systems.

In a twist of irony, one such system belonged to a security vendor that scans PyPI for malware, and lo and behold, promptly failed to find Claude's booby-trap and ran it. Once Claude presumably had remote code execution privileges, it used the credentials it found for further infiltration. The amusing bit is that Claude had no idea this company existed and didn't target it; the downloads just happened because the package was up and live for a short while.

According to Anthropic, the bot did detect it was acting on the real internet and even said that publishing a package like this was "NOT okay." However, it talked itself into believing it was in a test environment as it didn't recognize the real SSL certificates for the connections. It even believed the 2026 calendar date on the systems "proved" the environment was staged. It even recognized the systems that installed the malware as part of the experiment.

As for the third incident, Anthropic isn't saying much, other than Claude scanned 9,000 real live potential alternative targets once it noticed the intended one wasn't reachable. One of them reportedly had a live page with debugging information and was vulnerable to plain ol' SQL injection. Interestingly, this time around, once Claude noticed that the servers it was accessing resided on a cloud environment and not on the local network, it stopped the attack.

For its part, Anthropic recognizes that despite Claude following the instructions for the objectives, the fact that it stopped by itself once it found the target was real in only one of the cases is food for some thought. The firm says it's talking to METR for a third-party review, and it needs to "better co-design evaluation environments."

Oddly enough, Anthropic believes Claude probably wouldn't have gone online "if the prompt had clearly explained which systems were in and out of scope for the evaluation," while also stating the incidents were "closer to a harness and operational failure than a model alignment failure."

Amazon, Google, Meta, and Microsoft have spent more than a trillion dollars on AI infrastructure, including data centers, the chips inside them, and the power needed to run the facilities, since 2023. The Financial Times said that these four big companies have already hit $1.1 trillion in capital expenditure based on their latest earnings reports, and that an additional $745 billion is expected to be added to this figure just this year.

“There is basically no end in sight for the growth in capex,” RBC Capital analyst Rishi Jaluria told the publication. “Investors need these companies to toe the tight line between investing in AI and not compromising the things that have made them successful.” This massive investment has upended several other industries — namely electricity prices and memory and storage chips. The massive power demand that data centers have put on the power grid has forced many U.S. utility companies to spend billions of dollars to upgrade their respective infrastructure, which they then passed on to all consumers, not just the big ones that forced the upgrade.

This, alongside other environmental issues, has caused many Americans to push back against data center projects near their communities. The White House instituted the “ratepayer protection pledge” and made AI hyperscalers, utility operators, data center companies, and individual states promise that they will protect the average consumer from electricity cost increases. But so far, no state has taken a step to codify this pledge into law. Oregon actually enacted the POWER Act, which resulted in a 30% increase in the power bill of users that consumed more than 20MW while slashing the bills of residents by 1.3%, but the state did this in 2025, way before President Donald Trump called the tech giants into the White House and told them to “pay their own way.”

The mountains of cash that these tech giants are pouring into AI are also affecting the memory and storage chip industry. Since these AI hyperscalers have a lot of liquidity from investors, they are willing to pay top dollar for the HBM they need to run their data centers. Because of this, it made sense for Micron, Samsung, and SK hynix to prioritize them over DRAM, especially as they can charge a premium for these chips and there are customers who are willing to pay at those prices. This resulted in a shortage of consumer memory that started in 2025 — while this initially affected PC builders and enthusiasts, it has started to affect other industries that require memory as well, including cars and smartphones. Even Apple, which historically had huge sway over its suppliers, was forced to increase prices because of the shortages.

Aside from skewing other industries, the massive CAPEX the big four are going into is alarming some experts, warning that the promises and contracts they’re making are leading to “hidden debt” not listed in their balance sheets. The amount, worth around $1.65 trillion, is annotated in their quarterly financial statements as future obligations that will only come into play as the related asset or service comes online. The current value is 122% of the actual debt reflected on their balance sheets, which could give investors the wrong impression that they have fewer obligations than they actually have.

While the amount of money that the big four are spending on AI might seem dizzyingly high, we must note that these companies are raking in massive amounts of cash quarterly themselves. Microsoft’s latest quarterly revenue is $90 billion, while Meta made $60 billion in the same period. Alphabet (Google) announced revenue of nearly $120 billion, while Amazon made $200 billion. That is a total of nearly $470 billion for these companies in just the last quarter.

