Computerworld.com [Hacking News]

Microsoft is continuing its push to bring generative AI (genAI) into Excel, with new Microsoft 365 Copilot skills designed to automate common processes and a “plan” mode to provide more control over Copilot’s outputs when handling financial data.

Microsoft made Microsoft 365 Copilot generally available in Excel in late 2024 and since then has added several capabilities, including agentic tools, a Copilot function within Excel, and Python support for advanced data analysis.  

On Thursday, Microsoft unveiled a skills feature that lets users define processes Copilot can perform in Excel — such as building a discounted cash flow, Microsoft suggested, preparing a variance analysis, or refreshing a monthly reporting model.  

“Instead of starting from scratch each time, a skill guides Copilot through the steps, applying the right structure and formatting, and helping produce an output that is easier to review, reuse, and trust,” Brian Jones, vice president for Excel at Microsoft, said in a blog post.

Users can access a library of pre-built finance skills or create their own custom skills and save them as a SKILL.md in OneDrive, where the Copilot assistant can access them. Microsoft’s partners are also building their own skills, including finance software vendors such as LSEG, Ramp and Velixo — these are “coming soon,” Microsoft said. Custom skills are available today via the Insider channel and generally available next month.

A new “plan” feature is aimed at giving users greater oversight of the AI assistant’s proposed actions before it starts interacting with spreadsheet data. The Copilot assistant can now draft a list of planned interactions — such as changing a formula — and, before it gets to work, ask the user to “approve, edit, or answer clarifying questions,” said Jones.

After it has completed the list of actions, the Copilot assistant will post a link to any changes in the chat window. Edits made by the AI assistant will then appear alongside other those from human users in the Show Changes pane.

Copilot can connect to third-party platforms now, pulling in data from sources such as Moody’s, CB Insights, Morningstar, and PitchBook.

The features will roll out “progressively” for customers, Microsoft said, and are available to paid Microsoft 365 Copilot users. Microsoft offers two payment options: $30 per user each month for larger customers, or the Microsoft 365 Copilot Business plan, which costs $21 per user a month for organizations with fewer than 300 employees.

US authorities are getting decidedly twitchy about frontier AI models. Just a couple of weeks after ordering Anthropic to prevent foreign companies from getting hold of its latest release, Mythos/Fable 5, it’s been putting the squeeze on another AI company..

Now, the Trump administration is asking OpenAI to hold back on the general release of GPT-5.6, according to a report from Bloomberg.

OpenAI CEO Sam Altman reportedly told employees that the government is asking that the model be released only to a short list of trusted partners, initially 20, before being more widely disseminated.

Altman reportedly told staffers that the administration was getting nervous about the capabilities of the latest AI tools. It didn’t go as far as forbidding access to foreign users but it’s clear that the White House is looking to act as the power of the new models becomes more apparent.

The administration’s actions will undoubtedly cause some anxiety among AI companies, particularly in light of OpenAI’s and Anthropic’s upcoming IPOs. There will be concerns that new software developments could be postponed or even halted. However, it should also be noted that the administration was already displeased with Anthropic over its moral stance on defense issues, so the action against Mythos should be placed in context.

Indeed, the government is trying to play down such fears. Bloomberg quoted a White House official as saying that the Trump administration continues to collaborate with frontier AI labs to develop shared approaches for addressing the challenges of scaling the technology.

Chinese tech giant Tencent is set to launch an AI assistant inside WeCom, its Slack-like collaboration tool for enterprises. The new tool, Dayuan, is built on the latest large language models from Chinese AI developer DeepSeek.

Tencent announced the news in a post on Chinese messaging platform Weibo by Tencent’s public relations manager Zhang Jun. Dayuan will automatically understand user requests and will respond according to the demands of the user, he wrote, according to a translation by Bloomberg. “At any time within WeCom, simply swipe left to summon Dayuan. It can intelligently recognize the interface you’re on, understand what you’re asking, and help you resolve issues more effectively,” he wrote, according to the report.

In addressing the Chinese enterprise market, Tencent has an advantage over other companies in the AI space because it has a vast reservoir of customers who use WeCom. Earlier this month, it announced a range of AI productivity agents to address the demand for more AI tools across enterprises.

Tencent has been intensifying its efforts in the AI space in an attempt to beat US competition. In April, it launched an updated version of its Hunyuan model to catch up with more established AI companies such as ByteDance, Alibaba and DeepSeek.

The launch of Dayuan with its vast supply of user data will provide a step-up for Tencent and will reinforce Chinese efforts to establish serious AI competition to US products.

Following a seven-month investigation, the European Commission has reached a preliminary decision that Amazon’s and Microsoft’s cloud platforms — AWS and Azure, respectively — should be classified as “gatekeepers” under the EU’s Digital Markets Act (DMA), Reuters reports.

The DMA, also known as the Digital Markets Regulation, aims to limit the market power of dominant players. For cloud services, this would entail, among other things, requirements for increased interoperability and data portability, as well as restrictions on how these services might favor their own products and services.

The Commission pointed, among other things, to AWS and Azure’s large market shares, extensive investments, large customer bases, and high costs for customers who wish to switch providers.

If the decision is approved, the companies would be subject to the same type of regulations that apply to several of the largest technology platforms. Both Amazon and Microsoft were critical of the assessment. Amazon argued that the EU already regulates the cloud market through the Data Act, while Microsoft believes the EU is underestimating the growing competition from Google Cloud.

Amazon and Microsoft will have the opportunity to respond to the European Commission’s preliminary conclusions before a final decision is expected later this year.

Australia’s Security Intelligence Organization (ASIO) has uncovered an attack on a critical infrastructure operator’s network. State-sponsored actors had compromised the network and were preparing to sabotage it, according to its director general, Mike Burgess.

Other countries face similar cyber-threats to critical infrastructure.

It’s impossible to exaggerate the danger that the country is facing from cyberattacks on its infrastructure, he said, presenting ASIO’s annual threat assessment this week. “We categorize them into ‘threats to life’ and ‘threats to our way of life,’” he said.

In this case, the hackers had gained access to login details and passwords for active users of the networks, including the IT professionals guarding it. ASIO had set up a specific team to deal with the issue of cyber sabotage.

Australia isn’t alone in facing threats from the same state actors, Burgess said. “We struggle to find a single country in our region that has not been compromised by this state’s cyber apparatus.”

This meant that Australia is facing a persistent threat in the future, one that could have consequences for the way that the critical infrastructure is deployed and managed. “The biggest challenge is the cumulative one: in a degraded security environment defined by concurrent, cascading, compounding threats, when resources are limited, how and what do you prioritize?” he said.

This article first appeared on CSO.

Apple’s decision to raise prices in response to memory cost increases is not unique to the company. If Apple has to do it, everyone else will as well. 

Apple announced stiff price increases Thursday — up to 25% in some cases — that extended across most products, including refurbished Macs and iPads (which saw prices increase up to $330). Apple might have left iPhones out of the mix for now, but they’ll likely see price increases when new models appear this fall. Omdia believes the memory price crisis spells the end of low-cost smartphones.

The price hikes begin

“We had assumed a price hike of $100 to Pro and ProMax iPhones, and $50 hike to base models,” wrote IDC Senior Director Nabila Popal. “However, seeing the price hikes to iPads and Macs going as high as $300 for some models, my personal instinct says the hike to iPhones may be even higher than what we assumed — perhaps even $200 to the Pro/Pro Max models. I think the days of $50 price increases are over.”

What’s driving all this? The answer, increasingly, is AI. The buildout of large language model (LLM) infrastructure requires vast quantities of high-bandwidth memory and that appetite is accelerating faster than manufacturers can respond.

Will Apple suffer? Maybe, though perhaps not too much. Popal notes that the introduction of Siri AI will give a large number of existing users a reason to upgrade. Given most of these devices are sold on installment plans, even a $200 price increase over 36-months might not pose a major barrier to sales. IDC expects iPhone sales to decline, but only by 5% — while the rest of the industry shrinks.

The worst is yet to come

This may not be the worst of it. Major players now warn that the supply-demand gap could continue to widen in the coming years. The higher costs that result will spread across all types of electronic devices, so anything with memory, a processor, or storage will become more expensive.  

Microsoft announced its own price increase, adding up to $150 to the cost of Xbox this week. And Lenovo made its own contribution when it warned that high memory prices will become the new normal into 2030, and prices might never return to early-2025 levels.

Micron, Samsung, and SK Hynix describe the situation as beyond their control, saying they’re having difficulty meeting demand even for their top customers. These companies are generating massive profits all the while. 

In Lenovo’s case, the company claims that while it is introducing additional manufacturing capacity, this is not making a dent in the supply-demand imbalance. SK Hynix is expected to expand its manufacturing capability by accelerating its original 2040 expansion plans to 2030, at which time it should have tripled output; even that might be insufficient to meet demand. 

