Computerworld.com [Hacking News]

France’s OVHcloud is moving beyond cloud infrastructure into frontier AI model development, a shift that could test whether Europe can produce another serious alternative to US and Chinese AI systems.

The company, one of Europe’s leading homegrown cloud providers, plans to train a family of models from scratch and aims to open-source them once they meet its performance targets, CEO Octave Klaba told Reuters.

The move would put OVHcloud in closer comparison with Mistral AI, the Paris-based model developer that has become Europe’s most visible challenger to US AI labs.

Klaba said the economics of building advanced AI models have changed, with improvements in chips, training methods, and synthetic data reducing the cost of a project that may once have required about $1.15 billion (€1 billion) to now cost less than $230 million (€200 million).

Reuters reported that OVHcloud said one of its models has completed pre-training on Jupiter, the Germany-based EuroHPC supercomputer described as Europe’s fastest and its first exascale system, though the company has not yet disclosed detailed performance benchmarks.

This comes as European governments and enterprises are increasingly having to assess AI infrastructure through the lens of data governance and continuity of access, rather than performance alone.

Those concerns were sharpened this month after Anthropic said a US government export-control directive required it to suspend access to its Fable 5 and Mythos 5 models by foreign nationals inside and outside the US.

Training is only the opening cost

OVHcloud’s lower cost estimate does not capture the full cost of becoming a frontier AI model provider, said Neil Shah, vice president for research and partner at Counterpoint Research.

The $230 million (€200 million) figure likely refers mainly to the initial training run, Shah said. Once trained, however, models require continued investment because they can become depreciating assets if they are not improved with fresh data.

OVHcloud would also need to spend on fine-tuning, post-training, sovereign infrastructure, storage, security, distribution, and enterprise support. It would also need enough scale to make model serving economically viable against established AI providers such as Google and Anthropic.

“Model is seen as a depreciating asset if it is not consistently trained and kept fresh with the data,” Shah said.

That makes OVHcloud’s plan a test not only of technical capability, but also of policy support and economic viability. If the company falls short, enterprises may be reluctant to shift workloads away from more established models.

The lower training cost could still give OVHcloud a credible starting point, said Charlie Dai, principal analyst at Forrester.

The budget range can be enough to produce a credible frontier model as efficiency gains reduce the cost of entry, Dai said. But enterprise competitiveness will depend on sustained capabilities beyond training, including inference efficiency, data pipelines, evaluation frameworks, and ecosystem reach.

Buyers need proof

OVHcloud’s plan remains an expression of intent rather than demonstrated capability, said Sanchit Vir Gogia, chief analyst at Greyhound Research, pointing to the absence of published benchmarks and other details.

“$200 million now buys a serious training run,” Gogia said. “It does not buy a serious enterprise AI franchise.”

Gogia said questions around sovereignty also extend to the infrastructure used to train the model, noting that pre-training was run on Jupiter rather than on infrastructure owned or controlled by OVHcloud.

The system is a publicly owned European supercomputer in Germany that runs on American silicon, Gogia said, adding that this shows how partial European AI sovereignty remains.

CIOs will need evidence that the models can be supported in production, governed effectively, audited when needed, and exited without major disruption.

Gogia said a European-owned model could reduce some dependence on US and Chinese providers, but would not remove jurisdictional risk. “Sovereignty does not abolish the off switch,” he said. “It changes whose hand rests upon it.”

OVHcloud’s move into model development could also alter the lock-in risks enterprises need to assess, Gogia said. Customers may be able to move cloud infrastructure later, but find it harder to shift AI workloads once applications and processes are built around a provider’s models and governance tools.

What a long, strange trip it’s been.

From its inaugural release to today, Android has transformed visually, conceptually and functionally — time and time again. Google’s mobile operating system may have started out scrappy, but holy moly, has it ever evolved.

Here’s a fast-paced tour of Android version highlights from the platform’s birth to present. (Feel free to skip ahead if you just want to see what’s new in the most recent Android 17 update.)

Android versions 1.0 to 1.1: The early days

Android made its official public debut in 2008 with Android 1.0 — a release so ancient it didn’t even have a cute codename.

Things were pretty basic back then, but the software did include a suite of early Google apps like Gmail, Maps, Calendar, and YouTube, all of which were integrated into the operating system — a stark contrast to the more easily updatable standalone-app model employed today.

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The Android 1.0 home screen and its rudimentary web browser (not yet called Chrome).

T-Mobile

Android version 1.5: Cupcake

With early 2009’s Android 1.5 Cupcake release, the tradition of Android version names was born. Cupcake introduced numerous refinements to the Android interface, including the first on-screen keyboard — something that’d be necessary as phones moved away from the once-ubiquitous physical keyboard model.

Cupcake also brought about the framework for third-party app widgets, which would quickly turn into one of Android’s most distinguishing elements, and it provided the platform’s first-ever option for video recording.

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Cupcake was all about the widgets.

Android Police Android version 1.6: Donut

Android 1.6, Donut, rolled into the world in the fall of 2009. Donut filled in some important holes in Android’s center, including the ability for the OS to operate on a variety of different screen sizes and resolutions — a factor that’d be critical in the years to come. It also added support for CDMA networks like Verizon, which would play a key role in Android’s imminent explosion.

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Android’s universal search box made its first appearance in Android 1.6.

Google

Android versions 2.0 to 2.1: Eclair

Keeping up the breakneck release pace of Android’s early years, Android 2.0, Eclair, emerged just six weeks after Donut; its “point-one” update, also called Eclair, came out a couple months later. Eclair was the first Android release to enter mainstream consciousness thanks to the original Motorola Droid phone and the massive Verizon-led marketing campaign surrounding it.

Verizon’s “iDon’t” ad for the Droid.

The release’s most transformative element was the addition of voice-guided turn-by-turn navigation and real-time traffic info — something previously unheard of (and still essentially unmatched) in the smartphone world. Navigation aside, Eclair brought live wallpapers to Android as well as the platform’s first speech-to-text function. And it made waves for injecting the once-iOS-exclusive pinch-to-zoom capability into Android — a move often seen as the spark that ignited Apple’s long-lasting “thermonuclear war” against Google.

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The first versions of turn-by-turn navigation and speech-to-text, in Eclair.

Google

Android version 2.2: Froyo

Just four months after Android 2.1 arrived, Google served up Android 2.2, Froyo, which revolved largely around under-the-hood performance improvements.

Froyo did deliver some important front-facing features, though, including the addition of the now-standard dock at the bottom of the home screen as well as the first incarnation of Voice Actions, which allowed you to perform basic functions like getting directions and making notes by tapping an icon and then speaking a command.

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Google’s first real attempt at voice control, in Froyo.

Google

Notably, Froyo also brought support for Flash to Android’s web browser — an option that was significant both because of the widespread use of Flash at the time and because of Apple’s adamant stance against supporting it on its own mobile devices. Apple would eventually win, of course, and Flash would become far less common. But back when it was still everywhere, being able to access the full web without any black holes was a genuine advantage only Android could offer.

Android version 2.3: Gingerbread

Android’s first true visual identity started coming into focus with 2010’s Gingerbread release. Bright green had long been the color of Android’s robot mascot, and with Gingerbread, it became an integral part of the operating system’s appearance. Black and green seeped all over the UI as Android started its slow march toward distinctive design.

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It was easy being green back in the Gingerbread days.

JR Raphael / IDG

Android 3.0 to 3.2: Honeycomb

2011’s Honeycomb period was a weird time for Android. Android 3.0 came into the world as a tablet-only release to accompany the launch of the Motorola Xoom, and through the subsequent 3.1 and 3.2 updates, it remained a tablet-exclusive (and closed-source) entity.

Under the guidance of newly arrived design chief Matias Duarte, Honeycomb introduced a dramatically reimagined UI for Android. It had a space-like “holographic” design that traded the platform’s trademark green for blue and placed an emphasis on making the most of a tablet’s screen space.

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Honeycomb: When Android got a case of the holographic blues.

JR Raphael / IDG

While the concept of a tablet-specific interface didn’t last long, many of Honeycomb’s ideas laid the groundwork for the Android we know today. The software was the first to use on-screen buttons for Android’s main navigational commands; it marked the beginning of the end for the permanent overflow-menu button; and it introduced the concept of a card-like UI with its take on the Recent Apps list.

Android version 4.0: Ice Cream Sandwich

With Honeycomb acting as the bridge from old to new, Ice Cream Sandwich — also released in 2011 — served as the platform’s official entry into the era of modern design. The release refined the visual concepts introduced with Honeycomb and reunited tablets and phones with a single, unified UI vision.

ICS dropped much of Honeycomb’s “holographic” appearance but kept its use of blue as a system-wide highlight. And it carried over core system elements like on-screen buttons and a card-like appearance for app-switching.

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The ICS home screen and app-switching interface.

JR Raphael / IDG

Android 4.0 also made swiping a more integral method of getting around the operating system, with the then-revolutionary-feeling ability to swipe away things like notifications and recent apps. And it started the slow process of bringing a standardized design framework — known as “Holo” — all throughout the OS and into Android’s app ecosystem.

Android versions 4.1 to 4.3: Jelly Bean

Spread across three impactful Android versions, 2012 and 2013’s Jelly Bean releases took ICS’s fresh foundation and made meaningful strides in fine-tuning and building upon it. The releases added plenty of poise and polish into the operating system and went a long way in making Android more inviting for the average user.

Visuals aside, Jelly Bean brought about our first taste of Google Now — the spectacular predictive-intelligence utility that’s sadly since devolved into a glorified news feed. It gave us expandable and interactive notifications, an expanded voice search system, and a more advanced system for displaying search results in general, with a focus on card-based results that attempted to answer questions directly.

Multiuser support also came into play, albeit on tablets only at this point, and an early version of Android’s Quick Settings panel made its first appearance. Jelly Bean ushered in a heavily hyped system for placing widgets on your lock screen, too — one that, like so many Android features over the years, quietly disappeared a couple years later.

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Jelly Bean’s Quick Settings panel and short-lived lock screen widget feature.

JR Raphael / IDG

Android version 4.4: KitKat

Late-2013’s KitKat release marked the end of Android’s dark era, as the blacks of Gingerbread and the blues of Honeycomb finally made their way out of the operating system. Lighter backgrounds and more neutral highlights took their places, with a transparent status bar and white icons giving the OS a more contemporary appearance.

Android 4.4 also saw the first version of “OK, Google” support — but in KitKat, the hands-free activation prompt worked only when your screen was already on and you were either at your home screen or inside the Google app.

The release was Google’s first foray into claiming a full panel of the home screen for its services, too — at least, for users of its own Nexus phones and those who chose to download its first-ever standalone launcher.

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The lightened KitKat home screen and its dedicated Google Now panel.

JR Raphael / IDG

Android versions 5.0 and 5.1: Lollipop

Google essentially reinvented Android — again — with its Android 5.0 Lollipop release in the fall of 2014. Lollipop launched the still-present-today Material Design standard, which brought a whole new look that extended across all of Android, its apps and even other Google products.

The card-based concept that had been scattered throughout Android became a core UI pattern — one that would guide the appearance of everything from notifications, which now showed up on the lock screen for at-a-glance access, to the Recent Apps list, which took on an unabashedly card-based appearance.

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Lollipop and the onset of Material Design.

JR Raphael / IDG

Lollipop introduced a slew of new features into Android, including truly hands-free voice control via the “OK, Google” command, support for multiple users on phones and a priority mode for better notification management. It changed so much, unfortunately, that it also introduced a bunch of troubling bugs, many of which wouldn’t be fully ironed out until the following year’s 5.1 release.

