A new version of LibreOffice, a popular open-source alternative to Microsoft Office, won’t run on 32-bit PCs, or support the Windows 7 or 8 operating systems.

The Document Foundation earlier this month released version 25.8 of the free productivity suite, which was downloaded 642,564 times in its first week, according to a blog entry on the organization’s website.

Most Windows software comes in 32-bit and 64-bit versions, and most software vendors offer support for both for backward compatibility.

LibreOffice, which includes apps for word processing, spreadsheets, presentations, vector graphics creation, and more, is popular among Linux users and is included in many Linux distributions. A version is also available for macOS.

The base desktop software is free of AI tools and targeted for customers who want an alternative to Office without the frills. A cloud-based paid version of LibreOffice is available at Collabora Online.

But more AI tools are coming to LibreOffice desktop in the form of extensions. Earlier this month, StableDiffusion released an AI image generator for the software. Chatbots and automated writing extensions are also now available for LibreOffice.

The number of LibreOffice adopters remains modest, but some individuals and organizations are giving it a look as cloud and AI security concerns mount around the market-leading office suites, Microsoft 365 and Google Workspace. Denmark’s Ministry of Digitalization recently announced that it would move half of its employees to LibreOffice from Microsoft 365 this summer, with plans to cut off the Microsoft suite completely by year end.

The next version of LibreOffice is expected to be 26.2, which will be released next year.

Source:: Computer World

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Apple’s most senior leaders so far seem to have failed to find consensus on how to move forward with Apple Intelligence. They recognize the company appears to be lagging in the generative AI (genAI) race, but management appears split between those who want to build it in Cupertino, and those who hope to buy their way to industry leadership.

So far, neither side seems to have the room, with one thing holding everyone back being the insanely high valuations being thrown at AI companies at this point in the bubble. 

Why?

Look it this way – the question has to be, “Why spend billions when some of these companies will be worth a great deal less once the AI bubble bursts, as it inevitably will?” It’s a reasonable position, given that valuations at the current levels are not sustainable. And while governments everywhere want to sell themselves cheap to climb into figurative bed with the current crop of genAI billionaires, they face massive public resistance to adopting the dystopian result of their dollar-drenched trysts. Russia’s intrusive Max app seems ripe to inspire similar behaviors from other authoritarian governments.

Being different

The other issue is differentiation. Apple is a product company, and for all the blather about artificial intelligence, the only thing that matters is how the tech can become part of its product family. If you glance at the many existing machine intelligence features already in its products, you’ll see that most of these supplement existing hardware. When it comes to Apple Intelligence, for Apple the North Star must be the need to ensure it continues to offer something unique.

To some extent, doing this with AI is fundamentally difficult, as this kind of general purpose intelligence will eventually become a homogenous block of different models using similar data (all the data in the world) to inform responses to similar questions.

With that inevitable homogenization, AI services may yet become utilities rather than differentiated products. So, it could make sense for Apple to avoid developing its own general purpose AI, resolving to create specific solutions that work much more effectively for specific use cases and relying on partnerships with these emerging AI-as-a-Service companies (AI-ASS?). That seems to be the Apple Intelligence way. 

In that picture, how much difference would an AI acquisition make?

The problems of acquihire

The only way in which such acquisitions might make a difference is if the company were to both gain access to the models and the people who made those models. If that’s how the company is looking at it, then any acquisition talks must necessarily require some commitments around employment and long-term loyalty to Cupertino.

There’s no point doing an acquihire if everybody leaves, and this seems to be what’s been happening with the smaller AI purchases Apple has made. This may be an Apple-specific human resources problem, or a corporate culture problem, or it may reflect the insane competition for staff in the field.

What this all boils down to is that if Apple can’t find an acquisition target that includes an employee transfer of people genuinely committed to Apple, then — other than IP and/or any political advantage it might gain — it doesn’t have a deal. It would just have less money in the bank and still be unable to find the talent.

National security

It’s worth noting that at least one of the current rumored Apple acquisition targets (Mistral), is likely to be seen as a company of national strategic importance to France. Mistral is France’s best-known AI company, with partnerships across the government and leading tech industry players.

