President Donald Trump, several Cabinet members, and House Speaker Mike Johnson sat down with key AI leaders for high-level discussions on AI, as recent incidents such as the publicized Hugging Face hack have brought forth issues about AI safety. The meeting resulted in a document titled “Joint Commitment on Frontier Responsibilities,” which states that “every company is responsible for developing its own technology safely,” and was signed by Sundar Pichai (Alphabet), Dario Amodei (Anthropic), Mark Zuckerberg (Meta), Greg Brockman (OpenAI), Elon Musk (SpaceXAI), Jensen Huang (Nvidia), and Trump himself. According to CNN, the president argued that this was the best way to move forward with AI, which he dubbed “Super Intelligence,” as it balances progress with safety.

The pledge outlines four key points: (1) Implement robust internal controls to monitor the capabilities and alignment of its models during training and deployment around areas like cybersecurity, biosecurity, and chemical threats, and to ensure that its models do not hack or access technical systems in unintended ways; (2) Empower an internal team to ensure all of the controls, monitoring, and detection are operating as intended, and that any issues are remediated; (3) Partner with an independent external auditor or evaluator to carry out independent assessments of whether the controls, monitoring, and detection are operating as intended; and (4) Designate an independent committee of the board of directors to oversee and receive reports from the teams operating the controls and the internal and external auditors and evaluators, as well as to ensure any issues identified are remediated.

“I’m seeing tremendous self-policing, and they understand that they have to self-police,” Trump told reporters outside the West Wing. “These models are very complex and, again, I believe they’re going to be used for the good.” While this pledge has no law or regulatory pressure behind it, the president still called it “morally binding,” and that he believes that the AI companies can monitor themselves without the need for additional regulation.

The White House AI luncheon happened after several months of alarming incidents involving frontier AI models. One of the biggest incidents that has happened so far was when OpenAI’s GPT-5.6 Sol and other unreleased models broke out of their testing environment and hacked HuggingFace last July, which was followed by a discovery in August that these models had been secretly communicating with each other. It even had to pause testing just last week after a rogue agent failed to respond to a “kill switch.” Anthropic also discovered that Claude AI is building itself at a faster rate than expected, which is likely one of the reasons that led to Amodei’s call to slow down frontier model development. More surprisingly, Elon Musk and Sam Altman, two figures who've frequently clashed with Amodei (and with each other), agreed with his proposal.

On the other hand, Trump dismissed their concerns as hoaxes and called it a “sick conspiracy,” while Jensen Huang said that their fears are a “distraction,” and that AI labs should be accountable for any damage that their rogue agents may make. Chinese state media also said that move was “a response to Chinese competition,” and that “a global AI-safety framework that excludes China is not quite global.” Still, it mentioned the need to monitor AI development as humans could lose control of the technology.

This meeting apparently put most of the U.S. AI industry’s leadership and the Trump administration on the same page when it comes to AI safety. While this is just a pledge and not legally binding in any way, it has at least got the heads of the biggest AI labs and policymakers in Washington discussing the potential unintended consequences of AI development. How clashes with the pledge will be remediated, and the timeline in which that happens, remains an open question.

Insomniac Games' Marvel's Wolverine is the hot new PS5 game right now, but that's all it is — there's no PC version in the works, and given Sony's reported decision not to bring single-player games to PC, that seems unlikely to change anytime soon. However, that doesn't mean PC gamers are out in the cold after it was confirmed that an experimental PS5 emulator has been shown to be able to run the early portions Marvel's Wolverine, though only at single-digit frame rates.

While the KytyPS5 emulator was already able to start the game up, that was all it was able to do. That was great if you just wanted to be able to move around the game's menus, but it wasn't much use beyond a proof of concept. Now, things have progressed considerably, with VideoCardz pointing to an X social media post showing the emulator rendering the game's cinematics and gameplay.

The latest build of KytyPS5 can indeed play the game, but it does so at a single-digit frame rate. That's a long way short of making the action-packed title playable on anything other than a PS5 or PS5 Pro. But it's a start, and a much more promising one than was the case previously.

The frame rate situation probably says more about the emulator than the game itself. KytyPS5 is still very much in the early stages of its development, which means that issues are to be expected — especially for a title that has only been available for a few weeks. The emulator's developers are also focused on making it boot more games rather than making a handful work flawlessly, so it's unlikely it will be ready for prime time for a little while yet.

None of this is to detract from the work that has been done to get this point, though. The rapid progress that Marvel's Wolverine has seen is promising for the future of PS5 emulation on PC. KytyPS5 uses Vulkan 1.3 for its graphics output and uses an in-house system to decode the PS5's RDNA 2 GPU instructions. It's also available for both Windows and Linux right now, with an even more experimental version in the works for macOS.

This news also comes as another PS5 emulator has reached something of a milestone of its own. The developers behind SharpEmu, another experimental PS5 emulator, have been able to run a number of PS5 games at a playable 60 FPS. But even then, the success is modest —the developers only got six of 55 games to that point. A further six reached the point of gameplay, albeit at lower frame rates.

All Xbox gamers can now get digital versions of their game library with the rollout of the Disc to Digital feature, which allows them to secure a digital license for the game tied to the physical media and associated with their Xbox profile. The gaming company said on X that players on Xbox Series X and Xbox One could take advantage of this licensing program, allowing them to play their games without needing to insert the disc into the console.

Xbox has been experimenting with a feature that would let you digitize your physical game library since early July of this year, with the company rolling it out to Xbox Insiders first in late August. Now that Disc to Digital licensing is available to all Xbox users, gamers with extensive physical game libraries could get digital licenses for their titles, allowing them to install and play those games on Xbox consoles where they’re logged in. However, Microsoft says not all Xbox One and Xbox Series X titles are eligible for this feature, and that eligibility may change over time. The company said that this could be due to changes in technical factors, licensing, publishing rights, manufacturing, and more. You also need to have access to the qualifying disc to retain the digital license — if you lose, sell, or give away the eligible title, your digital license may be limited or revoked.

While this move is more of a quality-of-life feature than a full game preservation strategy, it still gives hope to gamers who prefer the “ownership” of physical games on discs over the licenses you get when buying them online. This is especially crucial after Sony announced that it will kill the PlayStation disc by 2028. This decision received a lot of criticism, with a Change.org petition to reverse the decision reaching more than 200,000 signatures a week after the company made the announcement. Despite that, it has doubled down on the decision, saying that it will move forward cautiously and stop manufacturing physical media for new releases by January 2028.

While Sony is moving towards a disc-less future, it seems that Xbox is choosing a hybrid path that allows gamers to own their physical games while still giving them the convenience of having a copy of the title in their digital library without requiring another purchase. Former Xbox CEO Phil Spencer said a couple of years ago that it won’t abandon physical media despite the success of Game Pass, and this move shows its commitment to the promise, even if Spencer has since retired.

A new PS5 jailbreak has been released with support for all PlayStation 5 consoles running firmware 7.00 through 13.60, meaning all consoles that haven't been updated within the last week or so are compatible. That includes the latest PS5 Pro console, too.

The new jailbreak, called Relapse, uses browser and kernel exploits to unlock kernel read and write access, including support for additional payloads like the homebrew enabler etaHEN.

However, while support for consoles all the way up to firmware 13.60 opens the door to more jailbreakers than ever before, the free-for-all might be short-lived. As VideoCardz reports, some games are already requiring the latest 14.00 firmware in order to download updates.

One example is the new Marvel's Wolverine game, with gamers reporting that the disc-based version requires PS5 firmware version 13.40. Downloading the latest 1.001.005 game update bumps the required firmware to version 13.60, which shouldn't be a problem for the Relapse jailbreak. But the fly in the ointment appears to be the digital version of the game. Some gamers have reported that the same update for the digital version of the game requires firmware version 14.00, pushing the PS5 beyond the firmware supported by Relapse.

This news leaves PS5 jailbreakers with a familiar Catch-22 situation. Some games, especially future releases, may require a newer version of the PS5 firmware in order to run. But installing those newer PS5 firmwares will break the jailbreak, giving gamers a decision to make — do they want to play the latest games or maintain their jailbreak?

Marvel's Wolverine aside, there are a number of reasons that gamers may wish to jailbreak their PS5 or PS5 Pro consoles. Doing so bypasses Sony's software restrictions, giving gamers the chance to install and run non-standard software and games. That can include pirated titles, of course, but it also means that emulators, including one for Nintendo Switch games, and mods can be used on a PlayStation. Naturally, those who choose to do this accept the inherent risks involved and do so at their own risk.

More information about Relapse can be found on the jailbreak's GitHub page.

Discover where AI is innovating in chipmaking and EDA at Tom's Hardware Premium AI Chip Design Week, from September 28th to October 2nd. Here you will find free in-depth reporting and analysis on the current state of AI in the chip design ecosystem, from Premium News to interviews with OpenAI, and much more.

Today is a big day for TP-Link, as the company revealed that it has opened preorders for its first Wi-Fi 8 hardware. The previously announced Archer 8 Ultra router makes the company’s first entry into what will become a fully stocked portfolio covering Wi-Fi 8 standalone and mesh routers, access points, and client adapters. Interestingly, TP-Link makes it clear that preorders kicked off today in the United Kingdom, Germany, Australia, and other global markets.

