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The internet never stays quiet. Every week, new hacks, scams, and security problems show up somewhere. This week’s stories show how fast attackers change their tricks, how small mistakes turn into big risks, and how the same old tools keep finding new ways to break in. Read on to catch up before the next wave hits. Honeypot Traps Hackers Hackers Fall for

The internet never stays quiet. Every week, new hacks, scams, and security problems show up somewhere. This week’s stories show how fast attackers change their tricks, how small mistakes turn into big risks, and how the same old tools keep finding new ways to break in. Read on to catch up before the next wave hits. Honeypot Traps Hackers Hackers Fall for Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

A federal judge has signalled that Elon Musk’s lawsuit challenging OpenAI’s transformation from a nonprofit to a for-profit entity will proceed to trial, adding legal uncertainty for enterprise customers that have built AI strategies around the ChatGPT maker’s technology.

US District Judge Yvonne Gonzalez Rogers said during a Wednesday hearing in Oakland, California, that there was “plenty of evidence” for a jury to consider Musk’s allegations that OpenAI violated its founding mission, according to Reuters.

“This case is going to trial,” Judge Gonzalez Rogers said at the hearing, Reuters reported. The judge indicated she would issue a written order addressing OpenAI’s motion to dismiss the case, but stopped short of a formal ruling.

The lawsuit alleges OpenAI co-founders Sam Altman and Greg Brockman fraudulently induced Musk to help establish and fund the organization in 2015 under the premise that it would remain a nonprofit dedicated to developing AI for humanity’s benefit, only to later pursue for-profit restructuring through a partnership with Microsoft.

Microsoft’s role under scrutiny

Microsoft, which has invested more than $13 billion in OpenAI since 2019, is also named as a defendant. The judge said she needs to determine whether to dismiss unjust enrichment allegations against Microsoft, which has accumulated a $135 billion stake in OpenAI and holds licensing rights to its technology, the report added.

A Microsoft attorney argued at the hearing that there was no evidence the company “aided and abetted” OpenAI, according to the report.

The case raises questions about vendor governance stability for enterprises that have integrated OpenAI’s models into business-critical applications through Microsoft’s Azure cloud platform or direct partnerships with OpenAI.

Vendor stability concerns for AI customers

The case comes as enterprises accelerate AI deployment, with global enterprise technology spending reaching $4.9 trillion last year, driven by AI investments.

The legal proceedings could affect enterprise confidence in OpenAI’s governance stability as companies evaluate long-term AI vendor relationships. OpenAI’s technology powers Microsoft’s Copilot products, which enterprises have integrated across Office applications and Azure cloud services.

The trial schedule remains unclear. Judge Gonzalez Rogers said she needs to determine trial logistics but did not set a specific date, the report added.

Governance structure at the center of the dispute

Musk, who co-founded OpenAI in 2015 and contributed approximately $38 million — roughly 60% of its early funding — left the organization in 2018 following disagreements over its direction, Reuters reported. He filed the lawsuit in August 2024.

OpenAI was founded as a nonprofit research organization with a mission to ensure artificial general intelligence benefits all of humanity. In 2019, the company transitioned to a “capped profit” structure, creating a for-profit subsidiary while the nonprofit parent retained control.

OpenAI is now pursuing further restructuring into a public benefit corporation.to become a public benefit corporation, which would significantly reduce the nonprofit’s oversight role. The restructuring is critical to OpenAI’s ability to raise additional capital and compete in the expensive AI development race. The company has said the nonprofit arm would remain and be well-resourced through the transition.

The lawsuit contends OpenAI abandoned its founding charter through these structural changes. Judge Gonzalez Rogers cited evidence, including a 2017 diary entry by OpenAI co-founder Greg Brockman in which he wrote, “We’ve been thinking that maybe we should just flip to a for-profit,” according to court documents referenced by Reuters.

Parties respond to the court decision

In a statement following the hearing, OpenAI called the lawsuit baseless, the report added. “Mr Musk’s lawsuit continues to be baseless and a part of his ongoing pattern of harassment, and we look forward to demonstrating this at trial,” the company said.

OpenAI attorneys requested that Judge Gonzalez Rogers enter judgment against Musk, arguing he had not shown a sufficient factual basis for fraud and breach of contract allegations. The company also contended Musk failed to bring his claims in a timely manner.

OpenAI has also filed counterclaims alleging Musk’s actions, including an unsolicited $97 billion takeover bid earlier this year, were designed to disrupt its business operations to benefit his competing venture. xAI and OpenAI did not respond to a request from ComputerWorld for comment.

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Chainguard, the trusted source for open source, has a unique view into how modern organizations actually consume open source software and where they run into risk and operational burdens. Across a growing customer base and an extensive catalog of over 1800 container image projects, 148,000 versions, 290,000 images, and 100,000 language libraries, and almost half a billion builds, they can see

Chainguard, the trusted source for open source, has a unique view into how modern organizations actually consume open source software and where they run into risk and operational burdens. Across a growing customer base and an extensive catalog of over 1800 container image projects, 148,000 versions, 290,000 images, and 100,000 language libraries, and almost half a billion builds, they can see [email protected]

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Pilotní úsek dálnice umožní bezdrátové dobíjení elektromobilů během běžné jízdy • Technologie využívá indukční cívky pod vozovkou a slouží hlavně k testování • Projekt cílí na flotily a má být hotový nejdříve roku 2029

Economists have time-tested models for projecting economic growth. But they’ve seen nothing like AI, which is a wild card complicating traditional economic playbooks.

Some facts are clear: AI will make humans more productive and increase economic activity, with spillover effects on spending and employment.

But there are many unknowns about AI. Economists can’t isolate AI’s impact on human labor as automation kicks in. Nailing down long-term factory job losses to AI is not possible.