It’s unclear whether France, which, like the rest of Europe, is now struggling with data sovereignty, will see letting the AI service provider slip into the hands of US Big Tech fit the national narrative. What I’m saying is that as the national importance of AI is revealed, the number of potential acquisitions Apple can reasonably expect government approval for will decline.

Made in California

Can Apple invent its own AI to match the others on the market? The latest reporting suggests Craig Federighi, Apple senior vice president for software engineering still believes it is possible, with Services Senior Vice President Eddy Cue in the “Add to Shopping Basket” camp. And it is possible that much depends on what surprises Apple can bring to the table once it ships its own context-savvy Siri in a few months’ time. 

Earlier this month, Federighi told staff that his AI team has achieved more than was originally promised. “This has put us in a position to not just deliver what we announced, but to deliver a much bigger upgrade than we envisioned,” he said.

If the company surprises and delights its audience, perhaps Apple will give those teams a little more time to build Apple’s own genAI solutions. If not, then perhaps those acquisition discussions will intensify. I can’t be certain but it is easy to imagine we’ll get a glimpse of some of what the company has put together during the iPhone launch on Sept. 9.

Partnerships

Then there’s partnership possibility. The problem with partnerships is that teaming up gives people credibility, and to some extent gives the AI companies the upper hand. There are also unexpected challenges — Apple’s current AI partner, OpenAI, is reportedly building its own AI products with former Apple Chief Design Officer Jony Ive. We can’t know the extent to which that hardware threatens Apple’s interests. But it can’t have gone completely unnoticed that Apple is apparently about to introduce additional support for Google Gemini and other AI services as alternatives to the existing support Apple Intelligence has for ChatGPT.

While the most recent chapter in Apple’s never-ending journey toward AI seems based on the narrative that the company can somehow buy its way to success, the truth is that any acquisition would be complicated. And in the absence of a determined management consensus, even with the company checkbook ready, no one seems to be making a move.

All the same, as tides turn toward new iPhones, don’t be too surprised if something makes your awe drop.

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Source:: Computer World

By Calum Chase AI agents are now being embedded across core business functions globally. Soon, these agents could be scheduling our lives, making key decisions, and negotiating deals on our behalf. The prospect is exciting and ambitious, but it also begs the question: who’s actually supervising them? Over half (51%) of companies have deployed AI agents, and Salesforce CEO Marc Benioff has targeted a billion agents by the end of the year. Despite their growing influence, verification testing is notably absent. These agents are being entrusted with critical responsibilities in sensitive sectors, such as banking and healthcare, without proper oversight. AI agents require…This story continues at The Next Web

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Cloud storage provider Dropbox has a generative AI (genAI) service called Dash that users will soon be able to download and install.

“We plan to launch a self-serve version of Dash,” Dropbox CEO Drew Houston said during an earnings call earlier this month. “Basically, a version anyone can download and start using similar to what we did with Dropbox 1.0.”

Dash, which will be available for download later this year, includes document search, summarization, AI chatbots, and writing assistance for content in its cloud and third-party workflow services.

“You can think of Dash as both a standalone product that allows us to reach a new audience of people beyond our file-syncing audience. And it’s also the AI layer across Dropbox FSS (file sync and share) for our existing customers,” Houston said.

Dropbox, which started in 2008, made its name in file sharing and storage. Dash is positioned as independent from the company’s mainstream file-storage offerings, though customers can buy a bundle with both offerings.

“It’ll be a separate product and separate subscription,” Houston said.

Dash creates intelligence from documents users store with Dropbox. For example, users will be able to search for rich media files within the storage service.

“We’re seeing growing adoption of Dash Chat for answering questions, summarizing long documents, and providing draft writing assistance,” Houston said.

Additionally, the service plugs into other workflow tools such as Slack, Salesforce, Microsoft 365, and Atlassian, from which it can analyze and locate documents, communications, records, reports, and contacts.

Dropbox is hoping the self-service model will attract more users to the company’s other wares. It did not offer information about pricing or target audience.

Since its inception, the storage provider has tried to convert free users to paid users. Virtually every subscriber started out as a free user in some form, executives said.

Dropbox has 700 million users, of which 18 million were paid users in 2024. (That figure is up from 15 million in 2020.)

Current file sync and share prices range from $7 to $199, depending on usage. Prices for business users start at $18.

