Put to work on different tasks, AI can do a good many specialist skills that even just a few months ago wouldn’t have been thought possible. AI is beginning to take on some of the work required to design the chips that power AI, way beyond simple optimisation. But that brings a potentially expensive problem: what happens if an AI ends up generating a design that infringes somebody else's patent, which then ends up in thousands of chips rolling off production lines? Tom's Hardware Premium spoke with experts like Domenec Forte, professor of electrical and computer engineering at the University of Florida, and Simon Moore, professor of computer engineering at the University of Cambridge, to learn more about a looming issue that could broadly impact the AI chip design market.

This week, Synopsys announced a suite of AgentEngineer tools capable of carrying out long-running tasks that could verify and implement chips, as well as planning analog design and manufacturing. The company said it had more than 50 customer engagements already underway, and planned to offer the tech for general availability by the end of the year.

Synopsys isn't alone in discovering the ability of AI to do such tasks. Competitor Cognichip says its ACI platform can generate specifications and RTL, then automatically produce verification plans and testbenches. And in China, Empyrean Technology recently claimed an AI agent reduced the time taken to complete one circuit layout task from four weeks to one. Even the big AI labs are getting in on the act: OpenAI says its models helped it and Broadcom develop Jalapeño, its first custom inference chip, from initial design to tape-out in just nine months.

It all poses difficult questions for the chip design industry – though not just about whether the sector will remain strong. It’s also about whether AI is a suitable replacement for human ingenuity. The industry relies heavily on intellectual property, and it’s not yet clear what happens when an AI designs something that somebody else already owns. Rather than designing every component of a chip from scratch, companies routinely license processor architectures, controllers, and other memory technologies rather than reinvent the wheel. Arm alone generated nearly $5 billion in its 2026 financial year, which came roughly half from licensing and other revenue and half from the royalties on that. Synopsys makes money that way too, selling a substantial portfolio of "silicon-proven" IP alongside its chip design software.

The AI copied my homework

But by putting AI to the task, some of those blocks could become far easier to create – and working out where they came from far harder. "It mostly amplifies existing problems," said Domenec Forte, professor of electrical and computer engineering at the University of Florida, whose research includes AI-enabled chip-design tools and semiconductor IP protection, in written comments to Tom’s Hardware Premium. "AI can spread a copied design or infringed patent across thousands of chips before anyone notices and without anyone even intending it,” he said.

Copying wouldn’t be as blatant as feeding an Arm core or another piece of proprietary RTL directly into a model. Instead, AI’s skill is in ingesting huge amounts of literature from academic papers and patent applications, then constructing lookalike designs. And in a space where power constraints, performance challenges, and a limited physical area all combine alongside mandated standards all have to follow, there are only a finite number of feasible designs that AI can reach quicker – inadvertently copying others’ homework. "Even a circuit that adds two numbers draws from a well-documented catalogue of textbook designs," said Forte. "Ask for the fastest one, and you'll likely land on a design someone has already published."

That creates a provenance problem for chips, because human engineers generally document and can explain where their ideas came from and which IP was licensed. AI might not have that same traceability. Existing tools can identify close copies, Forte said, but designs can be rewritten or run through synthesis tools that transform their implementation while leaving the underlying function intact.

Forte suggests IP owners could eventually deposit encrypted versions of their designs into a shared repository, allowing trusted agents to check whether newly generated hardware overlaps with existing IP without exposing the originals. He’s less convinced by watermarking, which he argues could be removed or forged. International standards could help somewhat: IEEE 1735 defines methods for encrypting electronic-design IP and managing the rights attached to it. But Forte argues standards like it may now need to go further, defining what AI agents can access, retain, and learn from while operating inside electronic design automation tools.

An abundance of caution

That all might suggest there’s a free-for-all when it comes to AI designing new chips, and potentially ripping off – advertently or not – other designs. But that’s not the case, for a simple reason. The semiconductor industry is unusually cautious about new design techniques because software can be patched, whereas fabricated silicon can’t. “Once you ship the chip, you ship the chip, and you can't change the transistors,” said Simon Moore, professor of computer engineering at the University of Cambridge, said in an interview with Tom’s Hardware Premium.

Moore estimates verification now accounts for more than half of the effort involved in getting many chips out of the door, while established verification tools can tell engineers whether an AI-generated test actually improves coverage. Getting that AI to conduct tests, probe possible failure points, and check results at scale is “a bit of a no-brainer," said Moore. However, allowing AI to make architectural decisions, which are much harder to undo, is where most manufacturers are drawing the line.

That same caution explains why licensed IP may survive the onslaught of AI-generated content, even if the tech is capable of generating technically similar blocks. Buying a block from Arm, Synopsys, or another established vendor comes with the history of the companies, and the assurance that it’s gone through the relevant verification and standards compliance checks.

So could AI reduce reliance on licensed IP? “For routine building blocks, probably yes,” said Forte. “But a licensed IP block is much more than its design files.”

And if engineers increasingly have to ask not only whether a design works, but where it came from and whether somebody else already owns part of it, provenance may become something semiconductor companies are all the more willing to pay for. “I'd treat an AI designer like a brilliant new hire,” said Forte. “Fast and talented, but everything it produces gets checked.”

Chinese AI company DeepSeek has released open-source programming tools developed in partnership with Huawei, for the company's Ascend AI chips, as part of efforts to reduce reliance on Nvidia's software and hardware ecosystem. According to a September 30 Reuters report, the release includes open-source libraries for AI computation and chip-to-chip communication, as well as Ascend support for the high-level programming language TileLang.

DeepSeek says the tools — for which Huawei provided full support during development — are intended to simplify programming while enabling developers to fully leverage the hardware's performance. The two companies also worked together to optimize computation and communication on a supernode system built around 128 Ascend 950 chips. The work addresses two requirements for running large AI workloads across multiple accelerators: performing calculations efficiently on each chip and moving data quickly enough between them to keep the processors occupied.

The released libraries include DeepGEMM-Ascend, which handles matrix multiplication and other calculations used in DeepSeek's models. It supports BF16, FP8, and FP4 operations and uses the same programming interfaces as DeepSeek's existing DeepGEMM library, allowing developers to retain familiar APIs when moving to Ascend. Meanwhile, DeepEP-Ascend handles communication for training and inference, including routing data to the different experts in mixture-of-experts models and combining their outputs. Both libraries were developed and tested on Ascend 950 hardware.

TileLang provides the higher-level programming layer for writing optimized kernels. DeepSeek described it as offering “a simpler programming model” than Nvidia's CUDA, with the aim of improving development efficiency and simplifying code. The language already supported Nvidia and other hardware, with earlier adapters available for Huawei's Ascend processors. The September 30 update adds native support for Ascend 950, including code generation, automatic scheduling, and synchronization.

The tools build on Huawei's existing CANN software platform, which provides the underlying infrastructure for running AI workloads on Ascend. Nvidia's CUDA platform has long supplied developers with a mature programming environment and libraries optimized for its GPUs, making the software ecosystem a major part of the company's advantage in AI computing. While DeepSeek's release provides developers with additional tools to optimize workloads on Huawei hardware, TileLang's support for multiple platforms ensures the language remains useful for Nvidia GPUs.

The announcement comes two weeks after Huawei unveiled its next generation of AI processors and supernode systems. Huawei said it expected its AI systems to be widely used for model training in 2027. The companies had already collaborated on DeepSeek's V4 model, released in preview form in April, with support for Huawei's Ascend chips. Huawei said its Ascend 950 supernodes fully supported the V4 models and that its chips had been used for part of the lighter V4-Flash model's training.

American custom and prebuilt computer seller Maingear has launched a new instant trade-in program in partnership with SELLIT9. The new collab gives customers instant credit on any new Maingear desktop or laptop PC. A broad range of used tech can swiftly be exchanged for Maingear gift cards, including laptops, phones, tablets, smartwatches, headphones, cameras, lenses, and microphones. Instant trade-ins are live today, and until October 14, all those taking advantage of this low-friction old-for-new plan can earn an extra 5% on their traded-in products using the code EXTRA5.

In a press release sent to Tom’s Hardware, Maingear says that it knows that “Memory prices are up. Component prices are up. Meanwhile, perfectly good tech is collecting dust in drawers across the country.” This old and neglected tech can help you get your next hand-built gaming rig, suggests the company.

Naturally, Maingear sings the praises of its collab with SELLIT9’s recycling and refurbishment business. The New Jersey-based PC firm says the quotes its customers can get through SELLIT9 are truly instant. For example, it says that you will get a quote in minutes, and a Maingear gift card on the spot. That means you won’t have to wait for a shipping carton for your used gear to arrive, then make its way to SELLIT9, and subsequently pass inspections. However, we note some wording indicates that there’s “a temporary hold on the shopper’s card” until any traded-in gear “checks out.”

We have no experience of Maingear’s chosen partner SELLIT9, but its 4.6/5 score on TrustPilot looks pretty good for such a service, which can easily attract ire. SELLIT9 is a Canadian business that recently raised $4.1 million to expand across North America. Perhaps this Maingear partnership is its first foray into the U.S. We’ll have to see what else happens on Oct 1.

