The past few years have been a bit of a struggle for Exynos chips. Low-yield issues with Samsung Foundry wafers have led to the loss of big customers. The demand for chip manufacturing at Samsung factories has been poor for some time now, with only Exynos SoCs and a few third-party contracts using them.

Poor demand prompts Samsung to shut down chip factories, including some Exynos makers

Many of Samsung Foundry’s semiconductor factories were still active but not actually producing products. So, according to South Korean media Chosung, the company has decided to suspend operations at 30% of its 4nm, 5nm, and 7nm chip fabs. This is not a permanent shutdown, but a temporary one. In fact, the equipment will continue to receive power, but in a low-consumption mode to save electricity-related costs.

According to the report, the percentage of “semi-off” fabs will increase to 50% by the end of this year. Among the mentioned wafers, only the 4nm one remains permanently active, manufacturing the Exynos 2400 and some Snapdragon S chips. However, third-party demand is so low that it is far from occupying the full wafer capacity.

Samsung still hopes to improve the performance of its 3nm wafers

Currently, Samsung Foundry is struggling with the low yield of its 3nm GAA wafers. The company even had to ditch the Exynos 2500 from the Galaxy S25 series. Reports claim that they are still working on trying to improve the performance of its 3nm wafers in order to implement the Exynos 2500 in the foldable Galaxy devices of 2025.

On the bright side, the situation looks better for Samsung’s 2nm process. The South Korean giant hopes to offer competitive factories that will attract the attention of big customers. Even Qualcomm has opened the door to working with Samsung again for flagship Snapdragon chips in the near future.

Things got complicated for Samsung Foundry in 2021

Samsung Foundry’s “nightmare” began in 2021 with the Snapdragon 8 Gen 1. Qualcomm had designed the SoC with high expectations, for which it even debuted a name change. However, Samsung Foundry 4nm wafers “ruined it,” delivering chips with high power consumption and poor thermal management. Low factory yield was also an issue back then, with the percentage of usable chips produced being far from ideal.

Qualcomm acted quickly to remedy things by moving chip production to TSMC for the second half of the year. The company used the same design but slightly increased the CPU clock speeds. The result was the Snapdragon 8+ Gen 1, one of the best chips in the company’s history. At the time, Samsung Foundry was widely blamed as the source of the problems with the original chip.

In fact, the Snapdragon 888, the predecessor of the Snapdragon 8 Gen 1, was already showing some signs that something was not quite right. While the chip did not exhibit the latter’s severe problems, it already presented worse thermal control and energy efficiency than usual. Nvidia, another big player in the tech industry, also switched from Samsung to TSMC. In this way, Samsung suddenly lost its main customers for the time.

If you’re excited, or even just a little curious, about the future of augmented reality, Meta’s Orion prototype makes the most compelling case yet for the technology.

For Meta, Orion is about more than finally making AR glasses a reality. It’s also the company’s best shot at becoming less dependent on Apple and Google’s app stores, and the rules that come with them. If Orion succeeds, then maybe we won’t need smartphones for much at all. Glasses, Zuckerberg , might eventually become “the main way we do computing.”

At the moment, it’s still way too early to know if Zuckerberg’s bet will actually pay off. Orion is, for now, still a prototype. Meta hasn’t said when it might become widely available or how much it might cost. That’s partly because the company, which has already poured tens of billions of dollars into AR and VR research, still needs to figure out how to make Orion significantly more affordable than the $10,000 it costs to make the current version. It also needs to refine Orion’s hardware and software. And, perhaps most importantly, the company will eventually need to persuade its vast user base that AI-infused, eye-tracking glasses offer a better way to navigate the world.

Still, Meta has been eager to show off Orion since at Connect. And, after recently getting a chance to try out Orion for myself, it’s easy to see why: Orion is the most impressive AR hardware I’ve seen.