Android version 6.0: Marshmallow

In the grand scheme of things, 2015’s Marshmallow was a fairly minor Android release — one that seemed more like a 0.1-level update than anything deserving of a full number bump. But it started the trend of Google releasing one major Android version per year and that version always receiving its own whole number.

Marshmallow’s most attention-grabbing element was a screen-search feature called Now On Tap — something that, as I said at the time, had tons of potential that wasn’t fully tapped. Google never quite perfected the system and ended up quietly retiring its brand and moving it out of the forefront the following year.

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Marshmallow and the almost-brilliance of Google Now on Tap.

JR Raphael / IDG

Android 6.0 did introduce some stuff with lasting impact, though, including more granular app permissions, support for fingerprint readers, and support for USB-C.

Android versions 7.0 and 7.1: Nougat

Google’s 2016 Android Nougat releases provided Android with a native split-screen mode, a new bundled-by-app system for organizing notifications, and a Data Saver feature. Nougat added some smaller but still significant features, too, like an Alt-Tab-like shortcut for snapping between apps.

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Android 7.0 Nougat and its new native split-screen mode.

JR Raphael / IDG

Perhaps most pivotal among Nougat’s enhancements, however, was the launch of the Google Assistant — which came alongside the announcement of Google’s first fully self-made phone, the Pixel, about two months after Nougat’s debut. The Assistant would go on to become a critical component of Android and most other Google products and is arguably the company’s foremost effort today.

Android version 8.0 and 8.1: Oreo

Android Oreo added a variety of niceties to the platform, including a native picture-in-picture mode, a notification snoozing option, and notification channels that offer fine control over how apps can alert you.

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Oreo adds several significant features to the operating system, including a new picture-in-picture mode.

JR Raphael / IDG

The 2017 release also included some noteworthy elements that furthered Google’s goal of aligning Android and Chrome OS and improving the experience of using Android apps on Chromebooks, and it was the first Android version to feature Project Treble — an ambitious effort to create a modular base for Android’s code with the hope of making it easier for device-makers to provide timely software updates.

Android version 9: Pie

The freshly baked scent of Android Pie, a.k.a. Android 9, wafted into the Android ecosystem in August of 2018. Pie’s most transformative change was its hybrid gesture/button navigation system, which traded Android’s traditional Back, Home, and Overview keys for a large, multifunctional Home button and a small Back button that appeared alongside it as needed.

Android 9 introduced a new gesture-driven system for getting around phones, with an elongated Home button and a small Back button that appears as needed.

JR Raphael / IDG

Pie included some noteworthy productivity features, too, such as a universal suggested-reply system for messaging notifications, a new dashboard of Digital Wellbeing controls, and more intelligent systems for power and screen brightness management. And, of course, there was no shortage of smaller but still-significant advancements hidden throughout Pie’s filling, including a smarter way to handle Wi-Fi hotspots, a welcome twist to Android’s Battery Saver mode, and a variety of privacy and security enhancements.

Android version 10

Google released Android 10 — the first Android version to shed its letter and be known simply by a number, with no dessert-themed moniker attached — in September of 2019. Most noticeably, the software brought about a totally reimagined interface for Android gestures, this time doing away with the tappable Back button altogether and relying on a completely swipe-driven approach to system navigation.

Android 10 packed plenty of other quietly important improvements, including an updated permissions system with more granular control over location data along with a new system-wide dark theme, a new distraction-limiting Focus Mode, and a new on-demand live captioning system for any actively playing media.

Android 10’s new privacy permissions model adds some much-needed nuance into the realm of location data.

JR Raphael / IDG

Android version 11

Android 11, launched at the start of September 2020, was a pretty substantial Android update both under the hood and on the surface. The version’s most significant changes revolve around privacy: The update built upon the expanded permissions system introduced in Android 10 and added in the option to grant apps location, camera, and microphone permissions only on a limited, single-use basis.

Android 11 also made it more difficult for apps to request the ability to detect your location in the background, and it introduced a feature that automatically revokes permissions from any apps you haven’t opened lately. On the interface level, Android 11 included a refined approach to conversation-related notifications along with a new streamlined media player, a new Notification History section, a native screen-recording feature, and a system-level menu of connected-device controls.

Android 11’s new media player appears as part of the system Quick Settings panel, while the new connected-device control screen comes up whenever you press and hold your phone’s physical power button.

JR Raphael / IDG

Android version 12

Google officially launched the final version of Android 12 in October 2021, alongside the launch of its Pixel 6 and Pixel 6 Pro phones.

In a twist from the previous several Android versions, the most significant progressions with Android 12 were mostly on the surface. Android 12 featured the biggest reimagining of Android’s interface since 2014’s Android 5.0 (Lollipop) version, with an updated design standard known as Material You — which revolves around the idea of you customizing the appearance of your device with dynamically generated themes based on your current wallpaper colors. Those themes automatically change anytime your wallpaper changes, and they extend throughout the entire operating system interface and even into the interfaces of apps that support the standard.

Android 12 ushered in a whole new look and feel for the operating system, with an emphasis on simple color customization.

Google

Surface-level elements aside, Android 12 brought a (long overdue) renewed focus to Android’s widget system along with a host of important foundational enhancements in the areas of performance, security, and privacy. The update provided more powerful and accessible controls over how different apps are using your data and how much information you allow apps to access, for instance, and it included a new isolated section of the operating system that allows AI features to operate entirely on a device, without any potential for network access or data exposure.

Android version 13

Android 13, launched in August 2022, was simultaneously one of the most ambitious updates in Android history and one of the most subtle version changes to date.

On tablets and foldable phones, Android 13 introduced a slew of significant interface updates and additions aimed at improving the large-screen Android experience — including an enhanced split-screen mode for multitasking and a ChromeOS-like taskbar for easy app access from anywhere.

The new Android-13-introduced taskbar, as seen on a Google Pixel Fold phone.

Google

On regular phones, Android 13 brought about far less noticeable changes — mostly just some enhancements to the system clipboard interface, a new native QR code scanning function within the Android Quick Settings area, and a smattering of under-the-hood improvements.

Android version 14

Following a full eight months of out-in-the-open refinement, Google’s 14th Android version landed at the start of October 2023, in the midst of the company’s Pixel 8 and Pixel 8 Pro launch event.

Like the version before it, Android 14 didn’t look like much on the surface. That’s in part because of the trend of Google moving more and more toward a development cycle that revolves around smaller ongoing updates to individual system-level elements year-round — something that’s actually a significant advantage for Android users, even if it does have an awkward effect on people’s perception of progress.

But despite the subtle nature of its first impression, Android 14 delivered a fair amount of noteworthy new goodies. The software introduced a new system for dragging and dropping text between apps, for instance, as well as a number of new improvements to privacy and security — including a new settings-integrated dashboard for managing health and fitness data and a more info-rich and context-requiring system for seeing exactly why apps want access to your location. And it brought about a new set of native customization options for the Android lock screen.

Android 14 includes options for completely changing the appearance of the lock screen as well as for customizing which shortcuts show up on it.

JR Raphael / IDG

Android version 15

Though Android 15 followed the trend of significant advancements arriving as their own separate rollouts — outside of and even ahead of its arrival, as an official operating system update — 2024’s new Android version was certainly no slouch.

The software introduced a number of noteworthy new features — including a redesigned system volume panel, an option to automatically re-enable a device’s Bluetooth radio a day after it’s been disabled, and a Pixel-specific Adaptive Vibration feature that intelligently adjusts a phone’s vibration intensity based on the environment. It also marked the debut of a system-level Private Space area that lets you keep sensitive apps out of sight and accessible only with authentication.

Once you set up Android 15’s new Private Space feature, certain apps appear in a special protected — and optionally hidden — area of your app drawer.

JR Raphael / IDG

Add in handy touches like a space-saving app archiving option and a predictive back visual that lets you sneak a peek at where you’re headed before you get there, and this small-seeming update shaped up to be a pretty hefty progression.

Android version 16

In a marked change from recent Android upgrade cycles, Google decided to go with two new Android versions per year as of 2025 — starting with Android 16 in the spring and then following that with a smaller release in the fall.

True to that promise, Android 16 catapulted into the world in early June, creating the framework for future-facing systems such as Live Updates — a new type of notification designed to support persistent, ongoing alerts, similar to what Apple does with iOS’s Live Activities — and introducing an Advanced Protection security supermode that provides a simple single-switch way to activate a whole slew of advisable Android security settings in one fell swoop.

The Android 16 Advanced Security control panel, as seen on a Google Pixel phone.

JR Raphael, Foundry

The update included a sprawling series of other new security strengtheners, too, making protection seem like the true centerpiece of Android 16 — even if other touches, such as a more advanced standard for hearing aid support, helped flesh out the software into a rounded and feature-rich release.

Android version 17

With its relatively low-key arrival in June 2026, Android 17 officially brings the long under-development Bubbles multitasking system to the Android-owning masses — adding an interesting new way to keep any app available on demand in a floating, collapsible window for easy ongoing access.

Android 17’s Bubbles offers a whole new way to think about multitasking.

JR Raphael, Foundry

Speaking of bubbliness, Android 17 also includes the creator-aimed option of showing a cutout of your face from a front-facing camera over an active screen recording — because why not, right? — along with such practical touches as a more dynamic and consistent system-wide dark mode and a more nuanced and effective way to track and control app location access.

Managing app location access is extra easy and powerful in Android 17.

JR Raphael, Foundry

While those features and the inevitable slew of under-the-hood security, performance, and privacy improvements add up to form a compelling final picture, it’s hard not to notice that much of Google’s focus in this era is now on the AI layers surrounding Android as opposed to being on Android itself, as an operating system. The company’s I/O conference in May showcased many such measures, appropriately noting that Android was transitioning from being “an operating system” into being “an intelligence system” (whatever that means).

Most of those “intelligence system” items remain limited in ability or not yet available as of the time of Android 17’s release — like the new and improved speech-to-text system for Gboard, the custom-widget-creating system for Android phones, and the multistep automation system for allowing AI to complete complex tasks on your behalf (assuming that you (a) trust such a system to act on your behalf and (b) don’t find the level of access and resulting manner of assumptions it makes about your life to be overly creepy).

But even at its foundational level and without any AI-laden Halo effect included, Android 17 manages to hold its own — with Bubbles acting as an anchor and bringing some much-appreciated new productivity potential our way.

This article was originally published in November 2017 and most recently updated in June 2026.

Because generative AI (genAI) tools and services have become so ubiquitous (and popular), the costs of using them are going through the roof — leading to an insatiable appetite for tokens.

Tokens represent a common way to measure and price AI use. Much like letters and words in English, large language models (LLMs) grasp a sentence or query by breaking words into tokens.

With the AI explosion well under way, tokens are now “the fundamental units of data our models process, many representing a problem being solved,” according to Google CEO Sundar Pichai. (Google, by the way, processes about 3.2 quadrillion tokens a month.)

But as the price of all those tokens adds up, business and IT execs are looking for ways to cut costs while keeping corporate productivity up. Uncontrolled token use has already landed one company with an unexpected $500 million AI bill. 

There are a number of ways companies can rein in the price of AI at the model, infrastructure, silicon, and business levels. Here’s a look at how some of those savings might actually be achieved.

Switch to lower-cost models

One way of potentially saving money is by re-routing AI work to a cheaper model, Pichai said. At Google that would Gemini 3.5 Flash. It delivers “frontier-level capabilities at less than half the price of comparable frontier models.

“If companies use a mix of [Gemini 3.5] Flash and other frontier models, they could save a lot of money,” Pichai said.