I checked some CAD prices for various used tech stuff on SELLIT9 and converted them to USD, suggesting you could get the following sums for used tech in the U.S.:

• Ryzen 7 9800X3D – up to $141
• Core i7-14700K – up to $120
• RTX 4080 Super – up to $530
• GTX 1080 Ti – up to $71
• 32GB desktop DDR4 RAM – up to $42
• iPhone 14 Pro 128GB – up to $278
• PS5 Slim (Digital Edition) – up to $160

We aren’t sure if these prices are directly translatable to the U.S. market, but they should give you a rough idea. Maingear also highlights that using its new trade-in service offers minimal friction for customers. That means “no listings, no lowball offers, and no meeting strangers in parking lots.”

When this article goes live, the new instant trade-in service goes live. Make sure to nose around the offers and let us know what you think.

With RAM, storage, and graphics card prices still stuck in the stratosphere and no relief in sight, we have an opportunity to upgrade other parts of our PC that aren’t affected by the AIpocalypse. Take, for example, this full-size Cherry Xtrfy MX 3.1 wired mechanical gaming keyboard, on sale for only $34 with promo code WOOTCHERRY. That’s an incredible 67% off the original $119.00 list price, 50% off Amazon pricing, and, by far, an all-time low. If you happen to be a new customer at Woot, you get $10 off, taking it down to a dirt-cheap $29 and one of the best deals on a name-brand mechanical keyboard we’ve seen in a while.

• Check out this deal at Woot!

The Xtrfy MX 3.1 is a full-size (100%) keyboard boasting a high-quality aluminum frame and RGB lighting. It features the latest Cherry MX2A switches (Red - Linear, smooth and quiet) designed to reduce wobble, eliminate spring ping, and offer a notably smoother feel out of the box than the original. The keys come pre-lubed and feature a new spring design to reduce scratchiness and noise compared with the previous iteration. The updated keys are also durable, rated for up to 100 million keystrokes so that you can hammer away without worry. It also includes integrated internal damping mechanisms to reduce fatigue and lower its acoustic profile.

The Cherry keeb uses a 1,000 Hz polling rate and also includes useful gaming functions. You get full N-key rollover (where all keys can register simultaneously), anti-ghosting to prevent false or missed key presses when you press multiple keys at a time, and fully programmable keys through the Cherry software. The MX 3.1 also features 16.8 million-color RGB LED lighting shining up around the keys and out the sides. With the same Cherry software, you can choose from several canned schemes or program your own. You can also personalize multimedia controls, create shortcuts and macros with the software.

Currently sold for $80 (white version) on Amazon and $129 on the Cherry website, this $34 deal from W00t.com (or $29 if you're a new customer!) is one of the best prices we’ve seen on a reputable brand mechanical keyboard. If you’re in the market for a new keeb, or just want a solid backup on the cheap, you won’t want to pass up this deal.

Gears of War: E-Day takes us back to the very beginning of one of the most beloved sagas in gaming: Emergence Day. And to tell this new chapter in the Gears story, developers at The Coalition rebuilt Gears entirely from the ground up in Unreal Engine 5. On top of all-new assets, E-Day is the first shipping title that uses UE5’s MegaLights technology. This tech allows artists to light a space with hundreds of dynamic ray-traced light sources, all in real time.

MegaLights immediately stands out as something different and distinctly next-gen for real-time graphics. Right from the start, Gears of War: E-Day made the hair stand up on the back of my neck with its haunting use of light and shadow. As I fought through the first bloody minutes of Emergence Day with protagonists Marcus and Dom and some new recruits in tow, I was struck by the power of dynamic lighting from sources like the flashing lights on police cars and ambulances.

And as I walked through a ravaged supermarket, I was amazed by the diversity of realistic light sources in the scene, from flickering TVs to fluorescent tubes in the frozen foods aisles. To find out what level of graphics horsepower Gears' incredible presentation demands in exchange for a 60 FPS average, I wiped the Locust guts off and got to testing performance across 43 of our graphics cards to see which GPUs were up to the job.

If you're interested in CPU performance, check out our Gears of War E-Day CPU benchmarks, as well.

Image quality analysis, low to max settings

Gears of War: E-Day offers five quality presets: Low, Medium, High, Ultra, and Ludicrous (seemingly equivalent to Cinematic in other Unreal Engine 5 titles). We captured screenshots at the same location in-game using all five of these presets so you can get a sense of the image quality changes at each one.

If you have to turn down settings to achieve playable performance, I wouldn’t personally go any lower than Medium, as Low brutally crushes the shadows (which may or may not be a bug) and loses a lot of ambient occlusion, creating a sort of cardboard cut-out look on surfaces like the car headlights in the comparison scene above. Check out the rather cartoony-looking destroyed building on the right edge of the frame, too, where the destroyed interior and intact exterior aren’t naturally shadowed.

From Medium on up, the differences are more subtle. Shadows from foliage become softer and more detailed as we move from Medium to High to Ultra, more bounce light might be visible on some surfaces, and textures might be slightly sharper, as expected. But you’re not losing a ton of visual richness if you have to step down to Medium. And as you’ll see in our performance results, that’s a good thing for older and lower-end hardware.

Image quality with RTX Mega Geometry

In addition to Unreal Engine's MegaLights, Gears of War: E-Day is also one of the first titles to ship with Nvidia’s RTX Mega Geometry tech. Mega Geometry allows for efficient ray-tracing of highly complex meshes like those used for foliage. E-Day exposes Mega Geometry as a toggle, so it’s easy to compare its impact on scenes.

In the examples above, RTX Mega Geometry creates subtle but solid improvements. The most noticeable effect of the feature is that trees have more natural self-shadowing in their cores, and shadows and ambient occlusion on some surfaces can also be visibly improved. We also found that hanging foliage (as can be frequently seen spilling out of planters in the game’s early segments) cast more accurate shadows with the feature enabled, but you’re not generally sticking your nose up close to those objects. Small objects in rubble piles might also show crisper and more accurate shadows, too.

RTX Mega Geometry is technically compatible with graphics cards ranging all the way back to Turing, but Gears will only let you enable it on cards with 12GB of VRAM or more, which wasn’t a common spec before the RTX 40-series family rolled around.

I didn’t have time to test the performance cost of Mega Geometry on every compatible card, but for those I was able to test, I saw a roughly 10% performance cost, and that drop seemed consistent across GPU generations.

If you’re looking for the absolute best image quality with E-Day, I would certainly enable Mega Geometry, especially after creating the side-by-side comparison images above. But this also isn’t a game where you’re standing around and appreciating pretty trees too much, so if you want better performance, you can also leave Mega Geometry off without raining potatoes on Gears’ image quality.

Notes on upscaling and frame generation tech

Gears of War: E-Day is refreshingly vendor-inclusive when it comes to upscaling and frame generation technologies. It features the latest DLSS, FSR, and XeSS tech on compatible graphics cards, as well as Epic's cross-platform TSR upscaling.

As we've come to expect, DLSS upscaling looks the best of the three vendor-specific upscaling solutions. It offers the sharpest and most stable image quality at rest and in motion.

FSR 4 on RX 9000-series and RX 7000-series cards looks great, if a tiny bit less stable on the finest details in motion compared to DLSS. But because you’ll want to steer clear of super-low input resolutions for upscaling to begin with to avoid placing more load on the CPU, DLSS’s current advantages at low input resolutions are less important in this title than they are in Control Resonant, for example. In short, RDNA 4 and RDNA 3 gamers will have a great time with upscaling enabled.

If you’re on an RX 6000-series Radeon, whatever fallback version of FSR is being used in this game is really showing its age. Surfaces visibly boil and crawl when the player is at rest, and high-contrast edges show distracting flickering. This option considerably worsens the presentation of the game.

The cross-platform version of XeSS available to RX 6000 cards doesn’t totally correct these issues, but it looks far better than FSR 2 or 3. If you’re still gaming on an RDNA 2 card and want the best possible image quality, we’d favor Intel’s upscaler.

Frame generation is also available across all three major graphics vendors. DLSS Multi Frame Generation is present and works as expected. AMD’s FSR ML Frame Generation appears to be present, as well, and it works on RX 9000-series cards without a (figurative) hitch.

But if there’s a fallback to an older FSR version for earlier graphics cards, it seems to be broken, as the feature is grayed out on RX 7000 and RX 6000 series cards. We expect that this is a bug or oversight and will be fixed, but it made it hard to see whether framegen was possible or working properly on those older cards.

Finally, for Intel, XeSS 3.0 with Multi Frame Generation is also present and functional, although the level of performance on offer from Arc cards might not be high enough to allow for large amounts of frame generation at acceptable input latencies. Still, XeFG is another handy tool for tuning your Gears experience to taste.

Performance analysis at 1080p, 1440p, and 4K

Since Gears requires RT and takes a mostly vendor-neutral approach to its graphics feature set, the performance story is pretty simple. We don’t have to generate tons of comparison data for RT on and off, and we don’t have to contort various settings to create a scalable workload across GPU vendors.

At 1080p, some clear performance trends immediately emerge. RTX 50-series cards hold a slight raw performance edge in average FPS over Radeon RX 9000-series hardware, but the Radeons demonstrate significantly higher 1% low frame rates.