A long journey ahead

“We currently do not have line of sight as to when memory supply will be able to catch up with increasing demand,” Micron CEO Sanjay Mehrotra said this week.

With no end in sight, Wedbush Securities analyst Dan Ives says he believes Apple is attempting to get ahead of the inflationary spiral, speculating that the latest price increases bake future increases into their model. 

Memory prices began to spiral out of control last fall, with prices today reaching levels no one anticipated. They rose as much as 98% in the first quarter of 2026 and are set to jump by another 58% to ​63% in the current quarter, according to TrendForce.

This consequential uncertainty is affecting tech stocks. Asian stock markets fell sharply on Friday, led by a sell-off in technology firms. Trading on South Korea’s Kospi was temporarily suspended as a result — for the third time this week. Apple shares are down again, and the sell-off is expected to continue.

So, who’s winning?

The beneficiaries of this memory squeeze are not hard to identify. While consumers face higher prices for phones, laptops and games consoles, the companies driving memory demand — the hyperscalers and AI firms building out server farms at extraordinary scale — are posting record revenues. The costs flow down; the profits flow up.

Some, including Naomi Klein, see it as a kind of strip mining of human intellect and ingenuity, a cultural wealth transfer in which human innovation is packaged and resold as competing chatbots, for a fee. 

There are signs this may not be sustainable. A UBS Group survey finds approximately 60% of companies have started curbing AI spending because of token costs, shifting to low-cost and open-source models. But even if AI-driven memory demand softens, there is little reason to expect consumer tech prices to follow. History suggests they rarely do.

Truncated dreaming

For Apple watchers, it was pleasant enjoying a few months in which the dream of a sub-$500 Mac was realized, only for that happy reverie to be dashed by the VC-funded race to deploy AI server farms for the benefit of those with pockets deep enough for the tokens to feed them. 

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For years, Microsoft has hyped Windows 11 cas an OS with AI, and the company is finally putting the building blocks in place for that transformation.

Microsoft execs shared examples of how the company is integrating AI in Windows 11 at its Build event earlier this month, highlighting how AI models and agents will make the OS smarter, allowing users to interact with it using natural language and intent.

Specifically, Windows 11 PCs will provide unmetered intelligence so users can run AI for free without a network connection. “No token cost. No sensitive data leaves the device. It also reduces latency,” Anastasiya Tarnouskaya, product manager for Windows ML, said during a Build session.

Hardware makers introduced AI-capable hardware before the applications were available. But Tarnouskaya said more than 500 million PCs are already running local AI workloads. “Thanks to recent advancements in AI models, hardware, and the software stacks that run them, today, every Windows PC is becoming increasingly AI-capable,” she said. 

The AI experiences are blended into apps and the Windows UI, not working only as chatbots such as those offered by ChatGPT or Gemini. 

Microsoft Office, Photos and Teams already use on-device AI capabilities, with Outlook, for instance, summarizing emails using Microsoft’s Phi Silica model and a GPU on the PC.

“And it’s not just developers that are betting on local AI…, [companies] from Adobe to WhatsApp are building some incredible local AI-powered experiences.” Tarnouskaya said. Other early adopters include Canva, Affinity, and Speechify.

AI apps for Windows 11 proliferated after Microsoft shipped Windows ML last fall, she said. (Windows ML helps developers create offline AI applications without accessing cloud models. It maps applications, localized AI models and hardware such as GPUs and neural processors.)

Windows ML is part of Microsoft’s “Foundry” portfolio of products, which includes Foundry Local for running open-source models on Windows devices, and Windows AI APIs that automate tasks such as conversation summarization, speech recognition, and video upscaling.

Microsoft is also turning to AI agents to change how users interact with Windows 11. Users can describe a task through natural language, and a long-running agent will get to work and complete the action. “Windows is evolving into a platform where natural language can map to real system outcomes,” said Samantha Song, product manager for Windows at Microsoft.

Song demonstrated how users could just tell or type how they want to personalize colors, wallpaper, or menus, and the agent will do it. “There is no manual set up against themes, setting or lighting. The system treats it as one coherent action,” Song said.

For the effort to succeed, developers will need to create a skills file that maps how an agent behaves. That skill can then be reused over and over again, Song said.

“At the enterprise level, you could imagine a world where a user switches into a secure finance mode, and the system aligns apps, access boundaries and environment automatically,” Song said.

Microsoft also demonstrated how OpenClaw can be used to create personalized agents to run Windows functions.

At Build, LLMware.ai demonstrated an agent on a Qualcomm laptop that collects Jira issues in real-time, summarizes them locally, and emails daily summaries of top issues to the team. The agent runs automatically without prompting.

“You can get optimized performance on the NPU [neural processing unit] by running the model locally…and you also implement a scheduled run of your automated agents,” said Darren Oberst, co-founder of LLMWare.ai.

Samsung, Lenovo and others are rolling out — albeit slowly and carefully — agentic AI features under the moniker of ‘personal AI,’” said Leonard Lee, principal analyst at Next Curve. “The problem is ensuring safe deployment,” he said.

Microsoft’s efforts to embed AI in Windows will force enterprises to rethink hardware strategies, said Jack Gold, principal analyst at J. Gold Associates. And since AI chips excel at different tasks, Microsoft will have to support multiple chips to offer choice to enterprises, he said.

“We recommend — and others do, too — that any new PC purchases, especially for enterprise, be done with this in mind and purchase AI PCs during any upgrade cycle,” Gold said.

AI is here, and we must help workers adapt: That’s the response of a new non-profit organization to the ongoing debate about whether AI will destroy jobs and cause catastrophe or take the economy to new heights.

Launched Thursday, Raise Us is a nonpartisan national organization that says it will partner with state governors, employers, workers, and training organizations to help the US workforce make a successful transition to an AI economy. It says it will design and pilot new corporate incentives to retrain and redeploy workers, develop new approaches to support people through job transitions, and create new training models tied to changing employer demand.

It has already raised more than $500 million and hopes to bring that up to $1 billion in multi-year commitments from philanthropists and industry sources. Anchor partners include Amazon, Microsoft, Anthropic, and the OpenAI Foundation, and more than two dozen other organizations including Cisco, IBM, ADP, AMD, and Deloitte have also signed on.

The organization is led by two former US state governors, Gina Raimondo, who will serve as CEO and co-chair, and Eric Holcomb, co-chair, and its initial government partnerships are with the states of Arkansas, Connecticut, Maryland, and Utah, which, it said, “will serve as the first proving grounds for outcome-driven pilots.”

However, analysts are skeptical that the initiative is anything other than a public-relations effort for the AI industry.

Jason Andersen, VP and principal analyst at Moor Insights & Strategy, said the organization’s initial communications are more marketing and posturing than strategy and action. “It also does not help that there are many CEOs involved who have already displaced thousands of workers due to AI,” he said.

The organization has chosen to work with state governments rather than the US federal government but even partnering at the state level may be too broad, he said, given that the industry makeup varies from state to state, with some leaning towards knowledge workers, while others are more manufacturing-focused.

AI-washing

Technology analyst Carmi Levy said Raise Us’s mission is laudable, but, “Unfortunately, for all its slick website and optimistic-sounding copy, Raise Us is possibly the highest-profile example of AI-washing thus far, a well-intentioned but ultimately futile attempt to illustrate that something is being done to cut through the uncertainty and empower the workforce of tomorrow. In reality, it’s a convenient vehicle for a diverse range of stakeholders, including state governments, educators, financial institutions, and companies like Amazon, Anthropic, Microsoft, and the OpenAI Foundation, to virtue-signal that they’re doing their utmost to solve the AI employment crisis and mitigate the impact of this generational technology deployment on everyday workers.”

Like Andersen, he noted that since many of those same companies have already had multiple rounds of tech-driven layoffs, “It’s more than a little rich for them to be joining an initiative whose mission revolves around protecting workers from the worst risks associated with AI.”

But, he said, “If Raise Us can deliver on its lofty promises, it could be an important first step in the great AI workforce transition. Otherwise, it could end up being little more than a high-profile exercise in creating plans and reports and conference panels with little on-the-ground execution to show for it. I’ll believe in its mission when middle-aged, mid-level administrative workers are realistically redirected into new AI-enabled career paths without a massive pay cut.”

Andersen, too, said he’s keeping an open mind, “but something more measurable and specific is in order, or this just looks like a public relations effort.”

Have you seen “The Founder”?

It’s the story of McDonald’s and how Ray Kroc (played by Michael Keaton) transformed the company from a local burger joint to a global landlord. According to the movie, Kroc’s accountant gave him the revelation: “You’re not in the burger business. You’re in the real estate business.”