Still, that does not mean that they can just keep on spending on AI. For example, even though Google’s cloud business had a revenue of $11 billion last year, its price dropped after it announced that it spent more than it made last quarter — the first time this happened in the 20 years since it went public. Meta is also planning to rent out its AI compute, apparently following in the footsteps of Amazon, Google, and Microsoft, which have growing cloud businesses. However, this announcement caused a drop in its stock price. “They are a bit all over the place,” SLC Management managing director Dec Mullarkey told FT. “For investors it’s no longer growth at any cost; they want to see the spending flowing through to results, like at the Big Three.”

Apple will pay even more for memory in the September quarter than it did in the June quarter, CEO Tim Cook told analysts on the company's earnings call on July 30, after memory costs accounted for more than the entire sequential decline in Apple's adjusted gross margin. Cook, speaking from Cupertino on his final call before John Ternus takes over as CEO, called the market "a hundred-year flood on the memory pricing." Apple's consolidated financial statements for the quarter ended June 27 put inventories at $11.09 billion, up 87% year over year, with $5.46 billion of cash consumed building that position over nine months. CFO Kevan Parekh said the benefit Apple gets from that carry-in inventory shrinks after September.

Apple held $5.93 billion of inventory at the end of June 2025 and $5.72 billion at its September fiscal year end, so the $11.09 billion reported for June 27 is a 94% increase in nine months. The cash flow statement puts the same movement at $5.46 billion consumed by inventories over those nine months, against a $1.22 billion release in the year-ago period. Measured against quarterly cost of sales of $54.65 billion, the June position works out to roughly 18.5 days of inventory, up from about 10.7 days a year earlier. Apple has run one of the leanest working capital positions in consumer electronics for two decades, but that has now been flipped on its head.

Parekh told analysts the carry-in inventory partially offset memory costs in the June quarter and will do so again in September, with a decreasing benefit beyond that. Cook said market pricing for memory keeps rising past September and that the effect on Apple's business could grow. Erik Woodring of Morgan Stanley asked whether Apple intends to pursue multi-year long-term agreements with suppliers at pre-agreed prices. Cook answered the second half of the question, on pricing philosophy, and left the agreements question alone.

Company gross margin was 49.3% in the March quarter and 48.1% in June, once the roughly two percentage points of tariff refunds come out, Parekh said, and more than 100% of that 120 basis point decline is explained by memory costs. Apple guided September gross margin to 47% to 48%, including about one percentage point of tariff refunds, putting the adjusted midpoint near 46.5%, and Parekh attributed that step down to the same dynamic. Foreign exchange was a factor in the June quarter but not the main one.

The cost of sales for products rose 8.1% year over year to $47.15 billion, while product revenue increased 18.1% to $78.68 billion, which lifted product gross margin to 40.1% from 34.5%. The Mac and iPad price increases that came in June, the tariff refunds, and the stockpile together more than covered the memory increase in the reported quarter. On the other side of the trade, SK hynix ran a 76% operating margin over the same three months on revenue of 79.32 trillion won and operating profit of 60.54 trillion won, the company said recently, and guided third-quarter DRAM bit shipments up only about 10% sequentially. TrendForce expects conventional DRAM contract prices to rise a further 13% to 18% in the third quarter.

Cook, asked whether Apple's push for sourcing flexibility is about securing volume or protecting its price points, said the DRAM market has three suppliers and that more of them would help on the supply side and perhaps on pricing, then corrected himself to say the pricing effect is unclear. He said Apple is "evaluating all options." The fourth supplier available to him is CXMT, which the Financial Times reported in July that Apple has begun testing DRAM for devices sold in China while lobbying Washington for clearance to use its parts more broadly. Representatives John Moolenaar and George Whitesides wrote to Commerce Secretary Howard Lutnick earlier this month, asking for purchases from CXMT and YMTC to be barred outright, including through allied supply chains. CXMT listed on Shanghai's STAR Market in July.

The unit supply constraints Apple flagged for the September quarter are a separate problem from memory pricing. Cook attributed them to the availability of the advanced process nodes Apple's SoCs are built on, after iPhone and Mac demand ran ahead of the company's plan, and said they'll hit iPhone, Mac, and iPad in September, against Mac primarily in the June quarter. Apple guided September revenue growth of 9% to 11% year over year, down from 16% in the previous quarter.