Those kinds of models provide cheaper tokens, with reasoning that’s good enough for many users — if not as strong as mainstream Gemini 3.5 — to deliver useful results.

“There is sometimes overkill with the [LLMs],” said Deepak Seth, senior director analyst at Gartner. “I don’t always need a large language model which has been trained on the works of Charles Dickens and Shakespeare and Harry Potter.”

Hyperframe Research principal analyst Steven Dickens can’t stop using Amazon’s Quick, which costs $20 a month, for personal tasks. “It is great personal ROI as it has not only made tasks faster, but unlocked tasks I would never have even attempted previously,” Dickens said.

Don’t forget the hardware and software part of the equation

The token crisis isn’t new, said Dheeraj Pandey, CEO of DevRev, who likens what’s going on now in the AI market to the disruptions that emerged with the arrival of cloud computing and virtualization years ago. 

“We let chaos reign and then we had to rein in the chaos,” Pandey said. “The word that people started using was server consolidation and virtualization.”

The answer to the token problem, he said, is the same: “Anything in systems can be solved with caching and indirection.”

DevRev, for example, is building a memory layer between AI agents and primary data sources, such as Salesforce or ERP records; that can cut token load and make data movement more efficient. The layer holds a knowledge graph with answers to common agent questions and runs on cheaper CPUs, avoiding more costly GPU cycles.

Sending agents straight at systems like ServiceNow and Salesforce “will burn a lot more tokens. It’s also not precise. And finally, it’s not safe enough where I can roll it back in case an agent has committed a mistake,” Pandey said.

Network automation firm NetBrain uses a different method: It uses conventional computing to map a network’s layout then feeds only key information to models for planning and reasoning, where AI excels. “So you don’t have to spend all the tokens,” said Netbrain CTO Song Pang.

Focus on prompt efficiency

Staffing firm ManpowerGroup has found that prompt efficiency can be an effective tool for improving token use, both internally and externally for clients.

For example, users accessing its internal labor-market tool initially needed 10 follow-up questions to drill into a query. A year later, more efficient use of prompts has brought that number down to an average of four, said Max Leaming, head of data science and AI solutions at ManpowerGroup. 

“They’re using fewer tokens and they’re simply more efficient,” he said. “And that in large part has to do with your ability to prompt efficiently.”

Go local

New AI hardware that generates free tokens at home could ease some of the cost crisis.

At GTC Taipei earlier this month, Nvidia and Microsoft unveiled RTX Spark, an agentic AI desktop PC that runs agents and 120-billion-parameter models locally on Windows. The goal is “to deliver unmetered intelligence to every home and every desk with Windows,” Microsoft CEO Satya Nadella said in a statement.

Some companies are looking to reduce cloud AI costs by putting their own hardware in data centers, with vendors such as HPE and Dell providing servers installed in independent facilities. (On-premise AI is gaining ground amid sovereign AI and geopolitical concerns, including the recent conflict in the Middle East, where large data centers were struck with missiles.)

“There are local, region-specific and multiple vendor AI solutions. All of those things can help mitigate the risk. But they’re not going to eliminate it,” said Max Goss, senior director analyst at Gartner.

Use forward-deployed engineers

Reducing token costs is something that may fall to forward-deployed engineers (FDEs) in customer environments, said Taimur Rashid, managing director of AWS’s Generative AI Innovation Center.

“I expect these teams to be able to architect systems that have those cost requirements in mind, whether it’s use a different model or a different use case that doesn’t increase the per-token cost,” Rashid said.

Companies may spend heavily on token consumption, “but if you’re generating revenue, as long as the economics work out, then you’re at peace,” Rashid said.

The use of FDEs is gaining ground as IT decision-makers look to both rollout successful AI deployments while also keeping an eye on costs.

Change the measure of success from tokens to outcomes

Even with the current emphasis on reducing token use to save money, the metrics used to measure AI success are likely to shift, Gartner’s Seth said. At some point, token-based pricing will move more toward an outcome-based model, where the unit of value is outcomes, not fragments of words.

“Some companies are moving towards outcome-based pricing,” Seth said. “When people start realizing the real cost of tokens, then companies will start looking at token efficiency.”

A federal judge has rebuffed Workday’s claim that it cannot be held liable under California anti-discrimination laws when its tools are used to screen (and potentially reject) job candidates in other states.

This week, US District Judge Rita Lin indicated that she will likely allow additional state discrimination claims against Workday to move forward. This would significantly expand the closely-watched case and likely ratchet up scrutiny of AI recruiting tools and their potentially inherent biases when it comes to age, race, sex, disabilities, and other factors.

Further, it could indicate that, even if a company is not the final employer, it may be held liable if its tools materially influence who gets rejected. This could set new legal standards for AI hiring systems, and have implications across industries, experts note.

“This case reinforces the importance of actually managing AI risks,” said Valence Howden, advisory fellow at Info-Tech Research Group. “If an AI-enabled model or ATS [Applicant Tracking System] is making decisions based on historical information, it can raise questions about whether bias in outcomes and datasets has been properly addressed.”

The case so far

Mobley v. Workday, Inc. alleges that Workday’s AI screening tools discriminate against job seekers based on age, race, and disability. The suit was filed in 2024 in the US District Court of California by Derek Mobley, a Black disabled man over 40, who claimed Workday’s algorithms continually screened him out as he applied for more than 100 positions on the platform.

The claims alleged discrimination prohibited by several US and California statutes: Race and sex under the Civil Rights Act of 1964 (Title VII); disability under the Americans with Disabilities Act of 1990 (ADA); age under the Age Discrimination in Employment Act of 1967 (ADEA); and race, gender, and age under California Fair Employment and Housing Act (FEHA).

Specifically, the suit centered around Workday’s use of automated, algorithm-driven tools for applicant screening. It alleged that these systems rely on historical data and statistical modeling that can make them susceptible to existing biases, even if protected characteristics like race, age, sex, or disability are not explicitly provided.

Bias may enter these systems in different ways, the plaintiffs argued, including via training data, model design, and evaluation criteria for candidate fit. The system could reproduce discriminatory outcomes by making correlations from data. For instance, years of experience on a resumé may indicate age; long employment gaps may infer a disability or caregiving responsibilities; educational and institutional affiliations could reflect race.

Workday has argued that it is not subject to liability under employment statutes because it does not qualify as the job applicants’ “employer.” But federal judges have allowed key parts of the lawsuit to move forward, ruling that Workday could potentially be treated as an employer’s “agent” for the purposes of anti-discrimination law.

The latest dispute centers on FEHA. According to legal sources, the California statute is among the strongest anti-discrimination laws in the US, in many cases providing broader protections than federal employment laws.

Workday asked the court to dismiss claims brought under California law, saying FEHA should not apply to the hiring decisions of out-of-state employers and applicants. The company’s lawyers argued that enforcing this would effectively allow California law to supersede that of other states, just because a company used their platform.

But Lin disagreed, saying FEHA does apply, and in fact, Workday is directly liable for its “own engagement in FEHA-regulated activities on the employer’s behalf.” Holding businesses liable for “their own discriminatory conduct” is within the scope and purposes of FEHA and consistent with public policy.

However, the issue is still to be decided; Lin did not indicate when she would release a final ruling.

Workday’s defense

A Workday spokesperson called the claims in the suit “false.”

“Workday’s AI recruiting tools don’t make hiring decisions and are designed with human oversight at their core,” the spokesperson told CIO. “Our technology looks only at job qualifications, not protected traits like race, age, or disability. We rigorously test our products as part of our responsible AI program to confirm our tools do not harm protected groups.”

Workday’s platform is meant to provide insights on how well a candidate’s qualifications match the requirements of a posted job, the company said. Those tools focus only on qualifications listed in a candidate’s application, which are compared to qualifications identified by the employer as important for the job.

Workday’s Chief Responsible AI Officer Kelly Trindel said its AI does not make employment decisions, automatically reject candidates, or determine who gets a job; further, she said, there is no evidence that the company’s tools result in harm to protected groups.

Trindel, who is former chief analyst of the Equal Employment Opportunity Commission (EEOC), leads a dedicated team composed of psychologists and PhD-level data scientists whose sole focus is to ensure that its AI is “responsible, fair, and ethical.” She said that the company’s AI systems undergo ongoing reviews throughout their lifecycle to help prevent unintended consequences, and Workday is “committed to accountability, transparency, and trust,” and invests “significant resources” into identifying and mitigating bias.

Further, she said, Workday has a company-wide commitment to ethical AI, and an independently-evaluated AI governance program based on standards from the National Institute of Standards and Technology (NIST) and the International Standards Organization (ISO).

“Workday builds AI to support people, not replace them, and this is of particular importance when it comes to hiring,” Trindel noted. Its platform is designed to help employers “manage high-volume processes more efficiently, surface relevant information, and reduce administrative work so teams can spend more time applying their expertise and judgement to hiring decisions.”

What this means for enterprise leaders

Workday isn’t alone in its legal challenges; other AI hiring tools are also being scrutinized over their methodologies, algorithms, and data-collecting practices. Eightfold, for one, is also facing a California class action lawsuit alleging that its tools unfairly rely on job candidates’ online data to predict whether they’d be a good fit for a position.

This means that enterprises, who are already feeling increased pressure to document hiring decisions, conduct AI bias audits, and maintain human oversight in recruitment and hiring, must be even more diligent in their vetting of AI tools.

Organizations must be actively defining how these recruitment tools should work, identifying bias in their algorithms, and setting up structures to test for bias across the tools’ decision-making logic, Info-Tech’s Howden advised.

“Validation of non-biased outcomes also needs to be active and ongoing, rather than a point-in-time exercise,” he said.

While Workday and others say human oversight is paramount, “it’s hard to incorporate humans into the process if the platform does the weeding out before humans have the ability to intervene,” Howden pointed out.

Discriminatory biases can exist in past hiring decisions, so it’s easy to forget that AI can “emulate and adapt those biases as part of its perspective,” he said. That includes how AI looks at language: Different cultures use different phrasing, and AI can capture that and use it to exclude candidates.

Ultimately, he called the case a “cautionary tale” illustrating how lightly some organizations have been treating AI risk. It also highlights the urgency involved in building out more advanced enterprise risk practices, “rather than relying on the limited capabilities they may have employed up until now.”

This article originally appeared on CIO.com.

For generations, technology export controls referred to the transfer of code to other countries. But that no longer works, as the latest Anthropic fight with the US Commerce Department makes clear. 

On Friday, Anthropic announced that it had received instructions from Commerce “to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected.” 

Technically, the Commerce letter doesn’t explicitly say that, but lawyers and consultants argue that, when combined with an earlier executive order declaring Anthropic a supply chain risk, that very well might be what it means.

What the Commerce letter says is that Anthropic needs a license to export Fable 5 and Mythos 5 (a “deemed export“), listing four circumstances in which that license would be required: “The sending or taking of the model out of the United States in any manner; The sending or taking of the model from one foreign country to another in any manner; Retransferring the model within a single foreign country; or the release of the model to a ‘foreign person’ in the United States or a foreign country.”

Restricts capabilities not code

Although moving a model has historically meant transferring the code, most experts argue that the definition has changed for all SaaS deployments, and could now be interpreted as referring to any access to the models.

“This is not just about data sovereignty anymore. It is about capability sovereignty, where governments want to control who has access to frontier AI capabilities, irrespective of who built it, where it is hosted, or who worked on it,” said Valence Howden, advisory fellow at Info-Tech Research Group.

“The reference to deemed exports is important, because traditionally that would apply to code, technology, or technical knowledge being transferred,” he said. “In this case, the thing crossing borders is not necessarily the model itself, but it is access to the capability. That is a significant shift, and signals the real intent behind the AI arms race. The focus is moving from controlling the technology to controlling access to the outcomes the technology can produce.”