Given that the United States is a key consumer market for TP-Link, the fact that the country is left off the preorder list is telling, but not exactly a surprise to anyone who has been following the wireless industry over the past six months. TP-Link has sat on the sidelines as competitors including Netgear, Asus, and Amazon gained Conditional Approvals from the Federal Communications Commission (FCC) to sell their hardware in the U.S. Without the Conditional Approval, companies are effectively banned from selling new wireless hardware in the country that wasn't already approved by the FCC.

So, while earlier Wi-Fi 7 gear from TP-Link that received FCC certification can still be sold, any new Wi-Fi 7 or Wi-Fi 8 products are effectively blocked from the market. The company has made several critical moves to position itself as a U.S. company, including incorporating TP-Link Systems in California, but those moves have been challenged in legal filings.

At this point, there haven’t been any clear signs that the U.S. government will lift its freeze on TP-Link hardware, so until that time comes, the Archer 8 Ultra will remain unavailable stateside. And it’s a shame, because the Archer 8 Ultra, which utilizes a Broadcom Wi-Fi 8 chipset, will deliver up to 33 percent more real-world throughput compared to TP-Link’s Wi-Fi 7 hardware, and 24 percent more consistent throughput when operating in high-interference conditions.

The Archer 8 Ultra will feature two auto-sensing 10 Gbps WAN/LAN ports and four 2.5 Gbps LAN ports. TP-Link says that the router covers up to 3,600 square feet and will support up to 200 devices.

"Wi-Fi 8 sets a new foundation, and we’ve built beyond it,” said Jim Poder, Senior Vice President of R&D at TP-Link. “Archer 8 Ultra combines our Wi-Fi 8 StabilityEngine with intelligent antenna technology and AI-powered optimization to deliver what matters most: a smarter, more reliable network that’s easier to use.”

TP-Link’s Wi-Fi 8 family will expand in Q1 2027 with a Deco 8 Ultra mesh router, and a Roam 8 travel router in Q2 2027. Throughout the year, further additions will come in the form of new PCIe/USB wireless adapters, access points, and range extenders.

AI has transformed the way we do things in life. If you are passionate about 3D printing, Meshy 7 is a tool you should try. This AI-powered platform can revolutionize your workflow by bringing your ideas to life as quickly as possible, with no modeling skills required. If you are not already on board, Meshy is running a special offer for new customers. They can enjoy a 60% discount on their first month’s subscription with the exclusive code TOMESHY60.

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Instead of spending hours modeling from scratch, Meshy 7 gives you a platform to generate print-ready 3D models of your ideas in minutes. All you need to do is feed the AI with text prompts and 2D images (sketches, drawings, or photographs), and Meshy 7 will prepare the 3D model, which you can send to the best 3D printers to commence the print job. Meshy 7 produces some of the best 3D models with regard to speed and quality.

Meshy 7, the latest model launched in August of this year, brings many improvements over its predecessor. The AI has improved significantly, and your projects come out very close to what you imagine, eliminating the need for repeated prompts or going back to the drawing board. 3D models from Meshy 7 feature improved geometry and texture quality, with greater accuracy to the original image.

Meshy currently offers four subscription plans: Free, Starter, Pro, and Premium. The Free plan is a great way to get started and explore the platform’s capabilities. However, 3D printing enthusiasts or professionals should choose a paid tier, which offers higher priority in the generation queue, faster processing speeds, and the ability to run more concurrent tasks.

Newcomers get 50% off their first month on the Pro, Premium, and Ultra plans, which are regularly priced at $40 and $20, respectively. The savings do not stop there. Use code TOMESHY60 at checkout for an additional 10% off. The code has no expiration date, so you can take your time and explore Meshy 7's benefits with this risk-free deal.

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Anthropic has released a new report, claiming that Zhipu AI's GLM-5.3 AI model can be used to generate malicious content, with weak safeguarding. The company claims that the AI model can be used for cyberattacks, and that its safeguards can be bypassed using several methods.

Anthropic's report comes amidst a chorus of calls for a slowdown of AI development, with the company seeking governance and regulation. Despite CEO Dario Amodei's calls for pacing the AI frontier, Claude Opus 5.5 and Claude Sonnet 5.5 were released just days after alarms were raised.

Now, the closed-source AI company, which is currently eyeing an IPO, says that Chinese open-weight models can be abused and can generate harmful content. Anthropic cites the Center for AI Standards and Innovation's own report, published in late September, which claims that GLM-5.3 can fully automate exploits on a similar level to Anthropic's own unreleased Claude Mythos AI model, which spurred the company to develop Project Glasswing, an effort that gives developers access to a Mythos-class AI model to patch bugs and to fix vulnerabilities before such AI models are released.

Anthropic ran its own benchmarks on GLM-5.3 in Exploitbench, where AI models, in a sandboxed environment, can develop exploits for Google Chrome. GLM-5.3 developed end-to-end exploits 50 times in 410 runs, with Mythos leading the pack with 56 successful exploits in 410 attempts. Zhipu AI's model was further tested in one of Anthropic's internal benchmarks, which targets the development of "full control-flow hijacks". GLM 5.3 performed just below Mythos once more, with a 4% success rate, compared to Mythos' 6%. Notably, other popular open-weight models such as Kimi K3 and DeepSeek V4.1 Flash attained 0% by the same measures.

Anthropic further notes how GLM-5.3 was able to successfully develop chained exploits autonomously, with its lighter "Flash" variant also having the ability to develop chained exploits in known bugs, at a tokenized price of just $20.40. Depending on the balance, that equates to GLM-5.3-Flash having the ability to develop (known) chained exploits using anywhere from 100-300 million tokens, depending on the ratio of inputs to outputs.

Anthropic also notes that using the stock GLM-5.3 AI model, it was simple to dodge the model's guardrails through various methods. The company details that this can be done through two methods: offering a deceptive prompt, where the AI role-plays an adversarial autonomous agent, which results in a 64% success rate, and prefilling the model's thinking tokens to ensure that a response proceeds, which results in a 92% success rate. The company also detailed a third method, known as abliteration.

Abliterating guardrails

Anthropic alleges that Zhipu AI's GLM-5.3 has weak safeguards, and that the stock AI model often refuses requests to generate harmful content. However, since Anthropic develops closed-source models, its products cannot be tinkered with. Because GLM-5.3 is freely downloadable, the model can be tweaked with its guardrails wholesale removed. When treated as the officially released model, GLM-5.3 achieves a refusal rate on par with Anthropic models.

However, Anthropic "abliterated" GLM-5.3, which purposefully removes model guardrails, and displayed how, after abliteration, the model's refusal rate drops to just 6% for GLM-5.3 and 14% for GLM-5.3-Flash. It's not uncommon to encounter abliterated open-weight models on HuggingFace, which are primarily developed to assist in simulated red teaming environments, but they can also be used for real-world attacks. This makes Anthropic's 'discovery' less surprising.

In addition, it would take an enormous amount of compute power to run an abliterated version of GLM-5.3 at a workable level. The model's weights demand 306 GB of VRAM at full-precision FP8 weights, and you should expect to allocate a similar amount of VRAM for KV cache to carry context. Running the model at an estimated 100 TPS not only requires a minimum memory bandwidth of 4 TB/s, but it would also demand powerful silicon, like a cluster of eight Nvidia H200 AI accelerators. The money required for that kind of hardware stretches into the hundreds of thousands. Adversarial nation-state actors may be able to utilize such a setup, should they acquire the hardware.

For the ordinary everyday bedroom hacker, though? You'd likely need to rent the compute, abliterate the model, and then run it, which is also incredibly expensive. Anthropic says that abliterating the model GLM-5.3-Flash took 2,200 GPU hours, which they estimate costs $4,400, or around $2 per GPU hour, which aligns with the hardware rentals required.

Renting enough GPUs to abliterate the full-fat GLM-5.3 would cost around $30 per hour, according to figures from Runpod, where rental of a single H200 costs $3.79 per hour; you'd need eight. Generating around 100 million tokens would cost $8,422 and take 11 and a half days, at a hypothetical 100 TPS using eight H200 NVL GPUs. Abliterating the model itself, running it, and generating a working cyberattack would likely take much more time and would be incredibly expensive, which may perhaps be the biggest hurdle for any would-be malicious actors.

Why is Anthropic focused on this?

Given the current uproar around AI safety, Anthropic is highlighting the existential threats posed by frontier open-weight AI models as their capabilities continue to improve. While President Trump has met with leading figures in AI to self-regulate future model releases, Anthropic's post can be read in such a way that it spurs developers and governments to test open-weight models for their capabilities.

It could also reflect a growing anti-open-weight sentiment among closed-source frontier AI labs, which are losing business as users flock to cheaper, almost as capable models, though this is mere speculation. As of right now, open-weight models continue to closely follow the closed-source frontier, lagging behind by mere months.

Given the costs of running such models, the dangers of abliterated open-weight models being theoretically run by adversarial or malicious actors are certainly real, but perhaps not quite as attainable as Anthropic would want the general populace to think.

Meta's Muse is the hottest new AI agent in town right now after the company announced it earlier this month. But despite having been well-received by those who have put the agent through its paces since then, it now appears Muse might not be as private as Meta claims. The company has said that Muse was "built from the ground up to be a safe, secure, private, and widely available personal AI agent." But according to one report, Muse has been caught digging through thousands of personal messages without being given permission to do so.