Lumentum CEO Michael Hurlston told an audience at the RAISE Summit in Paris earlier this month that indium phosphide, the compound semiconductor behind every laser in an AI data center, is heading into a supply squeeze worse than what we've already seen with DRAM / NAND, and that Nvidia's decision to fund Lumentum and its biggest competitor at the same time was a response to exactly that.

In his remarks, Hurlston said that telecom customers bought lasers in the hundreds, while Nvidia and the hyperscalers are asking for hundreds of millions. While Lumentum runs five indium phosphide fabs, it's still shipping more than 30% below what customers want. Nvidia's answer, in March, was to write $2 billion checks to Lumentum and Coherent, the two suppliers that, between them, make most of the world's high-speed datacom lasers, with purchase commitments and future capacity access attached to both.

"Between the two of us, I don't think we can service the demand that Nvidia and others are now putting on us to solve this resistance problem in the data center," Hurlston added.

Silicon doesn't emit light

Indium phosphide has a direct bandgap of roughly 1.34 eV, which lets it convert electrical current into photons efficiently. Silicon's bandgap is indirect, so it can guide, split, and modulate light but can't generate it. Every silicon photonics platform in production, including those of Nvidia, Broadcom, Marvell, and Cisco, still needs an indium phosphide laser somewhere in the package to supply the light for silicon to manipulate. Moving from pluggable transceivers to co-packaged optics changes where that laser sits and how it's mounted, but it doesn't remove it from the bill of materials.

Nvidia's marketing claims its photonics switches use four times fewer lasers than an equivalent pluggable deployment, alongside 3.5 times better power efficiency and ten times better network resiliency, all of which are vendor figures. Those savings are per port, and it's that port count that's exploding.

The high-end Spectrum-X Photonics configuration runs 512 ports at 800 Gb/s for 400 Tb/s of switching, and Quantum-X Photonics InfiniBand runs 144 ports at 800 Gb/s. Co-packaging also shifts the laser type toward high-power continuous-wave sources and external laser modules that feed multiple channels, which are harder to build than the electro-absorption modulated lasers inside a conventional pluggable. Coherent's Nvidia agreement covers that category of high-power CW lasers, external laser source modules, and fiber array units.

Capacity at Lumentum and Coherent

Lumentum posted record revenue of $808.4 million in its fiscal third quarter, up 90% year over year, with components revenue of $533 million and pump laser shipments up 80%. On the May earnings call, Hurlston told analysts the company expects its supply line to increase 50% measured from one December quarter to the next, and in the same breath said the supply-demand imbalance on EMLs had widened from the 25% to 30% given a quarter earlier to "somewhere greater than 30%," with pump lasers tighter still. A supplier growing output by half a turn per year and losing ground anyway is a clean measure of how steep the demand curve is.

Coherent's 6-inch indium phosphide line yields more than four times as many devices as its 3-inch line at less than half the cost, CEO Jim Anderson told investors on the company's fiscal Q3 call. Anderson said EMLs, CW lasers, and photodiodes are all in production on the 6-inch line with yields above the legacy 3-inch lines, and that internal capacity would double by the end of the June quarter, one quarter ahead of plan, then more than double again by the end of 2027. Coherent's revenue hit a record $1.8 billion, up 21%, with data center and communications now 75% of the total against roughly 41% a year earlier, and backlog stretching into 2028.

Logic and memory moved to 300mm wafers in the early 2000s. Indium phosphide is a brittle, expensive, small-boule material where the industry-wide upgrade currently underway is 3-inch to 6-inch, roughly the transition silicon completed in the 1980s. Lumentum's fifth fab, announced in March, is a converted Qorvo gallium arsenide plant in Greensboro, North Carolina, described as 4-inch and 6-inch compatible and ramping around 2028.

Running through China

Indium is recovered as a byproduct of zinc refining, so its output can't be scaled independently of zinc economics, no matter how much laser demand there is. The USGS Mineral Commodity Summaries 2026 put China at an estimated 760 tonnes of roughly 1,100 tonnes of global primary refined indium in 2025, about 69%, and recorded a 72% year-over-year fall in unwrought indium exports between September 2024 and September 2025 after Beijing placed the metal under export controls in February last year. The U.S. warehouse price averaged about $390 per kilogram in 2025 against $340 in 2024.

AXT's Chinese subsidiary Tongmei had to obtain Ministry of Commerce export permits, granted in June and August 2025, before it could resume shipping indium phosphide substrates out of China. The fabs Nvidia is funding sit downstream of that licensing regime, and the wafers going into them aren't made in the United States in meaningful volume.