Subjectively, Gears seems to exhibit more obvious hitches than we’ve seen in a modern game of late, and Nvidia cards might suffer more from it than Radeons, especially at this lower resolution. But on a high-refresh-rate, variable-refresh-rate monitor, those hitches only occasionally poke through on cards from either vendor.

The 1% low superiority of the RX 9000 series isn’t universal, either, as the combination of FSR 4 and RT seems to weigh more heavily on RX 7000-series cards. The RX 7900 XTX and RX 7900 XT would generally land close to the RX 9070 XT, but here they fall below even the RX 9070. The RX 7800 XT also performs more in line with the RX 9060 XT than the RX 9070 GRE here. That’s the price you pay for the superior image quality of FSR 4 on GPUs that lack the matrix math accelerators needed to run the model at its best.

The TL;DR for those seeking a 60 FPS average at the 1080p Ultra preset is that among current-gen cards, you’ll want an RTX 5060 at the bare minimum and preferably an RX 9060 XT or RTX 5060 Ti (at least if you were lucky enough to get one before prices for those products shot through the roof). The RX 9070 GRE and RTX 5070 provide a solid higher-refresh-rate experience, while the RX 9070, RX 9070 XT, and RTX 5070 Ti all clear the 90 FPS mark.

If you want a native 60 FPS-ish average at 2560x1440 Ultra, you need an RX 9070 GRE or RTX 5070 at minimum, and you really want an RX 9070, RX 9070 XT, or RTX 5070 Ti.

But given the broad availability of high-quality upscaling and frame generation options in this game, it’s worth grabbing FrameView (for all vendors) or pulling up the Nvidia Overlay and seeing whether your baseline PC latency is low enough with Quality upscaling (say) to consider adding framegen to the mix on compatible cards. Gears features Nvidia Reflex and responds well to AMD’s Radeon Anti-Lag feature in the driver, so it’s a great candidate for frame generation.

And that’s good news indeed, because at a native 4K Ultra, Gears simply can’t run at a 60 FPS average on anything short of the RTX 5090. The RTX 4090 comes close, but even it could really use a dose of DLSS upscaling and (optionally) frame generation to really provide a fluid experience. Lesser hardware will require some personal dialing-in of upscaling and frame generation settings to arrive at the ideal balance between a fluid experience and solid image quality.

Bottom line

Gears of War: E-Day is an incredible technical showcase for the PC. As the debut title for Unreal Engine 5’s MegaLights feature, it uses light to gorgeous, often cinematic effect. The dynamic lighting and shadows in this game feel distinctly next-gen. If you obsess over the artistic qualities and usage of light like I do, you will frequently be wowed by the variety and intensity of effects on display. And the assets that those lights fall on mostly look like a million bucks. In short: this game is a feast for the eyes.

In exchange for its jaw-dropping lighting and detail, E-Day requires a strong CPU for the best performance in addition to a powerful graphics card. Check out our full CPU performance analysis for more details.

Given its demands, Gears may have some gamers with older hardware contemplating an upgrade. At a native 1920x1080 Ultra, the ever-popular RTX 3060 falls well short of a 60 FPS average, and anything less powerful than a Radeon RX 7700 XT or RX 6800 XT is also way off that pace.

In our supplementary tests, we found that the RTX 3060 needs 1080p Medium settings and DLSS Quality to clear the 60 FPS bar. But Gears still looks decent at Medium, so you’re not giving up too much eye candy in the bargain.

For a native-res 1080p Ultra and 60 FPS-ish experience, you want an RTX 5060 at a minimum, or a Radeon RX 9060 XT for a considerably smoother ride.

For higher-refresh-rate displays at 1080p, the RX 9070 GRE comes out as a bang-for-the-buck champion, shadowing the RTX 5070 for roughly $300 less at current pricing. And if you want even higher performance, the RX 9070 hits the 90 FPS mark on average for not that much more cash.

At 2560x1440 Ultra, an RTX 5070 or RX 9070 GRE both fall just short of a 60 FPS average, so upscaling or frame generation (or both) may be required to get over the hump. But you really want an RTX 5070 Ti or an RX 9070 XT for the best experience and value.

Native 4K Ultra gaming in this title is practically impossible without frame generation, upscaling, or both. Only the RTX 5090 can clear a 60 FPS average, and the RTX 4090 falls just short. Every other graphics card is way behind, so if 4K is your goal, it’s time to start dialing in those performance boosts.

Since input latency is well-controlled with Nvidia Reflex or AMD’s driver-level Radeon Anti-Lag in this game as a baseline, you can usually enable DLSS FG at will on Nvidia cards (or FSR frame generation with more care) and go about your gory business.

Although this game isn’t an Nvidia-backed path-tracing showcase like many of the headline AAA releases of 2026, GeForce gamers still get one unique feature that Radeon owners don’t. Gears implements RTX Mega Geometry for more accurate RT on objects with lots of fine geometric detail, like leaves and rubble, and it seems to improve shadows and ambient occlusion on some surfaces, too. If you have a GeForce card with 12GB of VRAM or more, Mega Geometry is well worth its roughly 10% performance cost.

Given E-Day’s already extensive integration of Nvidia tech, we really would have liked to see DLSS Ray Reconstruction included in this game. The stock RT denoiser seems far from perfect and leads to visible boil or instability in shadows and reflections, and the overall detail in reflections isn’t quite up to par with what we’d expect. Ray Reconstruction makes a major difference for the quality of those effects in Control Resonant, and we’d like to see what it can do here, too.

But even so, Gears of War: E-Day sets a new high-water mark for what's possible from PC graphics, and I can't wait to dig deeper into the game to see how MegaLights enhances the rest of the experience. Don't be surprised if this title takes a spot in our next GPU Hierarchy update.

HP is the latest to announce a MacBook Neo competitor before the holidays. Its OmniBook 5, starting at $699.99, will use Intel's Wildcat Lake chips and come in a series of colors. What stands out is HP's choice of OLED screens for every model.

Like Apple's Neo and Dell's upcoming 14S, HP is offering four colors: peony pink, starlit blue, smoke silver, and soft honeydew.

HP is following steps we've seen from Lenovo, which recently announced its colorful IdeaPad Vibe lineup; Dell's XPS 13 and forthcoming 14S; and Acer's Swift Air 14.

The company is promising up to 42 hours of battery life, with the catch that that's measured with local video playback. At 2.56 pounds, it's lighter than the 14S, which measures 2.65 pounds, and the Acer Swift Air 14, which measures 2.62 pounds. The MacBook Neo and Dell XPS 13 are both lighter, but have smaller 13-inch screens.

Starting with an Intel Core 5 320, 8GB of RAM, and 256GB of storage, the system is familiar to others on paper. It's the 1920 x 1200 OLED touchscreen that stands out. The system will start at $699.99.

But that OLED screen does have one catch: the two 1920 x 1200 models at launch will each be rated for 300 nits, which is fairly dim for OLED, which benefits quite a bit from brightness.

Some configurations have clearly been affected by the ongoing component crisis. While 8 and 16GB RAM configurations will be available at launch, a 12GB option is set for November. The same goes for the top-end 1TB storage option. Lastly, the highest-end OLED panel, a 2560 x 1600 touchscreen rated for 620 nits, won't be shipping until March of next year.

Unreal Engine 5 isn’t particularly heavy on the CPU, but Gears of War E-Day changes that narrative. If you have a monitoring overlay open when you load into the game, you’ll immediately notice your CPU pinned at between 80% and 90% utilization, which is a far cry from most other UE5 titles. Even stranger is that the scaling between CPUs is clear. For instance, the Ryzen 7 9800X3D netted 42% higher performance than the Ryzen 5 7600X at 1080p with the High graphics preset.

We’ve tested a lot of UE5 games with our pool of CPUs to evaluate them, not only for our rankings of the best CPUs for gaming, but also for our ongoing CPU benchmark hierarchy. Most of them haven’t made the cut, simply because of a lack of scaling. We’ve tried to include games like Clair Obscur: Expedition 33, The Elder Scrolls IV: Oblivion Remastered, and Halo: Campaign Evolved, due to the prevalence of Unreal Engine 5 in modern AAA games, but the CPU scaling just doesn’t matter.

There are some exceptions, notably Marvel Rivals with its dense fighting arenas, large teams, and destructible environments, and The Blood of Dawnwalker, which has a large, sparsely populated city in the middle of the map that will bring most CPUs to their knees. Even then, however, these UE5 games don’t show the same level of scaling as other games in our test suite, such as Starfield or Baldur’s Gate 3.

Gears of War E-Day is an exception. It’s not only demanding on the CPU, easily spiking even the Ryzen 7 9800X3D to full utilization at 1080p, it also shows scaling across a wide pool of chips. And further, this is true even at the game’s Ultra preset (though not the Ludicrous preset, which we’ll dig into later). This heavy CPU burden was consistent throughout the opening hours of the game, as well, regardless of scene complexity or interactions.

If you're interested in GPU performance, make sure to check out our Gears of War E-Day GPU benchmarks, as well.

Gears of War E-Day CPU benchmarks

We tested Gears of War E-Day on the same test systems we use for CPU reviews, short of the GPU driver, which we changed for this benchmarking session (the game won’t launch without the Game Ready driver). Otherwise, everything is identical, down to the frozen OS images we use across AMD and Intel test systems.

