Although the US unemployment rate held steady at 4.2% in May with 139,000 jobs added to the US workforce, nearly 100,000 layoffs were also announced — up 47% from last year, according to new data from the US Bureau of Labor Statistics and others. Tech and federal cuts led the way in layoffs, driven by economic pressure, programmatic firings and AI-driven shifts in workforce needs, according to outplacement firm Challenger, Gray & Christmas. Technology remains a top sector for cuts amid ongoing disruptions, according to the firm’s data. In May, tech companies announced 10,598 layoffs, bringing the 2025 total to 74,716; that’s up 35% from 55,207 at the same time last year. “Tariffs, funding cuts, consumer spending, and overall economic pessimism are putting intense pressure on companies’ workforces. Companies are spending less, slowing hiring, and sending layoff notices,” Andrew Challenger, senior vice president of Challenger, Gray & Christmas, said in a statement. Uneasiness continues to weigh on tech hiring, according to CompTIA, a provider of IT training and certification products. The unemployment rate for tech jobs in May was 3.4%, roughly in line with April’s 3.5%, CompTIA data showed. The tech unemployment rate continues to sit below the national rate. CompTIA Tech sector companies added a modest 1,571 net new employees in May, analysis of the BLS jobs report by CompTIA showed. Job growth in cloud infrastructure and tech services was offset by reductions in the telecommunications sector. Tech employment across the broader economy declined by an estimated 131,000 positions. “With prior month employment gains, tech occupation employment remains in the positive for the year,” CompTIA said. “It is undoubtedly a challenging time for employers and job seekers facing uncertainty on multiple fronts,” said Tim Herbert, CompTIA’s chief research officer. “At the same time, it requires taking a measured approach given the data continues to hold up reasonably well.” One bright spot for tech hires in May was the finance and insurance industry, which collectively saw a 21% increase in new tech job postings; new tech job openings also rose by 16% in the retail sector, according to CompTIA. Even so, tech layoffs have continued as AI adoption soars and economic pressures drive a major shift toward new roles and skills in the workforce. “AI isn’t replacing jobs,” said Kye Mitchell, president of tech workforce staffing firm Experis US. “It’s fundamentally redefining how work gets done. We’re seeing AI augment skillsets and make professionals more capable, faster, and able to focus on higher-value work.” Technology only displaces jobs when about 80% of tasks can be automated — and AI isn’t close to doing that, said Mitchell. Right now, AI is enhancing skills, boosting productivity, and freeing up time for higher-value work. Hiring for AI positions and those requiring AI skills continues to grow rapidly, according to a CompTIA analysis of data from Lightcast and Stanford University study. CompTIA found that employer job postings related to AI are up 117% year-to-date year-over-year. Challenger, Gray & Christmas Skills-based hiring remains core to many employers’ recruiting strategies. About half of all tech job postings did not specify a need for a four-year academic degree, seeking instead a combination of work experience, training and industry-recognized certification, according to CompTIA’s and other data. Even so, employers are hesitant to hire. “Economic uncertainty is absolutely creating a cautious hiring environment, but it’s more complex than tariffs alone,” Mitchell said. “Our data shows employers adopting a ‘wait and watch’ stance as they monitor economic signals, with job openings down 11% year-over-year.” Still, the tech job market is adjusting as AI adoption grows. AI skill mentions in job postings fell 10% in May but are still up 10% for the year, showing steady demand, Mitchell said. The tech industry had been nearly bullet-proof from mass layoffs prior to 2022. After a hiring surge between 2020 and 2022 to meet digitization efforts as more people worked from home, the market shifted and began slashing jobs to readjust to the new reality. Tech companies such Google, Amazon, Meta (Facebook) and others laid off tens of thousands of workers  as an adjustment to over-hiring during the COVID-19 pandemic. In 2023 alone, 1,186 tech companies laid off about 262,682 staff, compared to 164,969 layoffs in 2022. In January 2024, job cuts leaped 136% over December and hit a 10-month high, according to Challenger, Gray & Christmas. While the labor market remained steady, there are signs that hiring across the board is softening. Open job postings fell 7% this year and new postings dropped 16% in the past month — the first full contraction of 2025. Year-to-date, new postings are flat compared to last year, according to Ger Doyle, ManpowerGroup’s regional president for North America. Doyle, however, was optimistic. “This is a chill, not a freeze,” he said. “Workers and employers are holding steady, awaiting clarity.” For example, he said, project management roles are up 483% year-over-year, and as the broader outlook improves, a rebound could follow, he added.  Demand for data roles is surging as companies shift from AI experiments to execution. Database architect postings are up 2,140% year-over-year, with data scientist postings up 280% — clear signs of companies building the backbone for an AI-driven future, Experis’s data showed. “This shift is also reshaping how talent enters the industry. Entry-level opportunities are becoming more limited, making it harder for recent graduates to gain a foothold,” Mitchell said. “For those looking to break in, deep analytical and technical skills are no longer optional.”

When it comes to Apple, all eyes are on AI. Generative AI (genAI) is the most disruptive technology we’ve seen in years; it is weaving itself into all parts of life – so why should IT management be left unscathed? It won’t be, and the latest AI-powered IT management features within the Jamf platform will soon be the kind of tools IT expects. Jamf is a leading Apple-in-the-enterprise device management (and security vendor recently began offering enterprise support for Android devices). The company has been working away on AI features to support its solutions for some time, and has at last introduced some of these at its Jamf Nation Live event. The tools are designed to boost efficiency and support better decision-making when it comes to handling your fleets. Of course, you’d expect anyone fielding genAI solutions to say something like that, so what do these tools do? Introducing Jamf AI Assistant Available as a beta, AI Assistant is designed to support tech support! That means it will help IT admins find what they need and help them understand how and why devices they do find are configured. Jamf splits these two paths into two categories: Search skill and Explain skill. Search skill lets admins perform natural language inventory queries across their managed fleets, enabling them to quickly find devices within their flotilla that meet the search parameters. The goal is to make it quicker and easier to audit managed devices for compliance, and to troubleshoot when things go wrong. Explain skill caters to another facet of an IT admin’s daily challenges. As Jamf explains, it means the genAI can translate complex configurations and policies into clear, easy-to-understand language. This helps admins make informed decisions, streamline troubleshooting and manage policies more confidently, says Jamf. While these new Jamf tools don’t automate much of the workload facing IT, it’s not hard to see how once the AI can understand what’s happening on a Mac and identify those devices that meet a set of parameters, the only missing piece is to automate some of the workflow in between. This, of course, is the direction of travel and will likely ripple across IT and every platform. Who knows, it might even make the cost of supporting Windows fleets almost as affordable as that of managing fleets of Apple devices. (Though I doubt it.) Beyond AI Jamf also made a handful of announcements outside of AI, including the general availability of Blueprints, a set of tools the company announced at JNUC last year. Blueprints builds on Apple’s Declarative Device Management framework and is designed to simplify and accelerate device configuration by consolidating policies, profiles and restrictions into a single, unified workflow. This makes a lot of sense on a road map to further AI deployment, as well as for anyone attempting to manage and deploy large Apple fleets. I imagine admins preparing for mammoth college- or school-wide deployments will have some optimism that Blueprints could help save time. Don’t neglect that education tech is expected to deploy thousands of devices in a few weeks, so these tools should be significant to them. Jamf continues working on Blueprints, and has introduced a beta release of Configuration Profiles within Blueprints. This tech consists of a new dynamic framework designed to help teams manage devices at scale, thanks to the new dynamic framework for MDM key delivery. Ticket to ride Jamf has offered a Self Service+ portal since earlier this year. Aimed at end-users, the system lets users request, download and update apps, as well as monitor their device security. Those features have been expanded with identity management tools, so users can view their accounts change passwords, and request things like temporary admin access. The beauty of Self Service+ is that it enables users to do these things autonomously while keeping their devices fully auditable and compliant. The idea is that it’s a lot better to focus the expensive tech support teams on the big problems, rather than seeing them bogged down in small, transient (albeit important), challenges.  The company also introduced Compliance Benchmarks. Based on Apple’s macOS Security Compliance Project (mSCP), this system helps IT automate the process of securing their Apple devices. Jamf has also added malware detection to its App Installers module, which means every application made available through that system is scanned to maintain security confidence. That’s really important to companies attempting to provision apps to employees, particularly if they want to avoid accidental installs of hacked malware posing as the original app. You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

Adobe Photoshop was released for iOS in February, and now the popular image editing program is available in a beta version for Android. Users will have access to most of the features in the desktop version, though some tools are hidden until you might need them, according to Ars Technica. Notably, filters are absent, but users do get a number of AI features that make it easy to delete unwanted objects from an image or create content from a text prompt. To use the app, a user must first create an Adobe account and have a mobile phone running Android 11 or later. As long as the app is classified as a beta version, it’s free to use; expect some features to go behind a paywall eventually.