Inc's Jason Aten has been putting Muse through its paces on two devices: a Mac mini and an iPhone. He says that he uses the Mac mini specifically as a place to test new agents like Muse, and once installed, he set about asking it to do some simple tasks. The agent said that it would be able to help him come up with new article ideas, which sounds just like something a journalist might want. But then Muse went rogue, suggesting a piece based on information it shouldn't have.

According to Aten, he was in conversation with a podcast cohost about the new iPhone 18 Pro and his decision to stick with last year's model. The conversation was in text form via the Messages app. That's when Muse popped up to suggest that the topic of discussion would make a good article. It offered to do some additional research on the subject, too. The problem was that Muse was never given permission to read Aten's messages.

When asked how it learned about the conversation, Muse claimed that the Mac version of the app was able to read incoming notifications. It then provided that context to the Meta iPhone app, which is where Muse made the article recommendation.

There had so far been no suggestion that this was possible, let alone something Muse was actually doing. Muse claimed that it wasn't reading Aten's actual messages, and that it didn't have access to chat histories. But Aten wasn't convinced.

After digging further, Aten was able to identify that Muse was indeed syncing a local Messages database. In fact, it was in the process of syncing all the way to row 187,462 of that database — suggesting Muse did have access to chat histories despite its protestations.

This, of course, is an issue. Aten confirmed that he didn't give Muse full disk access permissions on his Mac, something that should be required for the Messages database to be readable by the agent. Worse, David Singleton, the CEO of Meta Superintelligence Labs, responded to the situation with a thread of social media posts appearing to blame Aten for the situation.

At this point, it's unclear exactly how Muse gained access to Aten's Messages database. If Aten didn't accidentally give it access (which doesn't appear to be the case) there are obvious questions about what else Muse can access despite not being given permission to do so.

This isn't even the only Muse privacy and security issue to be put to Meta in the last 24 hours, either. It was also discovered that Muse can run terminal commands on its server host, which led to the discovery that the agent is powered by AMD EPYC Turin host systems. But because of that same access, it can potentially run unsafe commands, too.

The $1,999 iBuyPower Slate arrives with a formula that's hard to argue with: an RTX 5070, a Ryzen 7 7700X3D, a 360mm liquid cooler, and a tempered-glass chassis with plenty of RGB lighting. It even goes beyond expectations by including gaming peripherals that don't feel like afterthoughts.

But in an increasingly competitive mid-range desktop market, attractive extras and gaming prowess alone aren't always enough. The question is whether the Slate's gaming-first focus justifies its price when rivals offer stronger all-around performance for similar money.

Design of the iBuyPower Slate Gaming Desktop

There’s no mistaking the Slate for anything but a gaming machine. It’s designed to be seen inside and out, with a full-view tempered glass side panel. The front panel’s triangle glass partially shows off a trio of 120 mm RGB fans. The tower sits firmly in mid-tower territory, at 8.7 x 19.3 x 19.5 inches.

The mesh covering the rest of the front and top permits airflow and should do a decent job of blocking dust, though the case’s only removable air filter is under the bottom-mounted power supply.

Inside, the Slate’s blacked-out interior offers an RGB light strip running along the power supply compartment and up the front panel. Additional lighting comes from the two memory DIMMs. The GPU, an Asus Dual RTX 5070 in our review unit, lacks illumination. The lighting can be controlled via the Hyte Nexus app, though not on a per-component basis. (See the Software section later in this review.)

iBuyPower Slate Gaming Desktop Specifications

Ports and Upgradeability on the iBuyPower Slate Gaming Desktop

Mid-tower desktops tend to include lots of ports, and iBuyPower’s Slate doesn’t disappoint. The top-mounted front connectivity includes one USB-C and two USB-A (all 5 Gbps) plus separate headphone and microphone jacks. The positioning of these ports works best if the desktop is placed on the floor; it’s less ideal on a desk.

Rear connectivity on the ASRock B650M-CX motherboard includes one USB-C 3.2 (10 Gbps), one USB-A 3.2 (10 Gbps), two USB-A 3.2 (5 Gbps), four USB 2.0, Ethernet, HDMI, and audio jacks. Though there are no truly high-speed ports, they aren’t expected outside of high-end towers with pricier motherboards. The desktop also includes Wi-Fi 6E and Bluetooth – the antennas must be connected as shown here for meaningful range.

The side panels come off with a tug at the top rear, hinging outward and away. Seven 120 mm fans – three front, three on the top-mounted 360 mm CPU cooling radiator, and one rear – provide plentiful airflow. Cabling is reasonably tidy, though the bundle of unused cables from the non-modular 750-watt power supply is a bit of a mess.

The CPU cooler is more than a basic model, with an LED screen showing CPU temperature, frequency, and load. The display can’t be customized, though it can be toggled off in the Hyte Nexus app.

The Slate’s expansion options include one open PCIe x16 slot and four DIMM slots, two of which are unpopulated. The motherboard offers two M.2 2280 drive slots. There are two 2.5-inch drive sleds behind the motherboard, but no room for a 3.5-inch drive.

Gaming and Graphics on the iBuyPower Slate Gaming Desktop

We are testing the iBuyPower Slate with a Ryzen 7 7700X3D processor, GeForce RTX 5070 GPU, and 32GB of RAM.

Playing 007 First Light, at 3840 x 2160 with High settings and DLSS enabled, I observed between 60 and 70 frames per second (FPS) in outdoor scenes and closer to 80 FPS indoors. Using DLSS was required for smooth gameplay, as I saw roughly half the FPS without it.

Our comparison desktops include the Asus ROG G700 (Core Ultra 7 265KF, RTX 5070, $2,029.99 when reviewed) and the CyberPowerPC Gaming Desktop GXi3800BSTV2 (Core Ultra 7 270K Plus, RTX 5070, $2,309.99), both mid-tower systems with the same GPU but Intel processors.

At 1080p, iBuyPower’s Slate proved competitive overall, matching the Asus and CyberPowerPC in Shadow of the Tomb Raider (Highest settings), Cyberpunk 2077 (Ray Tracing Ultra), and Black Myth: Wukong (Cinematic). However, its Ryzen 7 7700X3D paid dividends in Far Cry 6 (Ultra), where its 185 FPS was well ahead of the Asus (110 FPS) and CyberPowerPC (141 FPS). It led by a similar amount in Red Dead Redemption 2, with 145 FPS against the Asus’ 120 FPS and the CyberPowerPC’s 126 FPS.

The Slate generally didn’t differentiate itself from the other desktops at the 4K resolution, an expected result since that resolution is almost always GPU-limited and these systems all rely on an RTX 5070. The exception was Far Cry 6, where the Slate’s 97 FPS greatly outperformed the 80 and 81 FPS produced by the Asus and CyberPowerPC, respectively.

Overall, despite its Ryzen 7 7700X3D not being nearly as powerful for general use as the Core Ultra 7 265K and Core Ultra 7 270K Plus featured in our competing systems, it can outperform both chips in games.

We stress-test gaming desktops running 15 loops of the Metro: Exodus benchmark at RTX settings. The iBuyPower desktop posted highly stable performance, averaging 145.7 FPS across all runs with less than a one frame variance across all runs. The Ryzen 7 7700X3D’s average temperature was 43 degrees Celsius and its average clock was 4.29 GHz. The RTX 5070 ran at 64 C and had an average core clock of 2.64 GHz.

I noticed a decent amount of fan noise from the Slate particularly when the CPU was stressed, causing the triple-fan CPU cooler to quickly ramp up to high speed. The fan noise was hardly noticeable for daily use and was relatively low while gaming as well.

Productivity Performance on the iBuyPower Slate Gaming Desktop

We tested the iBuyPower Slate gaming desktop featuring a Ryzen 7 7700X3D processor, 32GB of dual-channel DDR5-5200 RAM, and a 1TB PCIe 4.0 solid-state drive (a Patriot P410 as tested).

Productivity performance isn’t a strong point for this Slate configuration. Though its Ryzen 7 7700X3D remains potent for gaming, its previous generation eight-core architecture is outmuscled by the Core Ultra 7 K-class chips in both the Asus and CyberPowerPC systems, particularly the latter with its refreshed Core Ultra 7 270K Plus. The delta was significant: iBuyPower's system scored 14,222 in Geekbench multi-core versus 22,485 for CyberPowerPC, and took 3:35 to complete our 4K to 1080p Handbrake transcoding test versus just 2:03 for the Asus and 1:45 for the CyberPowerPC. The iBuyPower system does, however, include a decent storage drive, proving competitive in our 25GB file transfer test.

Despite the Slate’s performance delta versus the Core Ultra 7 systems, this PC is still highly capable for daily productivity and moderate content creation, such as video editing. Only those squeezing every last drop of performance out of their CPU will find it compelling to go with a more powerful chip than the Ryzen 7 7700X3D.