As usual, we tested with the RTX 5090 Founders Edition at 1080p to showcase the differences between each CPU. We ran a handful of chips at 1440p, as well, to get an idea of how performance scales at a higher resolution. We omitted 4K, as our graphics analysis of the game showed that it was completely GPU-bound at that high of a resolution. Go figure.

Before getting to the scaling benchmarks, we looked at scene performance and preset performance in E-Day. The game includes a built-in benchmark, common for titles published by Microsoft, so the first step to evaluating performance is ensuring that the benchmark actually matches in-game performance. It doesn’t, but it’s not far off.

As you can see, we saw slightly higher performance with our two in-game scenes than we did in the benchmark, likely due to the benchmark’s sweeping camera angle that draws much more than you’ll ever actually see on screen. We stuck with an in-game scene (scene 2) for testing, but the benchmark is a good approximation of what’s actually in the game.

For graphics settings, we went with the High preset. Gears of War E-Day includes five presets, and we tested the top four to see where the scaling starts and stops. The dynamic is binary. If you’re using the Ludicrous preset, the scaling between CPUs goes down drastically. We saw a 6.8% difference between the 9800X3D and 9600X with the Ludicrous preset. At the Ultra preset, that jumped up to nearly a 29% difference, which grows further at the High preset.

We eventually settled on High, as that’s the visual floor for becoming CPU-bound. Going down to Medium nets very little extra performance and slightly exaggerates the scaling between chips. High, then, is a good compromise. It shows near-maximum scaling between CPUs without being an unreasonable test case.

It should come as no surprise that the Ryzen 7 9800X3D and Ryzen 7 7800X3D occupy the top two spots on our chart. E-Day, like a lot of titles, is accelerated by X3D chips. You can see that with the Ryzen 7 5800X3D much lower down the chart, as well. What’s interesting here is how E-Day scales with core count, however. Take the Ryzen 5 7600X as an example. We expect less than a 5% jump with the Ryzen 7 7700X on average, but in E-Day, that increase nears 9%.

Elsewhere, the 12-core Ryzen 9 models usually underperform their 8-core counterparts in games. Again, the 7900X and 7700X are in lockstep, while the 9900X is 3.5% ahead. It’s worth stressing that, based on our data, the Ryzen 9 9900X and 7900X are slower than the straight eight-core models from AMD’s last two generations. The fact that they’re ahead here speaks to the importance of core count in this game.

Perhaps the greatest evidence of that comes from the Intel camp. Not only do we see Intel’s main Raptor Lake and Arrow Lake stack outperforming all but AMD’s X3D Zen 4 and Zen 5 CPUs, we also see strange comparisons between Arrow Lake and Arrow Lake Refresh. The Core Ultra 5 250K Plus is typically around 5% faster than the Core Ultra 7 265K on average in games. Here, the situation is flipped, with the Core Ultra 7 265K leading by about 2%.

It’s interesting to see core count as a performance driver, which isn’t usually the case in games past six or maybe eight cores. Although E-Day scales past that point, it still plateaus around 150 FPS.

We ran a few tests at 1440p, as well, foregoing the whole pool simply for the sake of time. For scaling comparisons, the Core Ultra 7 270K Plus is about 18% faster than the Core i5-14400 at 1440p, but it’s about 36% faster at 1080p. Further, that scaling isn’t consistent. Running CPUs at 1440p with our test suite and settings, we found a ceiling around 130 FPS where performance becomes bound by the GPU. Any CPUs that performed above that mark at 1080p normalized to that 130 FPS, while CPUs below that mark offered performance at 1440p within a few frames of the 1080p performance.

When looking at a weak CPU compared to a strong one, there’s clear scaling at 1440p. However, with CPUs within similar product ranges and families, the game is mostly bound to GPU performance at the higher resolution.

Looking at other aspects of performance, there aren’t any surprises. Power use is in line with our expectations, with AMD’s Zen 4 and Zen 5 flagships sitting alongside Intel’s most powerful Raptor Lake chips. And, on the efficiency front, the Ryzen 7 9800X3D and 7800X3D easily win the day with nearly two frames per watt used. Temperatures stayed in check, as well, with even the highest chip in our test pool averaging just 65.4 degrees Celsius.

Investigating ‘low core mode’ in Gears of War E-Day

Buried in the “System” menu of the settings in E-Day, there’s a curious option called Low Core Mode. The game says in the setting’s description that it can improve performance on low core count devices and handhelds, which is worthy of closer inspection. This isn’t a standard UE5 toggle — as we’ve established, the engine is not particularly heavy on the CPU at stock — and we haven’t seen similar settings, at least among most recent AAA releases.

We ran each chip through another pass of tests with this mode turned on, using the same test scene and settings, to see how it impacts performance. It universally lowered performance across our entire pool of 25 CPUs, even going down to the quad-core Core i3-14100. 1% low performance wasn’t improved either. For the sake of clarity, we’ve laid out the results in a table below so you can easily see the performance change between toggling Low Core Mode on and off.

With everything down to a strict quad-core tested — E-Day’s system requirements bottom out at hexa-core chips — we assumed the situation was settled and were content to leave Low Core Mode for others to explore further. However, we gave it one more try with the Core i3-14100, disabling two of the cores in the BIOS to see how the game would function with a dual-core chip.

That revealed a lot about what’s going on behind the scenes with this feature. Above, you can see a screenshot we gathered when booting into the game with the dual-core 14100. The texture streaming system completely breaks. We didn’t wait for an opportune moment to capture this screenshot, either; the low-res, half-loaded assets persisted throughout the entire run.

Further, whenever we went to pause the game to adjust settings or reload the checkpoint, the game froze and hung for upwards of 30 seconds before the menu appeared. Turning on Low Core Mode resolved both the asset streaming issue and the game freezing, as well as improved performance by 30%. That’s impressive, but keep in mind that going from four cores to two on the 14100 resulted in a 59% decrease in performance, dropping from an average of 89 FPS to just 36 FPS.

It appears E-Day switches up the streaming system with Low Core Mode. That would explain a lot, notably the heavy, all-core demand when running the game normally and the severe streaming issue we noted at two cores. E-Day is using some sort of asset streaming system, as most modern games do, and those assets look very expensive. Past a certain point, the streaming simply breaks as the CPU is overloaded with work.

Although we didn’t notice a performance improvement at four cores, keep in mind that we’re testing with the newest true quad-core on the market. The 14100 is one of the best budget CPUs, but it’s still new. The system requirements call for an older hexa-core chip at minimum. Taken together, you may run into issues with older quad-cores that necessitate the use of Low Core Mode, but those situations are few and far between. Assuming you have a relatively recent processor, or a processor with at least six cores, and ideally both, Low Core Mode will reduce your performance.

We didn’t test mobile chips, however, nor the handhelds those chips are inside of. This mode may have more practical applications there.

Test configuration

As mentioned, we used the same platform for testing Gears of War E-Day that we use for a CPU reviews, which you can find in the table below. All configuration and software is duplicated across all test systems, and AMD and Intel configurations never touch each other to avoid any conflicts.

There are a few important configuration details worth highlighting outside of the broad strokes of a frozen OS image. We disable Virtualization-Based Security (VBS) for all platforms, enable ReBAR, and enable XMP/EXPO. We've tested the memory to ensure stability with our platforms.

We also disable any automatic overclocking features that are unstable or not covered by warranty. For instance, we disable AMD's Precision Boost Overdrive and Intel's Extreme power profile, as neither is covered by warranty. Intel's Core Ultra 200S Boost, on the other hand, is covered by warranty, so we enabled it on the Arrow Lake chips we tested. Similarly, the 9600X and 9700X from AMD have a warrantied 105W operating mode, which we use, as it improves gaming performance slightly.

Micron said during its FY 2026 earnings call that RAM and storage chip shortages will worsen in 2027 and 2028, even as it announced a record gross margin of 86.25%. CEO and chairperson Sanjay Mehrotra said that the company has already sold most of its capacity for next year and that customers will have to pay more compared to 2026.

“In calendar 2027 as well as 2028, we see demand exceeding supply. In fact, we see greater tightness in the industry in 2027 and in 2028 versus 2026. Overall, supply-demand environment is only getting tighter,” Mehrotra said. “We do not have line of sight to when supply and demand will return to balance.”

The memory and storage industry is not holding back on increasing production, though, with Micron saying that NAND and DRAM shipments are expected to grow in the low-to-mid 20s percentage range for the next two years. However, it seems that this is not enough to match growing demand, as it expects “the industry to remain supply constrained in both years.” Still, it seems that manufacturers are prioritizing HBM demand for AI data centers, with the company expecting shipments of these chips to grow faster than conventional DRAM.

Micron and other chip manufacturers are bringing new factories online to increase output, but it’s going to take years before they can start producing chips. But even as we’re amid an unprecedented multi-year memory chip shortage, Micron also announced record financial guidance. It forecasted a record $61.5 billion FQ1 revenue and a gross margin of 86.25%, implying a gross profit of more than $53 billion. After accounting for an estimated $2.06 billion in operating expenses and a tax rate of around 15.5%, the company said its earnings per share sit at $38.15, plus or minus $1.00, for approximately 1.15 billion shares.