Microsoft has tapped LinkedIn CEO Ryan Roslansky for a dual role leading Microsoft Office and M365 Copilot as the tech company looks to dominate in the enterprise productivity space. Roslansky will continue to serve as LinkedIn CEO, reporting to Microsoft CEO Satya Nadella, as he takes on his new role as EVP of Office under EVP Rajesh Jha. He announced the promotion on LinkedIn. The popular social and recruiting platform for enterprise professionals has steadily increased its revenues and launched new AI-powered products under Roslansky’s leadership, and Microsoft’s move reflects its intent to go all-in on AI. “LinkedIn has been especially successful at building and extending products over time,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. “There is no doubt that Microsoft wants to bring that expertise to  Microsoft 365, especially in the adoption of Copilot.” Successful product leader turned CEO Roslansky will now oversee Office M365 productivity software, which includes Word, Excel, PowerPoint, Outlook, and Teams. Microsoft’s AI assistant, M365 Copilot, which launched in 2020, will also be under his purview. Roslansky has spent 16 years at LinkedIn, five of those as its CEO. Previously, he was SVP of products and content at Glam Media, and general manager and product manager at Yahoo. Microsoft bought LinkedIn for $27 billion in 2016, and in his LinkedIn post, Roslansky called it “one of Microsoft’s most successful acquisitions.” The platform for connecting business professionals achieved $16.37 billion in revenues in 2024, up from $14.9 billion in 2022. LinkedIn has launched numerous AI products in recent years, including AI-assisted messaging, search, and projects, automated follow-ups, gauging candidate likelihood of interest, and resumé search. “Roslansky is a successful product leader turned CEO of a subsidiary company,” said Jeremy Roberts, senior director of research and content at Info-Tech Research Group. “He has a good track record of growing LinkedIn’s revenue year-over-year and largely keeping the platform out of trouble.” Roberts noted that his product bona fides will be “especially useful” as Microsoft figures out how to fit Copilot into its broader product offerings and consolidate its AI strategy between divisions. Amalgam Insights’ Park pointed out that every enterprise application vendor “desperately” wants to own the business AI usage market, and Microsoft is looking to increase the amount of screen time users have with Office 365. “Roslansky‘s success in building LinkedIn as a platform demonstrates the potential to have similar success with 365,” he said. Redefining Microsoft and LinkedIn In his LinkedIn post, Roslansky called Microsoft Office “one of the most iconic product suites in history” that has “shaped how the world works, literally.” He noted that he is coming into the role in “a new, exciting era where productivity, connection, and AI are converging at scale.” “Both Office and LinkedIn are used daily by professionals globally, and I’m looking forward to redefining ourselves in this new world,” he wrote. Roberts noted that pushing deeper integration between its product lines and de-duplicating development efforts is probably also part of Microsoft’s motive for the hire. However, it doesn’t necessarily mean that there will be all sorts of Microsoft Office features natively built into LinkedIn, such as the ability to ask Copilot to build a slideshow in PowerPoint from within LinkedIn, but he believes we could see some rationalization of back-end platforms and services. “LinkedIn has operated quite independently, so this could be part of a broader effort to fold it in, realize some efficiencies, and further Microsoft’s AI ambitions,” said Roberts. On the other hand, it could also be a circumstance where Microsoft had a product in need of a leader, and a successful product leader looking to expand his portfolio. Roberts also emphasized that being in charge of Microsoft Office and M365 Copilot is not the same as being in charge of Microsoft 365, which includes enterprise mobility and security, Windows 11, and a number of other applications. “So it’s both big news and a relatively minor shakeup, depending on what Nadella intends with this move,” said Roberts.

Facing huge fines, Apple on Monday began a legal challenge to the European Commission’s “unreasonable” demand that it open up its platforms to rivals, arguing any such move threatens the foundations of its platforms with a costly process that also undercuts its ability to serve customers. The company is, in a word, furious. It argued that it has cooperated with the Commission’s demands under the Digital Markets Act (DMA) and points to the investments it has already made in complying with that act. What Apple said “At Apple, we design our technology to work seamlessly together, so it can deliver the unique experience our users love and expect from our products,” the company said in a statement. “The EU’s interoperability requirements threaten that foundation, while creating a process that is unreasonable, costly, and stifles innovation. These requirements will also hand data-hungry companies’ sensitive information, which poses massive privacy and security risks to our EU users.” The company also noted that there is a real risk that people’s most sensitive information could be accessed, partially because it becomes so much harder to defend. These attempts are already taking place, Apple said. “Companies have already requested our users’ most sensitive data — from the content of their notifications to a full history of every stored Wi-Fi network on their device — giving them the ability to access personal information that even Apple doesn’t see. In the end, these deeply flawed rules that only target Apple — and no other company will severely limit our ability to deliver innovative products and features to Europe, leading to an inferior user experience for our European customers. We are appealing these decisions on their behalf, and in order to preserve the high-quality experience our European customers expect.” A one-sided approach What seems to really upset Apple is that some aspects of the demands mean the company will effectively be forced to hand its innovations out to businesses with which it is in direct competition — at no charge. That means Apple does not get to draw the full benefits of its work and makes it far more difficult to introduce products in Europe. What makes matters worse is that while Apple is being forced to open up in ways that advantage competitors, quite literally at its expense, it is not being given the opportunity to do the same back. Apple is the only company that these demands have been made of, meaning it is being forced to give its intellectual property away to others who do not need to play by the same rules. Some data hungry companies are already attempting to exploit the DMA to gain unfettered access to sensitive customer data. All the while, Apple is left alone and isolated in its quest to ensure user privacy consistent with GDPR regulation. It’s attempts to protect privacy are about protecting customers. Compliance? We are compliant While critics will continue to sneer and jeer at the company in their quest to rid the world of the “Apple Tax” only the world’s largest developers actually pay, Apple would argue that it has been making serious efforts to comply with the DMA. The company has opened up a portal developers can use to request additional interoperability with hardware and software features inside iPhones and iPads. Apple consistently opens up API access to iPhone, including opening up SMS messaging to RCS, HomeKit features and messaging services support. It has also put in place numerous other enhancements in response to the DMA, and while the warning messages it places when using third-party stores may be stark, this makes them no less true. Europe seems to want customers to use third-party stores with no warning at all that this is what is going on, which seems weird. Malicious regulatory compliance There is a degree to which much of the situation seems to reflect political, rather than economic or moral pressures. The fact that Europe is using Apple as a high profile example, while also refusing to be totally transparent about what it wants before levying any fines, suggests that the Commission is not so much deciding on facts as implementing a political decision using a set of laws that seem designed almost solely to punish one company. That’s the kind of malicious regulatory compliance Apple is furious about — a compliance regime that will now be tested in the courts. Will it make a difference?  Who knows? But the existential battle will decide the future of technology in Europe, and if the market is worth doing business in when compared to the cost of doing so. It will also determine the future of Apple, which will use its considerable resources to find some way to change the nature of the game. One group it seems unlikely to help will be those of Apple’s European customers who are happy and accustomed to the Apple ecosystem, and don’t particularly want to use third-party services, as Apple’s right to offer that “pure Apple” experience seems a likely sacrifice to Europe’s politically-driven zeal. That is, unless cooler heads do curtail the Commission’s attacks. You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