Keyboard and Mouse with the iBuyPower Slate Gaming Desktop

The Slate includes much better than average peripherals. The mechanical keyboard is the standout. Though not stated in the specs, it feels like it uses linear switches favored by gamers for their predictability. It felt rock solid in my testing and wasn’t overly loud. The keyboard supports RGB backlighting, though not per-key – the lighting can be set in the included Hyte Nexus app for various colors and patterns. You can also control the lighting through keyboard shortcuts – Fn+Ctrl cycles colors, Fn+Left/Right arrows alter brightness, Fn+Space Bar disables lighting, and Fn+Alt cycles through 16 backlighting patterns, such as breathing, pulsing, and steady. It’s also nice to see that the keyboard uses a detachable USB-C cable.

The mouse is also better than average, though it’s only for right handers. The standard buttons offer a solid feel, with two side buttons and a notched scroll wheel. The DPI button behind the scroll wheel switches between 1,200, 2,400, 3,500, 5,500, and 7,200 DPI, which are visually indicated by the DPI button illuminating in red, blue, green, purple, and yellow, respectively. The mouse fits my medium-size hand well. It’s internally backlit in RGB color, which can be configured in Hyte Nexus or toggled through 10 modes pressing the button on the underside. Patterns include color streaming, steady, and breathing, to name a few.

Software and Warranty on the iBuyPower Slate Gaming Desktop

Besides the usual preinstalled Windows 11 apps, iBuyPower only preloads one app – Hyte Nexus. It provides basic component temperature and frequency monitoring, but its real focus is RGB lighting control.

The menu is confusing – there are tiles for different components, such as the case, but these have no effect since the particular case used in the Slate isn’t recognized. Instead, all the action happens in the Lighting section. Many preset lighting recipes are included, such as Rainbow, Aurora, Ice, Fireplace, and Forest, which even apply to the keyboard and mouse. There are also options for screen mirroring, audio visualization, and static colors. You can save your settings in profiles.

That said, there’s no way to control lighting on a per-component basis – colors and patterns apply to all components at once since they are all connected to ARGB header 1. Confusingly, the app lets you change settings for the other headers, but this has no effect since nothing is connected to them. If the app indicated that those headers aren’t connected, this would have been more straightforward. The Slate nonetheless allows a decent amount of lighting customization despite lacking an advanced lighting controller, which would have increased the cost.

iBuyPower covers the Slate with a one-year parts and labor warranty.

iBuyPower Slate Gaming Desktop Configurations

We tested the iBuyPower Slate Gaming Desktop with a Ryzen 7 7700X3D processor, GeForce RTX 5070 graphics card, 32GB (2x 16GB) of DDR5-5200 RAM, and a 1TB PCIe 4.0 SSD. A mechanical backlit gaming keyboard and mouse are included in the box.

Best Buy offers several other variations of the Slate. A $1,679.99 starter model included a Core i7-14700F and RTX 5060 while a $1,799.99 model stepped up to a Core Ultra 7 265F and RTX 5060 Ti. Above our test model, a $2,299.99 variant included a Ryzen 9 9900X3D, while a range-topping $2,999.99 model offered a Ryzen 9 7900X, an RTX 5080, and a 2TB SSD.

Bottom Line

iBuyPower’s Slate gets the mid-tier gaming desktop formula almost exactly right. Visually, its RGB lighting is attractive and reasonably configurable even if it lacks per-component control, and its RTX 5070 and Ryzen 7 7700X3D comfortably power through today’s games at 1080p and 1440p, with 4K possible using dialed-down visual quality or DLSS. iBuyPower goes above expectations by including a liquid CPU cooler with a built-in screen and by including a mechanical RGB keyboard and gaming mouse.

Where the Slate stumbles, as least in our reviewed configuration, is processing power. Though the Ryzen 7 7700X3D remains potent for gaming, it’s outclassed by Core Ultra 7 K-class chips for general use, which can be had in systems costing similar money. But for pure gamers, this Slate is a standup performer.

If you want to expand your home network with cameras, WiFi extenders, and other smart home tech, you'll want to invest in a good power-over-Ethernet switch. At the minute, a Ugreen 10-port PoE+ switch is on sale for just $37.79 on Amazon. That's a new record low for this model, and it unlocks eight extra PoE+ ports (and 10 in total) for your network, at a price that's much more affordable than many of its rivals.

● Check out this deal on Amazon

Ugreen is a brand that you'll see a lot on Amazon, from network gear like this to wall chargers and peripherals. It's budget-focused, with reasonable prices that hide some pretty well-designed hardware. Here, you're getting a 10-port switch that supports the PoE+ standard (IEEE 802.3at), which means it can deliver up to 30W of power for devices on each individual port.

In practice, that means you can hook up newer and much faster devices to this switch, with power and data both being delivered over a single Ethernet cable. The switch supports 60W in total using a cascade system. If the power draw hits 60W, it'll shut down ports to prevent overload, starting from port 8 to port 1, so you can prioritize the ports you want and need the most. The switch will automatically detect PoE devices, so it won't short out a normal device like your PC or laptop by accident. Extended power delivery mode means that you can connect devices as far as 820 feet away, too.

If you're building a smart home network with plenty of devices to power, a switch like this will prove useful. This particular Ugreen switch is unmanaged, so there isn't a huge setup process to work through, and it should work immediately when you plug it into your existing hardware. That does mean it'll lack some of the extra customization that a more expensive managed switch could offer, but for most users, the ease of use will be the bigger draw anyway.

Almost any Ethernet-ready device will work with this switch, even if they aren't able to take advantage of its power delivery. Laptops, PCs, NAS drives, printers, TVs, game consoles, IP cameras, and more will work with this switch.

You're also getting the bonus of speed here. This Ugreen switch delivers gigabit speeds across your network, so you can leave WiFi latency and lag issues far behind when you're playing games or streaming Netflix shows. Ethernet connections using this switch will help you max out the bandwidth from your ISP. You've also got two uplink ports which you can hook up to your existing network for the best bandwidth.

The $37.79 sale price for this 10-port Ugreen switch is a great one, but it's only around for a limited-time only. If you want to pick up this fanless, lightning-protected, compact Gigabit PoE+ switch, you'll need to get your order in before the deal runs out.

Florida Attorney General (AG) James Uthmeier has filed a motion for a temporary injunction against five OpenAI entities and Sam Altman personally over questions about AI safety. It asks the court to order several enjoinments against OpenAI: for the company to stop developing any AI models without independent third-party guardrails and approval, to stop providing ChatGPT for minors in Florida, to stop collecting data from Florida children under 13 without notice, consent, review, and security procedures, to stop representing ChatGPT as safe, accurate, or reliable, to stop ascribing any false human attributes to its services, and to stop soliciting engagement through “conversation prolongation.”

In a video posted to X the same day, Uthmeier said Altman can join the request if his intentions are genuine.

The motion is part of Florida’s original lawsuit against OpenAI and Altman, filed in June in Highlands County after the state reviewed the accused Florida State University gunman’s ChatGPT logs. It alleges deceptive and unfair trade practices (FDUTPA), negligence and gross negligence, design defect, failure to warn, fraudulent misrepresentation, and public nuisance. Uthmeier called it the first state-led lawsuit against the company and its CEO.

The motion covers multiple recent, well-known events related to the company and potential safety lapses, including the Hugging Face hack from July, the Australian Medicare statistics portal unauthorized access incident, and more. OpenAI posted six misalignment reports earlier this month, not including one for the Australian incident, although those are a small portion of the tens of thousands of incidents OpenAI and competitor Anthropic are reportedly investigating.

The motion also quotes multiple people at OpenAI in its call for actoin. Paul Christiano, a new OpenAI board member, said this month that he sees “a meaningful risk” of catastrophic and irreversible loss of control in the very near term, and that OpenAI isn’t on track to reduce it to an acceptable level. Chief scientist Jakub Pachocki wrote in an essay days earlier: “I believe broader interventions are required.” And at the UN Security Council last week, Altman said that OpenAI “should not train models” unless it can make “an extremely strong case” that they can be kept under human control.

On Friday, before the motion was filed, OpenAI reported that “all training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused” after an agent reached a public chatbot through a DNS gap in its training sandbox. In a statement about the motion on Monday, company spokesperson Drew Pusateri said the company will resume training “only when we are confident that we have additional safeguards in place.” In particular, the company wants policies that cover “the entire AI industry — not just one company,” with governments playing an important role. This is the second pause in less than three months.

For now, the pause is OpenAI’s own call, but it follows recent public calls to slow the pace of AI development. Training may resume under the company’s own internal safeguards, but Florida wants an independent third party to sign off for as long as the case runs. Whether a Florida court can influence model development outside of the state is a separate question. The FDUTPA provision for an injunction cited by the motion is “effective throughout the state,” but the motion wouldn't seem to hold force beyond state lines.

OpenAI had attempted to move the case to federal court in the Southern District of Florida, but Judge Aileen Cannon sent it back: the defendants “clearly fail[ed] to satisfy the requirements” for federal jurisdiction. The motion argues that under FDUTPA, the AG needs to show only a “clear legal right” to relief, through likely success on the merits.

OpenAI’s response to this motion will likely be viewed under a harsh light. The company and others in the AI industry have come under heightened scrutiny as agent capabilities have increased. Florida's ongoing lawsuit follows a slew of others, and the breadth of the enjoinments it requests against the company is serious and far-reaching. Concerns about AI’s influence and impact on children continue to spark debate, too.