When brothers Richard and Maurice McDonald transformed their San Bernardino, CA barbecue restaurant into a fast-food burger joint in 1948, their model was to make and sell their own burgers. They would succeed or fail based on making better food products than other restaurants. 

By the time Kroc bought out the McDonald brothers in 1961, the new model was leasing real estate to other people, and those other people would make the food. Whether the original San Bernardino McDonald’s succeeded or failed became irrelevant to the success of the McDonald’s corporation. 

SpaceX has done the same thing. Its San Bernardino location, i.e. xAI, can now succeed or fail without affecting the success of the parent company, which is SpaceX. 

The company this week signed a compute lease with Reflection AI, a pre-revenue startup founded by former Google DeepMind researchers. Under the agreement, Reflection pays $150 million per month for use of the Nvidia GB300 chips housed at Colossus 2, SpaceX’s expansion facility in Memphis, TN. If the lease runs for the full term, SpaceX as a landlord stands to make around $6.3 billion. (Reflection AI has shipped open-weight models, but has no widely adopted frontier model yet, and was reportedly raising capital at a $25 billion valuation.) 

Earlier this month, an S-1 filing revealed that Google agreed to pay SpaceX approximately $920 million per month for 32 months. SpaceX stands to make around $30 billion. And last month, SpaceX disclosed that xAI made a big deal with Anthropic that could bring in to SpaceX as much as $45 billion in revenue.

Regardless of whether xAI — or, for that matter, Reflection, Google, or Anthropic — succeeds or fails, SpaceX still wins. 

Like McDonald’s (which developed its super efficient burger-building system in order to succeed with its San Bernardino location and ended up using that system to succeed as a landlord), SpaceX is using the Colossus infrastructure it built for xAI’s Grok to succeed as an AI landlord.

Grok might succeed, or it might fail. But SpaceX makes bank if Grok’s competitors pay their rent. 

That makes Elon Musk, who is the CEO of SpaceX, the Ray Kroc of AI. 

Nvidia is Mayor McCheese

Colossus originally went online in July 2024, powered by 100,000 Nvidia H100 Hopper GPUs housed in a Supermicro liquid-cooled HGX H100 chassis. The company doubled that to 200,000 GPUs within a short time and it now comprises more than 220,000 Nvidia GPUs including H100, H200, and next-generation Blackwell-class accelerators. The entire fabric runs on Nvidia’s Spectrum-X Ethernet platform, specifically the Spectrum SN5600 switch built on the Spectrum-4 ASIC. 

Also: Nvidia is also heavily invested in companies that are renting compute power on Colossus. The company invests in Anthropic and Reflection AI — and, for that matter, xAI itself and, therefore, SpaceX. 

Nvidia has positioned itself as the Mayor McCheese of the AI industry, collecting taxes at every node of the AI economy. It supplies the GPUs and networking fabric that every frontier lab must train on and collects hardware revenue from the winner’s rivals, even as it profits from the winner’s success. 

So whether Anthropic, Google, Reflection AI, xAI, or some yet-unformed lab produces the dominant model, the computing power was bought from Nvidia and the landlord’s machine was built from Nvidia silicon. 

And Apple is the Hamburglar

Apple’s AI strategy is even more brilliant than Nvidia’s. 

It’s built on a three-tier routing system. When you ask Siri to do something, a built-in orchestrator in the operating system decides how complex the task is. According to third-party estimates, around 85% of requests are handled on your Apple device by Apple’s own small, efficient models. (It does things like summarizing text, prioritizing notifications, cleaning up photos, or suggesting replies.) Roughly 12% of all queries get sent to Private Cloud Compute, Apple’s own server infrastructure running Apple’s larger models on Apple silicon in Apple-owned data centers. Only the hardest 3% of queries get routed to an external partner model.

This design lets Apple avoid the ruinous cost of training a frontier model from scratch. Microsoft, Google, Meta, and Amazon each spend tens of billions of dollars per year on GPU clusters, energy, and research teams to build and run trillion-parameter models. Apple doesn’t. Its own models are deliberately small and run on chips Apple already sells you, so the inference cost is basically absorbed into the device. It only needs a frontier model for that tiny sliver of hard queries, which is where the partnership strategy kicks in.

While frontier labs are collectively spending trillions to build AI infrastructure, Apple is paying Google a mere $1 billion per year to license a custom Gemini model that powers the rebuilt Siri and Apple Intelligence’s complex-query path.

The reason Apple can swap partners is the Foundation Models framework, a native Swift API with a published LanguageModel protocol that any provider can implement. Google’s Gemini conforms to it. Anthropic’s Claude probably conforms to it. Any future model can theoretically conform to it. Apple’s orchestrator routes to whatever model fits the interface, so switching providers means changing routing logic, not rebuilding the whole system. 

Apple profits through several channels. Apple Intelligence requires recent hardware, driving upgrade cycles. Advanced features push users toward higher iCloud storage tiers. 

And so while everyone else is investing trillions, creating what is essentially debt that has to be repaid somehow, Apple is mainly just collecting billions without the massive investments needed by the frontier model companies. 

The AI industry has been telling itself a story: that the companies building the best models will win, that intelligence is the product, that the chatbot with the most capabilities and the cleverest training run will capture the market. That story is wrong. Some of the companies building the best models are tenants. The companies that rent out the compute are landlords. 

Ray Kroc would recognize SpaceX’s strategy immediately. The burger doesn’t matter; the land does. Right now, the most valuable land in the world isn’t in Silicon Valley. It’s a data center complex in Memphis full of hundreds of thousands of GPUs. 

The man who owns it just realized that he’s not in the AI business at all. He’s in the real estate business. (And he’s probably lovin’ it.)

AI disclosures: I don’t use AI for writing. The words you see here are mine. I used a few AI tools via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search as one part of my fact-checking for this column. I used a word processing product called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes. Why I disclose my AI use and encourage you to do the same. 

Microsoft’s controversial claim that its Majorana chip program will make possible a scalable quantum computer by 2029 has been thrown into new doubt by a scientific paper that questions whether the company has correctly interpreted its own experimental evidence.

According to a peer-reviewed paper by Dr. Henry Legg from the University of St Andrews, published this week in Nature, Microsoft’s Topological Gap Protocol (TGP) framework, designed to infer the existence of quantum states in theorized Majorana particles, is flawed.

“Last year Microsoft claimed they had built the equivalent of a precision Swiss watch. However, when I opened the case to examine the mechanism, I found what looked like a chaotic jumble of mismatched parts,” said Legg.

He believed the results gathered from Microsoft’s TGP software data analysis could also be explained by other effects, as well as being skewed by the data chosen for analysis. Because of this, he believed the company’s researchers had jumped to the wrong conclusions.

“Something was making noise, but it didn’t look like the breakthrough Microsoft had claimed. Despite the headlines, the vast majority of scientists in the field were skeptical of Microsoft’s claim from the start; my critique simply backs up that skepticism in the scientific record,” he said.

Topological qubits

The ability to create Majorana ‘zero modes’ that resist the errors suffered by traditional qubit-based designs is fundamental to Microsoft’s entire quantum computing strategy, stretching back two decades. This, of course, assumes the existence of subatomic Majorana fermions, named after the Italian physicist who first proposed them in 1937. To this day, they remain only theoretical.

In 2018, Microsoft said its researchers had detected evidence of their existence, an apparently major breakthrough it was forced to retract when the data was successfully challenged. Nature’s editors subsequently backed this up with the blunt note: “The results in this manuscript do not represent evidence for the presence of Majorana zero modes in the reported devices.”

Despite the setback, Microsoft persisted, in 2025 publishing a new paper in Nature that claimed the company had worked out how to exploit the principle in a new “transistor for the quantum age,” the Majorana 1 chip.

Earlier this month, the company launched its successor, Majorana 2, claiming that AI had helped to achieve a 1,000-fold improvement in reliability compared to the earlier chip. “With this progress, the team now expects to achieve a scalable quantum computer by 2029, cutting its original timeline in half,” the company announced.  

However, Microsoft again finds itself on the defensive after the latest criticism from Legg, who said, “I am simply reflecting what most in the field felt from the initial announcement. I felt that I needed to put these concerns into a formal scientific critique. It is good that it has now been peer-reviewed and published.”

His criticism relates to the transport data system, not the raw experimental data itself, which Microsoft had yet to make fully public in a way that would allow independent analysis.

Microsoft still confident

Microsoft said by email that it remains confident that the Majorana has brought usable quantum computing a step closer, pointing to its collaboration with DARPA as part of the Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program.

“We stand by our results and our roadmap,” said technical fellow and vice president of quantum hardware, Dr. Chetan Nayak. “At the end of the day, success is the delivery of a scalable quantum computer. We are confident in our ability to execute against our roadmap.”