Executives dropped hints that there could be some kind of free Dash service, with Dropbox hoping to convert those users to paid subscribers.

“Dash is a good example of providing a lot of new value to our existing free users beyond files, right? Because all of those free users have cloud content as well and are a good fit for Dash,” Houston said.

Source:: Computer World

“Disruptive products cannot be built from legacy chips,” Apple Senior Vice President of Hardware Technologies Johny Srouji said when he spoke at ITF World in Belgium in May. He was there to receive the prestigious 2025 IMEC Innovation Award and shared some of the lessons Apple has acquired since it began building its own processors with the A4 chip inside the iPhone. 

During his speech, he explained how the scalable architecture Apple chose for its chip development is enabling the company to transform all its hardware. And he shared a few tips for intelligent hardware design, such as to always use the best available tools and technologies during development. 

“I always tell my team, we want to be gated by physics, nothing else,” he said.

iPhone and the future of computing

Apple’s processor journey really began years before Apple Silicon. But it was only when it began work on the iPhone that it committed to taking complete control of the processor development process. 

“The iPhone was seen as a full-blown computer,” he said. “To support its full feature set, it required an incredible amount of performance — but needed to deliver that within a constrained power.”

The company understood that while the easy path might be to use third-party processors, doing so made it hard to design products that stood out. Shifting to custom silicon was difficult, but it enabled the company to design in chip efficiency and gave it the chance to “control our destiny.”

Mobile tech in a Mac

What that means in terms of actual hardware is now reflected in the power and performance advantage characteristic of Apple silicon. And it’s a direct result of the original design decisions made about to the iPhone — decisions such as building chips optimized in such a way that the most “efficient part of the performance versus power curve coincides with the maximum sustained thermal envelope of the device its going into.”

In other words, Apple’s chips are built to deliver their most efficient performance when the devices are being used for quite demanding tasks. This can scale up for short-term higher-end chores, and tick down when all you want to do is check email or pick up some more cookies and adware while browsing. That efficiency by design, in conjunction with the hardware and software Apple also designs, makes for longer battery life and higher performance per watt.

It also makes for the sort of power efficiency you need to run multiple operations on the GPU, which makes Apple silicon highly suitable for on-device AI. It might seem ironic, but the design decisions made for the iPhone directly contributed to the processor choices Apple has made for its fleet of private cloud compute servers.

The fundamental building blocks

Transistor density is fundamental. “We rely on transistors as the chips get more complex,” Srouji said. “As our chips are getting more complex, the transistor as the fundamental building block is getting more important than ever.”

The 190 million transistors crammed inside the A4 and A4X chips that powered the iPhone and iPad 15 years ago would occupy just one tiny corner of the M3 Ultra chip’s 183 billion transistors today. Those numbers are why, for example, GPU performance on the M3 Ultra is 17,000 times that of the A4.

It is also why innovations such as support for unified memory and better performance per watt feeds into the Mac. It means the M1 MacBook Air delivered equivalent performance to that of an Intel MacBook Pro, with system performance scaling up with every model after that. Progress has been so fast that Apple is expected to introduce a Mac that uses an iPhone chip later this year.

The ability to design technologies in which Apple is only constrained by physics enables better design, thanks to the integration of software, hardware, and processors. “And thanks to our vertical integration any innovation here we can transfer to millions of products reaching millions of customers,” Srouji said.

The future remains unwritten

Apple hasn’t finished with chip design yet. It’s likely in a decade’s time we’ll see more progress, more performance, and more computational ability squeezed into Apple’s processors — particularly as we move to whatever the successor may be to the 2nm chips the company is now expected to use. It means you can expect more transistor density moving forward, which means more performance. 

Can we anticipate 17,000-fold increases? 

I don’t know. But from what Apple’s silicon leader is saying, the only barrier will be the laws of physics. Of course, as development gets close to the finite constraints of physics itself, we’ll see ever more focus on small wins that lead to better efficiency. Seeking such wins, don’t be too surprised to see Apple’s chip designers lean even more deeply into AI than they already do as they work to identify and test new solutions.

“Gen AI techniques have high potential to get more design work done in less time,” Srouji said. 

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Source:: Computer World

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Nvidia CEO Jensen Huang sees a future where billions of robots serve humans, bringing in trillions of dollars in revenue for the company.