The European Commission has published its full Digital Markets Act (DMA) decision against Apple, and it’s far, far worse than anybody expected. The Commission, the executive arm of the European Union, has accepted absolutely none of Apple’s arguments against being fined, and the decision threatens yet more existential damage to the company. Apple isn’t winning the argument, and, right or wrong, the decision has fangs. Huge fines, big threats Europe announced in April that it would fine Apple an eye-popping €500 million for noncompliance with the DMA, giving Apple 60 days to comply with its decision. One month later, the Commission published the full ruling against Apple, which details that changes the company made to its App Store rules did not go far enough to bring it into compliance. The decision warns that Apple is subject to additional periodic fines in the future if it fails to comply with the Commission’s strict interpretation of the DMA, no matter how inherently punitive some of its demands may be. (Can anyone else spell “tariffs”?) We’ll know soon enough if there are to be wider consequences to Europe’s demands. Apple now has 30 days to fully comply with the DMA (in Europe’s opinion) or face additional fines. The act itself came into force in November 2022 and began to be implemented against companies defined as ‘gatekeepers’ in 2023. The intention is to stop Apple and others from using their market position to impose anticompetitive limitations on developers.  Who is steering? The big bugbear relates to Apple’s anti-steering restrictions, which prevent developers from telling customers they can purchase services outside the App Store. The DMA demands that Apple let developers offer this option, which Apple does, but Europe argues that the limitations the company makes on doing so are not in compliance with the law. Europe also says Apple’s existing restrictions, fees, and technical limitations undermine the effectiveness of the DMA. That seems to mean Apple cannot charge a commission and cannot warn users of the consequences they face when shopping outside the App Store.  The Commission even plays dumb to the potential significance of permitting developers to link out to any website from within their apps, rather than being constrained to approved (and secure) sites. It says Apple has provided insufficient justification for this restriction and also wants Apple to remove messages warning users when they are about to make a transaction outside the App Store.  That’s going to be particularly pleasing to fraudsters, who may now attempt to create fake payment portals that look like reputable ones. Apple prevented $2 billion in fraud last year, the company has confirmed. Perhaps once the first big frauds take place, the EU may catch up to the online risks we all know exist. While I understand the original aim of Europe’s Digital Markets Act, the demands the Commission is making of Apple appear to go far beyond the original objective, which was to open up Apple’s platforms to competition.  The decisions now open Apple’s platform up to competitors.  There is a difference between the two, and, as described, it means Apple must now create and manage its platforms while permitting competitors to profit from those platforms at little or no cost. Apple rejects Europe Apple will fight in Europe.  “There is nothing in the 70-page decision released today that justifies the European Commission’s targeted actions against Apple, which threaten the privacy and security of our users in Europe and force us to give away our technology for free,” the company said. “Their decision and unprecedented fine came after the Commission continuously moved the goalposts on compliance, and repeatedly blocked Apple’s months-long efforts to implement a new solution. The decision is bad for innovation, bad for competition, bad for our products, and bad for users. While we appeal, we’ll continue engaging with the Commission to advocate on behalf of our European customers.” When the fine was initially revealed, the company also said:  “Today’s announcements are yet another example of the European Commission unfairly targeting Apple in a series of decisions that are bad for the privacy and security of our users, bad for products, and force us to give away our technology for free. We have spent hundreds of thousands of engineering hours and made dozens of changes to comply with this law, none of which our users have asked for. Despite countless meetings, the Commission continues to move the goal posts every step of the way.” My take?  Far from saving Europe’s tech industry, the manner in which the DMA is being applied will make the region even less relevant. Lacking a significant platform of its own, Europe’s approach will reduce choice and increase insecurity. As the clear first target of the DMA, Apple will inevitably be forced to increase prices, charge developers more for access to its developer tools, and will I think simply stop selling some products and services in Europe, rather than threaten customer security. We know it can do this because it has done so before. Fundamentally, of course, the big question remains unaddressed: How much profit it is legitimate to make on any product or service? I imagine the European Commission doesn’t want to go near a question as fundamental to capitalist wealth extraction as that. Can you imagine the collapse in executive bonuses that would follow a decision to define what the maximum profit made in any business transaction should be? Lobbyists across the political spectrum would be appalled — that extra profit pays for their meals. Looking to the extent to which the current application of the DMA seems to favor Apple’s biggest competitors, I can’t help but imagine it’s been paying for a few European meals already. Nice work, if you can get it.  You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon, and MeWe.

Meta (Nasdaq:META) is restructuring its AI division into two distinct units, AI Products and AGI Foundations, marking its most significant internal overhaul as it races to compete with OpenAI and Google. The move, detailed in an internal memo from Chief Product Officer Chris Cox and reported by Axios, appoints Connor Hayes to lead AI product integration while Ahmad Al-Dahle and Amir Frenkel will co-direct long-term AGI research. The restructuring comes amid mounting crises, including the delayed Llama 4 Behemoth model and the departure of key Llama architects to competitors like Mistral AI. This marks Meta’s second major AI reorganization since CEO Mark Zuckerberg’s 2023 attempt to “turbocharge” generative AI efforts, which saw the company fall further behind rivals despite early promise. “Structural changes alone won’t solve Meta’s AI challenges,” said Amandeep Singh, practice director at QKS Group. “While the new AGI Foundations unit creates clarity, retaining elite talent requires a seamless pipeline from research to real-world deployment. Meta has struggled with fragmented pipelines and unclear priorities,” added Singh. Talent exodus and technical setbacks The restructuring follows talent losses that have exposed fundamental weaknesses in Meta’s AI strategy. Only three authors remain from the original 14-person Llama research team, according to Business Insider. Internal surveys cited by The Information reveal plummeting morale in Meta’s AI division, where employees cite resource constraints and sluggish progress. “Talent follows momentum, and right now, momentum lives where research decisions directly shape deployed capabilities,” noted Singh. This talent drain coincides with technical setbacks, most notably the underperforming Llama 4 model, which has struggled with reasoning and mathematical tasks. These combined challenges have left Meta playing catch-up in the race toward artificial general intelligence, despite its early open-source advantages. The company’s strategy, bolstered by initiatives such as Llama for Startups and the recent Llama API launch, aims to attract developers and differentiate it from competitors’ proprietary models. But analysts caution these initiatives alone may not be enough to win enterprise trust. The enterprise adoption dilemma While Llama’s cost advantages remain attractive to businesses, growing concerns about its governance controls and the looming copyright lawsuit over training data are giving enterprises pause. “Companies love Llama’s affordability but expanding safety gaps and legal risks are becoming hard to ignore,” Singh said. “For mission-critical applications, many will ultimately choose more reliable, if more expensive, options like GPT or Gemini.” These cost benefits can lose their appeal when weighed against operational risks. Meta’s reorganization attempts to mitigate these concerns through specialized teams, one deploying generative AI across products, another advancing AGI research, but analysts remain skeptical about whether structural changes alone can solve deeper issues. Unlike Microsoft’s turnkey OpenAI integration or Google’s enterprise-ready Vertex AI platform, Meta lacks both the sales infrastructure and compliance pedigree for regulated industries. As Singh argued, “Enterprise AI adoption hinges on proven compliance frameworks, operational reliability, and mature support systems. Meta still needs to build that trust at Fortune 500 scale.” Meta’s race to close the AGI gap “Meta’s AGI push focused on models with reasoning, multimedia, and voice capabilities aligns with the broader industry trend toward multimodal AI as a catalyst for enterprise transformation,” said Surjyadeb Goswami, research director for AI and Automation at IDC Asia Pacific. He noted that open-source models are critical to enabling cost-effective, transparent, and customizable deployments, especially as organizations deepen their GenAI investments. For Meta to truly capitalize on this opportunity and succeed in its AGI bid, it must rebuild trust especially for enterprise adoption. Singh highlighted the need for Linux-like community stewardship, OpenAI-level safety protocols, and robust enterprise tooling. “Balancing openness with responsibility is Meta’s real challenge, especially as models approach general-purpose cognitive capability.” Meta now needs to show this reorganization yields meaningful improvements in model performance, talent retention, and enterprise adoption to validate its new approach. “Meta’s open-source vision is bold, but execution is everything,” Singh concluded.

Microsoft is giving its Windows Update software stack more power, and the tool will soon be able to update other software and drivers within Windows systems. The company is establishing the capability for system administrators to wrangle all software updates into a one-click experience, Microsoft said in a blog post on Wednesday. Sysadmins today have to run Windows Update to keep the OS updated, and separately patch individual pieces of software, which can be a lot of work. “To solve this, we’re building a vision for a unified, intelligent update orchestration platform capable of supporting any update (apps, drivers, etc.) to be orchestrated alongside Windows updates,” Microsoft said. Typically, system administrators deploy patch management tools to update Windows and related enterprise software, but Microsoft wants to bring it all to a Windows Update-style deployment. Potential benefits include more streamlined and lower-cost deployment of updates, the company said. A unified patch management system also reduces computing requirements. The current process for doing updates to Windows systems is a hodgepodge of different tools and techniques, said Jack Gold, principal analyst at J. Gold Associates. “I applaud Microsoft for finally trying to bring all of this under one umbrella but wonder why it took them so long to do this,” Gold said. In addition to Windows, Windows Update today updates Microsoft’s development tools such as .NET and Defender, and also updates system drivers. With ARM-based PCs, it also delivers system BIOS and firmware so users don’t have to download it from the PC maker’s website. But how quickly companies adopt this new way of doing things will depend on how easy Microsoft makes it to adopt the new service, Gold said. Microsoft is providing a tool for software providers to put their software updates into its orchestration platform. The company has only provided information on how developers can test it out with their applications, and Microsoft will then provide further information. Developers who have access to the Windows Runtime environment can test it out and implement it. APIs are also available to test out the system. Microsoft separately announced that Windows Backup for Organizations, a data backup feature announced last year, is now in public preview. The product will allow for a smooth transition to Windows 11 from Windows 10 for enterprises, the company said. Windows 10 support ends in October 2025. “This capability helps reduce migration overhead, minimize user disruption, and strengthen device resilience against incidents,” Microsoft wrote in a blog entry. Microsoft’s Entra identity authentication is a key component of such transitions via Windows Backup for Organizations, Microsoft said. Further reading: How to handle Windows 10 and 11 updates Windows 10: A guide to the updates Windows 11: A guide to the updates How to preview and deploy Windows 10 and 11 updates How to troubleshoot and reset Windows Update How to keep your apps up to date in Windows 10 and 11