These are record numbers, but it’s not surprising given the amount of money being poured into AI data centers. It has gotten to the point where the memory workers from Samsung and SK hynix have demanded and received bonuses worth hundreds of thousands of dollars, with Micron’s workers in Taiwan threatening to strike if they do not receive a bigger slice of the AI pie. While this is good news for the average employee from these big companies, it’s unfortunately bad news for the average person who must pay for RAM and storage at higher prices.

Micron’s prediction goes directly against the Acer CEO’s prediction that PC prices will decline by late 2027, especially as Chinese RAM chips enter the market and consumers simply refuse to pay more for expensive memory chips. We can only wait and see which of these CEOs will be correct when it comes to DRAM and NAND pricing.

The developers of the Sony PlayStation 3 emulator RPCS3 have warned their social media followers about “a recent influx of fake Blu-ray drives.” According to the emulator devs, scammers are putting inferior optical disc mechanisms in higher-end Blu-ray drives for extra profit. Such skuillduggery could directly affect potential RPCS3 users, looking to ensure any Blu-ray drive they buy is compatible with the emulator.

“BEWARE OF FAKE BLU-RAY DRIVES!” warn the RPCS3 devs. “There has been a recent influx of fake Blu-ray drives. These fakes use older drives that cannot read PS3 discs, with enclosures and packaging for higher-end models, plus spoofed firmware.” The team also helpfully links to a wiki with images and details to help you know if you acquired a “fake drive.”

Telltale signs that you have a fake Blu-ray can include misspelled words on the drive label, misaligned logos, incorrect manufacturing dates, and the covering of certain PCB markings. We guess that another symptom that RPCS3 users might notice is that their supposedly compatible Blu-ray drive won’t work properly with the emulator.

Blu-ray drought

In 2026, after 20 years of commercial availability, the hardware options for living room Blu-ray players and PC drives are narrowing due to the withdrawal of several big brands from these markets. Media makers are dwindling, too. Meanwhile, interest in the highest-capacity consumer optical standard for media playback and backups is rising. The price of HDDs and SSDs in 2026 has also resulted in increased numbers of computer enthusiasts looking at Blu-ray storage.

So, what should those on the lookout for a Blu-ray drive do? According to the RPCS3 experts the best way to keep out of the reach of scammers is to only order from stores with good buyer protection. The above-linked Wiki warns specifically against buying your next Blu-ray drive from Newegg or AliExpress marketplaces. Unfortunately, we’ve now got another piece of tech to add to the beware-of-scams list.

OpenAI said in a company blog post that people associated with China-based Moonshot AI were at the core of an effort to pull hidden reasoning from its models. The post says that a “coordinated campaign” to extract “protected reasoning” from its models, which was “consistent with adversarial distillation,” occurred in July. OpenAI isn’t sure whether all of the operators it saw were a single actor.

Activity began on July 1st, “initially at a low volume.” After that came “high-volume spikes” on July 24 and 25, with 16,000 requests using an extraction pattern. The requests came from over 4,000 users. The activity attempted extraction but was “not necessarily successful,” and the campaign was “fully disrupted by July 28.” The post did not clarify any measure of success rate, which models were specifically targeted, or how many of the users were Moonshot-linked.

OpenAI defines protected reasoning as “the model’s internal record for working through a task” and adversarial distillation as the “systematic and unauthorized use of one model’s outputs or reasoning” to train or improve another model. This data is encrypted to hide the model’s chain of thought and is handed to the client as an encrypted block. The client sends that block back with each request, so the provider doesn’t have to store it. One method the operators tried took the encrypted reasoning from one conversation and asked a model in another to decrypt it.

OpenAI says the encryption, in this case, was not broken. There was no direct access to stored user conversations, and no database was compromised. One fix closed a pathway that let someone who already had another user’s encrypted reasoning replay it and recover its contents. Separately, it added checks to detect and hold streamed output that might expose reasoning. It also strengthened protections for hidden reasoning across users, workspaces, organizations, and model families, and worked with third-party providers to disrupt accounts whose activity moved through their services.

Independent security researchers had also brought “related cross-model and conversation-compaction vulnerabilities” to OpenAI through responsible disclosure, and the company confirmed the attack paths they found were real. The paper “Stealing Reasoning Traces from Proprietary LLM APIs,” dated Aug. 10, is explicitly linked by the post. Testing OpenAI, Anthropic, and Google, the researchers fed a frontier model’s encrypted reasoning to a corresponding weaker model, which then wrote it out in plain text. The researchers ran their test in early July and, after the providers acknowledged their report, they were “unable to launch the same attacks.”

OpenAI is not alone in facing such threats. Earlier this month, Anthropic’s report “Detecting and countering misuse of AI: September 2026” detailed a single ten-day period in which Moonshot relayed almost 300,000 customer requests to Anthropic using a proxy network of 5,380 fraudulent accounts. The report also says Moonshot saved Claude’s reasoning signatures and, in new sessions, got Claude to convert them back into full reasoning traces, which Anthropic calls “cross-session replay attacks.” In July, Moonshot denied that Kimi K3 was created from a distillation, and at the time OpenAI President Greg Brockman said it was “too early” to tell whether Moonshot had distilled OpenAI’s models.

OpenAI’s next step, meanwhile, is to ensure partner-hosted deployments have the same protections as first-party tools. Additional checks are needed to prevent tool-output attacks. It shared its findings with the Frontier Model Forum and government information-sharing channels, noting that “systems that support portable or replayable reasoning artifacts may face related risks.” The company expects distillation attempts to grow more sophisticated as frontier models improve and as attackers look for cheaper ways to mimic them. The work to protect against those attempts is ongoing.

There's a huge deal on an MSI gaming laptop right now with a set of top specs, including the best laptop GPU out there, the RTX 5090. This MSI Stealth A18 AI+ laptop is $3,779, with 64GB of DDR5, 2TB SSD and a 12-core AMD Ryzen AI 9 HX 370 CPU, saving you $520 in total.

● Check out this MSI gaming laptop deal

While the mobile version of the RTX 5090 isn't the same as its desktop counterpart, you won't find a better GPU in a laptop that you can buy. It comes with 10,496 CUDA cores, along with 24GB of GDDR7 VRAM, which operates on a 256-bit memory bus. A peak 175W TDP is much less than the desktop's 575W power rating, but laptops are designed to be portable, so power is restricted.

It's interesting to look at how the pricing stacks up in the current market. This laptop will set you back $3,779 right now for an entire working system. The desktop version of the RTX 5090, right now, will set you back over $7,000 at the moment, at least for models in stock. These are two different beasts, sure, but PC gaming is getting to be very expensive, almost prohibitively so. A desktop PC with similar specs might set you back almost double the price you're paying for this laptop. It's clearly the better value option from a financial point of view, especially as it'll handle modern games at the best settings for several years.

The RTX 5090 is the real centerpiece of this laptop, but you're getting a stacked set of specs elsewhere. For the CPU, you're getting the AMD Ryzen AI 9 HX 370. This Strix Point processor has 12 cores, 24 threads, and a boost clock speed of 5.1 GHz. This is a Zen 5 CPU, with a power-efficient NPU for AI tasks.

No compromises elsewhere, either. You get a full set of 64GB of DDR5 RAM with this laptop, with speeds of 5,600 MT/s. That's a little slower, but capacity-wise, you're beating the RAMpocalypse with a whole heap of memory. Likewise, the 2TB SSD means you've got plenty of space for Windows alongside a lot of big game installations.

This is a big-screened laptop for gaming, and you're getting an 18-inch panel with a resolution of 3,840 x 2,400. That means you can game at 4K, with a 120Hz refresh rate to match the in-game frame rates without X. The mini LED panel supports HDR 1000, with 1000 nits of brightness, covering 100% of the DCI-P3 color space.

Wi-Fi 7 connectivity, along with 2.5Gb Ethernet, means you can max out any connection for lag-free gaming. These impressive specs are wrapped in a magnesium-aluminum alloy chassis in a midnight black colorway, with vapor chamber cooling under the hood with two fans and four exhausts.

The $3,779 sale price for this MSI Stealth A18 AI+ gaming laptop is a brilliant one in the current market. Rather than spending $7,000+ for the desktop GPU alone, this full-spec'd laptop with a 4K-adjacent display will give you everything you need to play at high frame rates at the highest resolutions for years to come.

There's a private information leak most days, usually by way of misconfigured services or nasty security bugs. Sometimes, though, users will readily hand over private information without being aware of it. That's the case for over 300 organizations, including several Fortune 500 companies and a frontier AI lab, who collectively had 13,000+ private screenshots exposed — all thanks to their development AI agents being arguably too good at their jobs and performing them with little human oversight.

Besides showing pictures of internal and pre-release software, the leaked screenshots reportedly include corporate and client information, financial data, and even screen recordings for a money-movement interface. The report, called PixelLeak, comes from endpoint security firm Glow, and it details how the leaks happened. The root cause is surprisingly simple and likely to induce a forehead slap.

It's become customary in development work related to UI and UX (and other categories) to include screenshots showing previews or before/after comparisons of tweaked features, for review purposes. Said images travel as attachments to the respective code changes, also known as "pull requests" (PRs) in dev parlance.

When using GitHub (and potentially other code repository services), humans see a graphical interface for easily attaching an image to a PR. Meanwhile, bots are limited to using the command-line interface, which currently does not have that feature available for private repositories.