“Skepticism and rigor are hallmarks of the scientific process, which we appreciate and have supported from various academics,” he added. “We have participated in dialogue and our thorough rebuttal was accepted and published by Nature.”

Microsoft is not the only company researching quantum computing hardware, with Google, IBM, and Amazon also working on designs. But even if the hardware matures on the schedule claimed by Microsoft,  many believe the gap between technology and implementation inside enterprises could be more of a slow burn than a sudden jump forward.

This article originally appeared on NetworkWorld.

Apple’s threatened product pricing hike arrived Thursday with steep increases landing across all its products (except the iPhone) — and rapidly accelerating RAM prices are to blame.

Apple CEO Tim Cook had warned last week about the need for price increases, with Bloomberg reporter Mark Gurman subsequently telling us these were “imminent.”

The increases have been imposed as a result of the massive demand for memory and storage components generated by the rapid investment in AI servers. Prices have increased to meet this demand, with vendors shifting manufacturing to the advanced memory modules demanded by AI companies. In doing so, they have not invested in additional production capacity, further exacerbating the demand/supply imbalance.

“Unfortunately, price increases are unavoidable,” Cook told The Wall Street Journal. “We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable.”

The price increases are going to hurt

The price hikes even affect products Apple hasn’t updated in years, such as the HomePod and Apple TV. The extent of the increase is big in some cases — the Apple TV is up 55% while an M3 Ultra Mac Studio sees a 32.5% rise. While I had hoped any higher prices would be aimed at Apple’s high-end products, the extent and scale of the changes suggests the enormity of the component price increases the company has been struggling with.  

“We haven’t seen anything like this in modern Apple,” said Neil Cybart at Above Avalon.

Here’s what starting prices look like now:

Macs

• MacBook Pro, up $300 to $1,999.
• MacBook Air, up $200 to $1,299.
• MacBook Neo, up $100 to $699.
• iMac, up $200 to $1,499.
• Mac mini (M4 Pro), up $200 to $1,599. 
• Mac Studio (M4 Max), up $500 to $2,499.
• Mac Studio (M3 Ultra), up $1,300, to $5,299.

iPads

• iPad Air, up $200 to $749.
• iPad Pro, up $200 to $1,199.
• iPad, now starts at $449.
• iPad mini, now $599.

Other hardware

• Vision Pro, up $200 to $3,699
• HomePod mini, up $30 to $129.
• Apple TV, up $70 to $199.

There were no immediate changes to the iPhone, Apple Watch and AirPods — but no guarantees, either, that their prices won’t also jump.

This is AI-flation

The scale of these increases is like a dose of cold water on the already tepid optimism of investors. This was quickly reflected by a big sell off on Apple stock, which was down more than 6.5% at one point today. And the impact was contagious across the sector as investors realized that if Apple was forced to increase prices even at the lower end of the market it so recently secured, it’s likely other manufacturers will be forced to do the same. This can only further dampen market optimism.

Concerning the MacBook Neo, Francisco Jeronimo, IDC vice president for client devices, said: “The $100 increase takes the entry model from $599 to $699, close to a 17% increase, one of the sharpest percentage hikes in the announcement, and it hits one of Apple’s best-selling, most price-sensitive laptops right as it is winning share. Raising the price of its fastest-selling entry product indicates that Apple believes demand will stay strong, given the value the product offers. It also signals Apple is willing to give up some unit growth at the bottom of the range to defend margin, and it widens the gap the Neo had just closed against Windows entry-level machines.”

The fact of the matter is that consumers are effectively paying an inflationary fee for the benefit of the ongoing global AI rollout. That rollout is already consuming vast quantities of investor cash, despite the shaky business plan of existing incumbents. 

“We have never seen a component price increase this much, this quickly,” Apple ​said in a statement. “We have shielded our customers from these increases so far, but we have now reached a point where we need to begin ⁠raising prices on a number of products, including today’s increases for iPad and Mac.”

Apple and the entire industry will not want to maintain prices at these levels if they can avoid it. That’s going to mean the entire tech industry will now begin leaning on memory manufacturers such as Micron to do more about building out production capacity, or at least promising to do so. Of course, the other hope might be that investors finally see the folly of the AI business model and slow down deployment, though that seems unlikely.

“We know this is not welcome news, and we are working tirelessly to find solutions,” the company said.

Please join me on social media at BlueSky,  LinkedIn, or Mastodon, even better, please subscribe to The Core for your daily collection of human-curated Apple News.

As enterprises deploy increasing numbers of AI agents across applications and organizations, the Linux Foundation on Wednesday announced plans to launch a new Agent Name Service framework designed to establish identity, ownership, and trust for these systems.

The ANS framework, which is expected to allow systems and users to verify who an agent represents, what permissions it has, and whether its code and operational history remain authentic and unchanged, will be based on the existing Domain Name System (DNS), the Foundation said in a statement.

Just like DNS translates human-readable website names into internet addresses, ANS aims to create a standardized naming and discovery layer for AI agents, with the ability for enterprises to publish agent identities through domains they already control, enabling other agents and systems to verify who an agent represents and discover information about its capabilities and ownership before interacting with it, it added.

This, the Foundation further added, creates a federated mechanism for agent discovery and verification without any reliance on any proprietary registry or centralized control.

Growing demand for an agent identity framework

ANS solves an emerging problem for enterprises, especially in scaling AI deployments, said Charlie Dai, principal analyst at Forrester, too. “The agent identity problem is already emerging in early production deployments, particularly where multiple agents interact across tools, APIs, and organizational boundaries without consistent authentication and accountability models,” he said.

“We have seen growing concerns around provenance, authorization scoping, and auditability in agent-to-agent interactions, especially in regulated industries and multi-vendor environments,” Dai said.

Agent identity has become a more critical concern for enterprises, pointed out Jaishiv Prakash, director analyst at Gartner: “Agent identity has moved from an architectural consideration to an operational control-plane gap.”

“The evidence we see from enterprise clients is consistent: they need to know which agent acted, who it represented, what authority it had, and whether its runtime behavior matched its intended design,” Prakash said.

Beyond the problem ANS seeks to solve, analysts also said the framework’s architecture could prove equally important for enterprise adoption.

“For enterprises, one of ANS’s biggest advantages may be its reliance on DNS, especially since they already use it to manage domains and trust. It avoids creating a new registry and lets companies publish and verify agent identities using existing internet infrastructure, making adoption easier and cheaper,” said Pareekh Jain, principal analyst at Pareekh Consulting.

More so because enterprises don’t have to build anything new, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

However, there are downsides to being built on DNS, especially on the security front, Dai cautioned.

“DNS was not originally designed for high-assurance identity. This will make it susceptible to spoofing, hijacking, and latency or propagation inconsistencies that can undermine trust guarantees,” Dai said.

To bypass these security concerns, enterprises should complement ANS with IAM, workload identity, AI gateways, and API security controls, according to Prakash.

The Foundation, though, argues that DNS alone is not intended to serve as the sole trust mechanism inside ANS and the framework supports Decentralized Identifiers (DIDs) and Legal Entity Identifiers (LEIs), allowing enterprises to tie agents to existing digital and organizational identity systems as part of the broader identity verification model.

Battle of the standards

Even so, ANS is entering an increasingly crowded ecosystem of standards and frameworks that enable and govern enterprise AI agents.

While protocols such as MCP and A2A focus on connecting agents to tools and facilitating communication between each other, the Foundation itself hosts two standards that touch on agent identity, discovery, and trust.

One of them is DNS-AI Discovery (DNS-AID), a proposed framework that uses DNS records to help agents advertise their capabilities and make themselves discoverable across networks. Another is AGNTCY, a Cisco-led project that aims to provide a broader infrastructure stack for multi-agent systems, including capabilities for agent discovery, identity, messaging, and observability.

That raises the possibility of fragmentation if competing approaches evolve in parallel.

However, Prakash pointed out that the presence of multiple similar frameworks that touch on agent trust, identity, and discovery shows that the agent infrastructure market has entered its standards discovery phase, not its standards consolidation phase.

“Overlap in discovery, identity, messaging, and observability is expected at this stage,” Prakash said.

Enterprises, thus, the analyst added, should wait for “clarity and clearer interoperability guidance” before they treat any one initiative as strategic infrastructure.

The article originally appeared on InfoWorld.

As France seeks to reduce its dependence on non-European technology suppliers across the public sector, open-source software is playing an increasingly prominent role.

Among the projects that reflect this trend is Nuage, a file-sharing and storage platform developed by the Ministry of National Education for teachers, administrators and other staff. Aimed at its 1.2 million employees within the Ministry, there are now 400,000 active accounts, with around two-thirds of users accessing the service each week. 