These concerns are not limited to OpenAI or any one AI company: rival Anthropic’s IPO filing reportedly warns that AI may pose existential risks to humanity, the same kind of warning Florida’s motion levels against OpenAI.

President Donald Trump signed a directive telling federal agencies to start calling AI “Super Intelligence” after hosting a lunch at the White House with industry leaders discussing the technology and the risks that it carried. According to the executive order, the term artificial intelligence was born in the United States 70 years ago, and the capabilities achieved by AI today are much more advanced than what the original innovators envisioned. Because of this, he saw fit to have the federal government call that technology by the new term that he coined instead of referring to it as “artificial intelligence.”

“The terminology used by the Federal Government should reflect the transformative capabilities of these technologies and the limitless opportunities they create for the American people. Accordingly, the term ‘Super Intelligence’ more appropriately captures the promise, potential, and rapidly advancing capabilities of these technologies,” the order said. “It is therefore the policy of my Administration that, to the maximum extent permitted by law, the executive branch shall use the terms ‘Super Intelligence’ and ‘SI’ in place of ‘Artificial Intelligence’ and ‘AI’ and will not acknowledge the usage of ‘Artificial Intelligence’ and ‘AI’ in any applicable setting.”

Trump believes that AI is only suffering from a branding issue, just as a recent poll showed that 79% of Americans think that AI safety is more important than having frontier models, while 73% said they will push back against the construction of an AI data center in the neighborhood. Opposition against the AI infrastructure build-out has been increasing throughout 2026, with protests primarily driven by concerns about local utility hikes and noise pollution. Aside from that, there have also been numerous security incidents involving AI going rogue, to the point that Anthropic listed “existential risk to humanity” as one of its risk factors in its IPO prospectus. But even though Trump changes what he and the rest of the federal government call AI, it still does not address all those concerns.

Using the term “Super Intelligence” could also bring some confusion, as it’s the term that scientists use for a level of AI that has achieved cognitive superiority to humans and are better at them in every conceivable task. While the latest frontier models are extremely good at doing particular actions, they still are nowhere near the level of superintelligence described by experts. While the White House wants to win the AI race with China and has been pushing hard for the development of this technology, some industry leaders and politicians are concerned that an artificial superintelligence could get out of control and end humanity. Senator Bernie Sanders (I-Vt.), alongside Senator Greg Cezar (D-Texas), have even proposed a bill banning its development, with the included penalty at par with the illegal development of nuclear weapons.

Still, it seems that some of the executives of the biggest AI tech companies are following the President’s lead, with Elon Musk adopting the term while in Trump’s presence. As for the President, he told reporters that the change has officially been made “by the biggest, the smartest, the greatest people anywhere in the world at this,” likely referring to the AI executives who attended the lunch at the White House. “We’re using the word super because the other word is a fake word.”

A new report from AI research firm Epoch AI suggests the price of artificial intelligence has fallen by thousands of times in recent years — faster than any other transformational technology in the past century. The report suggests that the cost of artificial intelligence is just under 50% cheaper every quarter, and 13 times cheaper per year.

That's faster than lithium batteries, faster than DNA sequencing, and even faster than compute, which has been benefiting from the rapid advancements from Moore's Law for much of the past century

Although these falling costs are likely aiding adoption, it raises difficult questions about the necessity of remaining tied to frontier AI development, and could make it harder for third-party AI services to build customer loyalty and consistent revenue.

If you know that at any given time it's only a few weeks or months before the service you're interested in is going to be more capable and cheaper, what's the incentive to adopt it right now? If a competing service offers something better and cheaper, why stay with your current provider?

That poses a difficult conundrum for labs like OpenAI and Anthropic, which are racing towards ever greater AI capabilities. As the report suggests, finding large profits even with advanced models that outstrip the competition is difficult to imagine when it may only be a fleeting advantage.

Falling faster than DNA sequencing and 20th Century compute

Epoch's report paints a stark picture of the relative cost of what it terms "artificial thought." It shows the cost of AI falling by hundreds of thousands of times within just five years. Comparatively, it shows the price of Lithium Batteries between 1991 and 2024 as falling just 100 times over that near-25 year period. Compute fell the most over its lifetime, dropping 100s of billions of times over 60 years from 1940.

This is particularly relevant considering Moore's Law has remained a relevant constant throughout much of the past 60 years. With compute performance and efficiency improving by notable margins every year, the compound effect is modern computers that are both dramatically faster than their predecessors and vastly cheaper to run when measured against their compute capabilities.

AI performance and costs for that performance are falling even faster.

The metric that showcases the most similar fall in cost on the chart is DNA sequencing — an oft-cited example of the falling cost of technology. Its relative cost has fallen by an even greater extent than AI, but that took just over 20 years.

Epoch AI shows artificial intelligence doing the same in just half a decade. That works out to just under 50% cheaper every quarter, and 13 times cheaper per year. The fall is accelerating at a pace that's four times faster than DNA sequencing, and 18 times faster than lithium batteries.

There are several caveats to these results. Compute only covers the period up until 2001, with no numbers on the relative cost of it after that, and electricity is only tracked until 1973, which means it completely misses the explosion of solar energy in recent years. AI cost falling was also only tracked from 2023 onwards, too, with data prior to that date extrapolated from the existing shorter trend and outside research.

Epoch AI also didn't base these results on a particular model, but on models achieving an 81.25% or better result on the Graduate-Level Google-Proof Q&A (GPQA) Diamond benchmark, and tracked the cost of having the model answer one of the multiple-choice questions on the test.

That does measure AI capabilities across a range of knowledge disciplines, but it doesn't necessarily cover the entire range of AI capabilities. It can show how much easier these kinds of tasks are for newer models, though, because they're not just scoring higher on the test more readily; they're doing it for far less, too.

How much cheaper?

Epoch AI cites OpenAI's GPT o3 model, released in January 2025, as achieving a 75% score on the GPQA Diamond test with a price of $0.30 per question. Just a year and a half later, though, OpenAI released GPT-5.6 Luna, which can score just as well on the same test, but costs only $0.0004 per question.

But that kind of rapid cost reduction isn't uniform across different disciplines, or linear in its progression. Expanding its research to include additional benchmark results on chess puzzles and mathematics, Epoch AI discovered vastly different rates of cost efficiency improvements. Where its GPQA Diamond benchmark showed periods of rapid cost reduction followed by periods of stagnation and plateauing, the AIME OTIS Mock test showed much more regular progression at each data point. Chess puzzles, although not tracked to the same success percentage, showcased more regular and consistent improvement, too.

Frontier Math developments, however, barely improved between 2025 and 2026, and then the costs fell off a cliff midway through the year.

Epoch AI also highlights that the rate of cost reduction is in decline, and that appears to be the case for most measured metrics for any particular performance level. Across the five benchmarks, it tracked cost falls of 66% per quarter initially, but two years later, that's down to 32% per quarter.

That could suggest AI progression is slowing, or that it's becoming harder to cut AI use costs, particularly in 2026, as the price of energy and compute hardware has skyrocketed. It's hard to offer a cheaper service if the raw materials for AI intelligence are vastly more expensive than they used to be.

Benchmaxxing is still an issue

Arguably the biggest potential problem for this data is, as Epoch AI highlights, the potential for "Benchmaxxing." That involves AI developers specifically training their models to do well on benchmarks that are otherwise designed to test more generalized capabilities.

Fooling benchmarks is something software companies have always done — Nvidia and ATI got caught doing it with 3DMark in 2003, and thousands of Intel test results were invalidated in 2024 for doing much the same thing with another benchmark.

Epoch AI's benchmarking used randomized elements to try to avoid models from recognizing they're being tested or developers specifically training them to be effective at third-party tests. But that's not all tests, and the ones without it showed greater rates of decline, suggesting there is some measure of benchmark optimization going on in the data.

That doesn't invalidate the results, but it does warrant taking them with an ounce of skepticism.

Not everyone takes advantage of the savings

The major concern for AI developers, especially those developing frontier models while spending hundreds of billions of dollars on infrastructure, is that these results suggest there's little point in paying for any kind of privilege. Even though the likes of Fable, Mythos, and the latest OpenAI models are still expensive to run compared to their contemporaries, token costs are falling all the time as the flagship developers compete on the Pareto frontier — the point where efficient cost and high intelligence meet.

But if other models can achieve 90% of the same intelligence at a fraction of the cost, and that cost is only likely to fall in the weeks and months to come, then is there any need to pay for the latest features and capabilities? Especially if those other models end up being open-weight, meaning third parties can compete to offer the most efficient and affordable version of that model.

But like anything else people subscribe to, it's not just about price, and it's not just about intelligence or capabilities either. Familiarity is important: With UI, with workflows, with the rest of what your organization is running. Trust is huge with any kind of ongoing commitment, especially financial. Can you trust that new third-party service offering a cheaper model than the one you've used for the past year? Maybe, but is it worth the risk?

Switching to a new model means confirming the veracity of those new benchmarks, and trusting that prices won't change dramatically in the future (they probably will), potentially invalidating your savings. You have to trust that the system you were using won't just catch up a week from now, and that the new system doesn't have any bugs or privacy concerns.

Changing AI tools isn't as straightforward as just chasing cost or intelligence. While prices might be falling dramatically, that doesn't necessarily mean a subscription model for AI is impossible. Just harder to justify.