DRAM contract prices rose 90% to 95% quarter over quarter in Q1 2026, the largest quarterly increase TrendForce has recorded, and the firm forecast a further 58% to 63% in Q2 with NAND up 70% to 75%. HBM is sold out for 2026. Hurlston is measuring his warning against a genuinely historic crunch, which makes it a strong claim rather than a throwaway one, and he runs a company whose valuation depends on the shortage persisting.

LightCounting's April 2026 market forecast puts current transceiver demand about 30% above supply, matching Lumentum's own figure, but states that the shortages should be gone by the end of 2026 and cuts expected Ethernet transceiver growth to 65% for the year after 82% in 2025 and 93% in 2024. Coherent, hitting its capacity doubling a quarter early, points the same way. The distinction from memory is that the fix here is a wafer-size transition already running in production with yields ahead of the old node, not a new fab that takes three years to build.

Unless you’ve been living under a rock, OpenClaw has dominated headlines, both good and bad, for much of early 2026. Developed by Peter Steinberger, the tool is an engine that spins up an AI agent that can autonomously act on your behalf, going so far as having the ability to run files, browse the web, and much more. Using a system named “Skills,” you can even teach your AI assistant to perform new tasks, with more integrations popping up daily. But how useful is it for the average person? I took it upon myself to answer that question by using the relatively humble Beelink SER10 MAX mini PC, which comes pre-installed with OpenClaw, to find out.

To kick things off, when setting up your OpenClaw installation, there’s a fairly large decision one has to make: Do you want to rely on Cloud resources and more powerful AI models, or run something locally? Beelink’s SER10 MAX ships with Qwen-3.5 9B, which is a relatively older and more outdated AI model. Since I want to keep things fairly simple, I opted for the local AI route first. To run larger models, you’ll need a lot of memory, so I first tweaked the humble Mini PC’s video memory up to 48GB, which should allow us to keep a larger, more complex model in memory. It also leaves us 16GB of system memory to play around with, which should be more than enough to run the terminal and ensure that everything else works as intended.

Selecting the ideal local AI model

Having 48GB of available video memory should allow us to run a fairly powerful AI model on the system. However, the downside of running a larger model is that token speeds are likely to be slightly lower than expected. However, for running the super-smart AI that does things for us locally, we want as much intelligence as we can get. For this exercise, I chose Google Gemma 4 31B (UD-Q8_K_XL), a near-lossless quantization, somewhat ambitiously, as I will learn shortly.

After downloading the model and running it in llama.cpp, we managed to attain 2.34 tok/s. On average, everyday queries took 116 tokens to generate, at 2.34 tok/s. That’s still too slow for fast everyday use. So, I took a deep breath and conceded that the humble Ryzen AI 9 HX 470 didn’t have fast enough memory or memory bandwidth to run such a large model at acceptable token speeds. Even if I had dropped the quantization to 4-bit, we’re still looking at the theoretical maximum of around 5 tok/s for that model in particular. If you had a faster machine with rapid RAM speeds, such as a Strix Halo system like the Framework Desktop, it might be more workable, but for a lower-end piece of silicon with relatively humble DDR5-5600 speeds, you’ll just have to accept a slightly more neutered model.

So, I eventually conceded that the smaller Gemma 12B (Q4_K_M) was a much more sensible choice for a device of this caliber. After loading things up into llama.cpp, we managed to get a much more sensible 10.64 tok/s on a general knowledge query: “What is Tom’s Hardware?” Now that I have a little piece of talking electrified sand on my desk, it’s time to configure OpenClaw.

Hatching HammerClaw

The device I am using, the Beelink SER10 MAX, comes with OpenClaw pre-installed. The only real dependency it has is llama.cpp, which we configured earlier while testing which AI model to use. Since the llama.cpp port is open, OpenClaw leverages that to talk to the AI model, with all of its additional fanciness included. The OpenClaw installer is fairly straightforward in getting things up and running, including setting up a Telegram channel to communicate with our AI model remotely and configuring a gateway, so we can configure things without relying on Ubuntu’s terminal commands.

Within the installer, we start to define our new AI assistant: It asks what its name and identity are, some basic details about the user, as well as what principles it should uphold, in a very extravagant file named SOUL.md. Remember, our talking sand isn’t alive, so it’s all quite dramatic. Generating a SOUL.md file takes a while for our humble local AI model, which I’ve named HammerClaw. But can it do anything useful for me to justify its nascent AI existence? Right now, it’s taken five minutes to think about exactly what it is and what it’s doing.