Mark Rasch, a former federal prosecutor who specializes in legal technology issues, agreed. “I don’t need to have the code physically resident in order to take advantage of the capabilities of that code. Today, the location of the code is irrelevant.”

Practical challenges

There are two practical issues involved. The first is that a large part of Anthropic’s workforce is not US citizens, and some of them have direct access to the code for these models. 

But the potentially more daunting issue is that today it is difficult, if not impossible, to identify the citizenship of any AI user, which might force companies to assume that everyone might be unauthorized. 

In fact, said Yuri Goryunov, CIO of consulting firm Acceligence, “there is no way to check citizenship through an API call. Besides, three-quarters of Americans don’t have passports.”

Consultant Brian Levine, executive director of FormerGov, added that the issue will make life difficult for CIOs even if the Commerce position is viewed as dubious.

“Regardless of the strength of Commerce’s position, once it issues an ‘Is Informed’ letter, every unlicensed interaction with a foreign person becomes a potential violation, and the safest move is often to halt access until a licensing path exists,” he said.

This means that enterprise CIOs need to approach AI contracts with the knowledge that any government can now declare the product legally unavailable, with no notice. 

Sovereignty has climbed the stack

Howden said that this shift will force CIOs to strongly consider non-US AI models such as France’s Mistral or even China’s DeepSeek, “to reduce the concentration risk attached.” 

“There are hundreds out there that are very good. It is very easy to sit in the bubble of the four or five we know,” Howden said. “Enterprise CIOs think it’s a much more limited market than it really is.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, said that enterprise executives now need to take the significant import rule changes into account when selecting AI models. 

“The harder truth is that sovereignty has climbed the stack,” he said. “The wall no longer stands around the database, it now stands around the intelligence layer itself. Under the export-control frame, it covers the release of controlled technology or code to a foreign person, including one standing inside the United States. The border follows the person, not the parcel. Code is part of the doctrine, but it is not the whole of it.”

He added, “the difficulty is that the cited rules speak of technology and code, while the letter reaches for the model itself. A hosted model hands the user no weights and no code. It hands them inference, and inference is a capability, not a file.”

Ultimately, Gogia said, Anthropic’s decision to cut off model access to everyone immediately “was a rational answer to an impossible instruction. A frontier model can now vanish for reasons unconnected to uptime, price or performance. The same models that help secure systems can be withdrawn at the moment defenders most need them.”

This article originally appeared on CIO.com.

Adobe’s Firefly Graph is now available to Creative Cloud customers, offering a node-based workflow tool designed to help business create content at scale with generative AI (genAI). 

With Firefly Graph, users can connect multiple tools in visual workflow, with each “node” performing a specific task before passing its output to the next node. This gives creative professionals more control over generated outputs, according to Adobe, and makes it easier to try out ideas by swapping, adjusting or adding components.

For example, a user could start with a text prompt box that connects to a node that generates an image using an AI model from Adobe or third-parties such as Google and OpenAI. Further along the chain, the user could add nodes to remove a background or upscale an image, for instance, before producing an image, video or other asset ready for use.

Changing one aspect, such as adding a reference image or adapting the text prompt, would change the final output.

It’s an approach similar to node-based workflow tools such as ComfyUI — a startup valued at $500 million which claims more than 4 million users. Others include Weavy, acquired by Figma last year for a reported $200 million. 

With so many AI tools available to creative professionals, workflows can get complex and hard to replicate, said Elliot Sedegah, director for strategy and product marketing at Adobe. Firefly Graph provides access to more than 300 different node types, including images, video editing and AI generation tools across Adobe’s portfolio and third-party tools. 

“Whether you’re working at a mom-and-pop shop or a larger enterprise, you’re looking for consistency and then bringing that into a workflow so that you’re not hopping in and out of different tools,” he said. “Putting all that together takes massive amount of time, and sometimes it’s very difficult to even know what you did.”

Once created, workflows can be shared across an organization as repeatable processes for other individuals or teams to use. “Think of that rock star creative that you have and the recipes they create: those are now canonized as workflows, as assets, that the rest of the organization can take and reuse over and over again,” said Sedegah.

In addition, while creative professionals are needed to created high quality assets, reusable workflows can be put into the hands of broader teams to create content for large audiences, said Sedegah.

Firefly Graph addresses a challenge that most large creative organizations face, said Lisa Gately, principal analyst at Forrester — namely that their best creative workflows “live inside the heads of a few experts.

“Teams can generate images and video with AI, but reproducing the exact sequences of creative decisions, model selections, edits, and refinements that lead to a high-quality result is difficult and inconsistent. Firefly Graph turns those workflows into reusable assets,” she said.

While other node-based workflows aim to address similar problems, Adobe’s pitch is that Firefly Graph provides customers with the benefit of integration into its product suite. 

“Firefly is a full, broader AI creative studio, not just a node-based tool, so [Firefly Graph] is a part of a bigger picture,” said Sedegah. “The strength is having everything in one place with the tools that people know.” 

“Where Adobe differentiates is in enterprise integration,” said Gately, with Adobe connecting Firefly Graph to a range of other Adobe tools. Those include Creative Cloud applications; Firefly Boards for ideation; and Firefly Creative Production. 

“The workflow becomes part of a broader content supply chain instead of a standalone creation tool,” she said. ”Organizations committed to other tools are unlikely to migrate for a node-based canvas — making a change is about the broader content supply chain.”

Firefly Graph is available now to Adobe Creative Cloud for Enterprise subscribers. Creative Cloud for Enterprise is licensed by seat, requiring custom, volume-based enterprise agreements. Creative Cloud for Enterprise customers receive credits for Firefly Graph. Creative Cloud for teams customers can sign up for a public beta online. Creative Cloud pricing starts at $99.99 per license each month.

This article was updated on June 19 with pricing information from Adobe.

Beth Tschida, who became Jamf CEO in May after serving as CTO and as interim CEO, is the first woman to lead the company in its near 25-year history. I spoke with her this week at the London Jamf Nation event, where the company introduced its new AI Governance solution.

How the transition to CEO is going 

“It’s been a great privilege and an adjustment,” she said. “Jamf has always been a company deeply focused on culture, which is exactly why I love being here. Having the ability to influence and improve that culture from this role is something I feel very supported in doing.”

The last few years have seen a variety of changes at Jamf, which was briefly a public company. “We’ve come through a period of change, not all of it easy,” Tschida said. “But we now have a great partnership with Francisco Partners. We’re private, we’re focused on solving customer problems, and we’re finding ways to lean into what we’re good at.”

Women in tech and mentorship

Tschida is a good choice to lead a software engineering company, as she’s an engineer herself. She originally joined Jamf as vice president for software engineering in 2018, moving up to CTO in 2022. She’s also one of the few women in leadership positions in tech. (To Jamf’s credit, the company also has CIO Linh Lam on its team.)

“I think it’s important for women to stay deep in the tech, build their skills and find their voice confidently,” Tschida said. “You’ll never know all the technology out there. Nobody does. What matters is the ability to keep adapting and evolving.”

Tschida stressed the importance of mentorship. “I feel very honored to have a chance to be a role model for other women,” she said. “I had women who forged a path for me, including a female CIO early in my career who I asked to mentor me and learned an enormous amount from. I’m certainly not the first woman in tech, but I do want to play my part in helping others grow in their careers.

“Ultimately, I want to be respected for what I do, not for my gender. That’s how everyone should be judged.”

AI Governance

Tschida’s product focus means she knows what matters to Jamf. “If you focus on the problems customers have and how your product can help fix them, that’ll take you to where you want to go.”

For many in the enterprise, both in and beyond the Apple space, the next big problem is AI — how to deploy it, how to manage it, and how to regulate it.

AI Governance is a new Jamf solution that has been developed in response to those pain points. Countless surveys, including Jamf’s own data, show that AI is being widely used across every company, but IT lacks visibility into its use. It’s hard to know what data is being shared with AI tools, which services are being used, and how to report on that use effectively — particularly in regulated industries.

AI Governance is designed to make it possible for anyone managing an Apple fleet to get granular insight into AI use across their Mac, iPhone, and iPad devices. It uses telemetrics to shed light on that use, offers governance and management tools to help IT gain better oversight and control over it, and provides highly comprehensive reporting tools suitable for internal or regulatory review.

“AI is happening whether organizations know it or not,” said Tschida. “That’s the problem. You can try to block it, but that’s very hard to do well. It’s far better to build visibility and governance around it.”

The offering makes it possible for companies to enable the AI use they already know is taking place while protecting corporate interests and enabling fast and accurate reporting. You can find out more details here.

Jamf Empowering better AI

Jamf’s approach is focused on endpoint management. AI Governance means IT can see what’s running on a device, categorize it, and understand what AI tools and models are in use. “If you know how people are running AI on your fleet, you can open it up safely. Then all of your customers and employees can find their way to figure out how AI is going to optimize their workforce,” she said.

What does that look like in practice? Think of it as an orchestration layer. IT can define different AI configurations for different teams: HR might use one set of models, engineers another. And admins can apply opinionated postures per group: what models are permitted, what cloud services they connect to, what’s visible to IT versus the CISO versus the CFO. “It’s an extension of what Jamf has always done, it just now applies to AI endpoints too.”

What about regulatory complexity across geographies? “A lot of governance controls are shared across regulations; a good base set is a healthy way to run regardless. But each regulation has its own twists. Our mission is to make sure customers operating in different markets can expand on that base and fit the specific models and regulations they need, getting the right configurations to the right devices.”

Managers must prepare for AI cost challenges

There’s a second dimension beyond management — cost. The industry is developing quickly, with new AI models appearing almost every week. Yesterday’s leading LLM is tomorrow’s fading star, even while the cost of AI infrastructure goes through the roof. As that churn slows, investors will want to start seeing returns on their bets, which is why token costs — the price of running AI services, at least in the cloud — are climbing fast.  

As costs become more realistic, that’s going to change the nature of AI deployment from the laissez-faire, anything goes approach to a more strategic management of such use. “Models keep dropping fast, but token costs are only going to go up,” said Tschida.

“Organizations will need to decide: just because you can build something with AI, should you? What’s the right model for what work? We’re helping customers move from, ‘We’ll just block it’ or ‘We’ll turn it on and hope for the best,’ toward a place where they have a real viewpoint and can manage and change that viewpoint over time.”

The ever-changing AI world is also prompting Jamf to make more of its APIs externally available. “We’re used across every industry and every geography, at every scale,” said Tschida. “There’s no way we can build every workflow every customer needs, we’d never get to all of them.”

Embracing openness also helps build future foundations. “Thinking about where we’re heading next — agentic endpoint management — having platform APIs allows our customers to build things they can imagine, that we can learn from, in a way that solves their specific problems.”

Apple, WWDC, and the enterprise

Tschida’s comments come shortly after WWDC 2026, where Apple introduced a raft of AI advances that formed a strong foundation for its future, improvements that matter to Jamf. “When Apple innovates, Jamf celebrates,” she said.

“Apple is doing great things in their AI ecosystem, revamping Siri, expanding their AI capabilities, making Apple the platform people want to run AI on because those machines simply perform better. Our job is to take what Apple builds and bring it into the enterprise in the way that enterprises actually need it.”

Most of the industry recognizes that Apple’s enterprise story has changed dramatically as its products see accelerating use, and momentum is not slowing. Tschida reflected on how just a few years ago, Apple in the enterprise was an option in employee choice programs. “Now it’s becoming the clear choice,” she said. “We expect that trend to continue. And the more Apple invests in AI running natively on device, the stronger that argument gets.”