Meta has clearly gone to great lengths to make its AR glasses look, well, normal. While Snap has been mocked for its oversized Spectacles, Orion’s shape and size is closer to a traditional pair of frames.

Even so, they’re still noticeably wide and chunky. The thick black frames, which house an array of cameras, sensors and custom silicon, may work on some face shapes, but I don’t think they are particularly flattering. And while they look less cartoonish than Snap’s AR Spectacles, I’m pretty sure I’d still get some funny looks if I walked around with them in public. At 98 grams, the glasses were noticeably bulkier than my typical prescription lenses, but never felt heavy.

In addition to the actual glasses, Orion relies on two other pieces of kit: a 182-gram “wireless compute puck, which needs to stay near the glasses, and an electromyography (EMG) wristband that allows you to control the AR interface with a series of hand gestures. The puck I saw was equipped with its own cameras and sensors, but Meta told me they’ve since simplified the remote control-shaped device so that it’s mainly used for connectivity and processing.

When I first saw the three-piece Orion setup at Connect, my first thought was that it was an interesting compromise in order to keep the glasses smaller. But after trying it all together, it really doesn’t feel like a compromise at all.

You control Orion’s interface through a combination of eye tracking and gestures. After a quick calibration the first time you put the glasses on, you can navigate the AR apps and menus by glancing around the interface and tapping your thumb and index finger together. Meta has been experimenting with wrist-based neural interfaces for years, and Orion’s EMG wristband is the result of that work. The band, which feels like little more than a fabric watch band, uses sensors to detect the electrical signals that occur with even subtle movements of your wrist and fingers. Meta then uses machine learning to decode those signals and send them to the glasses.

That may sound complicated, but I was surprised by how intuitive the navigation felt. The combination of quick gestures and eye tracking felt much more precise than hand tracking controls I’ve used in VR. And while Orion also has hand-tracking abilities, it feels much more natural to quickly tap your fingers together than to extend your hands out in front of your face.

What it’s like to use Orion

Meta walked me through a number of demos meant to show off Orion’s capabilities. I asked Meta AI to generate an image, and to come up with recipes based on a handful of ingredients on a shelf in front of me. The latter is a trick I’ve with the Ray-Ban Meta Smart Glasses, except with Orion, Meta AI was also able to project the recipe steps onto the wall in front of me.

I also answered a couple of video calls, including one from a surprisingly lifelike . I watched a YouTube video, scrolled Instagram Reels, and dictated a response to an incoming message. If you’ve used mixed reality headsets, much of this will sound familiar, and a lot of it wasn’t that different from what you can do in VR headsets.

The magic of AR, though, is that everything you see is overlaid onto the world around you and your surroundings are always fully visible. I particularly appreciated this when I got to the gaming portion of the walkthrough. I played a few rounds of a Meta-created game called Stargazer, where players control a retro-looking spacecraft by moving their head to avoid incoming obstacles while shooting enemies with finger tap gestures. Throughout that game, and a subsequent round of AR Pong, I was able to easily keep up a conversation with the people around me while I played. As someone who easily gets motion sick from VR gaming, I appreciated that I never felt disoriented or less aware of my surroundings.

Orion’s displays rely on silicon carbide lenses, micro-LED projectors and waveguides. The actual lenses are clear, though they can dim depending on your environment. One of the most impressive aspects is the 70-degree field of view. It was noticeably wider and more immersive than what I experienced with Snap’s AR Spectacles, which have a 46-degree field of view. At one point, I had three windows open in one multitasking view: Instagram Reels, a video call and a messaging inbox. And while I was definitely aware of the outer limits of the display, I could easily see all three windows without physically moving my head or adjusting my position. It’s still not the all-encompassing AR of sci-fi flicks, but it was wide enough I never struggled to keep the AR content in view.

What was slightly disappointing, though, was the resolution of Orion’s visuals. At 13 pixels per degree, the colors all seemed somewhat muted and projected text was noticeably fuzzy. None of it was difficult to make out, but it was much less vivid than what I saw on , which have a 37 pixels per degree resolution.