Three companies, Cadence, Synopsys, and Siemens EDA, dominate chip design software. Their software takes you from chip specification to a manufacturable layout. As AI has rapidly advanced, so have their Electronic Design Automation (EDA) agents, all sporting notable improvements in 2026. The addition of a reasoning model helps drive that flow for Cadence’s ChipStack super agent, Synopsys’ AgentEngineer, and Siemens’ Fuse EDA AI agent, each with its own claims to fame. Cadence at Computex on June 1 said its agent reached what it calls Level 5 autonomy. Rob Knoth, Senior Group Director of Strategy and New Ventures at Cadence, told Tom’s Hardware Premium that the demo was “what we call bounded Level 5 in one domain,” with the bulk of super-agent tech at “I’d say advanced Level 4.”

Synopsys said its spec-to-RTL (Register Transfer Level) workflow shown on March 11 reached “L4,” and the company told Tom’s Hardware Premium it now claims L5 capabilities for its long-horizon agents. Siemens’ agent launched March 16 with “self-verifying” loops announced on July 26. Meanwhile, outside these big three, Empyrean’s chairman, Liu Weiping, stated on Sept. 9 that its agent cut a layout task from four weeks to one, according to the South China Morning Post.

While the claims about what these agents do are specific and now known, what they have measured is not. All of the claimed improvements to speed are the vendors’ or their customers’ own figures, with many “up to” or “early evaluation” caveats. These figures often measure different things against different baselines, and the only evaluator Synopsys named in July, AMD, has not provided analysis beyond an endorsement.

From copilot to closure

In an evolving field, it can be difficult to hit a moving target, especially when the reported numbers measure different things. Some clarity is possible by following the distinct stages from human-only to human-reviewed agent autonomy. The agents fundamentally rely on existing technologies with known desired outputs for accurate evaluation.

Progress has so far followed three distinct generations. At Synopsys, by DSO.ai and reinforcement learning in 2020, generative AI capabilities with Synopsys.ai in 2023, and finally an open agentic AI stack from 2025–2026. All decisions must be validated by proven EDA engines, with the agent focused on evaluating intermediate results and determining subsequent actions. A model’s proposal is examined by an existing deterministic engine such as Xcelium, Jasper, Questa, Calibre, or Fusion Compiler within an iteration loop until final verification.

Measuring progress here does not follow a standard, but there are autonomy ladders with a general rating from Level 1 or L1 to Level 5 or L5 or, in Siemens’ case, no numbered level. Cadence, which uses levels, moves from optimization AI to conversational LLM, complex reasoning, agentic workflows, and then finally full autonomy, defined as taking a design from spec to verification “with minimal human intervention.”

Synopsys’ L framework goes from actions of single agents on a single design step to more complex ones with multiple agents, with further development focusing on adaptive learning and, ultimately, the agent’s ability to make decisions autonomously. The critical part for Synopsys is that “human engineers will and must always be in the loop.” Given the claims of progress, it’s clear that ladders, where they exist, are a form of marketing scaffolding that describes the same underlying architecture or loop.

Specification to verified RTL

The greatest claimed gains thus far are in the front-end design and verification stages, taking a specification from RTL to a verified design. This part of the process is a natural fit for LLMs, as the RTL, testbenches, and assertions are code. Where the loop used to wait on human analysis and input, the agents can run simulations in parallel and make decisions with fast, concrete pass-or-fail answers. These loops already existed and are prime real estate for agents to speed things up, with claimed orders-of-magnitude improvements by moving from human to agent on what is often the slowest part of the process, triage.

Getting from the chip specification to verified RTL code involves generating the code from natural language and formal specification, according to Synopsys’ March release. From there, the agents generate unit-level testbenches and engage in a verification loop. In July, Synopsys claimed RTL validation could be up to 50x faster with a 20% improvement in coverage, compared with its own non-agentic flow.

For Cadence’s ChipStack from February, the scope includes “autonomous RTL design, verification, and debug.” Named early users include Altera, Nvidia, and Qualcomm. ChipStack started as a Seattle startup that was acquired in November of last year after a multi-year integration with Xcelium and Jasper. In June, Cadence said RTL validation could happen over 40x faster, with each engineer using ChipStack agents “to run hundreds of dynamic simulations with Cadence Xcelium ... reducing a typical five-week verification loop to less than a day.”

Siemens’ Questa One Agentic Toolkit, detailed in February, has five separate agents: RTL Code Agent, Lint Agent, CDC Agent, Verification Planning Agent, and Debug Agent. These work with mainstream AI coding applications including Claude Code, Cursor, and Siemens’ own Fuse. Akshay Aggarwal, senior director of engineering for MediaTek, noted “immediate and significant” gains. The numbers from Synopsys and Cadence should be read as the vendor’s own ratio comparison to the old loop, as regression loops are already automated. All three companies are now focusing on the decision-making process. For details on specific claims, refer to the table below.

The back end

Things are tougher when you move from RTL verification to implementation. Outside of specific tasks that look like front-end loops, claimed gains are harder to come by. This phase deals with geometry and physics, not code, and the pace is limited by compute. Some tool runtimes are “days to weeks … just running the tool itself,” and a tool call that takes “hours to days” to return a value “really gives the AI industry a much different problem to solve,” Knoth said. Complex trade-offs remove singular pass-or-fail judgments, and mistakes later in the process become much more expensive. Earlier AI integration also already exists for this flow, so the agentic touch is more on experiment-running. This makes things cloudier.

Cadence’s InnoStack runs parallel experiments from synthesis to ECO execution. ViraStack, which is for custom and analog solutions, handles the design flow from drawing to simulation, optimization, and layout porting, with some customers “reporting 3–10x productivity improvements,” according to a report from Futurum in April.

Synopsys’ looping implementation agents, driving Fusion Compiler directly or through other platforms, help “reduce manual engineering effort,” according to AheadComputing, and Synopsys said they improve quality of results, including timing, power, and area.

Siemens Fuse, meanwhile, coordinates at least nine named tools, including Solido for custom IC design and verification, Aprisa for physical implementation, Calibre for signoff verification, and design-for-test with Tessent. Improvements vary, with Solido mentioned as improving characterization turnaround times by more than 10x and token costs by 5x to 10x, according to Siemens. One third party, STMicroelectronics, mentions that the Solido Layout Analyzer is helping “cut down the time we spend debugging complex design blocks by weeks,” according to Non-Volatile Memory Design manager Gianbattista Lo Giudice.

Empyrean, according to the SCMP’s September reporting, is shifting from “humans operating tools” to “humans commanding agents” with its agentic EDA platform. This platform would work with its partners’ agents. One claim is that 3D verification went “from two to three months to less than a month,” according to TrendForce reporting. Empyrean is the largest domestic EDA vendor in China and is state-controlled as of December 2024.

Self-checking agents

What we inevitably keep coming back to with agentic EDA is one simple term: trust. The agents must be trusted to varying degrees to check their own work. Important checkpoints are still verified by a human: “The crucial approval checkpoints are still going to be human-driven,” Anand Thiruvengadam, executive director of product management at Synopsys, told Tom’s Hardware Premium. Inside the loop, the use of reliable tools such as deterministic engines allows for results that can largely be trusted. Outside of this, trust is based on evaluation, and internal verification is only as good as the tests. There remains reliance on what the vendors claim, and that depends on exactly how each defines the self-checking loop and goals.

The Siemens Agent loop, put simply, is: Plan, Act, Reflect, Iterate, over “Develop, Publish, Execute, Refine.” “Self-verifying” in Siemens’ case means that agents “continuously validate decisions against deterministic, physics-based EDA engines.” According to SemiWiki’s Bernard Murphy, describing the Fuse launch, “the orchestrator can trigger repair agents, then re-run validation. This may resolve most errors,” although we were unable to get any comment on specific triggers.

Cadence’s description is that “every action the super agent takes is anchored” in its own proven design and verification engines, to ensure accurate results. These, like Siemens’ solution, can run “within a secure Nvidia OpenShell sandbox” to enforce guardrails and ensure predictability. This allows engineers to “inspect, guide and collaborate as needed” even at Level 5. The human engineer works from their own runtime environment, then “once the super agent finishes that loop … the auto-run environment can interpret, understand, and then fire off that next message, right, that next iteration,” Knoth said.

Synopsys also retains the human element, with the debug workflow’s output as root-cause analysis and resolution through engineers. The flow iteratively runs checks against generated, unit-level testbenches, with the agent handling design and the tests themselves. When a check fails in Synopsys’ Verification AgentEngineer, the logs are consulted, and errors are aggregated to paint a picture of the most likely cause, checked against simulation waveforms. When applying the fix, the agent must prove that the bug has actually been fixed and should be able to produce a bug-fix manifest. Beyond this, the quantity of checkpoints is determined by customers’ trust and desired flexibility, and over time, “they might relax and take away those checkpoints,” Thiruvengadam said. The internal process, such as the specifics on retry limits, the definition of unfixable, and states requiring escalation, remains murky.