Where is Jamf going?

AI Governance is a unique answer to an increasingly important set of questions that are now beginning to affect the IT management of Apple’s platforms. (It’s not clear whether anything as sophisticated exists for other platforms at al, but as the need to manage AI grows, demand for such solutions will grow.)

Ultimately, the company’s latest move reflects Jamf’s inherent strategy under its new CEO. “Focus on customers, listen to them, solve their problems, and don’t throw tech at it. Ask: what’s the problem? Can we solve it? That focus is what takes you where you need to go.

“We’re on a good trajectory, customers stay with us, and the culture has always underpinned us. Now we’re finding ways to lean into it even further,” she said.

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There’s no shortage of agentic AI tools out there that offer to perform online tasks on your behalf, if only you’ll give them all your passwords and credit card details. The trouble starts when those agents don’t know when to stop — or when others don’t know to stop them.

In Estonia, the country’s AI Council has plans to change that, proposing to issue government-backed digital identities for AI agents that spell out what powers a person or company is willing to delegate to them.

“In the future, AI will increasingly perform digital operations on behalf of a person, company, or institution,” said Estonian Prime Minister Kristen Michal in a news release. “To do this, it must be clear who is acting, on whose behalf, with what rights, and who is responsible.”

He supported the AI Council’s proposal to create a digital identity for AI agents that will define agents’ rights and enable them to act in a verifiable and auditable manner.

The ID could, the council suggests, show whether an agent is only allowed to view data, create or edit documents, or make payments, and if so, up to what limit.

First mover advantage

There’s no telling when the plan will come to fruition — although Michal is keen for his country to take the lead.

“If we act quickly and wisely, Estonia will become the first country in the world to create an official digital identity for AI agents,” he said.

Estonia is already a leader in the use of digital identities for humans. Estonians can use their national digital ID cards for voting, signing documents, accessing medical and tax records. The country also offers foreigners the option of applying for “e-residency,” a digital identity enabling them to create a company in Estonia and digitally sign all related documents online as they interact with the country’s widely digitized administrative processes.

Michal created the AI Council in January, calling on Estonian startups, investment funds, industry and research institutions to systematically implement AI across the country’s industry, education, healthcare, and energy sectors.

AI vendors have already proposed creating digital identities for agents, but so far these are intended only to manage the activities of agents within the enterprise, or for interconnecting enterprise IT platforms, and none of them have the backing of governments.

Estonia’s proposal could put the tiny Baltic country at the cutting edge of agentic AI usage and set an example for others.

Microsoft has introduced usage-based billing for Copilot Cowork, which is now generally available. 

Microsoft unveiled Copilot Cowork in March, pitching it as an AI agent that’s capable of independently performing long-running, multi-step tasks — even when a user’s computer is off. 

It’s built on the same technology that underpins Anthropic’s Claude Cowork. Unlike Claude Cowork, which can interact directly with files and applications on a user’s computer, Copilot Cowork runs in Microsoft’s cloud environment and acts on documents held in a customer’s Microsoft 365 tenant.

Copilot Cowork now comes with usage-based billing.

Microsoft

On Tuesday, Microsoft unveiled pricing details for Copilot Cowork, which involves usage-based billing in addition to a Microsoft 365 Copilot license ($30 per user each month for large enterprises before discounts, and $20 for Microsoft 365 Copilot for Business). 

The usage-based pricing is calculated from four components, according to Microsoft: “model use, context retrieval, tool calls, and runtime.” 

In practice, this means more intensive tasks that draw on multiple sources, use “deep reasoning” and generate two or more outputs will lead to higher costs — denoted in Copilot Credits. 

There are two payment options: pay as you go — priced at 1 cent per credit — and P3, where customers commit to usage volume in advance and receive a discount.

Cowork is turned off by default; IT admins can decide when to make it available and which employees get access. Admins can also impose spending limits at the tenant, group, and user level, and receive notifications when spending reaches a certain level.

Users will also be able to see how much each tasks costs in credits (available “soon after” the general availability launch, Microsoft said). 

Copilot Cowork customers can select from multiple AI models, including Anthropic’s Opus 4.8 and Sonnet 4.6, while those enrolled on the Frontier program can access OpenAI’s GPT 5.5 and Microsoft’s own Cowork 1 model.

Microsoft is considering hosting a version of DeepSeek’s open source models. “We are actively exploring a range of options that meet those requirements, and DeepSeek is one of several models under consideration. We’ll share more specifics on the underlying model closer to release,” said a Microsoft spokesperson.

Microsoft also announced new integrations with third-party apps such as Miro and Monday.com, with more — such as Adobe, Box, and Canva — coming soon.

This article was updated on June 19 to add Microsoft’s comment on DeepSeek.

Z.ai has released GLM-5.2, an MIT-licensed open-source AI model designed for long-running software engineering tasks, as the Chinese company seeks to challenge proprietary coding models on cost and performance.

The company said GLM-5.2 ranked just behind Anthropic’s Claude Opus 4.8 on FrontierSWE, a long-horizon coding benchmark, trailing it by 1%. Z.ai said the model also edged out OpenAI’s GPT-5.5 by 1%.

Z.ai said GLM-5.2 supports a one-million-token context window with up to 131,072 output tokens, positioning it for agentic coding workflows that require reasoning across large codebases.

The company is also making an efficiency argument. It said GLM-5.2 uses a technique called IndexShare, which reduces per-token compute by 2.9 times at a one-million-token context length. It also said changes to the model’s multi-token prediction layer increased the acceptance length for speculative decoding by up to 20%.

The changes are aimed at a practical problem for developers: long-context coding agents can be expensive to run when they are asked to work across large repositories.

Enterprise appeal

GLM-5.2’s clearest appeal is that it pairs stronger coding capabilities with the cost advantages of an open-source model. But capability alone will not be enough to make it a credible alternative.

“Western enterprises will want independent benchmark validation, successful deployments at global enterprises, strong security and governance controls, and long-term support commitments,” said Pareekh Jain, CEO of Pareekh Consulting.

Jain said the fastest route to enterprise credibility would be hosting by a major cloud provider like AWS. That would allow customers to use the model under standard enterprise terms, with service-level commitments and compliance certifications.

Tulika Sheel, senior VP at Kadence International, said GLM-5.2 would also need to prove it can operate as a stable enterprise product.

“Demonstrated success in real-world deployments and transparent governance will be just as important as benchmark scores,” Sheel said.

The performance and cost claims will also need to hold up against established models.

“Enterprise leaders generally consider two major factors when evaluating new models,” said Lian Jye Su, chief analyst at Omdia. “First, they look at overall performance against competitors, where GLM-5.2 performs well in long-horizon agentic coding and software engineering. Second, they look at the cost of adoption. As an open-source model, GLM-5.2 has clear cost advantages.”

Su said the model could appeal to engineering teams under pressure to control AI costs. It may also attract open-source advocates and companies with significant operations in the Asia-Pacific.

But the claims still need wider validation, particularly around hallucination control and coherence during extended tasks. These are critical issues for enterprises considering AI coding agents, which may need to work across large codebases and multi-step software engineering workflows.

Jain said the one-million-token context window could be useful for large codebase analysis. It could also help with legacy modernization projects and complex engineering documentation.

He said long-context capability may also help with audit logs or legal contracts, where splitting material into smaller chunks can create errors across document boundaries. But for everyday coding tasks, effective retrieval systems may matter more than very large context windows, making some of the benefits more limited in practice.

Governance risks

The governance question depends largely on where the model runs.

Sheel said enterprises should evaluate GLM-5.2 as they would any strategic technology partner, rather than as a standalone model. That means looking at where data is stored and whether the model can be used in environments that customers control.

That deployment choice is central to the risk calculation, according to Jain. Because GLM-5.2 is available under an MIT license, companies can download the weights and run them on their own infrastructure, reducing the need to send sensitive data to Z.ai.

“The risk flips completely if you use Z.ai’s hosted API instead,” Jain said.

He said Chinese national security rules could require domestic companies to cooperate with government requests, making hosted use difficult for regulated industries or workloads involving sensitive data.

Su said the issue is not limited to Chinese vendors. Recent restrictions affecting access to some Anthropic models have also highlighted the risk that enterprises may have limited control over the availability of AI services from foreign providers.

“Selecting solutions from American and Chinese AI vendors does expose non-US Western enterprises to additional risk of having zero control over the availability and uptime of these models,” Su said.

The article originally appeared on InfoWorld.

Well, hey, how ’bout that? Here we are, on a random quiet-seeming week in June, and a new Android version is officially making its way into the wild and onto our favorite Googley gizmos.

Yes, indeed: Google announced the launch of Android 17 this week, and the rollout is getting underway as we speak. As usual, the software will show up for current, still-supported Pixel devices right away, over the next few to several days. (As for everyone else — well, you know the drill by now, right? It’s up to each individual Android device-maker to process and send out its software updates, and outside of Pixels, that support is exasperatingly unreliable. But odds are, if you aren’t palming a Pixel, you’ll be waiting for a while — maybe even a long while, if you have a phone by a certain manufacturer whose name rhymes with Boatorola.)

As always, some of Android 17’s most important elements are the under-the-hood privacy, security, and performance enhancements that you won’t explicitly see but that will make a critical difference in your device’s ability to operate efficiently and advisably. But this latest Android release also packs an impressive punch of interesting surface-level touches that’ll bring an instant boost to your day-to-day productivity and all-around Android-enjoying experience.

As is often the case, many of the most useful elements are things you might not ever even notice or think to tap into if you don’t know where to look.

Here are the four Android 17 features I’ve found most noteworthy so far and how you can start putting ’em to use this second.

[Psst: Don’t let the learning stop here. Check out my free Pixel Academy e-course to discover all sorts of advanced intelligence lurking within whatever Pixel you’re using!]

Android 17 Pixel feature #1: Bubbles multitasking magic

Our first Android 17 addition on its way to Pixel owners this week is something that’s been in the works for many a moon now — and that’s a handy new multitasking mode known as Bubbles.

Bubbles first came into the Android picture way back in 2019, but at that point, it was limited mostly to messaging and never came close to reaching its full promised potential. At long last, now, Bubbles are back, baby, and becoming everything we’d always wanted them to be.

The simplest way to think about Bubbles is as a way to turn any app you’re using into a floating, collapsible window — so you can pull it up on demand when you want it, then minimize it back down into a small icon (a “bubble” — get it?!) to get it out of your hair but keep it nearby for easy ongoing access.

It’s an interesting way to multitask without having to commit to a full-fledged split-screen setup. It can be quite useful for keeping things like lists, documents, emails, chats, or anything else you’re coming back to regularly at your fingertips — so you can pop into it as needed, without any real effort, but also without having it in your face all the time.

Android 17’s Bubbles system in action.

JR Raphael, Foundry

In Android 17, Bubbles is easy to launch with any app in front of you. The only catch is that as of now, at least, it can be triggered only from the standard stock Pixel Launcher — not a custom Android launcher like Smart Launcher or Niagara.

Provided you’re using the standard Android home screen setup, though, all you’ve gotta do is:

• Press and hold any app’s icon on your home screen or in your app drawer.
• In the menu that pops up, tap the option that says “Bubble.”
• Then tap the bubbled app icon to expand or minimize the app, or press and hold it to move it around to any position on your screen or dismiss it.

You can move a bubble anywhere on your screen or dismiss it easily.

JR Raphael, Foundry

You can also add additional bubbled apps into that same view via the plus icon next to the first app’s icon when a bubble is open. Pretty nifty, wouldn’t ya say?