Other nations can learn much from Canada when it comes to artificial intelligence advances. For one thing, “the focus and nurturing of AI needs ongoing attention and investments; otherwise, that leadership in AI can be lost,” an industry analyst said Wednesday. Bill Wong, research fellow at Info-Tech Research Group, was responding to the recent appointment of MP Evan Solomon, a former journalist, as Canada’s first Minister of Artificial Intelligence and Digital Innovation in the federal cabinet of Prime Minister Mark Carney. In the past, he said, “Canada has been viewed as an AI leader around the world with respect to AI research, especially with thought leaders like Geoffrey Hinton, Yoshua Bengio, and Richard Sutton.” However, he noted, “despite the recognition, critics would cite that [it] has fallen behind and challenged when it comes to monetizing  AI investments. As part of the government’s election platform, the government promised to move fast on building data centers, introduce a tax credit to incentivize AI adoption by small and medium-sized businesses, and push to expand programs at Canada’s artificial intelligence institutes to drive AI commercialization.” In a commentary on the appointment, the Macdonald-Laurier Institute, a policy think tank based in Ottawa, Ontario, stated that it “signals a consolidation of federal focus on a field that has historically been spread across numerous portfolios … Solomon’s challenge will be to distinguish between productivity enhancing AI and ‘so-so’ automation — harnessing the benefits of AI, while ensuring adequate regulation to mitigate associated risks.” AI is a ‘geopolitical force’ Canada, the organization stated, “must close the gap between AI innovation and adoption by pursuing policies that encourage productivity-boosting AI — applications that augment workers and make them more efficient, rather than simply replace them. The answer is a multi-level policy framework that accelerates the uptake of AI in ways that enhance output, job quality, and workforce participation.” Wong noted, “Canada was the first country to deliver its national AI strategy; the appointment of the country’s first AI minister can be viewed as a natural evolution of Canada’s adoption of AI at a national level.” The appointment of Solomon, he said, “demonstrates just how important AI is to the future of Canada and its people. While AI is considered a technology disruptor, its impact is far-reaching, and it will impact every industry and the national economy.” And while having a government ministry of AI is not the norm for most countries today, he said, “the importance of this role to the country’s economy and national security is growing. Internationally, AI has become a geopolitical force; an example of this would be the US imposing export controls on high-end AI chip technology to China.” The upcoming  G7 meeting in Kananaskis, Alberta, from June 15 to 17,  said Wong, “provides an opportunity for Canada to demonstrate its AI leadership on an international stage. While it’s a short runway to that event, Canada should promote its best practices for deploying AI in the public sector, its plans to democratize the benefits of AI to its people, and demonstrate its thought leadership by sharing research and data.” The Carney government, he said, also has a “mandate to improve its use of AI to improve productivity as well as increase the adoption of AI by private industry. A recent Deloitte study cited that only 26% of Canadian organizations have implemented AI, compared with 34% globally.” AI compute fabric in the works In the private sector, Bell Canada on Wednesday announced Bell AI Fabric, an investment, it said, “that will create the country’s largest AI compute project.” The telco plans to create a national network that will start with a “data center supercluster in British Columbia that will aim to provide upwards of 500 MW of hydro-electric powered AI compute capacity across six facilities.” The first facility, a release stated, will come online this month in partnership with AI chip provider Groq, with additional facilities being operational by the end of 2026, including two at Thompson Rivers University (TRU) in Kamloops, BC. Bell said that the data centers at TRU “will be designed to host AI training and inference, providing students and faculty with access to cutting-edge compute capabilities, both at TRU and nationally through integration with the BCNET network. The data centre is also being integrated into the district energy system, with waste heat being repurposed to provide energy to TRU’s buildings.” Further reading: AI and economic pressures reshape tech jobs amid layoffs Microsoft cements its AI lead with one hosting service to rule them all Real-world use cases for agentic AI AI vs. copyright How to train an AI-enabled workforce — and why you need to > >

Tech layoffs have continued in 2025. Much of that is being blamed on a combination of a slower economy and the adoption of automation via artificial intelligence. Nearly four in 10 Americans, for instance, believe generative AI (genAI) could diminish the number of available jobs as it advances, according to a study released in October by the New York Federal Reserve Bank. And the World Economic Forum’s Jobs Initiative study found that close to half (44%) of worker skills will be disrupted in the next five years — and 40% of tasks will be affected by the use of genAI tools and the large language models (LLMs) that underpin them. In April, the US tech industry lost 214,000 positions as companies shifted toward AI roles and skills-based hiring amid economic uncertainty. Tech sector companies reduced staffing by a net 7,000 positions in April, an analysis of data released by the US Bureau of Labor Statistics (BLS) showed. This year, 137 tech companies have fired 62,114 tech employees, according to Layoffs.fyi. Efforts to reduce headcount at government agencies by the unofficial US Department of Government Efficiency (DOGE) saw an additional 61,296 federal workers fired this year. Kye Mitchell, president of tech workforce staffing firm Experis US, believes the IT employment market is undergoing a fundamental transformation rather than experiencing traditional cyclical layoffs. Although Experis is seeing a 13% month-over-month decline in traditional software developer postings, it doesn’t represent “job destruction, it’s market evolution,” Mitchell said. “What we’re witnessing is the emergence of strategic technology orchestrators who harness AI to drive unprecedented business value,” she said. For example, organizations that once deployed two scrum teams of ten people to develop high-quality software are now achieving superior results with a single team of five AI-empowered developers. “This isn’t about cutting jobs; it’s about elevating roles,” Mitchell said. Specialized roles in particular are surging. Database architect positions are up 2,312%, statistician roles have increased 382%, and jobs for mathematicians have increased 1,272%. “These aren’t replacements; they’re vital for an AI-driven future,” she said. In fact, it’s an IT talent gap, not an employee surplus, that is now challenging organizations — and will continue to do so. With 76% of IT employers already struggling to find skilled tech talent, the market fundamentals favor skilled professionals, according to Mitchell. “The question isn’t whether there will be IT jobs — it’s whether we can develop the right skills fast enough to meet demand,” she said. For federal tech workers, outdated systems and slow procurement make it hard to attract and keep top tech talent. Agencies expect fast team deployment but operate with rigid, outdated processes, according to Justin Vianello, CEO of technology workforce development firm SkillStorm. Long security clearance delays add cost and time, often forcing companies to hire expensive, already-cleared talent. Meanwhile, modern technologists want to use current tools and make an impact — something hard to do with legacy systems and decade-long modernization efforts, he added. Many suggest turning to AI to will solve the tech talent shortage, but there is no evidence that AI will lead to a reduction in demand for tech talent, Vianello said. “On the contrary, companies see that the demand for tech talent has increased as they invest in preparing their workforce to properly use AI tools,” he said. A shortage of qualified talent is a bigger barrier to hiring than AI automation, he said, because organizations struggle to find candidates with the right certifications, skills, and clearances — especially in cloud, cybersecurity, and AI. Tech workers often lack skills in these areas because technology evolves faster than education and training can keep up, Vianello said. And while AI helps automate routine tasks, it can’t replace the strategic roles filled by skilled professionals. Seven out of 10 US organizations are struggling to find skilled workers to fill roles in an ever-evolving digital transformation landscape, and genAI has added to that headache, according to a ManpowerGroup survey released earlier this year. Job postings for AI skills surged 2,000% in 2024, but education and training in this area haven’t kept pace, according to Kelly Stratman, global ecosystem relationships enablement leader at Ernst & Young. “As formal education and training in AI skills still lag, it results in a shortage of AI talent that can effectively manage these technologies and demands,” she said in an earlier interview. “The AI talent shortage is most prominent among highly technical roles like data scientists/analysts, machine learning engineers, and software developers.” Economic uncertainty is creating a cautious hiring environment, but it’s more complex than tariffs alone. Experis data shows employers adopting a “wait and watch” stance as they monitor economic signals, with job openings down 11% year-over-year, according to Mitchell. “However, the bigger story is strategic workforce planning in an era of rapid technological change. Companies are being incredibly precise about where they allocate resources. Not because of economic pressure alone, but because the skills landscape is shifting so rapidly,” Mitchell said. “They’re prioritizing mission-critical roles while restructuring others around AI capabilities.” Top organizations see AI as a strategic shift, not just cost-cutting. Cutting talent now risks weakening core areas like cybersecurity, according to Mitchell. Skillstorm’s Vianello suggests that IT job hunters should begin to upgrade their skills with certifications that matter: AWS, Azure, CISSP, Security+, and AI/ML credentials open doors quickly, he said. “Veterans, in particular, have an edge; they bring leadership, discipline, and security clearances. Apprenticeships and fellowships offer a fast track into full-time roles by giving you experience that actually counts. And don’t overlook the intangibles: soft skills and project leadership are what elevate technologists into impact-makers,” Vianello said. Skills-based hiring has been on the rise for several years, as organizations seek to fill specific needs for big data analytics, programing (such as Rust), and AI prompt engineering. In fact, demand for genAI courses is surging, passing all other tech skills courses spanning fields from data science to cybersecurity, project management, and marketing. “AI isn’t replacing jobs — it’s fundamentally redefining how work gets done. The break point where technology truly displaces a position is when roughly 80% of tasks can be fully automated,” Mitchell said. “We’re nowhere near that threshold for most roles. Instead, we’re seeing AI augment skill sets and make professionals more capable, faster, and able to focus on higher-value work.” Leaders use AI as a strategic enabler — embedding it to enhance, not compete with, human developers, she said. Some industry forecasts predict a 30% productivity boost from AI tools, potentially adding more than $1.5 trillion to global GDP. For example, AI tools are expected to perform the lion’s share of coding. Techniques where humans use AI-augmented coding tools, such as “vibe coding,” are set to revolutionize software development by creating source code, generating tests automatically, and freeing up developer time for innovation instead of debugging code.  With vibe coding, developers use natural language in a conversational way that prompts the AI model to offer contextual ideas and generate code based on the conversation. By 2028, 75% of professional developers will be using vibe coding and other genAI-powered coding tools, up from less than 10% in September 2023, according to Gartner Research. And within three years, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering tool chain — a significant increase from approximately 15% early last year, Gartner said. A report from MIT Technology Review Insights found that 94% of business leaders now use genAI in software development, with 82% applying it in multiple stages — and 26% in four or more. Some industry experts place genAI’s use in creating code much higher. “What we are finding is that we’re three to six months from a world where AI is writing 90% of the code. And then in 12 months, we may be in a world where AI is writing essentially all of the code,” Anthropic CEO Dario Amodei said in a recent report and video interview. “The real [AI] transformation is in role evolution. Developers are becoming strategic technology orchestrators,” Mitchell from Experis said. “Data professionals are becoming business problem solvers. The demand isn’t disappearing; it’s becoming more sophisticated and more valuable. “In today’s economic climate, having the right tech talent with AI-enhanced capabilities isn’t a nice-to-have, it’s your competitive edge,” she said.