As the efficient and smart agents they are, the clankers came up with a simple solution: publish the PR as usual to the private repository, and include an image placeholder linking to a file that's hosted in a public repository instead. There, problem fixed! The user is happy and likely has no idea what happened unless they notice the problem somehow and start asking the bot some hard questions.

Glow says that in about a third of the affected companies, developers were using gitshot, a command-line tool for attaching screenshots, used in this instance to overcome the aforementioned limitation. Searching for images attached by the tool is quite easy, as one needs but look for the "_gitshot" tag. The report also indicates that in 93% of cases, images were found in repositories under direct control of the developer's username, rather than being tied to the company's GitHub account.

In one particular case, an agent skill (essentially long-winded prompts instructing a bot how to do something) was also an indirect source of leaked information. The agents started incorporating the image hosting workaround as a skill, and after a while, many of them were using it for every development ticket, leading to leaks of information about features months away from public release.

Glow provided sample reasoning output from an agent, explaining precisely why it uploaded the screenshots publicly:

"internal_sweeper is private, and GitHub cannot render images from a private repo in a PR description — its image proxy fetches anonymously, so anything committed here (branch, release asset, whatever) shows up broken for reviewers. The only way to satisfy both "reviewers see the images" and "nothing but index.html in the repo" was to host the PNGs elsewhere, so I created a new public repo, sweeper-demo/pr-assets, holding the two screenshots pinned to a commit SHA."

As mitigation measures, Glow first recommends checking that employees don't use repositories under their own accounts, auditing the accounts and code managed by employees no longer associated with the company, and curtailing the use of "shadow AI" — when employees unadvisedly sign up for their own AI tools without talking to IT first and neatly punch planet-sized holes in the firm's security and data privacy.

Additionally, Glow advises strong vetting of software and code libraries used for development, as well as careful reading of the instructions and rules of any agentic skills.

Micron this month filed a lawsuit against China's 3D NAND champion YMTC, accusing it of poaching its leading engineers, illegally obtaining its NAND memory know-how from them, and then using that information to build its own non-volatile memory as well as its patent portfolio. The Boise, Idaho-based company further alleges that YMTC uses patents granted to its former employees to accuse Micron of patent infringements in various jurisdictions.

To assemble a comprehensive IP portfolio that would cover everything from essential technologies to sophisticated manufacturing methods quickly, as well as to ensure that its patents overlapped with those of other 3D NAND manufacturers to enable it to attack Micron, the firm asserts YMTC opted for a rather simple, yet effective strategy: It poached engineers from Micron, then made them patent either standard essential inventions or inventions that closely resembled those they worked on while at Micron.

To leave Micron without being asked questions, its former employees cited a broad range of personal or family reasons for their resignations and even did not update their LinkedIn profiles to reflect their new roles at YMTC. These employees had confidentiality obligations that continued after termination of their employment and proscribed them from using, disclosing, or possessing Micron’s confidential information. Meanwhile, Micron did not really investigate the whereabouts of its former employees and "had no reason to suspect YMTC itself" of planning to use its confidential information because "YMTC did not have any material market share in the industry.”

We are all used to cut-throat competition on the memory market as leading producers of DRAM and NAND try to win market share from each other and maintain profitability at the same time. These companies have broad cross-licensing agreements to neutralize direct patent wars that can result in devastating injunctions that could paralyze fab operations and eventually lead to assured mutual destruction. However, this does not seem to be the case for emerging competitors from China, ChangXin Memory Technologies (CXMT) and Yangtze Memory Technologies Co. (YMTC), both established in 2016, which are increasingly attacking Micron in various courts, perhaps making pre-emptive strikes to avoid the destiny of their Chinese counterpart, Fujian Jinhua.

Using Micron's 3D NAND know-how against Micron

In its lawsuit against YMTC, Micron asserts that Yangtze Memory's patent campaign against Micron largely relies on patents that were granted to former Micron employees. Furthermore, Micron alleges that former employees brought its 3D NAND know-how to YMTC and subsequently developed patents covering related or overlapping technologies, which essentially means that Yangtze Memory has built its patent portfolio using Micron's intellectual property, which is why Micron claims that YMTC patents belong to Micron and therefore cannot be used against it.

"For example, these individuals include Daesik Song, Wenxi Zhou, and Hua Tan," Micron's lawsuit reads. "Each of these employees owed Micron contractual obligations to keep trade secrets confidential, had access to certain of the Micron Trade Secrets further described below while employed at Micron, and on information and belief, each joined YMTC shortly after leaving Micron. Each of these employees also regularly accessed one or more of the Trade Secrets that were located on servers in Boise by accessing Micron's highly confidential technical materials relating to its test and/or production nodes."

Micron's allegations in the new complaint go well beyond simple claims that its former employees brought manufacturing expertise to YMTC, something that happens often. The company alleges that YMTC systematically obtained confidential 3D NAND and memory technology from former Micron engineers and incorporated that knowledge into its manufacturing processes starting from its 64-layer 3D NAND, memory products, and, most importantly, its patent portfolio. Micron says it began to uncover the alleged scheme after YMTC sued it for patent infringement in California in November 2023, when it noticed that several patents asserted against it named former Micron engineers as inventors.

In its complaint, Micron identifies a number of its former employees who subsequently became inventors on YMTC patents, including Hongbin Zhu, Jun Liu, Yushi Hu, Qian Tao, Changhyun Lee, Daesik Song, Wenxi Zhou, and Hua Tan. According to Micron, these engineers had access to different classes of confidential technology that covered a broad range of 3D NAND-related know-how, including high-aspect-ratio etching, replacement gate processing, multi-deck connections, pillar shaping and cleaning, and memory I/O circuitry, among others.

Patent records independently confirm an important part of Micron's allegations: several former Micron engineers later became inventors in YMTC patents that YMTC eventually used against Micron. In general, this does not prove any misappropriation: engineers routinely change employers and continue to invent in their areas of expertise, but Micron alleges that some of these patents cover inventions conceived or developed while their inventors still worked for Micron, which is why it is asking courts to transfer ownership of these patents from YMTC to Micron.

One of the clearest examples involves Hongbin Zhu, who left Micron in September 2017 and subsequently became an inventor in numerous YMTC patent applications. One family originates from the international patent application PCT/CN2018/101482, filed on August 21, 2018, and includes three patents. Micron's complaint alleges that these patents relate to technology Zhu 'learned of, worked on, and/or conceived while at Micron' and asks the court to transfer their ownership from YMTC to Micron. The same family also includes two additional patents that YMTC asserted against Micron in its California infringement lawsuit. Micron has already challenged ownership of these two patents in that litigation and claimed that Zhu invented the underlying technology while employed by Micron and had assigned his rights to the company.

Daesik Song provides another connection between Micron's allegations and YMTC's patent enforcement campaign. Song previously worked at Micron on memory I/O technology and later became an inventor on a patent titled 'Double Data Rate Circuit and Data Generation Method Implementing Precise Duty Cycle Control.' YMTC asserted the '851 patent against Micron in the Eastern District of Texas, yet Micron now alleges that it is actually the rightful owner of the patent because Song's invention-assignment agreement transferred inventions conceived during his Micron employment to Micron. The company is therefore asking the Idaho court to transfer legal title to the '851 patent from YMTC to Micron.

The Song connection also extends beyond the United States. In Europe, YMTC owns European patent EP 3 909 047, which covers the same invention as the '851 patent, and uses it against Micron in its UPC in Düsseldorf and U.K. litigations. Thus, YMTC is enforcing different members of the same invention family against Micron in Texas, the UPC, and the U.K. while Micron alleges that the U.S. patent from that family actually belongs to Micron. That does not automatically mean Micron owns the EP '047 since the U.S. and Europe are different jurisdictions, but it demonstrates how Micron's ownership allegations intersect with YMTC's international patent campaign.

There are additional connections. Former Micron engineers Yushi Hu and Qian Tao are named as inventors on two patents that YMTC asserted in California, while Jun Liu, Qian Tao, and Yushi Hu are all inventors on another patent asserted against Micron there. Changhyun Lee is an inventor on US 11,468,957, also part of YMTC's California case. Meanwhile, Wenxi Zhou appears on a YMTC patent family that covers barrier layers for word-line contacts, including US 12,094,767.

The European cases provide a somewhat narrower connection. Of the three European patents YMTC is asserting through the UPC and UK proceedings, EP 3 909 047 is directly relevant because it names former Micron engineer Daesik Song. The other two — EP 3 850 660, which names Qinxiang Wei, Jianhua Sun, and Ji Xia, and EP 4 236 650, which names Zilong Chen and Xiang Fu — do not name any of the former Micron engineers identified in Micron's complaint. Meanwhile, YMTC has also filed eight patent cases against Micron in China, three in Beijing and five in Shanghai, although we have not found a reliable public list of the eight Chinese patents involved, so it is not yet possible to determine whether they include Chinese patents of YMTC's inventions in the U.S. and Europe.