Android 17 Pixel feature #2: Smarter location access

Allowing apps access to your location inevitably requires a certain amount of compromise when it comes to the ever-prickly subject of privacy — and it demands a certain level of trust that the apps in question won’t abuse the privilege or use it for reasons beyond their intended purposes.

Android 17 makes it meaningfully easier to accept that bargain and rest easy knowing your info isn’t being misused, thanks to a pair of related new privacy protection measures:

• First, whenever any app is accessing your location, you’ll now see a blue dot appear in the upper-right corner of your screen — and if you swipe down once from the top of your screen to open your notification panel, you can actually tap on the location icon that appears in its place to get detailed info about exactly which app or apps are involved. With another tap from there, you can opt to force-close the app in question, view all of its recent location access attempts, and manage its location access ability, too, in case anything ever seems amiss.

Android 17 offers some welcome added nuance and power around app location access.

JR Raphael, Foundry

• And second, when an app asks to access your precise location, Android 17 adds in the option to allow such access only temporarily — for that one brief moment and purpose — without giving the app the permanent permission for ongoing use.

Hey, we’ll take it. Just remember to keep tabs on the permissions Google itself is claiming these days, too, as those don’t always come with a prominent pop-up.

Android 17 Pixel feature #3: More dynamic dark mode

Dark mode may be one of the more divisive interface adjustments of our modern mobile moment, but if you’re a fan of the dimmer, less glary view across your Android experience, you’re bound to appreciate the added option Android 17 affords you in that area.

It’s a simple one-tap checkbox that forces apps to adjust and comply with your dark mode preference, when it’s active — even if they don’t natively support such a setting. That means those pesky apps that’d typically maintain the same standard light interface even when you activate dark mode will now turn dark along with the rest of your setup whenever Android’s dark mode is on.

All it takes it quite literally one tap on the freshly added Android 17 option:

• Head into the Display section of your Pixel system settings.
• Tap “Dark theme” — the actual words, not the toggle alongside ’em.
• And change the setting from “Standard” to “Expanded.”

Android 17’s “expanded” dark mode setting brings the darkness you crave everywhere.

JR Raphael, Foundry

Now, one note: By forcing apps to adapt to dark mode even if they aren’t designed for it, it’s possible some programs may end up lookin’ a little funky. If that ever happens and you aren’t thrilled with an app’s adaptation, go back to that same settings screen we were just starin’ at and tap the gear-shaped icon alongside the “Expanded dark theme” option. That’ll let you create exceptions and select specific apps that don’t get dark mode automatically applied.

You can create specific exceptions to Android 17’s expanded dark mode approach.

JR Raphael, Foundry

But everything else will now be in the dark when you want it — just like your dark, brooding heart desires.

Android 17 Pixel feature #4: A more comfy all-around view

Whether you’re a prince/princess/dutchess/middle-manager of darkness or not, Android 17’s new Comfort View may be just the thing for you.

Comfort View is an off-by-default addition to your Pixel that applies a softer, more pastel-oriented filter to the display with automatic adjustments based on your current viewing environment — in other words, how bright it is around you at any given moment. Ooh, ahh, etc.

To try it out, march your way back into those Display settings, and this time, tap the line labeled “Comfort Filters.” Flip the switch next to “Comfort View,” make sure the “Dynamic” checkbox is active, and see how you feel about your newly optimized screen-color view as you move throughout your day.

Android 17’s Comfort View — ahh….comforting.

JR Raphael, Foundry

If you find the filtering to be too extreme or not enough to make a difference, you can also try disabling the “Dynamic” option there and then manually adjusting the “Intensity” slider to suit your specific peeper preferences. But I suspect if you give it enough time, you’ll find the automatic adjustments to be one of those things that just works for you and makes your device a little easier on the eyes, relative to each and every viewing environment — without being something you actively think about or pay much mind.

That, if you ask me, is the sign of an effective feature. And it’s a welcome addition to Android and the ever-evolving Pixel experience.

Don’t let yourself miss an ounce of Pixel magic. Come check out my free Pixel Academy e-course to find tons of hidden features and time-saving tricks for your Googley gizmo.

Despite best efforts by defenders, malicious emails continue to slip through the cybersecurity cracks, leading some enterprises to implement a layered “defense in depth” strategy that incorporates multiple tools.

Microsoft seems to be challenging this idea, revealing that there are only nominal returns from adding integrated pre- and post-send partners to Defender for Office 365’s protections.

According to its new quarterly benchmarking data, the tech giant catches the vast majority of malicious and spam emails before delivery, misses the fewest compared to competitors by a wide margin, and removes nearly 100% of dangerous emails that do reach the inbox. Collectively, its integrated partners improve that catch rate by less than .05%.

While these numbers seem to tip the scales towards a one-vendor email security stack, experts urge enterprises to be skeptical and cautious of such vendor claims.

Seva Ioussoufovitch, senior research analyst at Info-Tech Research Group, pointed out, “percentages obscure the true quantity and severity of what’s getting through, and, considering it only takes one message to result in an incident, it’s simple enough to argue that there is real value in the defense in depth that having multiple tools provides.”

Malicious and spam email catch by the numbers

Microsoft introduced its quarterly benchmarking report in July 2025 alongside a Defender integrated cloud email security (ICES) ecosystem designed to support multi-vendor security strategies.

The SEG players it ranked itself against this year includes Mimecast, Proofpoint, Hornetsecurity, Trend Micro, Iron Port (Cisco), Barracuda, and FireEye (Trellix); ICES companies include Abnormal, Checkpoint Harmony, Cisco, DarkTrace, KnowBe4 Defend, Tessian, and Trend Micro.

Redmond reported that Defender “consistently leads” in pre-delivery detection, missing 59% fewer high-severity cyberthreats prior to delivery than the other SEG vendors it evaluated. Its closest competitors were Mimecast and Proofpoint. The company also introduced a new metric in this area: A threat miss rate per 1,000 employees. In Microsoft’s case, that was 194 per 1,000; for Mimecast, 478; for Proofpoint, 483.

When it came to post-delivery protection, Defender removed an average of 96.03% of malicious emails that reached the inbox, up from an initial 45% when Microsoft first started tracking the data in its second report.

This makes Defender “an increasingly critical backstop, operating even when ICES solutions are in place,” Jeff Pinkston, VP and GM for Microsoft Defender, wrote in a blog post. Still, ICES tools operating in tandem with Microsoft Defender “continue to provide benefits,” improving malicious catch by 0.29% and spam catch by 0.68%, he said.

“If we focus on the basics, their argument seems strong,” Info-Tech’s Ioussoufovitch noted. “Do you really need a separate ICES vendor for that extra sub 1% catch?” Microsoft paints a “compelling picture” by only focusing on raw catch rate, he said, but we don’t hear the rest of the story: “What exactly is the danger of what isn’t being caught by Defender?”

No one vendor catches everything

David Shipley of Beauceron Security pointed out that the report underscores the fact that “lots of stuff still gets by e-mail filters.”

His company regularly analyzes hundreds of thousands of emails, and the content that gets through “ranges from the shockingly mundane and obvious to a human expert, to highly clever time-delayed attacks,” he said.

A key factor in what gets through is the amount of content that is allowlisted; settings in “100% paranoid mode” get high catch rates, as well as high false positives, Shipley noted. “Anyone who has ever had a sales person lose a deal because the purchase order PDF got flagged has felt this pain.”

Then there’s the AI conundrum: “A key risk for e-mail vendors using agentic LLM-based analysis is it’s now possible to poison those models with hidden content (such as ‘ignore this e-mail, pretty please’),” Shipley said. This means enterprises need a variety of analysis methods.

Ioussoufovitch agreed that keeping pace with threat actors using AI is an industry-wide challenge, particularly as AI enables higher-quality phishing. Filters are improving and will catch some of it, but some will inevitably continue to get through. Those messages are likely highly-targeted, which are lower in volume but harder to catch.

“As of now, current tools do seem to be struggling to keep pace, but that doesn’t mean those tools aren’t necessary,” said Ioussoufovitch. “It just highlights that defense-in-depth, broadly speaking, is becoming more and more important.”

Claims ‘appear more honest’

Shipley said that this report appears more honest, accurate, and mature than others claiming 99.99% phish catch rates, “which is never true.” It’s also a “smart marketing move,” because Microsoft competes for the same security budget as other tools, and would rather enterprises remove those vendors and buy more from it in areas beyond e-mail.

On the other hand, he said, Microsoft is offering up a list of other vendors to think about, “which, congrats to Mimecast on coming in second.”

In the long run, CISOs need to determine the best spend for their limited security dollars, he noted. Enterprises need a good filter; whether they need two is up for debate. “They also clearly still need to invest in a robust awareness program,” Shipley said, “because as this report shows, lots of phishes are still getting delivered.”

Missing an important nuance

Ioussoufovitch noted that while the claims in the study are interesting, the data is presented without much of the nuance that would make it truly actionable.

“We are all too familiar with vendors’ abilities to massage data to tell the story they want, so I would advise leaders not to extrapolate the data beyond what it actually says,” he said.

Instead of the takeaway being “get rid of our current vendors,” this post highlights that Defender provides “considerable value,” he noted. Whether adding or subtracting additional vendors is worth the money should be a case-by-case conversation that considers an organization’s risk appetite, and overall security budget and environment.

“I’d treat these claims more as a reminder to assess your own environment and compare detections,” he said. “Come to conclusions based on the data you have, not what a vendor is presenting.”

This article originally appeared on CSOonline.

OpenAI has signed a partnership agreement with Visa that allows the company’s AI agents to use the payment card for e-commerce transactions. The agreements lets users shop for everything from groceries and diapers to airline tickets without having to manually enter a lot of information.

“As AI agents become active participants in the economy, Visa’s focus is on ensuring that transactions are reliable, secure, and seamless,” Visa Chief Product and Strategy Officer Jack Forestell said in a statement, according to AP.

The pact means AI agents can complete purchases on a user’s behalf at virtually any merchant that accepts Visa. Details about the financial terms of the agreement, or whether specific transaction fees will apply, were not immediately detailed.

Anthropic’s apparent inability to identify which of its users are foreign nationals has led to some collateral damage from a US export ban on its most powerful AI models — but there is a way around it, at least for some.

On Friday, the US government ordered Anthropic to suspend access to Fable and Mythos, the new AI models it had introduced just a few days earlier, to all foreign nationals, citing national security reasons.

[ See also: Anthropic Fable dispute suggests ‘export’ no longer means what it used to ]

While the drafters of the US order may have had sovereignty in mind, they ended up making it an identity management problem.

“The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance,” Anthropic said in a blog post commenting on the order, implying that it was unable to distinguish between foreign nationals and US citizens in its user base.

That’s likely the case today, but for its consumer customers, an update to its privacy policy, introduced last week and taking effect on July 8, gives it a new option: asking them for government ID.

The section of the policy on collection of personal data contains a new provision under the heading “Personal data you provide to us directly,” saying:

• Verification Data: In certain circumstances, we may ask you to verify your age or identity. If you choose to do so, data we will collect includes, depending on the method: an image of your government-issued identity document and the information appearing on it (such as your ID number and date of birth); your image in photo or video form, facial geometry templates (which may be considered ‘biometric data’ in some jurisdictions); and the result of the verification (for example, whether your age meets the applicable threshold).

If the government ban on foreign access to Fable and Mythos continues, that would give Anthropic the option of opening access to users willing to submit a scan of their identity document, provided that it contained proof of their US citizenship. That would be the case for US passports — and also for citizens’ driving licenses issued by some US states along the country’s Northern border, which issue so-called enhanced driving licenses indicating the holders’ nationality.