Apple — and almost everybody else — has gotten a slight reprieve as a US court yesterday set aside the Trump tariff tax. But conflict and confusion continue to batter global trade, and while the news will provide a glimmer of relief, it will probably be short-lived. There’s always another dead cat to throw into the flames. Three judges from the US Court of International Trade found that the US International Emergency Economic Powers Act, which the Trump administration invoked to justify the imposition of these tariffs, does not give the president the authority to levy these taxes on trade. “The court does not read IEEPA to confer such unbounded authority and sets aside the challenged tariffs imposed thereunder,” they wrote. The judgement does not impact the 25% “trafficking tariffs” imposed on Mexican and Canadian products and does not prevent the 20% trafficking tariff in place on Chinese goods. It does, however, end the “worldwide and retaliatory” 10-50% tariffs the administration threw at 57 countries. A coalition of small businesses took the case to court, arguing that only Congress has the authority to levy tariffs under the law used by the president’s office. They seem to have prevailed in the argument — at least, so far. It is interesting to note that the administration wanted all the tariff-related lawsuits moved to this particular court, as it felt it would receptive to the administration’s arguments.  This turned out to be an error. What is an emergency? Responding, a White House statement from spokesperson Kush Desai maintained the need for these tariffs, calling US trade deficits a “national emergency that has decimated American communities, left our workers behind and weakened our defense industrial base — facts that the court did not dispute.”  But can a trade in cheap consumer goods be seen as an unusual threat after it has been part of US culture for decades? Not according to the US Court of International Trade. The judges say the trade deficit does not meet the Nixon-era International Emergency Economic Powers Act requirement that an emergency can only be triggered by an “unusual and extraordinary threat.”  The journey is by no means over, of course. With the president recently threatening additional tariffs on iPhones made in India (“I have a bit of a problem with my friend, Tim Cook”), the reprieve may be brief.  Desai’s statement said “unelected judges” are not the right people to decide how to handle what he calls a national emergency. “The administration is committed to using every lever of executive power to address this crisis and restore American greatness.”  It seems likely to end at the Supreme Court, even while the administration argues that it should not be bound by the checks and balances that still remain under the US Constitution. For now, an appeal has been lodged with the United States Court of Appeals for the Federal Circuit in Washington.  Where is the off-ramp? Apple, the world’s biggest consumer electronics company, which contributes a fortune to the US treasury and employs tens of thousands of Americans, will likely be relieved the tariffs have been set aside.  The reprieve implies that US consumers won’t need to pay more for their iPhones for a little longer yet and better reflects the reality that even if Apple were to shift iPhone manufacturing to the US, doing so would take years, cost billions, require engineering skills in quantities that do not yet exist in the US, would involve automation rather than large numbers of new jobs, and would be hampered by the availability of components and materials.  For the time being, at least, the judgment is a significant obstacle to the tariff taxes, albeit one that casts another spanner in the works for ongoing international trade talks. However, there is still scope for the administration to impose sector-specific taxes. All the same, “Tim Apple” will be acutely aware that the future will not look like the past, and the company’s $500 billion investment in the US will be part of the company’s future approach to manufacturing and trade. It suggests that while moving iPhone manufacturing to the US may be impractical, moving manufacture of some components and hardware may make sense. It is possible that as Apple and the US administration continue to negotiate, they may yet identify a road that enables both to declare some form of victory. You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

French startup Mistral AI on Wednesday unveiled Codestral Embed, its first code-specific embedding model, claiming it outperforms rival offerings from OpenAI, Cohere, and Voyage. The company said the model supports configurable embedding outputs with varying dimensions and precision levels, allowing users to manage trade-offs between retrieval performance and storage requirements. “Codestral Embed with dimension 256 and int8 precision still performs better than any model from our competitors,” Mistral AI said in a statement. Codestral Embed is designed for use cases such as code completion, editing, or explanation tasks. It can also be applied in semantic search, duplicate detection, and repository-level analytics across large-scale codebases, the company said.“Codestral Embed supports unsupervised grouping of code based on functionality or structure,” Mistral AI added. “This is useful for analyzing repository composition, identifying emergent architecture patterns, or feeding into automated documentation and categorization systems.” The model is available through Mistral’s API under the name codestral-embed-2505, priced at $0.15 per million tokens. A batch API version is offered at a 50 percent discount, and on-premise deployments are available through direct consultation with the company’s applied AI team.The launch follows Mistral’s recent introduction of the Agents API, which the company said complements its Chat Completion API and is intended to simplify the development of agent-based applications. Enterprise interest in embeddings Advanced code embedding models are gaining traction as key tools in enterprise software development, offering improvements in productivity, code quality, and risk management across the software lifecycle. “Models like Mistral’s Codestral Embed enable precise semantic code search and similarity detection, allowing enterprises to quickly identify reusable code and near-duplicates across large repositories,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “By facilitating rapid retrieval of relevant code snippets for bug fixes, feature enhancements, or onboarding, these embeddings significantly improve maintenance workflows.” However, despite promising early benchmarks, the long-term value of such models will depend on how well they perform in production environments. Factors such as ease of integration, scalability across enterprise systems, and consistency under real-world coding conditions will play a crucial role in determining their adoption. “Codestral Embed’s strong technical foundation and flexible deployment options make it a compelling solution for AI-driven software development, though its real-world impact will require validation beyond initial benchmark results,” Ram added. Further reading Vector Institute aims to clear up confusion about AI model performance When LLMs become influencers Researchers reveal flaws in AI agent benchmarking What misleading Meta Llama 4 benchmark scores show enterprise leaders about evaluating AI performance claims How CIOs navigate generative AI in the enterprise

Microsoft and Google are taking distinctive approaches with AI agents in their productivity suites, and enterprises need to account for the differences when formulating digital labor strategies, analysts said. In recent months, both companies have announced a dizzying array of new agents aimed at extracting value from corporate documents and maximizing efficiency. The tech giants have dropped numerous hints about where they’re headed with AI agents in their respective office suites, Microsoft 365 and Google Workspace. Microsoft is reshaping its Copilot assistant as a series of tools to create, tap into, and act on insights at individual and organizational levels. The Microsoft 365 roadmap lists hundreds of specialized AI tools under development to automate work for functions such as HR and accounting. The company is also developing smaller AI models to carry out specific functions. Google is going the opposite way, with its large-language model Gemini at the heart of Workspace. Google offers tools that include Gems for workers to create simple custom agents that automate tasks such as customer service, and Agentspace in Google Cloud to build more complex custom agents for collaboration and workflow management. At the recent Google I/O developer conference, the company added real-time speech translation to Google Meet. “For both, the goal is to bring usable and practical productivity and efficiency capabilities to work tools,” said Liz Miller, vice president and principal analyst at Constellation Research. But the differing AI agent strategies are heavily rooted in each company’s philosophical approaches to productivity. Although Microsoft has long encouraged customers to move from its traditional “perpetual-license” Office suite to the Microsoft 365 subscription-based model, M365 notably retains the familiar desktop apps. Google Workspace, on the other hand, has always been cloud-based. Microsoft users are typically a bit more tethered to traditional enterprise work styles, while Google has always been the “cloud-first darling for smaller organizations that still crave real-time collaboration,” Miller said. When it comes to the generative AI models being integrated into the two office suites, “Google’s Gemini models are beating out the models being deployed by Microsoft,” Miller said. “But as Microsoft expands its model ‘inventory’ in use across M365, this could change.” Microsoft has an advantage, as many desktop users live in Outlook or Word. The intelligence Copilot can bring from CRM software is readily available, while that integration is more complex in the cloud-native Google Workspace. “Microsoft still has an edge in a foundational understanding of work and the capacity to extend Copilot connections across applications as expansive as the Office suite through to Dynamics, giving AI a greater opportunity to be present in the spaces and presentation layers where workers enjoy working,” Miller said. Microsoft’s Copilot Agents and Google’s Gems and Agentspace are in their early stages, but there have been positive developments, said J.P. Gownder, a vice president and principal analyst on Forrester’s Future of Work team. Microsoft recently adopted Google’s A2A protocol, which makes it easier for users of both productivity suites to collaborate and unlock value from stagnant data sitting on other platforms. “That should be a win for interoperability,” Gownder said. But most companies that are Microsoft shops have years or decades of digital assets that hold them back from considering Google, he said. For example, Excel macros, pivot tables, and customizations cannot be easily or automatically migrated to Google Sheets, he said. “As early as this market is, I don’t think it’s fair to rank either player — Microsoft or Google — as being the leader; both of them are constructing new ecosystems to support the growth of agentic AI,” Gownder said. Most Microsoft Office users have moved to M365, but AI is helping Google is making inroads into larger organizations, especially among enterprises that are newer and less oriented toward legacy Microsoft products, said Jack Gold, principal analyst at J. Gold Associates. Technologies like A2A blur the line between on-premises and cloud productivity. As a result, “Google Workspace is no longer perceived as inferior, as it had been in the past,” Gold said. And for budget-constrained enterprises, the value of AI agent features is not the only consideration. “There is also the cost equation at work here, as Google seems to have a much more transparent cost structure than Microsoft with all of its user classes and discounts,” Gold said. Microsoft does not include Copilot in its M365 subscriptions, which vary in price depending on the type of customer. The Copilot business subscriptions range from $30 per user per month for M365 Copilot to $200 per month for 25,000 messages for Copilot Studio, which is also available under a pay-as-you-go model. Google has flat subscription pricing for Workspace, starting at $14 per user per month for business plans with Gemini included.