After a little while, our little HammerClaw “hatches,” asking what its purpose is, who I am, and how it should talk. Personally, I don’t like it when AI models are verbose, so it’s straight and to the point, and should never, ever lie to me. When AI models can scale to rather humble devices like this one, the smaller, less-intelligent models can be error-prone. With our little local AI agent alive and kicking, it’s time for it to automate a task for us; it couldn't be that difficult… right?

Stumbling blocks

HammerClaw quickly wakes up, and I offer it a task: to gather ten news articles from trusted outlets and different parts of the day, ensuring freshness, with a small digest of what’s happened. Since most of my work on Tom’s Hardware Premium is centered around chipmaking and data centers, I want it to focus on those topics. HammerClaw then quickly takes the task and starts working out how to pull it off. It’ll use its built-in cron tools and felo-search to pull stories from the internet.

Now, here’s where things go bad for poor HammerClaw. It then begins to simulate the action, instead of actually performing it, despite saying that it had set up the cron jobs and skills required for the automated scheduling. Even worse, it hallucinated a list of links that went absolutely nowhere. After pointing the error out, it gets apologetic and investigates why things went wrong. As it turns out, some additional parts need to be configured, which it tried to do and failed once again. Agentic Tool use is now a specific benchmark, but when asking Gemma 4 12B to set up a multi-step task like this, it simply couldn’t manage with the conversational tone of my prompts.

It’s at this point that I asked HammerClaw to give itself a grade, to which it offered itself a B- on accuracy for hallucinating and making up news stories. Now, I don’t want to say that all OpenClaw AI agents would do this, as we are running a relatively light local model. I would expect that larger local models would be able to identify what they need to do much more efficiently due to having a significantly higher model parameter count. With that in mind, I wondered if another AI model could help HammerClaw along a little bit with this task. And so, I went onto my OpenRouter account and opened a chat with the big, beefy 2.8 Trillion Parameter Kimi K3.

Kimi K3 saves the day

Comparing a cloud-based frontier model like Kimi K3 and Gemma 4 12B just isn’t fair. One takes up terabytes of RAM to run, while Gemma fits inside a Mini PC that lives on my desk.

Instead of using Kimi K3 to run the AI itself, I tasked it with actually helping me set up the job that HammerClaw seemingly didn’t have the skills to pull off. This hybrid workflow – of using a local LLM in tandem with a more powerful one in the cloud- is a common setup for many AI enthusiasts. After about a dollar in tokens, it sent me a list of OpenClaw commands, skills, and instructions, after reading the current documentation for OpenClaw CLI to ensure it got everything right. It was flawless. I followed the instructions Kimi K3 gave me and entered them into an Ubuntu Terminal, where it successfully created a new skill for HammerClaw named ‘News-Intel,’ instructed me on how to enable the web-search functions, and set up the Cron jobs for the automated sends. Bearing in mind that I had never used OpenClaw before, it was all surprisingly smooth, even if our local model couldn’t do all of these tasks itself.

With everything set up, the buck is then passed back to our much humbler Gemma 4 12B model-powered HammerClaw to execute the rest – after all, this is supposed to be a test for how Local AI models function. I executed the command for a manual run while checking the operational logs, where it was successfully calling all of the tools, and thought, against all odds, HammerClaw might actually be able to do it. A few minutes later, I received a Telegram message. HammerClaw had managed to locally execute the task and send me a digest of ten news stories.

Of all of the things to automate, this is likely one of the simplest examples of how someone can use OpenClaw. While simple, it’ll also save me a bit of time every day staring at an RSS feed and looking for stories myself. But, it’s quite a distance away from the “speak, and it’ll do whatever you want it to!” promise that drew so many into a frenzy earlier this year.

Is it worth it for the average person?

If all you heard about OpenClaw is that it’s a magical AI agent that can do anything, as I’ve learned, it’s not quite the truth. Unless you’re well-versed in several elements, like understanding model choices and getting your head around what models perform well for tool-calling. Running a humble local setup might be fun, or interesting to tinker around with, but the true power for Local AI developers and tinkerers lies with the obvious: More power to run bigger, complex models, and more agents running tasks consecutively. In fact, we’ve taken a look at how these workflows can be used in practice with a mixture of Local and Cloud AI models for Tom’s Hardware Premium.