To meet that goal, Nvidia on Monday unveiled a new computing device that will go into high-performing robots that could then try to replicate human behavior.

The Jetson Thor robotics computers are capable of AI intelligence. The devices will help robots hear, detect patterns, make decisions and act. Many of the AI capabilities arise from generative AI (genAI) technologies available today.

“Thor…is the ultimate platform for physical AI and robotics built for the age of reasoning AI running at the edge — low power, high performance, real time,” Deepu Talla, vice president of robotics and edge AI at Nvidia, said during a news briefing.

The boards have Nvidia’s flagship Blackwell graphics processor, which power the latest genAI models. Users currently have to access those GPUs in the cloud, which is why the robotic implementation of Blackwell in Thor is different. Robots make real-time decisions, and the GPUs can run AI on the device without cloud access or internet connections.

“…Typically, your actuation is in fractions of a second…,” Talla said. “If you go to the cloud, typically the latency is hundreds of milliseconds…. That’s why you want to do as much as possible at the edge.”

The Thor developer board delivers roughly 2,070 teraflops of low-precision FP4 data performance; that pales in comparison to Blackwell datacenter GPUs, which deliver 20 petaflops.  While 10 times faster than the robotics GPUs, they also consume significantly more power.

The robotics computers draw between 40 and 130 watts of power. Given that robots will operate on batteries, that adds up to two times the power draw of standard work laptops, which draw between 30 watts to 60 watts of power.

Robots with Thor are expected to be used in heavy industries with physical labor such as manufacturing, oil and gas, and mining. “Our vision for robotics is not just humanoid,” Talla said. “We think general purpose robotics is extremely important so that they reason out about different tasks that they encounter in the real world.”

Nvidia has recently made a variety of announcements involving AI robots. Earlier this month, the company announced Cosmos Reason, a vision AI model that helps robots make better decisions by evaluating their surroundings. The model can process pixels in video input to process information.

Thor is the hardware where these AI enhancements will come to life, Talla said. “Because of reasoning we are able to tackle any model — adding reasoning to it allows it to generalize even more, which enables us to solve problems that [were] previously unsolvable,” Talla said.

The $3,499 AGX Thor computer system has 14 ARM CPU cores, 128GB of RAM, and Gigabit Ethernet ports. Nvidia also has the T5000 and T4000 production boards on which developers can build robots. 

The T5000 board draws 130 watts of power, while the T4000 draws 70 watts, making it suitable for robots with smaller batteries.

More Nvidia news and insights:

Nvidia turns to software to speed up its data center networking hardware for AI

Nvidia: ‘Graphics 3.0’ will drive physical AI productivity

Nvidia launches Blackwell-powered RTX Pro GPUs for compact AI workstations

Nvidia’s new genAI model helps robots think like humans

Nvidia patches critical Triton server bugs that threaten AI model security

Source:: Computer World

US President Donald Trump’s announcement Friday that the US government is taking a 9.9% stake in Intel to defend national interests will shift the dynamics of IT procurement globally.

“Intel’s new identity as a government-backed ‘national champion’ represents a structural shift in how enterprises must evaluate supplier relationships,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “Technology buyers have framed procurement primarily in terms of cost, performance, and roadmap alignment. Intel’s repositioning disrupts that calculus.”

President Trump rounded up the size of the government’s stake when he announced it on Truth Social: “It is my Great Honor to report that the United States of America now fully owns and controls 10% of INTEL, a Great American Company that has an even more incredible future.”

 “The United States paid nothing for these Shares, and the Shares are now valued at approximately $11 Billion Dollars.,” Trump wrote.

Intel’s version was a little different: It said that the US government “will make an $8.9 billion investment in Intel common stock, reflecting the confidence the Administration has in Intel to advance key national priorities and the critically important role the company plays in expanding the domestic semiconductor industry.”

The truth lies somewhere in between: The $8.9 billion comprises the capitalization of a $3.2 billion grant from the Department of Defense’s Secure Enclave program and $5.7 billion in remaining CHIPS Act grants which, with the $2.2 billion in CHIPS Act grants Intel has already received, make up Trump’s $11 billion figure.