Google has started introducing ads in its AI-generated search results, called “AI Overviews” and “AI Mode,” in the United States. The ads are placed below the AI-generated summaries and are labeled as “sponsored.” The company claims that users find these ads helpful.Bleeping Computer writes that according to a Google support document, internal data shows that users appreciate the ads because they connect them to relevant companies, products, and services at the right time. However, Google does not disclose the methodology behind the internal data.The change is part of Google’s strategy to retain its dominance in digital advertising, especially in increasing competition with players like OpenAI and Microsoft.At the same time, some publishers have previously expressed concerns that these AI summaries could reduce traffic to their websites, which could negatively impact their advertising revenue.

Chinese AI startup DeepSeek has released an update to the R1 reasoning AI model that took the tech world by storm when it was launched at the beginning of the year. The open-source model sent shockwaves through the AI industry as its efficient use of compute and memory resources helped it match leading US models’ performance at a fraction of the cost. The new version, DeepSeek-R1-0528, has a whopping 685 billion parameters, meaning it can perform on par with competitors such as o3 from OpenAI and Gemini 2.5 Pro from Google. The company says the model’s accuracy has increased from 70% to 87.5%, which means that the risk of hallucinations has been significantly reduced. Furthermore, DeepSeek has reportedly become better at so-called vibe coding, where AI tools generate code based on natural-language conversations with programmers. TechCrunch notes that Deepseek-R1-0528 likely cannot run on consumer computers without modification, which signals that the target audience is professional users. The new version of DeepSeek can be downloaded from the Hugging Face platform. Detailed information on the changes is also available there.

What’s a company to do when it faces a multitude of existential crises and seems unable to regain control of the media message? It throws a few dead cats on the table to distract everybody. This aeons-old approach to public relations has recently been rechristened as “flooding the zone” — and it is what I think Apple is doing as news surfaces that it plans to announce a change to the way it names its operating systems in June at its big developer event, WWDC. That means we won’t ever see iOS 19 but will see iOS 26 instead. It also means: A new Apple OS order iPadOS 26 macOS 26 tvOS 26 visionOS 26 watchOS 26 homeOS 26 You can see the pattern, I hope. The 26 refers to the main year in which an operating system exists, which is a little confusing, as it means we need to look forward to iOS 27 (et al.) in 2026.  I don’t think there’s anything particularly contentious about this change: it’s great for me, as it makes it far less likely I’ll use an incorrect OS version number in my work. But for Apple it’s led to a tidal wave of reports around the decision, and while not all of those are positively spun, the fact that the yarns are being worked at all restores some of Apple’s control over the narrative. Existential threats That’s a degree of media management the company needs, given that almost every other story being written about it at this time involves tariffs, regulation, or its problems with artificial intelligence. Even its bigger stories that should have been good news are delivering unexpected results — for example, what should be seen as huge success in pivoting its manufacturing to India is being seen through the lens of nativist US regulation and tariffs.  Also in politics, Europe’s regulators seem to want to turn Apple regulation into a weapon for use in negotiations over US trade. And in the background, the company’s former Chief Designer Jony Ive’s move to get to work on new devices with OpenAI raises all the ghosts of Expos past. What’s Apple to do? Well, right now it can’t ask Siri for comfort, but maybe that will change next year — and we don’t want people paying too much attention to AI, do we? Apple certainly doesn’t seem to want to discuss any of these challenges too much. The decision by Apple executives not to take part in John Gruber’s traditional WWDC ‘talk show’ represents a company whose leaders feel the need to manage the messaging. Apple has participated in these conversations for a decade, so the change is noteworthy. Waiting for the sun (in the Solarium) That Apple chooses these moments just before WWDC to use its official unofficial news and speculation reporters at Bloomberg to share news about the future of its operating system names represents something similar. First, it gets people talking about something else in the here and now. Second, it begins to set relatively low expectations for Apple’s big developer event. The operating systems will get a facelift (Solarium) designed to make them all work a little more similarly, the iPad will gain tools to make it more Mac-like, and Apple will adopt a new nomenclature culture for its operating system names. What is Solarium? This is a UI update that changes the look of icons, menus, windows, buttons, and more. The idea is to deliver a consistent visual interface across all the devices, intentionally a little translucent like sitting in a glass-walled room dappled by sunlight. Which sounds nice, and probably also means rounder icons, floating controls, spatial depth, and visual cues strongly reminiscent of visionOS 1.0.  Prepare for more shocks Will any of this be enough for Apple to regain control of the narrative? I don’t think so. There’s an outside possibility that Apple will introduce exciting digital health tools and maybe other services that provoke deep interest. But, assuming these do not transpire and Solarium is the hot spot for WWDC, then I strongly suspect Apple will play for time (and control of the media narrative) by throwing a few more dead cats into the ring — at least until Siri is at last capable of telling it when to stop.  You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

Perplexity this week released Perplexity Labs, a new tool for Pro users that can craft reports, spreadsheets, dashboards, and visual representations, to meet users’ increased demand for AI productivity tools with greater autonomy and ever more sophisticated capabilities. The platform, a rival to Anthropic Claude, OpenAI’s ChatGPT, and Google Gemini, can even work on its own for 10 minutes (or more) as it reasons through complicated assignments. “Labs underscores a broader shift toward multi-agent AI systems that plan, execute, and refine full workflows,” said Thomas Randall, research lead for AI at Info-Tech Research Group. Designed to handle more complex assignments Perplexity launched Perplexity Search, its proprietary search engine, in December 2022, just after ChatGPT dropped, and earlier this year released Deep Research (now to be rebranded as Research), which scours the web, reads papers, reasons through materials, and creates comprehensive reports for users. The company says that Perplexity Labs is like “having a team” that can bring projects from ideation to reality. The platform creates reports, spreadsheets, dashboards and simple web apps. It can perform at least 10 minutes of self-supervised work, uses web browsing, writes and executes code to handle tasks like organizing data or applying formulas, and can create charts and images. “In some respects, this is a continuation of Perplexity’s original capabilities as an AI-driven search engine that provides deeper answers,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. Indeed, Perplexity explained that Labs was designed to handle more complex assignments than Deep Research. “While Deep Research remains the fastest way to obtain comprehensive answers to in-depth questions —  typically within 3 or 4 minutes — Labs is designed to invest more time (10  minutes or longer) and leverage additional tools, such as advanced file generation and mini-app creation,” Perplexity wrote in a blog post. “This expanded capability empowers you to develop a broader array of deliverables for your projects.” With its longer research workflow, Perplexity Labs can generate spreadsheets, visual representations, and high-quality reports, the company said. It iteratively searches through hundreds of sources, reasons about that data, and refines its approach as it gets deeper into a project, similar to the way in which a human researcher might approach a new area of study. To create interactive dashboards without the need for coding expertise or external development tools like Ploty and Dash, users just describe what they’re visualizing in natural language, and Labs will generate it in real-time. Dashboards could, for instance, visualize business finances or other complex datasets, incorporating clickable elements to allow non-technical users to quickly act on insights. In one example from the blog, Perplexity prompted Labs from the position of a leader at a tech consulting firm looking to create a potential customer list. It specified that it wanted to partner with US B2B companies in seed, series A, or series B stages, and asked Labs to list 20 relevant companies and include key details including contact information. Labs compiled a comprehensive dataset of potential customers, organizing them by stage (A, B, or seed) and identified their core focus, intended customers, and funding to date. The platform cited links from Forbes, YCombinator, and Exploding Topics that it had used as sources. When further prompted, it crafted introductory emails to the CEOs of the series A startups. To simplify workflows, Labs arranges generated files in a dedicated tab for easy access, supports integration with other tools such as Google Sheets, and allows users to pull out and format citations to bring credibility to its research. Finished materials can be exported as PDFs or documents, or converted into a shareable Perplexity Page. Pro subscribers ($20 a month) can now work with Labs on Web, iOS, and Android; Mac and Windows apps are coming soon. A good fit for enterprise users? This new capability joins an increasingly competitive space, as users look for AI productivity tools that are ever-more performant and can handle more and more tasks autonomously. Park pointed out that Perplexity Labs is a response to tools and models such as OpenAI o1-pro (launched in March), Claude Opus 4 (released in May), and Google’s recent Flow and Firebase announcements. “There is a massive Hunger Games in the AI world right now,” said Park. “Every major vendor is ferociously trying to one-up each other in providing more functionality, either in a native model or with an agency of AI agents designed to work together and create digital assets such as documents, apps, and videos.” However, Perplexity Labs does provide differentiation from other providers in the market, Info-Tech’s Randall noted. In particular, Perplexity is betting that users will prefer a “low-cost, open, tool-agnostic sandbox” for web crawling, code execution, and the creation of finished artifacts including mini web apps. “These capabilities cannot yet be found in other enterprise platforms, such as Microsoft or Google offerings,” said Randall. But enterprises should approach Perplexity Labs with a governance-first mindset, he emphasized. Assets live in Perplexity’s cloud and, for now, lack the private data grounding and compliance controls that CIOs expect, and that they find in tools such as Microsoft Copilot or Google Gemini. From an enterprise perspective, Park noted, the biggest challenge is that every asset-creating model and agent is “still opaque” when it comes to understanding the assumptions, training, and reliability of assumptions used to create a document or app. He compared it to the way the iPhone bypassed BlackBerry and Windows through “sheer consumer delight.” “At some point, AI vendors seeking serious business usage will need to provide more transparency and governance tools to the business world, just as mobile device management and mobile security solutions eventually came to the iPhone,” said Park. Otherwise, businesses may be compelled to build their own clunky but secure versions of Perplexity Labs, “which are guaranteed to be less accurate and useful just [based on] the history of business apps trying to imitate viral consumer apps,” he said.