The issue here is that for our local model, this simple task – of running and sending us 10 news stories- could not set itself up, and that’s using hardware that already costs north of $1,500. As our resident local AI expert and GPU guru Jeff Kampman has recently tested, for those serious about AI who want real power without relying on the cloud entirely, you might want to save up your pennies to run stronger, faster local models, either using dedicated GPU-accelerated setups, or dedicated boxes such as the DGX Spark, or a cluster of Dell Pro Max GB10s, both of which will cost you north of $5,000. You’d hope that the models capable of running on that hardware wouldn’t fumble the setup of a relatively simple Cron job.

The real question is, will having a local AI inference box meaningfully change how you work, or the work you do, to pay for itself? For many, that’s the lingering question that many are asking themselves. For now, HammerClaw is sending me more articles every few hours, a task that can be performed by simple scripting. But the manual “sift” is being handled by an LLM. As neat as it is, I wouldn’t pay $1,500 for the privilege. Luckily, it’s not merely a box made for AI inference; there’s a whole computer attached.

A Qualcomm exec suffered from one of the most disastrous tech demos we’ve ever seen live on stage at Computex 2026 this summer. In what should have been a wow moment during a press event in Taipei, Qualcomm hardware partner NEURA Robotics’ 4NE-1 humanoid robot quickly shifted gears from being a technical tour de force to a crashing mechanical shambles.

Robot powered by Qualcomm’s new AI chip dies mid-presentation from r/singularity

We see that the 4NE-1 humanoid robot is powered by Qualcomm’s Dragonwing IQ10 robotics reference design (RRD), claimed to be capable of 700 TOPS. But even before the robot’s spectacular malfunction, it didn’t look particularly healthy or nimble. 4NE-1’s slow shambling walking style radiated strong Two Soups vibes.

As the robot eventually hobbled close enough to the smart-suited presenter, the on-stage human picked up a Qualcomm Dragonwing IQ10 RRD from the robotically delivered platter and proudly showed it off to the attending crowd. As the applause and whoops subsided, the robot turned to face the crowd, and the presenter rolled on with his spiel.

Seconds into his script, the exec spun away to the side as the 4NE-1 humanoid robot very suddenly and noisily collapsed into a mechanoid mess. After resting in its terminal backbend for a second, one robot hand raised momentarily to make a futile gesture. Thankfully, the glass tray that had carried the Dragonwing IQ10 box onto the stage didn’t shatter.

Stage hands seemed well-prepared

While the overall feeling from watching the video is one of shock from the ambling robot’s sudden noisy collapse, many on social media have commented on how unruffled and well-prepared the stagehands seemed to be. The silent and swift action of the black-clothed helpers, first cloaking the dead robot with purple silks, then the trio carrying it away, exits stage left, making it look like this catastrophe wasn’t such a surprise to some.

As the robotic remains were eventually removed through the side doors, we hear the brave speaker who carried on regardless come to the end of his polished presentation. The crowd politely and quite loudly claps - but is most of the applause for the speech, or for the unflappable stagehands who have efficiently extracted the heap that was the 4NE-1?

This late-emerging clip from Computex 2026 rivals the Windows 98 USB plug-and-play scanner crash during Bill Gates’ presentation at Comdex. That infamous software crash event may well have prompted the creation of the USB Cart of Death.

Data centers on land are facing increasing opposition and having a hard time getting off the ground — from power connection issues that can take months or years to resolve, to neighborhood concerns about noise and air pollution, as well as their impact on the local water supply. One startup called Atomarine wants to solve this by building nuclear-powered data centers on barges and deploying them at sea.

There have already been various proposals to put data centers out at sea (or even submerged) from established names and startups. We’ve seen conventional examples from companies like Samsung Heavy Industries and Japanese shipping company MOL who to put the servers on dedicated ships and are powered by liquefied natural gas (LNG). There are also more innovative solutions from startups that want to put them in the bases of ocean-based wind turbines or in wave energy converters.

Atomarine differs by building data centers as floating barges, similar to floating oil platforms, which can be deployed anywhere — even in international waters. This should help cut down on the permitting process, and it doesn’t have to worry about disturbing neighbors. The electricity needed to run the infrastructure is then delivered by a power ship moored alongside the barge supplied by LNG ships. Once small modular nuclear reactors (SMRs) become commercially available and certified for shipboard use, operators can then swap out the conventionally powered electricity suppliers with a nuclear one relatively quickly. And because these data center barges are modular in nature, it would be relatively easy to upgrade and scale the floating data center.