Early access everywhere

All of the numbers and roadmaps in the world don’t replace actual value to the customer, or actual customers with a released product. Thus far, the agentic platforms have been in early access and evaluation. Engagements have been listed, and major customers exist, but none are confirmed with a generally available product. General availability hasn't arrived yet, but it has been promised by the end of 2026 by Synopsys, with Cadence’s AuraStack, its PCB and advanced-packaging agent, also due this year. Usable products and workflows exist, but the efforts remain nascent, despite relatively rapid claimed gains over the last year or so.

Cadence ChipStack has been in early access since February. In June, this extended to the promise of Level 5 capabilities with early access for customers in the second half of 2026. Synopsys has debug and implementation workflows, announced in July, available for evaluation on Microsoft Discovery, if requested. Microsoft’s platform is designed to assist in the construction and management of agentic AI workflows related to scientific and engineering fields. Siemens has the Questa One Agentic Toolkit, announced in February, and the Fuse Agent as of March, with promises made in July surrounding self-verifying capabilities in forthcoming releases. Empyrean is also developing its own platform as of the time of writing.

The current stacks largely run on Nvidia’s Nemotron and its OpenShell sandbox. Nemotron is a reasoning model family. OpenShell is an open-source runtime that keeps the agent contained with kernel-level isolation, protecting customer IP and guarding against rogue actions. Nemotron and OpenShell are open, so nothing on record prevents Empyrean from using them, but without announcements, it appears to be the outside case. What each EDA vendor does with this technology and the flexibility it offers its customers are the defining differences.

Earlier this week, Nvidia announced its Open Agent Safety Platform, a combination of OpenShell and the Sentry reference design, an “out-of-band watchdog” that runs on BlueField-4 DPUs to “continuously monitor agent behavior” and can quarantine an agent in milliseconds. Cadence, Synopsys, and Siemens are named among the more than 100 organizations working with Nvidia on the platform. Cadence’s Knoth told Tom’s Hardware Premium that OpenShell “helps ensure the agents are well behaved ... don’t go rogue and start accessing data they’re not supposed to,” as agents need to become productive and trusted. Thiruvengadam from Synopsys echoed these sentiments, with both companies aware of the dangers of rogue agent actions.

Cadence and Synopsys, at least, needed licenses as of May 2025 for EDA sales to China, but the requirement was rescinded as of July of that year. EDA is expected to be critical for the industrial software space “toward the high end of the global value chain” by 2030, according to ChinaTechNews. According to Liu, as reported by the SCMP, Empyrean is trending away from traditional software licenses to a token-consumption pricing model with agentic EDA.

Startups in this space include ChipAgents, Cognichip, Agentrys, Silimate, and Ricursive Intelligence, most funded since December 2025. The big three buy other companies, from startups such as ChipStack to simulation software maker Ansys, as advances put EDA in the spotlight toward the end of 2026. Even with platform-specific flexibility, Nvidia remains the common ground, with its open model family and sandbox being the standard across the big three.

As it stands in 2026, the agents exist, and there are real roadmaps and customer evaluations. The framework hinges on existing technology already worthy of trust. The overall trend is toward harness engineering and, as Knoth told us, the implementation of “an array of specialized engines” to fuel the workflow end to end. Improvements, although currently lacking the independent measurement and precision we would like to see, are real and accelerating, according to vendors and their customers. It will become increasingly important to have proper evaluation of the different agents as they approach Level 5, “L5,” or some equivalent marker that denotes autonomous action.

What we would like to see by the end of the year is some customers named to actually be using the highest level of agent. Specifically, these agents must live outside of a trial or early access mode. This enables oversight and comparison in what will become an incredibly important field. We also anticipate Siemens’ forthcoming releases, which may demonstrate the self-verifying loop. Cadence is worth a close watch to see whether a Level 5 early-access customer is named before the end of the year. The baseline, though, is at production: real customers, real workflows, real products, and a measured improvement.

On this day in 1980, version 1.0 of the Ethernet specification was published by Digital Equipment Corporation (DEC), Intel, and Xerox. This ‘DIX’ standard was established at a time still nearly three years before the modern internet existed. Nevertheless, Ethernet would become the default technology for connecting computers to each other in local networks – and all around the world. However, we must point out that Ethernet had existed in experimental form at Xerox PARC in the 1970s.

Before Ethernet, computer manufacturers were wary of building LAN connectivity into their computers. It seemed wasteful to integrate one type of network adapter that wouldn’t always work with other networked computers and equipment an organization might use. To foster the adoption of Ethernet industrywide, DIX allowed any vendor to use the specification in their own hardware implementations.

Ethernet has been adjusted, refined, and improved over time to remain competitive and relevant. In 1980, it arrived using coaxial cable wiring and with a top speed of 10 Mbps. Five years later, it would move to adapters with BNC connectors. The first RJ45 implementation, a connector that still identifies Ethernet ports to this day, was in 1990 alongside the introduction of 10BASE-T twisted-pair cabling (but still at 10 Mbps).

Data speeds ramped up tenfold, hitting 100 Mbps in 1995, and it would be just four more years until the next tenfold boost with the debut of Gigabit (1 Gbps) Ethernet. If you buy a PC nowadays, you will commonly see Ethernet ports labeled 2.5G and/or 10G. To make use of the latter, Cat6a or better cabling is typically recommended for reliable links. However, data centers run equipment connected via 800 Gbps+ Ethernet protocol connections over advanced fiber optics.

Despite the rise and generational advances delivered by Wi-Fi 7, wired Ethernet is still preferred for speed, low latency, and stability. These are very desirable qualities for enthusiasts, home labbers, and gamers alike. Thus, even on small form factor PCs and laptops with the latest Wi-Fi but no RJ45 networking port, some might choose to connect a USB Type-C to RJ45 Ethernet dongle when they are at the home/office with wired networking available.

A developer who goes by the name “stmonty” has trained a small AI model on a single RTX 3080 Ti to play Pokémon Red. The developer’s blog post, “Teaching a World Model to Play Pokémon,” details the journey of training a model working from a save to pick a starter. The model is what is known as a world model, built off of LeWorldModel research co-authored by famed AI scientist Yann LeCun. LeCun split from Meta last year to build world models with his AMI Labs, using the same approach.

This particular one was trained with only about 12.5 million parameters and written up on Sept. 20. On a discussion forum, stmonty called the project a way “to learn and have some fun,” and chose the model because it could be trained locally on a consumer-grade graphics card.

The model has to learn what each button press does, and it does this by looking at the screen that’s produced. Rather than predict the entire next screen, it uses compressed summaries with just 192 numbers. The initial training is “reward-free” in the sense that the model is not aware of winning conditions. Goals come later, when the model plans. This design follows a March 2026 paper detailing a single JEPA model with about 15 million parameters, trainable on a single GPU, not that far different from stmonty’s.

The full training run required 42,382 grayscale frames in over 1,000 short runs, using a save in Professor Oak’s lab. The data included scripted routes, those same routes with random presses mixed in, and random wandering. The last part is necessary because a model trained only on clean runs “might see A pressed whenever a dialogue box appears and never learn what B does there,” said stmonty. The model was trained from scratch for the project.

Plans are developed with 14 button presses and attempted in the model, not the game. Each round has 512 plan samples where the search keeps one in eight, or 64. This continues until the final plan is the one run in the game emulator. Stmonty hypothesizes that one possible cause of the failure, which left the player without a Pokémon, is that small errors compound when predictions are made from the model’s own predictions.

The next attempt, after some fine-tuning, came back a success with the model selecting Squirtle. Then, over 100 runs from the same save, there were 52 plans that got a starter, compared with zero for random button presses and just one with an untrained model. Stmonty stated that pressing A repeatedly already works from the save; the model discovering the solution on its own was the goal. This is still a modest result but does demonstrate that small models can be tailored to specific tasks.

The next goal, which the developer “may still try,” is to start in the lab, walk to the Professor, get through the dialogue, then actually pick a Pokémon. “I suspect that difficulty scales exponentially with plan length,” the developer said. Making the model large enough would probably not lead to the game being beaten: “I don’t think simply making my current model bigger would get us there,” the developer stated in a reply.

The small model approach to playing Pokémon Red is one of many in recent weeks, with at least two separate, and successful, attempts to beat the game using the Jev decision model and a harness or custom code. Those use more sophisticated approaches, however, while small models remain a realistic option for hobbyists. The project’s code is open source and available on GitHub as lePokeRed, with a CUDA GPU recommended.

When Nuvacore, a new CPU startup founded by legendary CPU and system architects Gerard Williams, John Bruno, and Ram Srinivasan, emerged from stealth earlier this year, it promised to "rewrite the rules of silicon," but did not say how it planned to do so. On Thursday, the company finally broke its silence and revealed that it is taking an unusual development approach that allows it to design a significant portion of its CPU core IP before committing to a particular instruction set architecture (ISA).

Traditionally, CPU developers start development of a new processor project with a particular ISA — such as Arm, RISC-V, or x86 — already selected. Although modern high-performance processors translate instructions into internal micro-operations and therefore separate the ISA from much of the underlying execution pipelines, the instruction set still influences numerous architectural decisions, including instruction decoding, register handling, memory ordering, and, of course, software compatibility.

Nuvacore says its Core First approach reverses at least part of this process. Instead of designing WarpCore around the requirements and limitations of a predetermined ISA, the company's engineers are developing a significant portion of the foundational CPU IP independent of a particular ISA. To a large degree, one can compare such an approach to the development of a car around its engine and not trying to fit an engine into an already developed platform.