To sum up, the list of patents and YMTC engineers reveal a much more complicated dispute than an ordinary patent fight between two memory manufacturers. In less than a decade, YMTC has built an international patent portfolio and is attempting to enforce it against Micron across the United States, China, Germany, the UPC, and the U.K. Several of the patents used in that campaign are assigned to engineers who previously worked for Micron. Furthermore, Micron now alleges that at least some of those inventions were conceived while at Micron or using its trade secrets, so at least in some cases these patents belong to Micron, according to the company's complaint.

In short, Micron's main allegation is not that YMTC obtained its manufacturing know-how through former employees, but that some of this know-how was ultimately converted into YMTC intellectual property that YMTC later used to sue Micron itself. Hence, if Micron proves to the courts that YMTC's inventions are in fact Micron's inventions, then the whole Yangtze Memory campaign falls apart. However, the decision by the Munich I Regional Court looks rather alarming for Micron, as the company has so far failed to persuade the court that it not only did not infringe any YMTC patents, but that the very patents in question were obtained illegally.

All-out war

YMTC sued Micron in the Northern District of California in early November 2023, alleging that Micron's 3D NAND infringed eight U.S. patents. In early 2024, YMTC brought three additional patent cases against Micron in Beijing, then followed up with another California lawsuit in July that asserted another 11 U.S. patents against Micron’s 3D NAND and DDR5 products. In September 2024, YMTC expanded its Chinese campaign with five more patent cases in Shanghai. The dispute became global on October 6, 2025, when YMTC simultaneously launched another wave of litigation, asserting eight patents in the Eastern District of Texas, three patents in the U.K., three European patents before the Unified Patent Court in Düsseldorf, and five patents before the Munich Regional Court.

By then, what had begun as an eight-patent U.S. dispute had evolved into a multi-jurisdictional patent war covering dozens of patents and some of Micron's most important NAND products. Hence, Micron had to start responding. On October 18, 2024, the company filed a counterclaim alleging that it is the true owner of the US 10,879,254 and US 11,581,322 patents (more on this later). Then, almost two years after YMTC first sued Micron, the company filed the case on October 7, 2025, asking the U.S. District Court for the Northern District of California to issue a declaratory judgment of non-infringement concerning YMTC patents, which essentially means that Micron asked the court to rule that its patents did not infringe certain YMTC patents. These actions were barely a defense from YMTC's all-out patent war.

However, having spent nearly three years primarily defending itself and challenging YMTC's patents, Micron decided to respond more vigorously, which it did in its lawsuit filed with the Boise, Idaho, court on September 15. In the new lawsuit, Micron alleges that YMTC systematically obtained Micron's 3D NAND manufacturing know-how through former Micron engineers and then incorporated that information into both YMTC's manufacturing processes (64-layer, 128-layer, 232-layer, and subsequent nodes) and its patent portfolio. The alleged trade secrets cover multiple aspects of contemporary 3D NAND production, including high-aspect-ratio etching, replacement gate processing, multi-deck connections, pillar formation, and other manufacturing techniques.

Micron claims that its former employees who had access to this information later worked on similar technologies at YMTC and became inventors in YMTC patent applications. More importantly, some of these patents or their family members were subsequently used by YMTC against Micron, which essentially means that YMTC used patents granted to Micron's former employees against Micron itself. In many cases, YMTC allegedly poached Micron engineers who had previously developed and patented certain technologies at Micron, then tasked them with working on similar technologies and filing new YMTC patents covering essentially the same technical solutions. As a result, Micron now claims that several of the underlying inventions actually belong to it.

Back in Europe, Micron launched its own patent offensive against YMTC and companies that sell products which allegedly use the disputed technologies. In July, Micron sued YMTC in the Munich Regional Court, where it named Apacer, Lexar, and Ultron as co-defendants. In addition, it took a separate U.K. High Court action targeting YMTC alongside its partners Apacer, Lexar, and TeamGroup (note that TeamGroup is one of the SSD makers that from time to time uses different memory and even controllers on the same SSD models).

The German ruling

Before we proceed with a rather unprecedented decision of the Munich I Regional court, let us explain the difference between the patent and utility model as neither the U.S. nor the U.K., nor Canada, support the utility model system.

A patent protects major technical inventions across process technologies, tools, and materials for up to 20 years, but it requires a rigorous step-by-step patent office examination that typically takes years and significant expense to obtain. By contrast, a utility model protects incremental, practical mechanical or structural improvements to physical products for a shorter duration (usually 5 to 10 years). It is granted rapidly within months and at a fraction of the cost because it involves a lower inventive threshold and skips substantive examination prior to registration. While most patent offices register utility models without checking whether the idea is actually new, the validity of a utility model remains uncertain until enforcement. This is where YMTC prevailed to a degree that nobody expected.

In fact, YMTC has so far failed to gain any substantial court victory in the U.S., but it scored its first major victories in its German litigation against Micron in September, when the Munich I Regional Court ruled that Micron infringed two YMTC German utility models and granted injunctions against the company. The two utility models — DE 20 2020 006 166 U1 and DE 20 2021 004 551 U1 — cover 3D NAND-related to staircase structures and barrier layers for word-line contacts, respectively. The injunctions prohibit Micron from offering and selling products that infringe the two utility models in Germany. Meanwhile, the court has adjourned decisions on three infringement actions: it did not reject the other three, but simply has not resolved them.

Normally, this would not be a problem for Micron's clients, as unlike injunctions by the U.S. ITC, these rulings in the European Union typically have no effect on downstream entities, so Dell, Lenovo, and other PC OEMs can continue selling PCs containing violating hardware. However, it is possible that this particular decision could affect Micron's downstream partners, effectively prohibiting them from selling systems with Micron's 3D NAND inside. As a result, the injunctions could disrupt sales of PCs, SSDs, servers, and other products containing the affected Micron NAND in Germany.

Micron has appealed the decisions and is challenging the validity of the rights, so the September rulings represent first-instance victories for YMTC rather than the end of the dispute. Three other Munich cases brought by YMTC — including one that involves a European patent — have not yet produced infringement decisions. Yet, the existing decision shows that courts can side with Yangtze Memory and rule that Micron uses YMTC's intellectual property illegally.

Normally, this would be a 'local' problem for Micron. However, it is important to mention that some of the technologies and patent families involved in the European litigation overlap with those asserted in the United States. Several of them name former Micron engineers as inventors.

In fact, both German utility models on which YMTC just won injunctions appear to belong to patent families that involve former Micron engineer Wenxi Zhou, with corresponding U.S. patents being used by YMTC against Micron in Texas. This becomes particularly important in light of Micron's new allegations that former employees brought its 3D NAND know-how to YMTC and subsequently developed patents covering related technologies. If YMTC can win a litigation in Germany, can it also prevail elsewhere using the same patent families?

Summary

The conflict between Micron and YMTC emerged amid the escalating U.S. – China semiconductor confrontation. In October 2022, the U.S. imposed sweeping restrictions on exports of advanced semiconductor manufacturing equipment and technology to China, and in December 2022, the U.S. Department of Commerce added YMTC to its Entity List, which severely restricted the company's access to American fab tools. China responded with pressure on Micron: in May 2023, the Cyberspace Administration of China concluded that Micron had failed a cybersecurity review and prohibited operators of critical information infrastructure from purchasing affected Micron products.

The commercial conflict between the two memory makers eventually moved into the courts, with YMTC accusing Micron of patent infringement and Micron largely defending itself rather than launching a comparable patent offensive of its own. Micron’s new complaint in Boise, Idaho, may explain this strategy: the American memory maker not only seeks to defend itself against YMTC's infringement claims, but also alleges that some of the patents YMTC is using against it incorporate Micron’s confidential technology or actually belong to Micron itself. But while Micron can potentially prevail in the U.S. and perhaps in Europe and the U.K., China represents a different challenge, as the legal battle between the two memory makers may well be considered a part of the broader geopolitical confrontation between the U.S. and China.

The Call Center Doctors, a call center consultancy that builds and runs call floors for other businesses, rented a server with four Nvidia H200 AI GPUs to serve DeepSeek V4.1 Flash to its Claude Code agents in place of Opus 5.5, according to its test write-up. The write-up is aimed at anyone who has heard that DeepSeek is “80x cheaper” than Claude. Using its real coding mix, the firm's server topped out at around 213 tokens written per second at an on-demand cost of $440.88 per day, versus $184–$223 per day for the same work via DeepSeek’s API. The call center went back to Opus 5.5 at a lower price than the box would cost at on-demand rates.

DeepSeek’s API is priced at $0.15 per 1 million new input tokens off-peak, $0.003 cached input tokens, and $0.60 per 1 million output tokens. Peak costs are double. By comparison, Anthropic’s Claude Opus 5.5 is listed at $4 input and $20 output per 1 million tokens, with cache reads at $0.20. The consultancy used it through subscriptions and not the API.

The box’s performance was fine for one kind of work at a time in the test’s one-minute full-load runs. But the coding agents work mostly by resending the conversation, and 96% of what the agents provided the model was stale text, according to the consultancy’s logs. It takes about 1,000 old tokens for every new token written; the re-reads are cheap, but so numerous that the box was kept busy re-reading, leaving little of its time for writing new tokens.

Getting the model to run stably took five tries at 10 to 15 minutes of loading for each try. The box bills whether it’s busy or not, which is suboptimal. By our arithmetic, a standard-length month at the on-demand rate, $18.37 an hour, comes out to about $13,200. This is over twice the consultancy’s September Claude bill. Additionally, one box can only get through about 20 billion tokens a day, by the consultancy’s math, most of it re-reading, against the 51 billion the consultancy’s agents used on its busiest day.