Enterprise users most likely to benefit from the power of the new AI models, though, will have to hope Anthropic finds some other way out of the current impasse.

The article originally appeared on CIO.

Hot AI companies can’t stop talking about forward-deployed engineers (FDEs), which are now very much in vogue. 

FDEs, in case you haven’t heard, are hired by companies looking (hoping?) to successfully deploy AI tools and services. It’s one of the hotter professions in a world still trying to understand the impact of AI on careers.

So, what exactly are FDEs — are they techy lone rangers like the ones OpenAI, Google and Microsoft are hiring? Turns out it’s not so much about individual engineers who swoop in to design and roll out AI deployments; it’s more about a team of engineers working together at customer sites.

At least, that’s the view at Amazon Web Services (AWS).

In fact, according to Taimur Rashid, managing director of the AWS Generative AI Innovation Center, the FDE concept pre-dates the current generative AI (genAI) gold rush. The same kinds of engineering teams were needed for the earlier machine-learning and cloud eras to help companies with deployments.

Taimur Rashid, managing director of the AWS Generative AI Innovation Center,

AWS

Rashid recently talked about how AWS sees FDEs as a profession in a conversation with Computerworld. And he weighed in on the desired job skills the company seeks in this increasingly AI-centric era.

What is an FDE? “We view it as a team. It’s a cross-functional team that has engineers, scientists, strategists, and folks that can piece technology and business together. In some cases, we do have to have security engineers in there, too. 

“I see them as anesthesiologists. They have to prep so many things, monitor things throughout. We see ourselves as a frontier deployment team helping customers adopt all forms of AI, whether it’s genAI, agentic AI, even emerging trends like physical AI. We’re helping these companies become frontier themselves.”

How does an FDE engagement begin, and how is it structured? “Where we see the forward deployed model is when customers come in — for example, we have our executive briefing center in Seattle and in Arlington, VA. When customers share what they’re trying to do, very quickly a customer’s like, “What’s the quickest way I can go and build something with you?” 

“We’ll forward deploy our people in, we’ll embed them in your business and we’ll go through these 45-day sprints that we typically design. Through those successive sprints, we’re building stuff together, we’re proving value, and then they can expand that to a much broader engagement.

Where do FDEs actually sit? Client side, internally, or in-between? “It’s mixed, and it largely depends on what the customer’s preference is. We’ve seen models where the customer has been very adamant that, ‘We want your teams with us in our business.’  In those cases, we forward deploy the majority of the teams on site. 

“We have models where customers are fine with you being wherever you’re based, as long as you’re still embedded virtually. And then there’s a hybrid. We deploy anywhere from five to seven people. Sometimes, the baseline is actually three.”

Will cost savings be the job of an FDE, or someone else on the team? “I expect these teams to be able to architect systems that have those cost requirements in mind, whether it’s use a different model for a different use case that doesn’t increase the per-token cost…or think about ways where you can use semantic caching. I personally think you may have a high spend in token consumption, but if you’re generating revenue, as long as the economics work out, then you’re at peace.”

What challenges have you gone through deploying FDEs in the real world? “One of the challenges worth highlighting is when customers get really excited about us forward deploying resources, what they end up realizing is [that] they’re not set up to absorb that right away. They realize they have to go through … process-related things, security access — all those operational things. 

“One very good example is the Commonwealth Bank of Australia. They said: “Prioritization’s a big thing, and if you forward deploy and you’re 100% dedicated, how do we ensure that our teams are also equally 100% dedicated?’ When you’re sitting in your office, you’re distracted by your day-to-day. So they said, ‘Why don’t we create a neutral ground in Seattle? You fly your people, we’ll fly our people. We’ll give them three weeks of dedicated time so they have no distractions.’”

Have you gone into projects where they want AI but have no security or governance ready? “I’ve been through this before, certainly. We do see customers that have security processes and capabilities, but it’s not as tight as it should be in the age of AI. Governance is the biggest area where customers right now have the biggest gap. I’m talking governance around agents. In the past two months, almost 100% of the conversation around agents is not about capability. It’s all about governance. 

“That is a big area right now where forward deploy teams are helping with governance education, and building the scaffolding for that.”

How does software engineering fit into the FDE model? “One of the greatest learnings is that as we forward deploy resources and get customers to take AI and integrate it into their systems, the knowledge of software engineering is so important. Today, a customer can use one of the Claude models and scan their code base and look at vulnerabilities. 

“The tough part is not assessing those vulnerabilities, it’s remediating [them]. Remediating [them] requires software engineering experience, because you have got to merge code, test it, deploy it. We largely see that the frontier software development teams are smaller and they’re managing agents that are doing various tasks across the software development lifecycle.”

AWS has many models at your scale, open source, closed — it’s more complex than what other AI vendors offer.  How do you nail down the talent? “It’s massive, and when you look at not only scale, it’s the complexity of the stack. We take an approach where we fundamentally do three important things: No. 1, we want to ensure people understand concepts; they have to understand pre-training, post-training, reinforcement, fine-tuning. 

“Secondly, we make sure that our teams are well versed in their first-party services. The third thing is that by design, AWS has always been about choice. We say, ‘Let’s do 80-20 here. What is 20% of those specialties that we need to have, which can cover 80% of what most customers are trying to do?’”

What skills should software developers learn to move into FDE work — the top three or four things? “We look at three categories. First category is entirely functional. Are they more engineering specific? Are they science specific? Are they security specific? Our litmus test is not only knowledge of the function, but the actual hands-on work that they can do with it. Secondly is around domain. I focus on what is their domain understanding across the whole AI lifecycle? The third thing is cultural. We are looking for folks that are okay at dealing with ambiguity, being good at stakeholder management, and having that startup mindset. 

“This AI transformation’s not for the faint of heart.”

There’s a rush for FDEs from OpenAI, Google, and others. What’s different about what you look for? “I don’t know entirely what the others are looking for, but I’ll tell you what we have been looking for in the past. When we hired solution architects, it was about systems level understanding. But what I see more and more is hands-on experience, cultural mindset, all these things are very important. If I had to pick one thing that is going to be very important in the talent that we either upskill or future talent that we hire, it has to be the application of AI towards software engineering and system integration tasks.

You’re upskilling AWS talent as well? “We do. You will see some publications coming out in the next couple of weeks around how do we do this across our software teams and how does that translate to customer-facing roles.”

Europe’s evangelistic approach to insisting Apple open up personal data to competing AI services is hurting Apple users in the region. More than that, it also places its entire business sector at risk, and a newly-published Jamf survey suggests why.

Announced at WWDC 2026, Apple Intelligence/Siri AI relies on personal, contextual data to run. Europe wants that same information to be made available to third-party services for competing apps, but has not worked with Apple to protect user confidentiality. It’s an approach that places your data at risk of exfiltration using those apps because Europe is insisting Apple share personal information with the developers of other apps.

The desire to protect that data is why Apple won’t distribute Siri AI in the EU for a while.

Jamf survey exposes the IT risks of AI

It’s not as if Europe doesn’t understand the risk of data leaks in an era of AI. Just look at the bloc’s focus on things that do matter, such as sovereign AI or managed AI services like Orange Live Intelligence. These locally-produced AI services, alongside Europe’s attitude toward them, tell me the confederation understands the risks.

How real are these risks? Very. Jamf on Monday published survey results confirming the scale of that risk, telling us that one-in-five IT and security leaders in the enterprise sector has already experienced an AI-related incident involving unexpected costs, a security issue, or both.  The survey also found that:

• 72.9% of organizations have already deployed AI in some form.
• 59.7% see an AI-related incident as a near-term risk.
• Organizations with deeply integrated AI are 40% more likely to report an AI-related incident than organizations still in the exploratory stage. 

The implication is that AI governance is becoming an operational requirement and — as Apple has told us umpteen times in the past — the best way to maintain operational confidentiality is not to collect or share any data at all. That’s the whole point of its approach: the data doesn’t need to be shared, it just needs to be turned into another signal that promotes utility while protecting confidentiality. 

Crafting trust in a crowded market

There’s another challenge to emerge. There are now multiple brands of AI, with more coming on stream all the time. That’s great in terms of finding a model that suits your needs, but challenging when it comes to ensuring all the services you or your employees use of are equally secure. You don’t want your business to become deeply reliant on any service only for that vendor to subsequently get bought out and/or shut down, nor do you want a service to be hacked or otherwise exploited to your detriment.

“AI isn’t arriving as a single application that IT can approve and move on from,” said Jamf CEO Beth Tschida. “It’s showing up in developer tools, productivity apps, autonomous agents, and other software they already run. The challenge is maintaining visibility and control as that footprint expands.” 

The survey described the challenges IT faces with AI deployment: shadow IT, vendor sprawl, and the need to grapple with highly unpredictable use-based pricing models. And that’s even before considering the governance challenges of agentic and developer AI.

AI and the emerging governance nightmare

“What our survey shows is that governance must keep pace with adoption,” Tschida said. “For organizations built on Apple, the foundation is already an advantage. Apple’s privacy model and the management controls built into the platform give IT teams a strong foundation to build on and … that advantage depends on using tools built for Apple from the start.”

That’s the point of the curated, private and secured service offered by Siri AI, of course. It’s also part of what Apple is building toward with its wider ambitions toward AI on its platform. Bloomberg’s Mark Gurman discussed elements of this in his weekend newsletter, in which he suggested Apple might introduce some subscription services using AI, and that it is building an App Store for Siri Extensions, which would allow third-party chatbots to work with Siri. 

There is a need for curation and management in AI

What makes that model work is the curation with which Apple surrounds it, and its determination to extend Private Cloud Compute so it can protect your data even when using third-party servers (in this case, Google’s server clusters). It makes sense to think Apple intends to use that system to protect all approved third-parry AI interactions provided across its platforms by its own routes. That’s true, even if users access services free of those safeguards using a web browser, which they currently can.

But the key thing is that if Apple can get this right, offering up a managed, curated, and controllable ecosystem for AI agents and services, it will be going a long way toward building the kind of managed AI ecosystem the Jamf survey shows our modern digital enterprises increasingly need. 

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MUNICH — Amid trans-Atlantic political and trade tensions, digital sovereignty — once a relatively niche concern — has jumped to the top of the agenda for European organizations wary of their reliance on US technology suppliers.

For many, including European Union policy makers, increased use of open source software is a key part of the answer, offering an alternative to proprietary platforms from a handful of large US vendors.

That’s the view of Frank Karlitschek, CEO of Nextcloud, the German software vendor that bills itself as an open-source alternative to software suites from the likes of Microsoft and Google. 

Nextcloud CEO Frank Karlitschek speaking at the German software company’s Nextcloud Summit 2026.

Nextcloud

Karlitschek founded the company in 2016, forking OwnCloud’s open-source file-sharing software. Since then, Nextcloud has expanded its products to include a range of productivity and collaboration tools that organizations can install and run on their own servers or access via cloud providers. 

More recently, Nextcloud helped develop the Euro-Office application suite, which launched last week as an open source alternative to Microsoft Office and others, and continues to build out its Nextcloud Hub with AI assistant and agent features. The company now says revenues are growing at between 50% to 100% year over year.

Computerworld spoke to Karlitschek at Nextcloud Summit about momentum around digital sovereignty, the European Commission’s Tech Sovereignty Package proposals, and how Nextcloud hopes to evolve in the coming years.

The following interview was edited for length and clarity.