Each person is allocated 100GB in storage for documents, PDFs, videos, and images, though the average usage level is around 3GB. Workers can also use the platform to share and co-edit documents with colleagues.

Open source as a model for others

The Ministry’s project is an example of how open-source software used at scale and could serve as a model for other organizations looking to embrace digital sovereignty. Interest in that approach has risen in recent months amid geopolitical and trade tensions, with heightened concerns in Europe that the Trump Administration could suddenly access to certain technologies.

For the French agency, Nuage allows the ministry to retain control over sensitive student-related data stored by teachers, said Benoît Piédallu, national project manager for digital services at the French Ministry of National Education. “We didn’t want this data to go to the US, to Microsoft, to other systems like that. It was important to us to be on premise,” said Piédallu. 

Cost is another factor. The Ministry of Education can allocate around 10 euros per user each year for the project, said Piédallu. “My budget for the platform is less than two million [euros] a year, so price is, of course, a big issue in this matter,” he said.

The Ministry for Education’s digital services team is responsible for design and delivery of the Nuage platform, with the work taking place between the initial deployment in 2020 to the final version release in 2022. 

“The major challenge about deploying an open-source platform is that we have to do everything [internally],” he said, such as running virtual machines, and installing and configuring Linux Debian. “We need to manage everything on this virtual machine, and, of course, to install and configure Nextcloud on it.” 

The Ministry has two dedicated staff members managing the file-storage platform, while another handles the infrastructure. The Nuage platform is hosted at two state-owned data centers: one near Paris, the other in the south of the country near the Pyrenees.

Nuage’s file storage and synchronization features are built on Nextcloud Files, open-source software developed by German vendor Nextcloud. Nuage also includes a document editor app built on Nextcloud Office, which uses Collabora’s open-source software.

User uptake on the upswing

Piédallu said user uptake of the file-storage service is a sign of its success. (A smaller proportion of that 400,000-strong group — 80,000 workers— use the Nuage file sync client on their desktop. Nuage stores 570 million documents with 1.2 petabytes of data.)

This level of adoption has been achieved largely without \ efforts to encourage use internally, said Piédallu. “We didn’t do any major, national communication for our users to start using the service, to make them know they have the opportunity to use 100 gigabyte of backup on this file system,” he said. Even so, adoption continues to rise, with around 40 terabytes more storage required each month to meet demand.

“We have a very linear increase. It is incredible to see that,” said Piédallu.

But with rising storage hardware costs, the Ministry actually hopes to slow the pace of adoption to avoid added infrastructure costs, said Piédallu. “If I do a communication tomorrow it will accelerate usage, and I know that I have a limit in my data center.” 

Even without an adoption push, the Ministry forecasts uptake will increase to 600,000 users by the end of this year. 

Although the digital services team doesn’t have full visibility into how Nuage has been received, Piédallu said feedback is positive, with the file storage and sync system largely invisible to users and operating well — aside from some bugs around synchronization at times. “They are very happy to use it. They don’t make a comparison to Google Drive or OneDrive…, it’s just working,” he said. 

The Collabora-based office application suite has been less well-received, in part because its interface is unfamiliar to many users. “When they want to edit documents, work on a [spreadsheet] or something like that, they want it to be exactly like they are used to — if they have Microsoft Office, they want [it] to work the same, to have the same options,” he said. 

Local administrations and school districts are not required to use Nuage; they can choose whether to deploy the platform or rely on proprietary software. Microsoft SharePoint and Office tools are still in use, for instance, though there are no figures available for how many people are using the software. The Ministry pays around 2.5 million euros a year for Windows licenses, for instance, across 50,000 devices for Ministry staff.

Looking toward tech independence

Digital sovereignty has become a growing priority for the Ministry in recent years, and across the French public sector more broadly, said Piédallu. 

“A few years ago, free software and digital commons were very important; now, it is sovereignty…, to deploy some tools that are sovereign, and that we can deploy on our side with no ‘kill switch,’ et cetera,” he said. “It is something that in the administration, the French administration, we push, and politicians are pushing.”

Nuage is just one of numerous open-source initiatives under way within the French public sector. Other examples include the introduction of LaSuite, an open-source productivity and collaboration suite developed by France’s Interministerial Directorate for Digital Affairs (DINUM). It includes services such as messaging app Tchap and Visio, a video meeting platform. The French government has also set out plans to replace Windows with Linux in parts of the public sector.

Piédallu said other public sector organizations considering open-source software should look to peers that have completed similar projects for guidance. He added that many senior decision-makers overestimate the difficulty of moving away from established technologies.

“Most of the decision people in the hierarchy think that it will be very hard to do. Of course, there is work to do to embrace the change, to help people, to be sure that everything that has been thought about,” said Piédallu. “But at the end, it is possible, it is something that we can do.”

Anthropic has accused Alibaba of using nearly 25,000 fraudulent accounts to extract capabilities from its Claude AI models, in what the US AI company described as the largest known attack of its kind against it.

The campaign, carried out between April 22 and June 5, generated more than 28.8 million exchanges with Claude, according to a June 10 letter Anthropic sent to senior members of the US Senate Banking Committee, Reuters reported.

Anthropic said the effort involved “distillation,” a technique in which a less capable AI model is trained on the outputs of a more advanced system, potentially allowing rivals to replicate some of its capabilities at lower cost.

The company said the campaign was conducted by operators affiliated with Alibaba and Alibaba Qwen, Alibaba’s AI lab, according to the report.

The allegation comes as businesses adopt generative AI tools across business functions, putting pressure on vendors to show they can detect misuse while keeping services available for corporate customers.

The dispute also comes as AI development becomes more closely tied to US-China technology tensions. Anthropic said the alleged campaign could help accelerate China’s ability to reach the capabilities of its advanced Mythos Preview model, while US officials have stepped up scrutiny of advanced AI systems over fears they could be used by military or intelligence users in countries of concern.

In February, Anthropic said it had identified similar campaigns by DeepSeek, Moonshot AI, and MiniMax to extract capabilities from Claude, with the alleged activity ranging from more than 150,000 exchanges by DeepSeek to more than 13 million by MiniMax.

Alibaba did not immediately respond to a request for comment.

A new supply chain risk

If Anthropic’s claims are true, the alleged campaign could allow Alibaba to build a comparable model in a short period of time and offer it at a much lower cost, said Anand Joshi, an AI analyst at TechInsights.

Analysts said the alleged campaign also points to a broader pattern beyond the two companies. Viewed alongside previous incidents cited by Anthropic, they said, model extraction appears to be escalating rather than remaining an isolated risk.

“The enterprise supply chain no longer ends at software, APIs, and cloud regions,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “It now includes rented intelligence, and rented intelligence can be copied and redeployed well outside the safety controls it was born with.”

Gogia said distillation should be a board-level concern because a weaker model trained on a stronger one can inherit its capabilities without the governance and controls around the original system.

For enterprises, the allegations point to a potentially more serious risk than conventional intellectual property theft: reverse engineering at scale. If proven, they would suggest that AI models can be copied systematically, turning model extraction into a new AI supply-chain risk.

“If a rival can clone the exact brain of the AI your company relies on, they can easily find its blind spots, hack your automated systems, or cause the AI vendor to panic and shut down services that your business needs to run every day,” said Pareekh Jain, CEO of Pareekh Consulting.

Mitigating the risks

The allegation raises questions about the controls AI vendors have in place and how customers can protect themselves.

“Vendors should provide verified accounts, smart rate limits, abuse detection, usage monitoring, contractual bans on distillation, incident disclosure, and audit rights,” Jain said. “Enterprises should ask how the vendor detects and blocks large-scale model extraction and can demand contracts that guarantee backup plans and financial refunds if the AI service gets attacked or suddenly shut down.”

Joshi said enterprise customers should also press vendors for greater transparency around model development and safeguards.

“Enterprise buyers should ask what training data was used, how it was trained, what guardrails exist, how they can audit it, and so on,” Joshi said. “Model publishers will have to come up with watermarking technology in models as well as model responses. So if the model ‘skills’ are stolen, they should be able to find the thief.”

The article originally appeared on InfoWorld.

Claude Tag is Anthropic’s latest attempt at getting Claude out of your DMs and into your team’s Slack channels.

AI assistants are increasingly showing up in the workplace to perform research, coding, writing, and analysis, but the results of those interactions typically remains tied to individual conversations rather than being shared across projects and teams.

That limitation is what Anthropic is addressing with Claude Tag, a new Slack channel-based experience for its Enterprise and Team customers, designed to give them a shared AI collaborator that retains context across conversations and participates in work with multiple employees.