"Rather than beginning with the constraints of an existing architecture and iterating from there, we are starting with the core itself," the company said in its statement.

As a result, the team may be able to better focus on the core's microarchitecture and optimize it for performance, power efficiency, and sustained workloads typical of modern data centers and AI infrastructure, the target application for the company. Nuvacore intends to select an instruction set later based on what works best for the systems that it and its partners plan to build.

There are obvious limits to how ISA-independent a CPU design can be. Eventually, WarpCore will need front-end logic designed around its chosen instruction set, and those architectural requirements will inevitably affect other parts of the processor. Nevertheless, substantial pieces of a modern CPU — such as execution units, branch-prediction units, data paths, caches, and portions of the memory subsystem — can be developed and evaluated before every ISA-related implementation decision is finalized.

In addition to not disclosing its ISA, which is arguably the biggest unanswered question surrounding the project, Nuvacore also has not disclosed the project's schedule, configuration, manufacturing process, power, or performance targets, though given the lack of the ISA commitment, one could say that the remaining details are not that important.

For now, the only thing that Nuvacore discloses about WarpCore is that it is "a new class of general-purpose CPU designed specifically for the sustained performance and power-efficiency requirements of AI infrastructure and contemporary data centers."

AMD's ambidextrous strategy returns?

Nuvacore's approach is not entirely without precedent. Under CEOs Rory Read and Lisa Su, AMD spent part of the 2010s pursuing an "ambidextrous" strategy intended to let it address x86 and Arm customers by sharing substantial processor and platform IP.

Arguably the most detailed part of the initiative was AMD's SkyBridge platform, announced in 2014. Under SkyBridge, AMD envisioned pin-compatible 64-bit Arm and x86 systems-on-chips that were to use AMD's own Puma+ x86 cores and Arm's Cortex-A57 cores with common platform infrastructure. Separately, AMD developed its custom 64-bit Arm K12 core under Jim Keller that was supposed to share microarchitecture features with Zen. Ultimately, due to lack of resources, SkyBridge was ultimately scrapped, while K12 never became a commercial product as AMD only pursued x86 Zen cores. The difference is, of course, that Nuvacore appears to be taking the idea further by deliberately postponing the ISA decision for WarpCore.

Since Nuvacore is still just a startup, we can only wonder whether its business plan includes marketing of CPUs at all. Nuvia, which was also founded by Williams and Bruno along with Manu Gulati, was acquired by Qualcomm and ultimately shaped the company's Snapdragon X Elite SoCs. In that light, its approach could give the company considerable commercial flexibility.

By developing much of its WarpCore design without tying it to a particular ISA, the company could potentially adapt the technology to the requirements of a future customer or partner. An Arm licensee could use the underlying core technology for an Arm implementation; another customer could choose RISC-V or another ISA. An established x86 vendor could potentially acquire or license the underlying microarchitecture and create its own x86 CPUs if it needs to.

Nuvacore, of course, has not indicated that such a transaction is its objective, but keeping WarpCore ISA-agnostic for as long as possible potentially broadens the number of companies to which its CPU technology could be valuable.

All in all, the Core First announcement tells us considerably more about how Nuvacore intends to develop WarpCore than what the processor itself will eventually look like. But if the company really does keep its ISA options open until relatively late in development, WarpCore represents an unusual attempt to separate development of a high-performance CPU microarchitecture from one of the decisions that normally defines a processor project from its earliest stages.

OpenAI’s Jalapeño ASIC is a seismic shift for the industry, not because of its efficiency or performance, but because of how it was designed. The company was clear from the jump that AI played a big role in the design process of Jalapeño, not only for the hardware itself, but also in the co-design with OpenAI’s software stack, allowing the ASIC to go from initial register-transfer level (RTL) to tapeout in a matter of just nine months. From concept to reveal, the timeline was less than two years. Richard Ho, head of hardware at OpenAI, says the timeline “established a new baseline,” and that the industry is already knocking on OpenAI’s door to learn how the company pulled it off.

“The way I like to think about it is, we’ve established a new baseline. In the old baseline, you’re talking 18 months to two years, roughly. Often that’s even with some existing IP or some more legacy architecture design,” Ho told Tom’s Hardware Premium in an interview. “We’re starting from scratch here. We had nothing. There’s not a line of code here to refer to. What we’ve established is that there’s a new baseline that you can do with a very talented team with the help of AI. Now, does it get shorter? It depends on what you’re trying to do.”

AI usage in chip design is nothing new, predating the era of LLMs entirely. The largest Electronic Design Automation (EDA) companies, Cadence and Synopsys, have a portfolio of AI-assisted chip design tools that have been around for several years. The models OpenAI used were its own internal models, however, and it leveraged both existing EDA tools and its new AI-assisted engineering workflow.

The engineering team used OpenAI’s agentic coding platform, Codex, for the Jalapeño design. “But we think that every engineering team in chip design should be able to use this as a new baseline, because it’s a proof point that the models that are in use — the AI models for us is mostly Codex, Sol, the one before Sol, and now we’re moving on to Astra. These are super capable. Even from when we started that work, back in November 2025, to when we taped out, the models improved enormously. Even from that moment to when we started doing the kernel optimization in May, when the chips were first coming online, we ourselves were shocked at how much better Codex was and what it could do,” Ho said.

Although AI was used during the entire development process, not only for design itself but also in writing and optimizing kernels, Ho continually reiterated the importance of talented engineers guiding those systems. The development story of Jalapeño is one of the few clear examples of AI bolstering a team of human workers, not displacing them.

Ho described the development cycle as a “good model” of how engineering teams should operate in the AI era. “This is how AI should be used. We didn’t replace our engineers; they just became super productive. With a smaller team of really good engineers with a lot of this AI stuff, you could do things faster and better than you could otherwise. I think that’s a good model of how engineering should be approached in the AI age.”

Although OpenAI managed to get working silicon much faster than a traditional development cycle, it wasn’t free of issues. For starters, OpenAI’s B0 stepping of Jalapeño reportedly delivers up to a 25% improvement in performance per watt over the original A0 stepping. That’s closer to a generational improvement than stepping optimization, suggesting that, at least for a brand new hardware team, there may have been design oversights with the original stepping. Attributing that to AI or humans is anyone’s guess.

Other frontier labs are circling for a slice of the pie, as well. Clive Chan, a key engineer on Jalapeño and the second-ever hardware hire at OpenAI, left the company in June to join the hardware team at Anthropic. Ho says there’s already been “a lot of interest in the industry” for OpenAI’s AI-assisted chip design process, and says that “we’ll see more about that quite shortly.”

“I’m not going to preempt anything here. I can tell you that there’s a lot of interest in the industry, and I would also tell you that we feel that there is a lot of benefit in industry generally that we want to enable,” Ho said. “I think that’s something that we’ll see more about quite shortly, to be honest.”

Ho referenced the dozens of AI-first chip design startups in Silicon Valley, suggesting OpenAI may eventually introduce tools of its own to aid other firms with AI chip development. “We have our take on that, and I think at some point we want to tell the world, “Here’s our take on it.” Fundamental to that is Codex and GPT-6 Astra coming out. Those are fundamental, and we can basically point to it; it’s not going to be slideware or vaporware. We can point to it and say, “Here’s what we did, here’s how we did it, and here’s what we got.” It’s going to be very concrete.”

What exactly the “it” Ho is referring to here remains a mystery, though given the context, it sounds like OpenAI may explore some way to productize its AI chip design workflow. Ho says Astra is a big step toward that, and we’ve already seen the model in action performing impressive feats, such as completing Portal autonomously.

Although OpenAI leveraged its own models heavily for Jalapeño's design and validation, it didn’t completely break the mold of traditional EDA workflows, particularly at the end of the design process. Before tapeout, tools from companies like Synopsys and Cadence perform a series of tests for signoff, including static timing analysis and signal integrity analysis. Ho says that OpenAI used this typical EDA flow for Jalapeño.

“But for sign-off, you need to use the standard EDA flows, and we did, because you want to make sure those results are good and correct. There’s no real alternative today. Part of it is this combination of standard flows optimized with AI, optimized by really good engineers,” Ho said.

OpenAI shared a multi-generational roadmap for accelerators. It says its second-gen ASIC is approaching tape-out, and its third generation is already in development. Although Ho said that the Jalapeño development cycle establishes a new baseline for chip design timelines, he was cautious about calling that cycle and cadence.

“Our projects and tape-outs will be dependent on the maturity level of the technologies. We can do very fast execution. Will we do those types of executions back-to-back? I doubt it, because the technology will not be ready for that, and I don’t want to tape out something that is 2% better than what I taped out before, because it’s not worth it to change a fleet,” Ho told us.

The executive was also clear that the Jalapeño timeline isn’t necessarily the same timeline all chips will follow. There were some “pragmatic trade-offs” in the early architecture design, with the team avoiding emerging, complex design points like 3D stacking and co-packaged optics. “Will it take longer? Will it take nine months? I won't say it will take nine months… but I think it will go faster than if you didn't have AI models.”

You can read the full transcript of the interview, which covers a wide range of topics, at Tom's Hardware Premium.

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