Meta’s VP of Wearable Devices, Ming Hua, told me that one of the company’s top priorities is to increase the brightness and resolution of Orion’s displays. She said that there’s already a version of the prototype with twice the pixel density, so there’s good reason to believe this will improve over time. She’s also optimistic that Meta will eventually be able to bring down the costs of its AR tech, eventually reducing it to something “similar to a high end phone.”

What does it mean?

Leaving my demo at Meta’s headquarters, I was reminded of the first time I tried out a prototype of the wireless VR headset that would eventually become known as Quest, back in 2016. Called at the time, it was immediately obvious, even to an infrequent VR user, that the wireless, room-tracking headset was the future of the company’s VR business. Now, it’s almost hard to believe there was a time when Meta’s headsets weren’t fully untethered.

Orion has the potential to be much bigger. Now, Meta isn’t just trying to create a more convenient form factor for mixed reality hobbyists and gamers. It’s offering a glimpse into how it views the future, and what our lives might look like when we’re no longer tethered to our phones.

For now, Orion is still just that: a glimpse. It’s far more complex than anything the company has attempted with VR. Meta still has a lot of work to do before that AR-enabled future can be a reality. But the prototype shows that much of that vision is closer than we think.

As 2024 nears its conclusion, the state of play in enterprise technology is that companies of all sizes and domains are keen to leverage their data in generative AI applications that improve internal (employee-facing) or external (cusomter/partner-facing) processes.

However, ensuring that they do so securely is another challenge — especially for companies that don’t specialize in security. For many enterprises, their existing security solutions may also be inadequate or unprepared for the AI era and the many capabilities they want to unleash with their data piped through AI.

Enter Noma, an Israeli startup specializing in AI enterprise security, that today exits stealth mode wih a Series A round led by $32 million Ballistic Ventures and supported by Glilot Capital Partners and Cyber Club London, as well as angels including the chief information security officers (CISOs) from companies like McDonald’s, Google DeepMind, Twitter, Atlassian, BNP Paribas, T-Mobile, and Nielsen.

Noma provides a comprehensive security platform that ensures integrity of enterprise customer’s data from the very start, before they do anything to it, all the way through to leveraging it to train and/or deploy AI models and custom applications.

The platform is already in use by several Fortune 500 companies.

Tackling security challenges in the data and AI landscape

Niv Braun, co-founder and CEO of Noma, told VentureBeat in an interview about the pressing need for targeted security in AI workflows. “

“Today’s AI and data science models face unique security risks, like prompt injection and data leakage, that simply aren’t covered by standard security tools,” he said.

These issues are becoming more common as organizations experience security incidents due to misconfigured MLOps tools and unverified open-source models.

This gap inspired Braun and his co-founder, Alon Tron, to create Noma.

“My co-founder Alon and I served together in the military, and we both saw firsthand the gap in security tools for data science and AI workflows,” Braun said. “In application security, we had tools that helped software engineers work securely, but for data teams—data scientists, engineers, and analysts—there was nothing similar. They were left unprotected.”

Both co-founders served in Israel’s elite 8200 intelligence unit. Combining expertise from their backgrounds in security and data science, they quickly a team skilled in AI and application security.

What Noma’s three-tiered platform offers

Noma’s platform is designed to safeguard every stage of AI model development and operation, incorporating security tools that cover:

1. Data & AI Supply Chain Security: Ensures secure environments, pipelines, and development tools, mitigating the risk of compromised data and AI supply chains.
2. AI Security Posture Management (AI-SPM): Provides a comprehensive inventory and security management solution for both first- and third-party AI models, aiming to protect assets before they enter production.
3. AI Threat Detection & Response: Actively monitors AI applications to detect adversarial attacks in real-time and enforce safety protocols during runtime.