Tag will replace Anthropic’s previous attempt at this, Claude in Slack, would only interact with one person (although it’s responses were visible to all in a channel) and its context was limited to the last 20 messages in a channel.

Claude Tag has a much larger context and can be asked to complete tasks on its own, returning with results and a log of how it completed the task for review. It can also schedule follow-up work for itself, enabling projects to continue over hours or days without constant prompting, Anthropic said.

Tag also has an “ambient” mode: when this is enabled, it proactively surfaces relevant information from other channels and connected tools, notifying teams about updates that may be important, and following up on unresolved discussions or tasks, the company said.

Shared context could unlock productivity gains

These features could act as an immediate productivity enhancer for enterprises by reducing coordination overhead and improving collaboration across engineering, developer, and business teams, analysts said.

The biggest benefit for enterprises is the reduction in time spent finding information and rebuilding context across AI interactions, according to Pareekh Jain, principal analyst at Pareekh Consulting. “Because Claude remembers what’s been said across channels, it acts like shared team memory, so no one has to repeat context or hold endless catch-up meetings.”

That reduction in coordination overhead, according to Amit Jena, AI development manager at IT consulting firm Kanerika, could deliver productivity gains that go well beyond the incremental improvements associated with traditional AI assistants.

“For engineering teams, Claude Tag will help reduce time spent on debugging through fragmented Slack discussions, summarizing long incident threads, pulling context across repos, tickets, and logs, and documenting decisions after the fact,” Jena said, while for business teams, “It could enable faster decision-making from thread summaries while reducing follow-ups in cross-functional work.”

Sohail Dev Majumdar, principal analyst at Gartner, though, sees greater benefits than mere productivity gains, particularly for CIOs and other technology leaders.

CIOs may need new governance and ROI metrics

The new offering reflects growing demand among enterprises for AI systems that can work across teams, retain organizational context, participate more actively in day-to-day workflows, and generate more measurable return on investment, he said.

On that last point, though, he warned that CIOs will need to change how they measure ROI for collaborative AI systems compared to traditional AI assistants: “ROI measurement must go beyond license counts, focusing on both hard metrics like time savings and error reduction; and soft metrics, such as employee satisfaction and innovation.”

Jena said CIOs will also need to reconsider auditability and governance around Tag, as it can access context, data, and tools outside individual user boundaries and influence downstream systems.

“CIOs should rethink who can assign tasks to AI agents, what data a channel-level agent can access, how AI-generated outputs are reviewed and approved, how long conversational memory should persist, and. how compliance logs map to AI actions,” Jena said.

With this in mind, Anthropic is shipping controls that will enable system administrators to filter access to data, tools, and Slack channels along with spending limits. These controls, the company said, will also enable administrators to create separate Claude instances for different teams, with each instance limited to the channels and information assigned to it.

Teamwork incentivizes

To encourage adoption, Anthropic is offering a one-time pool of launch credits to eligible organizations, enabling employees to experiment with the service before it begins consuming their regular usage allocation. Eligible Claude Enterprise customers will receive $25,000 in promotional credits, while qualifying Claude Team customers with at least 10 paid seats will receive credits worth $2,500, the company said.

The credits can be used only for Claude Tag interactions in Slack channels and will remain valid through September 1, 2026.

Tag will replace Claude in Slack on August 3, 2026 — or administrators can opt in early in the next 30 days.

Apple’s iOS 27/macOS 27 cycle is revealing something new: AI is only as good as the operating system that supports it. The latest beta releases show that after two years in which the company has promised to become AI-native, testers finally believe it’s happening as Apple prioritizes improved system performance and Siri AI.

For example, the second developer beta (released this week) has clarified the vague “indexing” prompt that showed up two weeks ago, replacing it with a clearer message reading “Optimizing Search and Siri.”

What is indexing doing?

Developers digging into the code found the system is proactively building contextual maps of messages, notes, and photos, allowing the updated on-device architecture to swiftly pull up personal data without compromising privacy. It still takes time, but at least its purpose has been clarified.

The improved indexing seems to deliver smoother device performance overall, reflecting the deep architectural improvements supporting the entire release. Across communities like Reddit, early beta testers are reporting an unprecedented level of refinement for such an early build.

Siri gets snappier

Where external server support isn’t required, beta testers indicate Siri is responding faster. Many are also trying out the new Write with Siri feature that appeared in the second beta with a dedicated command for this feature situated above the keyboard in supported apps.

Write with Siri replaces the earlier Writing Tools panel with a natural language interface fully integrated with Siri, which means it can write responses informed by relevant information from messages, email, and other documents that Siri AI can access. When you tap the prompt, a text input field slides down from the Dynamic Island. The new tool will write contextually-relevant messages in Notes, Mail, and Messages on request, while the original Writing Tools remain.

Testers note that while the personalized context works remarkably well for tracking down past vacation details or messages, some functions such as Visual Intelligence and cross-app action tools are heavily restricted or throttled in early builds.

This reflects that Apple has only actively deployed limited server capacity at this point in the beta cycle, with more capacity scheduled to support full operation once the final versions ship. It is important to note that Apple continues to operate a wait list before providing beta testers with access to Siri AI.

The quiet stuff that matters

The update also checks off a massive list of quality-of-life bug fixes. A notorious glitch that broke screenshot cropping in the earlier beta has been resolved, and chronic Wi-Fi connection drops have been stabilized. Native utilities such as the standalone Passwords app received layout upgrades, adding a swift “+” button directly to the main dashboard to bypass multi-step menus.

The new betas also introduce a clutch of interesting cross-device tools; developers can now fully interact with their phone’s interface using a Mac keyboard and trackpad, which makes testing apps during development much easier. They also note that audio routes flawlessly through desktop hardware when working this way.

For most iPhone users, the big improvement is that Apple has made Handoff faster and more responsive. You’ll also find solid RCS upgrades in Messages and a new Insights feature in Wallet to help improve financial management.

An ecosystem getting ready

The new operating systems also appear to support future product development plans, with code identified in tvOS 27 reportedly including a variety of Apple Intelligence frameworks. That’s going to turn HomePods and Apple TV devices into useful, integrated AI devices — just as watchOS 27 will turn Apple Watch into the most widely-used wearable AI platform.

Apple plans to release the first iOS 27 public beta in July, with the final version arriving for everyone this fall. Sadly, the new Siri AI features that arguably underpin the release will not be made available in Europe or China due to regulatory problems, something that has upset customers.

Apple introduced a beta that feels remarkably close to a public release. But the bigger picture is that by improving the inherent architecture across its platforms, Apple is much better able to support the integrated, ecosystem-wide AI on which its future will be based.

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The AI IPO tsunami on the stock market has only recently gotten under way, with SpaceX’s more-than-$2 trillion IPO likely to be followed in several months by OpenAI’s and Anthropic’s IPOs — each of which is likely to hit $1 trillion.

That will mint three new trillion-dollar AI companies in a matter of months, all of which compete with Microsoft. 

Wall Street has never seen anything like it. Previously, the most money raised by all IPOs in a single year was $671 billion in 2021. It took 38,644 deals to get to that figure. Compare that to three deals this year that by themselves will likely total $4 trillion.

The numbers are eye-popping. 

For Microsoft though, it’s not the numbers themselves that are important. It’s what will happen to the company once it as three newly minted trillion-dollar AI competitors. Until recently, when it came to AI, Microsoft was king of the hill. But can it keep that place?

Microsoft’s weakened position

The IPOs come at a particularly fraught time for Microsoft. At one point, it was the most valuable AI company in the world, with a big head start on the tech industry.

No longer. Microsoft’s stock price has tanked in the past 12 months, even as the S&P index has soared. In the last year, Microsoft’s stock price has dropped 24%, while the S&P has jumped 24%. The two were pretty much in sync until last fall..

Microsoft has fared even worse against its most powerful AI competitor, Google. In June 2025, Google’s total value was $2.1 trillion, well behind Microsoft’s  $3.57 trillion. By  mid-June this year Google was worth $4.5 trillion, Microsoft, $2.8 trillion. That’s entirely due to Google’s AI push and Microsoft’s failure to improve Copilot significantly.

Matt Vellosso, who worked for Microsoft for 14 years — including four as technical advisor to CEO Satya Nadella and then as Partner Director for fostering AI innovation in Windows before leaving in 2023 — is scathing about what he views as Microsoft’s AI failures. 

He warned: “Microsoft missed the internet wave, the mobile wave and now it missed the AI wave.”

Show me the money

The most immediate likely impact on Microsoft after the three massive IPOs take place will be on the company’s stock price, which could well take another hit. Investors don’t want to miss out on the AI boom, and until now, there’s been a relatively small number of companies in which they could. When Anthropic and OpenAI join SpaceX as publicly traded companies, investors will have three more choices than they did only a few months ago. That could make it tougher for Microsoft to attract AI-focused investors. And that, in turn, could knock down its stock price. 