The deal makes the US government Intel’s largest shareholder, although it will have no representation on Intel’s board, and has agreed to vote with the board on shareholder matters. Intel has also given the government the right to purchase an additional 5% stake if the company loses majority control of its foundry business, it said.

“As the only semiconductor company that does leading-edge logic R&D and manufacturing in the US, Intel is deeply committed to ensuring the world’s most advanced technologies are American made,” Intel CEO Lip-Bu Tan said in the statement.

The new procurement reality

This recasting of Intel’s role in the industry as a government-backed national champion creates a fundamental shift in supplier evaluation, moving beyond traditional cost-performance metrics to include political considerations.

Neil Shah, VP for research at Counterpoint Research, agreed: “With Intel’s growing role as a national champion, IT leaders need to recalibrate their procurement strategies. Intel’s government-backed status provides a more robust supply chain, reducing disruption risk from global geopolitical tensions.”

The recalibration becomes more complex when considering Intel’s dual role as a commercial vendor and national security asset, which creates potential conflicts enterprises must understand when evaluating future technology roadmaps.

The core concern centers on resource allocation. “The risk for commercial customers is that engineering bandwidth may be diverted away from accelerating competitive product roadmaps in AI, data-center silicon, and edge workloads,” Gogia warned.

However, Dr. Danish Faruqui, CEO at Fab Economics, disputed this concern: “R&D resource allocations for developing leading edge fabrication technologies are the same for both public and private productization, so enterprise customers should not worry about delay in technology maturity.”

National security vs financial security

The impact may be more subtle, affecting Intel’s commercial focus. Shah noted that government support “could lead Intel to prioritize national security initiatives over commercial needs, potentially impacting its product roadmaps and cost competitiveness.”

Beyond these resource allocation concerns, the US government’s move creates a two-tiered global market structure affecting enterprises differently based on geographic location and regulatory requirements.

International enterprises face the most significant challenges. “For European and Asian CIOs, Intel’s government-backed monopoly raises concentration risk, as supply will inevitably be prioritized for US customers,” Gogia warned.

Paradoxically, Intel’s government support could strengthen competition by freeing rivals from confrontation with a subsidized competitor. Shah suggested this “could create an opening for rivals like AMD, Nvidia, and Qualcomm to innovate more freely in commercial markets.”

The competitive landscape benefits from broader CHIPS Act funding. Faruqui emphasized that domestic sourcing extends beyond Intel: “Whether leading edge fabrication happens at Intel, TSMC or Samsung foundry on US soil, there should not be any forced choices for customers.”

Concerns persist for Intel’s foundry business

These competitive dynamics become particularly relevant when examining Intel’s foundry division, where government backing provides financial stability, but analysts questioned whether this resolves fundamental business challenges.

The deal offers crucial breathing room for Intel’s struggling manufacturing-for-hire business, according to Alvin Nguyen, senior analyst at Forrester. “This $8.9B investment provides a financial lifeline as they find foundry customers and develop new processes,” said Nguyen. However, he cautioned that “this does not mean the risks associated with their foundry business are gone.”

The core issue remains attracting customers rather than capital availability. Gogia explained that “despite substantial subsidies, the foundry division continues posting multi-billion-dollar losses with limited customer traction.” The problem, he noted, is that “subsidies extend Intel’s financial runway but don’t address structural competitiveness weaknesses” such as yield rates and process maturity that customers demand.

Industry experts emphasized that government cash alone cannot solve market-driven challenges. “US government equity holdings is no solution for attracting foundry customers,” Faruqui noted.

This suggests Intel must still prove its technical capabilities and service quality to win business from major chip designers, regardless of federal support.

Keep a close eye on the roadmap

Given these multifaceted challenges and opportunities, analysts provided guidance for navigating Intel’s transformation into a government-backed supplier, emphasizing continuous monitoring and strategic portfolio management.

Performance tracking becomes crucial given Intel’s hybrid status. “IT leaders should closely monitor Intel’s progress on its technology roadmap and price competitiveness to ensure government backing translates into tangible benefits,” Shah said.

Strategic positioning requires balancing stability against limitations. Gogia recommended treating “Intel’s foundry as a politically secured option, useful for compliance-heavy workloads where domestic sourcing is required. But it should not be relied upon as the sole provider of leading-edge capacity.”

Source:: Computer World

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