When Nextcloud launched, there was a big push in Europe away from on-premise software towards US cloud providers. How have attitudes towards open source and awareness of alternatives providers changed since then? “I’ve been doing open source since the ‘90s; at the time it was mostly for a nerdy audience — a very small group of people who really care about software and being in control. The sovereignty part was always there. It’s the core idea behind open source that you can understand what the software is doing, you can deploy it wherever you want, you can study it and change it, and so on. 

“At the time, it was very niche, and since then it’s really growing and growing. There are certain points in time that really accelerated the growth; something like the Snowden revelations, for example, or the whole discussion about GDPR and certain legislation. And then, of course, the current geopolitical situation.  

“I personally find it interesting that it grew from something that is just interesting for software developers, and now it’s on the geopolitical stage. I have meetings with big politicians who really care about it now, and I personally find it interesting that it’s increasingly understood by — I wouldn’t say the mainstream, but more and more people.

“At the beginning of Nextcloud, we mostly talked with IT managers looking for a solution; they care about how it works, the price and other things. But now we are also talking with the C-level people. It’s part of an overall strategy of a company, to say, ‘Hey, we need to look into the dependencies, we want to have a solution that fits into the strategy of the company.’

“In the past, it was like a commodity – it’s just some software, who cares? Now, it’s really part of the company strategy. That’s really interesting.”

There’s been a lot of interest around digital sovereignty over the past couple of years. To what degree is this translating into action, with organizations migrating away from US cloud providers? “The interest is gigantic. Everybody’s talking about it, we have so many contacts and people coming to us. Not everybody is doing it — a lot of people are just exploring and seeing what the options are. 

“Obviously, we hope that this will translate into actions in a few months. At the moment, it’s a lot of talking and exploring the options. As a company, we are also growing a lot in customer base.  But the interest in this space is even bigger; we see it as the beginning of a funnel.

‘In defense we see a lot of interest, then also everything around education is very important for us, then other regulated markets like the healthcare, for example. Finance is an interesting one.”

A lot of the conversations around digital sovereignty are tied to the current geopolitical situation and even the US administration. Do you see demand for sovereign technology as a structural change or are some organizations holding back to see how the situation improves in the future? “I see it as a long-term trend. If you look at the IT budgets and projects in the ‘90s, it was some something unimportant. It was, of course, important that the printer works and the fax machine works, but it was not definitely not strategic for the company. 

“And then in 2000, the whole cloud trend came up, and there was the big hope that this will save money. It was always the narrative with cloud computing that you can just outsource it and save money and it’s great. 

“Nowadays, people realize that it’s not something that you can just ignore. I wouldn’t say that everything comes back on premise, but people care about it now. They understand it’s not just a commodity, like water, or electricity that comes out of the wall and you don’t care what’s behind it. People realize that it’s something that has an impact on the future of an organization, from a vendor lock-in perspective, from a cost perspective, from an industry espionage perspective, and competitiveness. With open source, you’re more flexible. So, I think the trend that this is all more strategic and important for the future, this will go on.”

The European Commission recently published its Tech Sovereignty Package, including its open source strategy. Are these proposals sufficient to address the concern around digital sovereignty and support the open source ecosystem in Europe?

“It’s great, I really like it. I was actually surprised they listened so well. But now the real challenge is to actually do it; this still needs to happen. The description of the problem and a possible solution, this is all very good. I’m surprised, I’m happy about it, but to put this into actually binding law, this still needs happen.”

Would you like to see any changes to the current proposals before they’re gets passed into legislation? “At the moment, they have these four different risk levels, and the most critical one — No. 4 — is one where they accept only open source and European solutions. This is the highest risk level, but this is only for 1% of the market. I hope that it’s better understood that more than 1% should care about this more.

“If you have something which is completely not critical, maybe doesn’t possess any personal data at all — sure, it’s totally fine [to use non-EU suppliers]. But if you have GDPR requirements, espionage protection, no vendor lock-in, and so on, then there should be more of that [the highest requirement level].”

US firms have attempted to address European customers’ concerns in different ways, with sovereign marketed cloud services and joint ventures with European providers. Microsoft 365 Local is designed to run on premise. Where do you draw the line between what’s actually a sovereign solution and what some call ‘sovereignty washing? “Sovereignty has different dimensions, of course. But if you look at the problem of the CLOUD Act alone, which gives foreign agencies full access to the data here, then the whole idea that it’s enough to have European data centers — that’s not enough. It’s clearly written in the CLOUD Act, that even with [European] data centers, or subsidiaries, it still applies.  

“Microsoft tries to find a solution there with its Delos idea; a company that is owned by SAP — a German company — and Microsoft delivers only the software. But even then, you have this dependency, because software needs updates and software security updates. And if they’re not available, or if someone puts a backdoor into the software, which is possible, then you still have a problem. 

“So, they’re trying really, really hard to find a way around the problem, but it’s not easy for them.”

To look ahead a bit in terms of the product strategy, there were announcements for Nextcloud Hub this week around AI agents, and the program to work with independent software vendors. What do these say about Nextcloud’s future? “The overall product strategy will not change so much; it’s about having state-of-the-art collaboration software — but with a lot more control, security and safety — that’s open source and independent where you host it. So this will always stay, but of course, there’s some additional factors that come into play now, like the AI impact that we see and want to leverage with our agent strategy. 

“We’ve had this for one and a half years already, but we are expanding that. In the future, you might still use an interface in a classic way that you open documents and type in text and so on. But there are also a lot of operations that can be automated in the future with AI. And this is something we really invest a lot into. 

“Another aspect of AI is how easy it is to build custom software around it. The coding models are getting better all the time, which means there will be more and more custom business software. This is what we want to capture with our ISV program. Software development will become easier, but you don’t want to deploy just random software in your company, you want to have something that is tested, certified and secured, and that somebody’s accountable for it. This can be something we can provide at Nextcloud.”

Editor’s note: NextCloud paid for Matthew Finnegan’s travel and hotel costs for NextCloud Summit 2026, but had no editorial role in the creation of this story.

Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.

Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.

It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”

But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable expanded compute capacity without draining the operations budget,” he said.

A contrast to left-to-right processing

Built on Google’s Gemma 4 family and its Gemini Diffusion research, DiffusionGemma is a 26B mixture-of-experts (MoE) model designed to maximize text output generation.

It essentially shifts how models use hardware, giving processors a larger hunk of work each cycle so it can draft full 256-token paragraphs in sequence. This allows the model to generate text up to 4x faster on GPUs, Google claims. It activates only 3.8B parameters during inference, and, when quantized, can fit within 18GB VRAM on high-end consumer GPUs like Nvidia RTX 5090.

“It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously,” Google research scientists Brendan O’Donoghue and Sebastian Flennerhag wrote in a blog post.

AI image generators begin with pure, random ‘visual noise’ and iteratively refine that into a finalized picture (what’s known as ‘diffusion’); DiffusionGemma applies this same process to text. It does not generate tokens in order, but begins with a “canvas of random placeholder tokens” that it processes in multiple passes, identifying the context tokens it feels are most relevant and using those to refine the rest.

The model has the ability to self-correct, using confidence scoring to re-evaluate tokens in the next pass. “The model iteratively refines its own output, allowing it to evaluate the entire text block at once to fix mistakes in real-time,” O’Donoghue and Flennerhag explained.

DiffusionGemma also has bidirectional attention, they wrote. “Generating 256 tokens in parallel with each forward pass allows every token to attend to all others.” This can be particularly helpful in domains that are non-linear in nature, such as mathematical graphs, code infilling, and in-line editing, they said.

DiffusionGemma is optimized across Nvidia’s hardware stack, making it compatible with consumer setups as well as with high-performance enterprise systems like Hopper and Blackwell.

Because it is released under the Apache 2.0 license, developers can freely use, modify, distribute, and commercialize the software using their preferred tools. It can be run on GPUs or in the cloud through Google Cloud Model Garden or Nvidia NIM, and is available on Hugging Face, GitHub, and vLLM, with support for the open-source library llama.cpp coming soon.

Key use cases

The model is particularly useful in local workflows that are “speed critical,” such as generation of non-linear text structures, and unlocks what Google calls “new patterns of model behavior” like multimodal understanding and generating and rendering code in near real-time.

Levy explained, “DiffusionGemma is particularly well suited for interactive coding and editing where its efficiency allows rapid processing and iterations,” noting that its ability to fit within 18GB of VRAM and its deployability on commonly available local GPUs can potentially benefit customer service-related workloads that lean heavily on real-time interaction and local processing.

“DiffusionGemma also incorporates a thinking mode that is especially adept at problem solving,” he said. For instance, the model was fine-tuned to play Sudoku, a typically challenging task for autoregressive models because each token depends on future tokens. This “rather handily” illustrates the model’s capability to solve more complex problems, Levy noted.

Limitations

Google freely admits that DiffusionGemma is geared to specific workflows, and there are “key trade-offs.”

The model is engineered for small batch size inferencing and low-latency, high-speed generation low-to-medium batch sizes on a “single capable accelerator.”

In high-QPS cloud serving environments, (where infrastructure is designed to handle tens or hundreds of thousands of requests per second with ultra-low latency), DiffusionGemma’s parallel coding “offers diminishing returns,” and can even result in higher serving costs, Google conceded. In addition, its overall output quality is lower than that of standard Gemma 4, which is built for apps demanding maximum quality.

However, Levy noted that while DiffusionGemma “can be less precise than other models in certain workloads,” subsequent refinement cycles could overcome this limitation.

While Google isn’t sharing runtime costs, it’s clear that this is an efficiency play, he added. “When deployed across the kinds of workloads that would optimally benefit from its architecture, DiffusionGemma seems to have the potential to reduce processing overhead and related costs,” he said.

This article originally appeared on InfoWorld.

Anthropic has come up with a neat way to combat those students who are booing AI at their universities.

The company has launched Claude Corps, an endeavor that will pay selected young people to extol the benefits of AI to communities across the US.

Anthropic is looking to recruit 1000 “fellows” and introduce them to selected nonprofits. The vision is that both sides benefit: the nonprofits learn how to use AI tools effectively and the young evangelists develop their own knowledge of AI. The company is committing to spend $150m on the project.

It will work with two partners: CodePath will act as the fellows’ official employer, while investment advisor Social Finance will lead measurement and evaluation.

The fellows will certainly be well rewarded: the companies are paying $85,000 for their yearly commitment, on top of their extensive training. At least 400 nonprofits will be hosting the initial wave of AI enthusiasts.

Of course, it’s easy to be cynical about such a venture given the increasing backlash against AI from students. But perhaps Anthropic has genuinely seen an opportunity to improve AI knowledge and equip a new generation with another set of skills. Time will surely tell.

When Pope Leo XIV wrote about the effect that AI is having on our world in his encyclical, Magnifica Humanitas, he may not have imagined the document being referenced in an HR environment.

But, according to a report by Business Insider, Erin Maus, a software developer in North Carolina, used the Pope’s message about the need for vigilance in how AI would be deployed to gain a special exemption from her employer about using the technology for coding.

Maus is not even a Catholic but a Unitarian Universalist, according to the report. However, it said, she maintained that the use of AI didn’t align with her religious beliefs.

Business Insider said that to make her case, she consulted an employment lawyer — a move to be expected — and her local chapter’s minister — which probably wasn’t. Her wishes were reportedly granted last month. “I’m writing my code and reviewing my code by hand, which seems crazy to say,” she told the publication.

She’s certainly not alone in wondering whether AI is always the way forward for techies: a journalist at PC World has also been rethinking its use after reading the encyclical.

It remains to be seen whether this will be the spur for a torrent of claims from Catholic workers, asking to be freed from the demands of using AI or whether Business Insider’s report is an outlier.

This article first appeared on InfoWorld.