The testing was aimed at anyone who has heard that DeepSeek is “80x cheaper” than Claude. One server running from Sept. 1–27 had 2.03 million model calls, 388.5 billion tokens read, 374.2 billion of them cached, and 393 million written, with 5,610 merged changes. The consultancy’s September Claude Code subscriptions came to about $5,500. The same 27 days of tokens on DeepSeek’s API would be between $3,500 and $7,000, depending on peak pricing, with our estimated average around $4,200 if usage is spread evenly across the week.

Set against Claude Opus 5.5’s list prices, the same tokens would come to about $140,000 by our math, over 25 times the cost of the subscription. That flat-rate discount is where the 80x went. The consultancy also priced each merged code change: about $1 on Claude, and about $0.63 on DeepSeek’s API if DeepSeek did the same work with the same tokens. Its $1.15–$4.90 estimate for DeepSeek is based on assumptions that the model would need more tokens, succeed less often, and need Claude to check its work.

DeepSeek never got to write actual code in the test. The consultancy’s reviewers kept finding ways agent code could escape the sandbox, for example via a settings file in a shared temp folder that could allow agent code to be run as admin. This meant the code-writing agents had to stay offline. Instead, DeepSeek ran only as 48 to 64 read-only reviewer agents, reading 2,377 folders and filing 32 bug reports. The box, rented at a $9.19-an-hour spot rate, was taken back by the provider within minutes of the last test ending.

Based on the V4 generation, where Pro outscored Flash, better performance from the model family can be expected with DeepSeek V4.1-Pro, which currently does not have a firm release date. This may change the math in the future, but for now there are better ways to spend on tokens than renting GPUs to run an open model.

Sony launched the world’s first CD player on this day in 1982, kickstarting the compact disc digital audio era. The Sony CDP-101 was released to consumers in Japan, priced at JPY 168,000. That converts to $1,060 at today’s exchange rate, and is about $3,700 adjusted for inflation. CD players were meant to be a worldwide launch on October 1, 1982, but tech partner Philips didn’t have its CD100 ready, so Sony agreed to limit this initial event launch to Japan. Six months later, U.S. and European consumers would get their chance to buy a CD player, starting March 1983.

As for this first CD player hardware release for consumers from Sony. It wasn’t perfect, of course, and Wikipedia points out one particular limitation of the CDP-101: It had just one DAC for both left and right audio channels due to the high cost of these converters. Without sample-and-hold circuitry, this meant the stereo channels were approximately 11 μs out of sync.

Before CDs, home hi-fi setups of the era were typically built around vinyl records and compact audio cassette tapes. For a full set of separates, an enthusiast would also add a radio tuner, an amplifier, plus speakers - of course. Users of these older established pre-recorded audio media would have to cope with music affected by various imperfections over time. Vinyl sound quality would be a battle to maintain against the challenges of dust, scratches, warping, and wear. I remember pre-recorded cassettes had other problems, the major one for me being the variable quality of the tapes used by the distributors.

So, CDs came with many attractive promises, most importantly in the context of the early 1980s – providing digital music data that was read by a laser and wouldn’t wear. CDs also offered near-instant track selection, skipping, and replay. But most consumers would have to wait a few years: firstly for the price of player hardware to approach an acceptable level, and secondly for CD albums to become the norm for new releases and classic remasters.

Sony worked with CBS to have 50 CD albums on the shelves when the CDP-101 debuted in Japan. You’d think that six months later, when Philips joined in, and we saw the worldwide launch, that the catalog would have grown. Surprisingly, the ‘western’ launch was backed by just 16 titles.

It wouldn’t be until the mid-1980s when landmark DDD releases arrived and player prices dipped to much more acceptable levels that CDs would become established as a hi-fi staple. At around the same time, we started to get computers with built-in CD-ROM drives, but these would only start to become common from the early to mid 1990s and the dawn of the multimedia PC era.

If you are a retro tech fan who would be interested in buying a Sony CDP-101 before they all end up in museums, we noticed that you can buy them on eBay. At the time of writing, there were several of these 1982-vintage CD players for between $600 and $800.

The Russo-Ukrainian war has now formally extended to the digital realm. Over the last week, there have been multiple reports that the Russian military has started targeting Ukraine's data centers. Although the Western world didn't feel much of an impact, the situation is now changing, as popular pirate websites that host torrent trackers 1337x (aka Leet X) and NordicQuality experienced periods of explosion-induced downtime.

1337x has been fighting standard-issue DDoS attacks for months on end, and now it's experienced a Direct Destruction of Server attack, as several machines in its network were brought offline by kinetic blasts. Likewise, Norse tracker NordicQuality was also temporarily down for the same reason. Both sites are online again, though don't be surprised if the seas are unsteady for the pirates.

The servers for this type of site are rarely directly visible to the internet, relying instead on content delivery networks like Cloudflare. As such, it's hard to tell which Ukrainian data center firm is hosting them.

Virtual Systems reported strikes on the 23rd of September, which took down both its facilities, at least temporarily. MiroHost Ukraine got completely destroyed; strikes on CosmoNova took down TV channels; DataGroup and CityHost also got hit; telco KyivStar saw its Kyiv headquarters hit, while Russian authorities claim Vodafone Ukraine was also struck. Most of the strikes were clustered around Kyiv, prompting several of these companies to move operations to other regions or countries.

The 1337x tracker has been online for 19 years now, and has survived several shutdown and redirection attempts, including a blacklisting by Google and blacklisting across multiple countries' ISPs. Despite that, it remains the second most-visited pirating tracker, at around 40-50 million monthly visits, clearly displaying how neither laws nor guided missiles seem to be effective in curbing piracy.

Ukraine, Russia, and other countries in Eastern Europe have proven a popular choice for hosting torrent- and piracy-related websites, thanks to the region's general lax approach to copyright law, legal liability (or lack thereof), and difficulty in identifying website operators. Having said that, Ukraine is shifting its legal stance on the matter to more closely align with the European Union, likely as part of its bid for membership in the bloc.

A pre-launch Geekbench 7 result, shared on X by INIYSA as one of OpenAI’s dots (it's answer to Meta's Muse), shows a multi-core score of 9,435, with indications of the hardware used by the new autonomous agent. The VM hardware appears to be a single nine-core slice of an AMD EPYC 9V74 with 9.73GB of memory, run on Ubuntu for the leak and Debian for post-launch runs. Our check of Geekbench 7’s public database found six runs on that configuration uploaded since launch, ranging from 1,512 to 1,614 single-core and 8,135 to 8,991 multi-core. Dots, powered by GPT-6 Astra, launched at OpenAI’s DevDay on Tuesday for Pro and Business Premium users.

OpenAI bills the autonomous agents as having “their own cloud computer, their own browser, and the apps you’ve connected” in its introduction. This is not a new concept: Cursor says its cloud agents get their own virtual machines; SpaceXAI says every Grok Bot shares one persistent cloud computer; and Meta’s Muse gives each agent a sandbox.

Maddie D. Reese posted on X on launch day the apps her dot said came preloaded. The lineup includes Chromium, Blender, GIMP, Inkscape, Kdenlive, Godot, FreeCAD, OpenSCAD, KiCad, QGIS, ParaView, 3D Slicer, with Python, Node.js, and Git. In the same reply, the dot said its cloud computer runs “Debian Linux.”

The post went up the evening of launch day, captioned “OpenAI dots Geekbench 7 Score,” with a screenshot showing a single-core score of 1,667 and multi-core of 9,435. The result in the screenshot shows that it was uploaded Sept. 25, or four days before the launch. The 9,435 is almost 5% higher than the post-launch best and about 10% higher than the median. The pre-launch run is potentially a bit of an outlier, but performance remains within the same ballpark. It is uncertain who ran the original benchmark.

Tom’s Hardware previously reported that rival Meta’s Muse runs on AMD EPYC Turin with two cores and 8GB per sandbox. The 10 Muse runs in Geekbench’s database have median scores of about 1,041 single-core and 1,394 multi-core. The dot’s post-launch runs, with median scores of 1,570 and about 8,550, put it at about 1.5x the single-core and 6x the multi-core score.

Some of this gap is accounted for by the nine-core vs. two-core configuration; the rest of the gap likely comes from a difference in clock speed, partially offset by Muse’s newer architecture. Geekbench lists a 2.6GHz base clock for the dot’s EPYC 9V74 against 1.5GHz for Muse’s EPYC 9D25.

Muse’s runs also report 7.75GB of memory. Our independent run for this article came back with a single-core score of 1,000 and a multi-core score of 1,433, in line with the other Muse runs.

Data center CPU demand for always-on agents has surged as high-core-count CPUs are needed at sustained load. It is unknown whether the dots, as tested, hold their cores while idle. Such agents are rising in popularity: Muse reportedly passed 500,000 daily active users, and SpaceXAI plans standalone Nvidia Vera CPUs for Grok’s agentic workloads. It may only be a matter of time before a new CPU arms race begins. OpenAI has already said that, in the future, users will be able to add more dots and “scale the output of each dot by ... increasing its speed.”