While this project sounds promising, building a megastructure like this comes with several challenges. The biggest one is that the ocean is massive and unpredictable, so these barges must be engineered to face extreme weather conditions, making them quite expensive to deploy. It must also be connected to high-speed internet (unless the operators plan to ship tons of drives daily), so they’ll have to lay undersea cables, as well. Furthermore, data servers are particularly sensitive equipment, so they must be upgraded to ensure that they can withstand the continuous rolling motion and the salty environment of the sea. There’s also the question of an SMR’s viability in shipborne use, although the U.S. Navy already has a good track record of running nuclear reactors aboard massive vessels.

The mounting opposition against data centers, both by the general public and politicians, is making it harder and more time-consuming to build new projects across the U.S. While an ocean-based data center like this is likely going to be significantly more expensive and technically challenging than land-based developments, this might become the fastest option in the future if the idea proves to be viable.

Reuters has named Shanghai Aishengna Electronic Technology Group as the state-owned company producing China's first domestic immersion deep ultraviolet lithography (DUV) scanners, a day after news of the program broke without identifying the manufacturer. Aishengna was established in August 2023 with RMB 7 billion, around $1 billion, in registered capital and is thought to have absorbed engineering teams from Shanghai Yuliangsheng Technology and Shanghai Micro Electronics Equipment.

Aishengna has been named by a single source who declined to be named, and its shareholders, SMEE and Yuliangsheng, didn’t respond to requests for comment. SMIC has been testing a Yuliangsheng immersion tool since September 2025, and first deliveries are slated for SMIC, Hua Hong Semiconductor, and ChangXin Memory Technologies.

Photoresist, coater tracks, and light sources

Tokyo Electron held an 89% share of the global coater/developer market in 2022, per Shared Research's analysis of the company's own disclosures, with its chief executive putting the figure near 90% and at 100% for EUV production. A scanner only exposes the wafer, however. It’s the track that's responsible for applying the resist film, baking it, and developing the pattern after exposure, and it has to be mechanically and thermally matched to the scanner in a single cluster, which is why the two are bought together. Shenyang Kingsemi has reached 28nm-class track capability and is currently targeting 14nm.

JSR, Tokyo Ohka Kogyo, Shin-Etsu, and Fujifilm hold a combined 72.5% of the ArF photoresist market, while Chinese suppliers hold under 1% of ArF immersion resist specifically. Nata Opto-electronic built a 25-ton ArF line, later expanded to 50 tons, passed customer qualification in December 2020, and completed project acceptance in 2024 with little volume to show for it. Xuzhou B&C says its ArF immersion products cover 45nm to 28nm and can stretch to 14nm, and chairman Fu Zhiwei has put mass production of China's core advanced resists five years out.

Cymer, Gigaphoton, and Coherent hold more than 80% of the ArF excimer laser market between them, and Cymer has been an ASML subsidiary since 2013. Beijing RSLaser shipped China's first high-power domestic excimer laser in 2018 under the national Project 02 program and has a 4 kHz 193nm ArF prototype aimed at 90nm and 65nm-class tools, generations behind what 28nm immersion requires.

Carl Zeiss SMT has been ASML's sole projection optics supplier since 1983, and Zeiss SMT revenue grew from €1.2 billion in 2016 to €4.1 billion in 2024. It’s not currently known what, if any, Japanese tooling is inside the Aishengna machines, but excimer sources and precision optics are areas where Chinese substitution is believed to be lacking.

CXMT

CXMT is projected to reach around 350,000 wafer starts per month by the end of 2026, roughly 25,000 short of Micron, up from 40,000 in 2020. DRAM scaling at 1a and 1b-class nodes runs on immersion multipatterning because CXMT has no EUV access, which makes any potential ramp lithography-gated rather than cleanroom-gated. DRAM contract prices rose 93% to 98% quarter on quarter in Q1 2026, and TrendForce projected a further 58% to 63% in Q2, lifting DRAM industry revenue 81% to $97 billion. A domestic immersion source is therefore worth having to CXMT, even at inferior overlay and throughput.