Braun emphasized the consolidation that Noma’s platform offers to customers. “Our platform includes three products: data and AI supply chain security, AI security posture management, and AI runtime defense.”

But, for those that wish, each of the three domains can be applied ad-hoc, a-la-carte.

“A major strength of our platform is that it consolidates everything into one solution,” Braun explained. “While customers can choose just one part, most prefer the comprehensive approach.”

Braun clarified that Noma offers a choice between an all-inclusive enterprise license and a modular, product-based option, both on an annual software-as-a-service (SaaS) subscription basis. He said 95% of customers have so far chosen the integrated, all-in-one approach.

Braun’s comments suggest that the enterprise license is positioned as the most cost-effective, flexible choice for customers looking for extensive, organization-wide access to Noma’s solutions.

Maximum flexibility and ease-of-use

Noma’s platform is compatible with diverse environments, supporting cloud-based, SaaS, or self-hosted configurations, and installs within minutes without requiring code changes.

“Integration is easy,” said Braun. “All customers need to do is connect our platform via API, and we automatically map and scan everything in their environment.”

This frictionless setup means data science teams can implement security controls without disrupting their workflows, a feature that Noma highlights as essential in high-velocity, AI-powered development.

Kobi Samboursky, Founder and Managing Partner at Glilot Capital Partners, extolled the value of Noma’s unified approach in a press release: “AppSec evolved over decades with fragmented tools for static and dynamic analysis, open source, supply chain, and runtime. Security teams have come to realize that they need consolidated solutions. Noma is uniquely positioned to tackle this problem from the start, consolidating multiple use cases into a single platform.”

In addition, Noma can be applied by those without extensive training in security or data infrastructure.

“We engage with both data and AI teams as well as security teams, and our platform doesn’t require deep expertise in either field,” he said. “Even in cases where security teams ran POCs (proof of concepts) without data science teams involved, they found it easy to integrate and use.””

At the same time, the platform turns these subjects into digestible, easy-to-understand insights for employees working in all departments.

“The platform itself is very self-educating,” Braun noted. “It explains the basic principles of security in a way that application security teams are familiar with, but with a new ‘data and AI’ layer.”

Addressing industry wants and needs

As security and compliance become more critical in AI adoption, Noma aims to facilitate collaboration between data science and security teams.

“Our mission is to bridge the gap between data science and security teams, making it easy for both to collaborate on securing AI workflows,” Braun said.

Noma’s approach is designed to improve transparency and simplify security processes.

“We make security simple for both teams, providing clear, understandable risk information and steps for remediation,” he added. “It’s all about reducing friction and improving collaboration.”

Jake Seid, Co-founder and General Partner at Ballistic Ventures, emphasizes the importance of security from the outset in a statement in a press release.

“As security and compliance become more top of mind for organizations adopting AI, embedding security from the start ensures that innovation can flourish without compromise,” Seid said. “Noma’s approach gives AppSec teams full visibility and confidence while empowering data science teams to move fast and drive business value.”

Noma’s ambitions are to lead the emerging field

Noma’s entry into the market marks a significant step in securing AI-driven business operations at scale.

With the growing use of AI in critical applications, the potential for security vulnerabilities in AI workflows becomes more acute.

Noma’s platform provides a much-needed safeguard, allowing enterprises to harness AI’s potential without compromising on security.

In addition, Noma is actively contributing to AI security standards and has participated in the development of U.S. government guidelines, such as NIST SP 800-218A, through its involvement with the OWASP AI Exchange.

With $32 million in fresh funding and early traction among high-profile customers, Noma seeks to become a leader in the emerging field of data and AI lifecycle security.

Eight years on from its initial launch, Amazon has introduced multi-factor authentication (MFA) to its business cloud-hosted email service, WorkMail.

Better late than never appears to be the justification behind the near-decade delay, especially for one of the most basic forms of identity verification that has been standard practice for several years now.