A lower stock price means the company will have a harder time raising money when it needs it. In addition, Microsoft won’t be able to as easily buy other companies in stock-only deals, because the value of its stock will be less.

Still, the IPOs are not all bad news for Microsoft’s stock position. It does, after all, own 27% of OpenAI. So if OpenAI is valued at $1 trillion after its IPO, Microsoft has a $270 billion stake in it.

Thumbs up for increased Azure revenue

One area where these mega IPOs should be unalloyed good news for Microsoft is in Azure revenue, which should significantly. Microsoft’s final divorce settlement with OpenAI requires the latter to buy $250 billion in Azure services through 2030. And its  estimated trillion-dollar valuation ensures the company will be around for the long term, meaning Microsoft can count on that revenue — and possibly more on an ongoing basis.

Microsoft will likely also get tens of billions of dollars from Anthropic for Azure cloud services. Anthropic might spend $43 billion annually on Azure cloud services by 2030, according to estimates by HSBC, one of the world’s largest banking and financial services companies.

Thumbs down for the effect on Copilot

Although Azure use will boom thanks to the IPOs, Microsoft’s own Copilot could face significant competition at a time when it’s having serious problems gaining traction. In its April 2026 earnings call, the company said Microsoft 365 Copilot had 20 million paid seats. That’s up from 15 million paid seats as of January, but still a minuscule number, considering Microsoft has 450 million Microsoft 365 commercial subscribers. That means only about 4% of the company’s business customers have been willing to pay for it since its launch in late 2023.

Beyond that, developers have been leaving GitHub Copilot and turning to Anthropic’s Claude Code and SpaceX’s recently purchased Cursor. A survey by the software development company JetBrains of more than 10,000 developers found that 29% used GitHub Copilot, 18% used Cursor and 18% used Claude Code. 

Early in 2025, GitHub Copilot had a commanding 67% market share.

More downside than up from the IPOs

Although the IPOs do have some upside for Microsoft — dramatically increased Azure revenue and several hundred billion dollars in OpenAI stock holdings — mostly, they’re bad news thanks to increased competition. 

Unless Microsoft significantly improves Copilot for businesses, developers and consumers, the company’s one-time AI dominance will erode even further.

AI has created a tough job environment for entry-level workers and things aren’t getting better anytime soon — even those with AI capabilities now need “senior-level” skills to land a job.

“AI-exposed entry-level roles are seven times more likely to require traditionally senior-level skills such as judgement and leadership,” consulting firm PwC said in a study released this month.

That’s because AI is changing the traditional career ladder. Companies are increasingly looking for candidates that use the cutting-edge tools and services to amplify their performance and grow faster. “Organizations must rethink how they mentor and train junior staff, helping them step up to complex decision-making much earlier in their careers,” PwC said.

Entry-level job seekers with or without AI skills are already dealing with stagnant wages,  layoffs, and stalled hiring. 

(The PwC findings echo similar concerns raised late last year in McKinsey’s State of AI report. Many companies are reducing headcount by deploying AI agents to take over entry-level jobs.)

Early-career AI job postings “have flatlined in highly AI exposed sectors,” and listings for junior roles with mid-career or senior-level skills have grown 35% since 2019, PwC said.  The consulting firm largely discounted the notion that AI is taking jobs away, though other studies point in the opposite direction. 

By the end of May, AI-driven job cuts had reached 87,174 for 2026, already outpacing the total of around 54,836 in 2025, according to figures released by Challenger, Gray and Christmas earlier this month.

The AI-driven layoffs haven’t reached the “jobpocalypse” stage yet, and workers are more productive with it, said Andy Challenger, chief revenue officer at Challenger, Gray and Christmas. But companies are rethinking hiring and long-term operational strategies as AI becomes a routine component in daily workflows and processes, he said.

Businesses are “restructuring aggressively as they reposition for an AI-driven economy,” he said.

That’s putting downward pressure on entry-level hiring as AI tools absorb more routine work, said Kye Mitchell, head of Experis US, a part of ManpowerGroup. “That doesn’t remove opportunity, but it changes the expectations. Employers now expect candidates to come in with hands-on experience, AI familiarity, and the ability to contribute faster,” Mitchell said.

Compensation remains strong for specialized, in-demand skills, while more commoditized roles such as customer service, helpdesk, and some entry-level positions  are flattening. “The shift overall is toward skills-based hiring, where demonstrable capability matters more than credentials alone,” Mitchell said.

Graduates who combine technical fundamentals with practical experience, AI fluency and strong communication skills stand out quickly. Job candidates can’t rely solely on academic credentials. 

“Employers are moving away from ‘train-from-scratch’ hiring and looking for talent that can contribute earlier and continue to adapt,” Mitchell said.

The PwC study also focused on the productivity gap between companies that have invested heavily in AI and companies lagging in adoption.

Since ChatGPT showed up in 2022, AI-exposed companies have seen productivity gains of 40% versus other companies. “The companies achieving the biggest productivity gains from AI are not using it only to cut costs,” PwC said.

AI-forward firms are also raising headcounts and wages. “Far from being a job killer, AI may actually be a job expander when used to unlock growth and enter new markets,” PwC said. 

Workers who use their domain expertise to supplement AI tools can advance, with AI-exposed roles “2.5 times more likely to rely on skills like empathy, judgement, and creativity that become even more valuable as AI absorbs some routine work,” PwC said.

Few things are as delightfully divisive as Android’s dark mode.

Some phones now ship with Android’s darker-style interface activated by default. Most reasonably recent devices offer it as a swift ‘n’ simple toggle. And most people, in my experience, have amusingly strong preferences about which approach they prefer — the standard Android “light” mode, in which screens tend to be bright and with shades of white as a foundation, and the dark mode (a.k.a. “dark theme”), where black and dark gray dominate and everything is much more muted and muddy.

It really is a night and day difference, so to speak — but no matter where you fall on the light vs. dark preference spectrum, it’s well worth your while to noddle over two pertinent points:

1. Android’s dark mode doesn’t have to be an all-or-nothing decision. With the right setup, you can use it as a dynamically activated sometimes switch that enables itself automatically based on different variables and gives you a darker, less glary motif when conditions call for it while leaving you with the lighter, brighter look the rest of the time.
2. Regardless of how often you’re using dark mode, a few easy adjustments will make it meaningfully more complete and effective as an end-to-end interface style for whatever you’re doing on your device.

It occurred to me recently that we’ve gone over several smart Android dark mode enhancements over the past weeks and months — and that, put together into a single power-pack bundle, these small-seeming items can add up to create a pretty dramatic difference in your Android-using experience, whether you’re a full-fledged dark mode convert or a more light-preferring vampire skeptic.

Here, specifically, are five easy ways to make Android’s dark mode meaningfully better for you.

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Android dark mode power-up #1: The app expansion

Up first is a feature that arose as part of our Android 17 discussion and sparked the entire idea for this collection — and that’s the one-tap switch in the latest Android version that forces every app on your phone to follow your dark mode preference, whether the program technically supports such a setting or not.

In Android 17, finding and flipping that switch will make every app turn dark whenever the system-wide dark mode is active. It eliminates the irksome exceptions that’ve traditionally stayed stubbornly light (due to developer laziness) even when your dark theme is on.

If you’ve got Android 17 on your phone already, it couldn’t be much easier to make it happen. Just look in the Display section of your system settings, tap the words “Dark theme,” then change the setting that shows up next from “Standard” to “Expanded.”

Android’s new “Expanded” option lets you force apps into dark mode compliance, even if they aren’t designed to do it on their own.

JR Raphael, Foundry

No Android 17? No problem: On devices with reasonably recent pre-Android-17 system software, you can actually find a switch buried deep in some developer settings that’ll let you enable the exact same option without any waiting.

Follow these instructions and enjoy your new universally consistent darkened dynamic.

Android dark mode power-up #2: A darkened web

That first trick fixes the issue of certain apps not following your dark mode preference — but what about the web? Most of us spend a fair amount of time in our browsers these days, and most websites won’t follow a dark mode setting and adjust their interfaces accordingly.

They absotively can, though. With the flip of a single switch buried within your browser’s bowels, you can force every website into a darkened motif whenever your system-level dark mode is up and running.

See?

Viewing a website in Chrome normally, at left, and with dark-mode-associated web darkening, at right.

JR Raphael, Foundry

Here’s the secret:

• Open up Chrome on whatever Android device you’re using. (And note that this will also work with any Chromium-based Chrome alternative, like Brave, Edge, or Vivaldi.)
• Type chrome:flags into the address bar.
• Type darken into the search box at the top of the screen that comes up next.
• See the line labeled “Darken websites checkbox in themes setting”? Tap the “Default” box beneath it, and change its setting to “Enabled.”
• Tap the blue “Relaunch” button at the bottom of the screen.