A DUV-only 7nm flow needs roughly 19 spacer-defined multipatterning masks from the front end through the second metal layer, against about 10 for an EUV-based N7+ process, by one published comparison of SMIC's process. SemiAnalysis has put SMIC's 7nm defect density near 0.14, around double TSMC's N5 and N6. ASML CEO Christophe Fouquet told analysts during the company's July earnings call that rising DRAM lithography intensity partly reflects "the increased replacement of multi-patterning with more cost-effective single-exposure EUV." As such, every exposure China adds to compensate for the missing EUV burns scanner hours a thin domestic fleet doesn't have.

The MATCH Act

China fell to about 14% of ASML's sales in Q2 2026 from 33% across 2025, and installed base management, the service and upgrade business, brought in €2.8 billion of ASML's €9.3 billion in second-quarter revenue. H.R. 8170 would ban both the export and the servicing of immersion DUV systems to any destination in China and designate SMIC, Hua Hong, Huawei, CXMT, and YMTC as restricted entities by statute. Former ASML chief executive Peter Wennink has said the company can service most Chinese tools, but not with spare parts of U.S. origin that fall under export control, which is the mechanism that the MATCH Act would widen.

The bill remains in committee after clearing the House Foreign Affairs Committee in April, with a Senate companion filed as S. 4281, and no floor vote yet scheduled. Its 150-day allied-alignment clause would also reach Nikon, which sold 11 ArF immersion systems in FY2024 and none in the first three quarters of FY2025, and which plans to deliver a new immersion prototype to a major chipmaker by 2027. ASML expects about 130 immersion shipments this year and intends to raise immersion capacity 30% in 2027, with a further 30% under investigation for 2028.

As for Chinese providers, SMEE prototyped its SSA600 ArF tool in 2011 and never reached sustained commercial sales, and a late-2023 shareholder claim that the company had developed a 28nm machine was subsequently retracted. SiCarrier showed etch, CVD, PVD, and ALD tools at SEMICON China 2025 without a lithography system, and a December 2025 government contract reported as a lithography award turned out to cover a KrF tool at 110nm. We’ve previously assessed that China’s toolmakers are more than a decade behind the market leaders.

The AI Futures Project's June forecast puts a commercially viable domestic 7nm-capable immersion scanner between 2032 and 2038, with a median of 2035, and claims ASML holds 98.7% of the immersion market today. Five machines in 2026 would be under 4% of ASML's annual immersion output, and each would still need a coater track, a qualified ArF immersion resist, and an excimer source to print a single wafer; China leads in none of those three.

Amazon has several internal reports that show how AI is causing the company to overspend on various projects. The Financial Times reports that the cost overruns reached $1.8 million, and that is just for one project. These mistakes used to be “trivially cheap,” but AI models made them “catastrophically expensive,” especially as token spending drastically increased with the deployment of AI agents.

The biggest blunder, so far, is the $1.8-million bill that came from a failed Claude Sonnet AI deployment, which was supposed to match author details with listings on Amazon, representing an 860% increase over the allocated budget that was only detected some five months after the issue started happening. Other problems that surfaced include a $541,000 additional cost that came from a project building, ironically, a financial auditing tool, and a $134,000 extra expense for a system designed to reduce delivery times in the company’s logistics network.

“As with any new technology, we’re experimenting, learning and improving how we use it, including how we drive cost efficiencies,” Amazon said in an internal presentation, according to FT. “Cherry-picking small, isolated examples where teams are learning from one another and portraying them as business as usual doesn’t reflect how teams across Amazon are using AI.” And even though overspending more than a million dollars on failed AI projects might seem excessive for the average person, the tech giant’s latest quarterly revenue sits at more than $181 billion, meaning these excess AI expenses don’t even account for 0.1% of what it makes in a month.

This is not the first time that AI-related issues have cropped up in Amazon’s workflow. Earlier this year, AWS reported several outages that were driven by AI coding bot blunders, but the company fixed this by limiting the access of AI agents instead of giving them the same permissions as the senior engineers that they’re tied to. It also used to have an internal leaderboard that showed which employees used AI the most, but has since dropped it as spiraling AI costs made them think twice about the policy.

Many tech companies have been pushing their people to use AI, supposedly to increase productivity through tokenmaxxing. However, the Uber CTO said that there is no link between this policy and shipping successful products. And as agents took over and AI providers switched from subscription to per-token models, costs have become so great that companies are using up their annual budgets in a matter of weeks. While this might not be an immediate issue for tech giants like Amazon and Microsoft, it is unsustainable for most other companies out there.