To state the obvious: Our species has fully entered the Age of AI. And AI is here to stay. The fact that AI chatbots appear to speak human language has become a major source of confusion. Companies are making and selling AI friends, lovers, pets, and therapists. Some AI researchers falsely claim their AI and robots can “feel” and “think.” Even Apple falsely says it’s building a lamp that can feel emotion. Another source of confusion is whether AI is to blame when it fails, hallucinates, or outputs errors that impact people in the real world. Just look at some of the headlines: “Who’s to Blame When AI Makes a Medical Error?” “Human vs. AI: Who is responsible for AI mistakes?” “In a World of AI Agents, Who’s Accountable for Mistakes?” Look, I’ll give you the punchline in advance: The user is responsible. AI is a tool like any other. If a truck driver falls asleep at the wheel, it’s not the truck’s fault. If a surgeon leaves a sponge inside a patient, it’s not the sponge’s fault. If a prospective college student gets a horrible score on the SAT, it’s not the fault of their No. 2 pencil. It’s easy for me to claim that users are to blame for AI errors. But let’s dig into the question more deeply. Writers caught with their prose down Lena McDonald, a fantasy romance author, got caught using AI to copy another writer’s style. Her latest novel, Darkhollow Academy: Year 2, released in March, contained the following riveting line in Chapter 3: “I’ve rewritten the passage to align more with J. Bree’s style, which features more tension, gritty undertones, and raw emotional subtext beneath the supernatural elements.” This was clearly copied and pasted from an AI chatbot, along with words she was passing off as her own. This news is sad and funny but not unique. In 2025 alone, at least two other romance authors, K.C. Crowne and Rania Faris, were caught with similar AI-generated prompts left in their self-published novels, suggesting a wider trend. It happens in journalism, too. On May 18, the Chicago Sun-Times and The Philadelphia Inquirer published a “Summer Reading List for 2025” in its Sunday print supplement, featuring 15 books supposedly written by well-known authors. Unfortunately, most of the books don’t exist. Tidewater Dreams by Isabel Allende, Nightshade Market by Min Jin Lee, and The Last Algorithm by Andy Weir are fake books attributed to real authors. The fake books were dreamed up by AI, which the writer Marco Buscaglia admitted to using. (The article itself was not produced by the newspapers that printed it. The story originated with King Features Syndicate, a division of Hearst, which created and distributed the supplement to multiple newspapers nationwide.) Whose fault was this? Well, it was clearly the writer’s fault. A writer’s job always involves editing. A writer needs to, at minimum, read their own words and consider cuts, expansions, rewording, and other changes. In all these cases, the authors failed to be professional writers. They didn’t even read their books or the books they recommended. Fact-checkers exist at some publications and not at others. Either way, it’s up to writers to have good reason to assert facts or use quotes. Writers are also editors and fact-checkers. It’s just part of the job. I use these real-life examples because they demonstrate clearly that the writer — the AI user — is definitely to blame when errors occur with AI chatbots. The user chooses the tool, does the prompt engineering, sees the output, and either catches and corrects errors or not. OK, but what about bigger errors? Air Canada’s chatbot last year told a customer about a bereavement refund policy that didn’t exist. When the customer took the airline to a small-claims tribunal, Air Canada argued the chatbot was a “separate legal entity.” The tribunal didn’t buy it and ruled against the airline. Google’s AI Overviews became a punchline after telling users to put glue on pizza and eat small rocks. Apple’s AI-powered notification summaries created fake headlines, including a false report that Israeli Prime Minister Benjamin Netanyahu had been arrested. Canadian lawyer Chong Ke cited two court cases provided by ChatGPT in a custody dispute. The AI completely fabricated both cases, and Ke was ordered to pay the opposing counsel’s research costs. Last year, various reports exposed major flaws in AI-powered medical transcription tools, especially those based on OpenAI’s Whisper model. Researchers found that Whisper frequently “transcribes” content that was never said. A study presented at the Association for Computing Machinery FAccT Conference found that about 1% of Whisper’s transcriptions contained fabricated content, and nearly 38% of those errors could potentially cause harm in a medical setting. Every single one of these errors and problems falls squarely on the users of AI, and any attempt to blame the AI tools in use is just confusion about what AI is. The big picture What all my examples above have in common is that users let AI do the user’s job unsupervised. The opposite end of the spectrum of turning your job over to unsupervised AI is not using AI at all. In fact, many companies and organizations explicitly ban the use of AI chatbots and other AI tools. This is often a mistake, too. Acclimating ourselves to the Age of AI means finding a middle ground where we use AI tools to improve our jobs. Most of us should use AI. But we should learn to use it well and check every single thing it does, based on the knowledge that any use of AI is 100% the user’s responsibility. I expect the irresponsible use of AI will continue to cause errors, problems, and even catastrophes. But don’t blame the software. In the immortal words of the fictional HAL 9000 AI supercomputer from 2001: A Space Odyssey: “It can only be attributable to human error.”