Now, when Chrome comes back a second or so later…

• Tap the three-dot menu icon in its upper-right corner.
• Tap “Settings” in the next menu.
• Scroll down until you see “Appearance.” Tap it, then tap “Theme.”
• Make sure the newly added box for “Apply dark theme to sites, when possible” — which we just magically made appear via our last little modification — is checked and active.

Chrome’s web-darkening option appears only after you’ve enabled an out-of-the-way flag.

JR Raphael, Foundry

And that’s it: From that moment onward, whenever your Android device is switched into its dark mode, any website you’re viewing within Chrome will automatically follow suit. Nothing more to it, and no further thought or action ever required on your part.

Not bad, eh?!

Android dark mode power-up #3: A dark mode schedule

Even as someone who isn’t into dark mode as a 24/7 sort of thing, I can definitely appreciate the presence of a dimmer, less glary look on my device in certain specific scenarios.

It’s incredibly easy to overlook or forget, but Google’s actually got a way to handle that for you. In fact, it’s been built into Android itself since 2020’s Android 11 release.

Just look in the Display section of your system settings and tap the line for either “Dark theme” or “Dark mode settings.” If you see a toggle alongside that line, make sure you’re tapping the actual words next to it — not the toggle itself.

Then look for the option to create a schedule.

An Android dark mode schedule, as seen in Google’s standard Android interface (at left) and in Samsung’s Android style (right).

JR Raphael, Foundry

You can create a time-based rule for when your device’s dark mode turns on and back off again, or — more intelligent yet — you can set it to automatically activate at sunset, wherever you are at any given moment, and then turn itself back off and switch you back over to light mode at sunrise.

You can also integrate dark mode into Android’s rarely noticed Bedtime Mode so that the screen getting dimmer is part of your pre-sleep winddown routine, if you really wanna get wild.

Or — ahem…

Android dark mode power-up #4: Contextual dark mode

A dark mode schedule is pretty forkin’ sensible. But the reality is that even with a time-based setup or a sunset-driven activation approach, you’ll still be using your phone in bright rooms with dark mode active and vice-versa.

And if you want Android’s dark theme present only when you’re actually in a dark room — as makes the most sense in my mind — there’s an even more intelligent option.

It comes our way via a handy little free app called Adaptive Theme. That app does one thing and only thing only: It automatically adjusts your device’s dark mode setting based on the actual ambient light around you, using your phone’s sensors rather than an arbitrary time or a not-always-relevant sunset status as a guide. It makes so much sense, you’ll find yourself wondering why your phone didn’t just work that way from the get-go.

The app does unavoidably have a slightly complex one-time setup, which I outline step-by-step here. It’s perfectly safe to do, though, and it shouldn’t take you more than a couple minutes to pull off.

And once you’ve done that, your Android dark mode will just work for you — flipping on when the lighting around you is dim (to your exact specifications) and flipping back off when you’re in brighter surroundings.

Adaptive Theme lets you take total control of how and when Android’s dark mode activates based on your environment.

JR Raphael, Foundry

Yes, please — and thank you. Last but not least…

Android dark mode power-up #5: The wallpaper wizard

Superficial as it may seem, the one piece of the puzzle we haven’t yet addressed — that isn’t ordinarily affected by Android’s dark mode setting — is your home screen wallpaper.

By default, whatever wallpaper you set at the system level stays the same even as your interface moves between its dark and light states — and when you’re anglin’ for a dimmer, less glary look in dark environments, that can be pretty darn jarring.

An app called, rather aptly, Dark/Light Wallpaper Scheduler is the answer you never knew you needed. It’s pretty self-explanatory — you tell it which wallpaper you want when your phone is in dark mode and light mode, then it automatically switches ’em out for you based on that status — but I wrote about it in detail here, if you’re interested in reading more about how exactly it works and how you can make the most of it.

And with that, my fellow Android-appreciating animal, your dark mode power-pack is complete. Now, would someone please turn off the lights? I don’t know about you, but all this talk of darkness has me hankering for a nap.

Wake up to even more useful Android wisdom every Friday with my free Android Intelligence newsletter — one practical new trick to try each week, straight from me to ye.

An extensive program at Meta to gather a wide range of data from employees to train its AI model has been frozen after employees reportedly broke through its guardrails and accessed restricted data, and then did so again after Meta claimed to have fixed the vulnerability.

Whether or not the data collection by the $201 billion owner of Facebook was a good idea, analysts argue that the data protections deployed were woefully inadequate, given the extreme sensitive nature of the collected data.

“Meta had the resources to get it right, and yet they failed exponentially,” said Karianne Michelle, a director with consulting firm Acceligence. “That is what it looks like when the policy decision and the technical execution are happening in two different rooms that are not fully in sync. It is the kind of gap you see often enough at organizations under structural strain.”

Fritz Jean-Louis, principal cybersecurity advisor at Info-Tech Research Group,  agreed.

“What we just observed from the Meta story is a classic failure mode in AI-era data strategy: collecting high-risk telemetry without equally mature access controls,” Jean-Louis said. “At that scale, a single misconfiguration turns internal data into a systemic exposure.”

The story, detailed in a Wired report, involved a program that Meta rolled out in April called the Model Compatibility Initiative (MCI), which collects computer inputs such as mouse movements, click locations, and keystrokes, as well as screen content, the story said. Meta employees were initially not allowed to opt out. 

The data collected included full prompts and transcriptions, private conversations, people and performance data, Wired said, adding, “Meta executives have repeatedly defended the data-gathering project, saying it was necessary to train AI systems to operate computer software the way humans do, and that employees were the best examples for the artificial intelligence to learn from.”

Wired also quoted Stephane Kasriel, a Meta vice president overseeing AI research, who saying that the company discovered that unauthorized employees were found to have accessed MCI data on June 18, and that the hole was closed “within four hours.” But, he added, “ the initial fix didn’t stick, and access to the data had to be further locked down.”

In an email statement, Meta confirmed that the program was being halted for the time being. “We have carefully designed this program with privacy safeguards, and while we have no indication at this time that any data was improperly accessed by Meta employees, we’re pausing it while we investigate,” Meta said. 

A ‘liability surface’

Analysts, consultants, and industry practitioners said they were more concerned about the inadequate protections than the underlying data exposure.

Carmi Levy, an independent technology analyst, said that although there should be concerns about Meta’s “Orwellian oversight of workers’ keystrokes and mouse movements,” the bigger issue is the paper-thin protections that it used to protect that data.

“As creepy as MCI was and is, the reason Meta has hit the pause button has nothing to do with the moral and ethical fuzziness of everyday employee surveillance, and everything to do with its failure to secure the data it collected in the process,” Levy said. “Conceivably, it will resume monitoring and data collection once it fully understands how highly sensitive data, such as employees’ private conversations, performance data, and transcriptions, ended up being inadvertently shared with the entire workforce.”

One of the critical background issues is that while the data collected was highly sensitive, it was not, from a strict compliance law perspective, PII (personally identifiable information). That distinction might have lulled Meta into a false sense of security and convinced it that the data did not merit strong protections. 

“I think companies can get a little too comfortable saying, ‘Well, it’s not PII,’ as if that makes the data low-risk,” said Tom Findling, CEO of Conifers.ai. “But internal prompts, transcripts, chats, data tables, and performance notes can tell you a lot about how a company works, what it’s building and where things are messy or exposed. That’s sensitive, even if it’s not someone’s Social Security number.”

Findling argued that Meta executives “wanted to pretend that they didn’t understand” how sensitive the collected data was, and that was their excuse for why they should not have to protect it sufficiently. “There is no doubt that Meta did not tag this at an appropriate risk level,” he said.

Info-Tech’s Jean-Louis took particular umbrage at the particulars of the collected data. 

“Employee behavioral data, such as keystrokes, screenshots, and usage patterns, is effectively sensitive by default. If you’re using it to train AI, you have to treat it like production secrets, not analytics exhaust,” Jean-Louis said. “When thousands of internal tables are broadly accessible, you have a liability surface rather than a data platform. Trust nowadays is a security control. Once employees believe their data is overcollected or underprotected, you introduce both insider risk and reputational damage at the same time.”

Acceligence’s Michelle echoed Jean-Louis’ concerns.

“The data Meta exposed is not the real risk. Security policy only works if people believe it, and belief is exactly what is now in question,” Michelle said. “That gap is where incidents like this one do their damage: once employees stop trusting what leadership says about their own data, the doubt follows every policy that comes next, producing workarounds, quiet noncompliance, and employees who stop raising flags.”

This article originally appeared on CSOonline.