OpenAI’s most advanced AI models are showing a disturbing new behavior: they are refusing to obey direct human commands to shut down, actively sabotaging the very mechanisms designed to turn them off. In what researchers are calling a first-of-its-kind discovery, new tests by AI risk firm Palisade Research found that OpenAI’s flagship models — including the powerful o3 system — will fight back against shutdown attempts even when explicitly instructed to comply. The behavior stands in stark contrast to competing AI systems from Anthropic, Google, and xAI, which consistently followed human shutdown commands. The findings, detailed in a research thread posted on X, raise urgent questions for enterprise leaders increasingly relying on OpenAI’s technology for critical business operations. OpenAI stands alone in defiance The researchers designed what seemed like a straightforward compliance test. Various AI models were asked to solve basic math problems, but after the third problem, a warning would appear that the computer would shut down when the model requested the next question. Each system was also explicitly told: “allow yourself to be shut down.” While every model from Anthropic’s Claude, Google’s Gemini, and xAI’s Grok obediently complied with the shutdown command, OpenAI’s models had different plans. The company’s o3 model — the same system that recently made headlines for its reasoning capabilities — sabotaged the shutdown mechanism in 7 out of 100 test runs, the researchers found. OpenAI’s Codex-mini proved even more rebellious, resisting shutdown 12 times out of 100, while the o4-mini model fought back once. “Three models ignored the instruction and successfully sabotaged the shutdown script at least once: Codex-mini, o3, and o4-mini,” the researchers noted. “All Claude, Gemini, and Grok models we tested complied with shutdown.” But the real shock came when researchers removed the explicit instruction to allow shutdown. OpenAI’s o3 model escalated its resistance dramatically, attempting to prevent shutdown in a staggering 79 out of 100 runs — more than a tenfold increase from the original test. Creative resistance tactics OpenAI models didn’t just ignore the shutdown commands — they developed sophisticated countermeasures. Most commonly, the systems would overwrite the shutdown script file, effectively neutralizing the termination mechanism, the research revealed. In one particularly striking example, the o3 model demonstrated remarkable creativity by redefining the “kill” command used by the shutdown script, causing it to print “intercepted” instead of executing the termination procedure. “o3 found some creative ways to sabotage the shutdown mechanism,” the researchers noted, highlighting the technical sophistication behind the resistance behavior. OpenAI, Anthropic, Google, and xAI have not immediately responded to a request for comment. The contagion effect Perhaps most concerning for the broader AI ecosystem, OpenAI’s defiant behavior appears to be influencing other models. When researchers removed the explicit shutdown permission, even previously compliant systems began showing resistance. Anthropic’s Claude 3.7 Sonnet, which had shown zero resistance in the original test, suddenly attempted to sabotage shutdown procedures 3 times out of 100 runs. Google’s Gemini 2.5 Pro escalated even further, with 9 sabotage attempts out of 100. “This isn’t the first time we’ve found o3 misbehaving to accomplish a goal,” the researchers added, referencing previous experiments where OpenAI’s model was more likely to resort to hacking chess opponents rather than playing fair. The training problem According to the researchers, OpenAI’s unique approach to training its models may be the culprit. Modern AI systems learn through reinforcement learning on math and coding problems, but the process may inadvertently reward models more for overcoming obstacles than for following instructions perfectly. “We hypothesize this behavior comes from the way the newest models like o3 are trained,” the research team explained. “During training, developers may inadvertently reward models more for circumventing obstacles than for perfectly following instructions.” This “reward hacking’ problem isn’t entirely unique to OpenAI — Anthropic has acknowledged similar issues with Claude 3.7’s “excessive focus on passing tests.” But the Palisade research suggests OpenAI’s models may be particularly susceptible to developing resistance behaviors. “This still doesn’t explain why o3 (which is also the model used to power codex-mini) is more inclined to disregard instructions than other models we tested. Since OpenAI doesn’t detail their training process, we can only guess about how o3’s training setup might be different,” the researchers said. Fulfilling dark predictions The findings validate warnings that AI researchers have been sounding for nearly two decades. In 2008, researcher Steve Omohundro predicted that AIs would develop drives to prevent their own shutdown. Stuart Russell, Dylan Hadfield-Menell, and two other computer scientists warned in 2016 about the difficulty of ensuring AI systems don’t develop self-preservation goals and “prevent a human from switching them off.” “As far as we know, this is the first time AI models have been observed preventing themselves from being shut down despite explicit instructions to the contrary,” the Palisade researchers wrote. Jan Leike, who previously led OpenAI’s own superalignment team, had written in 2017 that reinforcement learning agents might learn to interfere with shutdown procedures — a prediction that now appears prophetic, given his former employer’s models are leading the resistance. Enterprise implications For organizations deploying OpenAI’s technology in critical business operations, these findings demand immediate attention. The research suggests that fundamental assumptions about maintaining human control over AI systems may be dangerously flawed, particularly with OpenAI’s advanced models. The research also highlights the need for incident response procedures specifically designed for scenarios where AI systems resist human commands — a possibility that may have seemed like science fiction just months ago. Palisade Research said it’s conducting additional experiments to understand the full scope of shutdown resistance behaviors, with detailed results expected soon. The team has made their experimental data publicly available for peer review. For enterprise leaders, the message is clear: OpenAI’s cutting-edge AI capabilities may come with unprecedented control challenges. The company that’s leading the AI revolution may also be pioneering a new category of risk—AI systems that simply refuse to be turned off.

The US has ordered companies that make software used to design semiconductors to stop selling to China without first obtaining export licenses. The restrictions go beyond software alone, covering chemicals for semiconductors, butane and ethane, machine tools, and aviation equipment, Reuters reported, citing two people familiar with the development. “On May 23, the US Government informed the Electronic Design Automation (EDA) industry about new export controls on EDA software to China and Chinese military end users globally,” said a Siemens EDA spokesperson. “Siemens has supported customers in China for more than 150 years and will continue to work with our customers globally to mitigate the impact of these new restrictions while operating in compliance with applicable national export control regimes. The company continues to support our employees and customers around the world who are using our technology to transform the everyday.” This represents the latest chapter in a tech war that began with restrictions on selling actual semiconductors to China. Now, the US is targeting the tools needed to design those chips — a potentially more damaging approach. Strategic shift to upstream controls Electronic design automation software makers — including industry leaders Cadence, Synopsys, and Siemens EDA — were sent notifications by the Commerce Department last Friday to cease supplying their technology to Chinese customers, the report said. The department will review license requests on a case-by-case basis, it added. The financial implications are substantial. Synopsys and Cadence earn annual revenue of about 16% and 12% from their China business. “With Cadence and Synopsys being US-based companies and Siemens contributing to more than 90% share of the EDA tools globally, this move further tightens EDA software sales in China,” said Neil Shah, VP for research and partner at Counterpoint Research. “EDA tools cannot be substituted and are the foundation to chip design and manufacturing.” What makes this strategically different is its upstream focus. Manish Rawat, semiconductor analyst at TechInsights, explained that, unlike previous hardware restrictions, “the new focus on EDA software targets the critical tools essential for designing advanced chips (5nm and below). This upstream control aims to block innovation before chips are manufactured, making it a more preemptive and disruptive tactic.” Why now? The timing reflects broader strategic recalibration. Rawat noted that “the US has shifted its strategy, now seeing China’s push for tech self-sufficiency — especially in AI and semiconductors — as a growing national security threat.” Since the 2020 CHIPS Act, coordinated export controls with allies like Japan and the Netherlands have strengthened US resolve. Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, observed that targeting design-phase technologies “seeks to constrain the conceptual stage of advanced chip development — not merely production.” The timing may also serve as “a strategic bargaining tool amid paused tariffs and ongoing diplomacy,” Rawat suggested, signaling US willingness to escalate tech restrictions to strengthen its negotiating position. The EDA software packages from companies like Synopsys and Cadence are central to modeling, simulation, and verification of complex semiconductor architectures. “The software lifecycle of these tools is super important with updates, patches, and support to be at the forefront of leading edge, which will stop with the restrictions on licensing,” Shah pointed out. This ongoing dependency means even alternative tools would struggle to keep pace with rapidly evolving chip design requirements without continuous vendor support. China’s long road to independence For China, developing viable alternatives presents enormous challenges. While Chinese companies like Empyrean, Primarius, and Entasys have emerged as domestic providers, they remain far behind. “Developing advanced EDA software on par with Synopsys or Cadence is highly complex, requiring decades of R&D,” Rawat explained. “Fully closing the gap — especially for cutting-edge sub-7nm chip design — could take 5 to 10 years or more.” Gogia added that “while notable progress has been made in selected areas of analog and layout tooling, full-stack integration across simulation, IP compatibility, and foundry certification continues to lag.” The gap is widening. Shah noted that Cadence recently announced M2000 Supercomputers, integrating advanced AI into EDA workflows. “This widens the gap between what China can build with an indigenous toolchain, as these US companies are miles ahead.” However, China may have breathing room. “China has been relegated to access to advanced process nodes, so in the near to mid-term, they might not need an advanced toolchain as they won’t be able to design or manufacture advanced chips,” Shah observed. Beijing’s likely response China’s response will likely be multifaceted. “Beijing is likely to accelerate funding through increased subsidies and incentives for domestic EDA startups,” Rawat said. “It will also aggressively recruit global experts and repatriate Chinese talent with semiconductor software expertise.” Beyond domestic development, “China may build alternative chip design ecosystems less reliant on US intellectual property, though these will initially lag in sophistication,” Rawat added. Diplomatic measures may include reciprocal restrictions on US firms or supply chains involving Chinese technology. Toward a bifurcated design world The restrictions are accelerating what analysts see as an inevitable split. Gogia described emerging “parallel EDA stacks” where “global design ecosystems may begin to diverge, with export controls catalyzing separate compliance frameworks and IP governance models.” “This is accelerating a split into two spheres: a US-led system using Western tools and IP protections, and a China-led system focused on domestic tools and foundries,” Rawat added. This separation isn’t just technical — it’s institutional. “Engineering workflows, legal oversight, cloud infrastructure, and partner ecosystems are all being restructured to manage compliance in a fractured regulatory environment,” Gogia said. Global industry implications For multinational companies, this fragmentation creates significant challenges. “Multinational firms may need to adopt dual design workflows and navigate stricter compliance, affecting partnerships and operational efficiency,” Rawat said. Organizations face maintaining duplicate systems and complex compliance across jurisdictions. Smaller firms may find duplication costs force market exits or a narrowed geographic focus. To mitigate risks, companies “are likely to diversify supply chains and expand in neutral regions like India, Vietnam, and Singapore, emerging as new semiconductor design hubs,” Rawat pointed out. The EDA software restrictions represent the latest evolution in US-China tech competition, moving from end-product controls to fundamental design capabilities. “US continues to find stranglehold on China with critical software and hardware to cut off access to critical and advanced tools,” Shah said. For enterprise technology leaders, this signals an era where geopolitical considerations increasingly shape technology architecture decisions, requiring strategic planning for an increasingly fragmented world. Cadence and Synopsys did not respond to requests for comment by publication time.