AI also complicates capital expenditure projections. Heavy money is going into data centers and power plants, but how much this will translate into productivity gains — and thus whether demand for AI services will remain high — remains unclear.

Economists are weighing the likelihood of a slowdown in the US and global economy against the productivity gains AI is expected to bring. The Peterson Institute for International Economics, for instance, predicts that global gross domestic product (GDP) will slow in 2026, with AI offsetting some of the decline.

The Conference Board, a nonprofit economic think tank based in New York, estimates that the US GDP will grow around 1.9% annually from 2025 to 2039, down from 2.4% growth from 2000 to 2024. AI will lift some of that decline, said Erik Lundh, senior global economist for The Conference Board Economy, Strategy & Finance Center.

To arrive at this projection, TCB factored AI’s uncertain crosscurrents — such as AI productivity gains — into its models along with established variables, such as long-term trends in total-factor productivity, labor, and capital.

But the projection “does not adequately capture the potential of a sea change… like artificial intelligence,” Lundh said.

Computerworld sat down with Lundh to understand AI’s big-picture impact, how it is being quantified, and how such metrics help business and policy planners. This interview has been condensed and lightly edited for clarity.

The Conference Board projections show US GDP growing at an average rate of 1.9% from 2025 to 2039, slower than the 2.4% growth from 2000 to 2024. Does AI meaningfully offset some of that slowdown? “Yes. The US GDP projection of 1.9% from 2025–2039 … reflects that there’s going to be less bang on the capital and labor side. Productivity associated with technological developments — including AI — does offset more of the slowdown.

“We’re seeing an increase in terms of productivity enhancements over the next decade and a half. While it doesn’t capture AI directly… there is all kinds of upside potential to the productivity numbers because of AI.

“The same is true of the global economy. Emerging markets are going to be growing faster than advanced economies are — and they have been  — but again, there is an expectation that AI will play a role in terms of augmenting the kinds of productivity that we see over the coming years.”

As AI becomes a bigger part of the economy, will it change the way we measure growth? And as we go forward, will AI’s impact on GDP keep increasing? “It helps to make a distinction in terms of AI’s contribution. On one hand, we’re seeing a lot of stories about data centers being built, electricity demand rising, and power plants being dusted off or newly planned to support AI. When you build a data center or a power plant, you create real economic activity — the planning, the materials, the labor that goes into erecting these things. That shows up as capital contribution to growth because it’s physical investment.

“But beyond that, you also get productivity enhancements afterward. It’s similar to infrastructure buildout. If you build a new port or airport, you spend money up front, but then it becomes cheaper to ship goods or move workers, and that long-term efficiency shows up on the productivity side.

“AI will likely have similar spillover effects once the infrastructure is in place. How large those effects will be is unclear, which is the core challenge… estimating the relationship between AI and productivity.”

How exactly could AI change productivity and investment patterns across the economy? “There are basically two ways this can go. You can get more output for the same input. If you used to put in 100 and get 120, maybe now you get 140. That’s an expansion in total factor productivity. Or you can get the same output with fewer inputs.

“It’s unclear how much of either will happen across industries or in the labor market. Will companies lean into AI, cut their workforce, and maintain revenue? Or will they keep their workforce, use AI to supplement them, and increase total output per worker?

“R&D spending is also a question mark. AI can allow researchers to do more, faster, and with fewer resources. But that could either mean less R&D spending is needed, or it could inspire even more investment because the return on R&D becomes higher. We don’t yet know which direction it will go.”

The US is spending much more on AI than the rest of the world. Does that make your US productivity projections different from other economies? “Yes, the productivity numbers we’re seeing in the US modeling work are elevated, both compared to what we had previously projected and compared to some historical periods. But we’re also seeing upticks in other parts of the world. China, for example, shows increased productivity projections as well, and that reflects its serious investments in AI capabilities.

China is in the process of developing its next five-year plan — the 15th — and a lot of attention is going into building a more advanced manufacturing environment and next-generation technologies like artificial intelligence. Of course, it’s a moving target: access to high-end chips, the development of domestic alternatives, and broader geopolitical dynamics all play a role.

But China has a large technical talent base and significant government funding aimed at making AI a key part of its growth environment over the next decade.

The US and China are ahead in the AI curve. For developing economies, how does AI change their growth paths? “One of the advantages many of them — like Vietnam, Bangladesh, Kenya, or parts of sub-Saharan Africa — have historically relied on is a labor-arbitrage system, where it simply costs less to produce goods because labor is cheaper. That’s how countries such as China, Taiwan, and Singapore worked their way up global value chains over time.

“But with AI, that can become disruptive. If AI and automation remove the human element from labor-intensive manufacturing, that cost advantage erodes. It makes it harder for developing countries to use cheap labor as a stepping stone toward industrialization.

“At the same time, businesses and consumers in these economies… can still use AI tools to become more efficient. That’s the tailwind.

“So there are both headwinds and tailwinds for emerging markets that may not have the resources or technical know-how to build out AI domestically but will still feel its effects as the technology spreads.”

VCs say they don’t want to fund yet another coding tool or AI search engine. They want AI that transforms the physical world, like robotics, safety tech, or manufacturing tools. That’s where they see trillion-dollar impact. How do you view that? “It’s interesting, and I agree to an extent. But the US is a services-oriented economy, so even if AI eventually reshapes the physical world, the more immediate impacts will be in services. That’s the largest share of our economy. And you don’t need a robot to see disruption.  See AI call centers, chatbots, automated accounting, paralegal tools. These can replace tasks that used to require people, and do it for a fraction of the cost.

There may eventually be a pivot back toward manufacturing as physical AI develops, and some in the political world would like that. But in the near term, AI’s biggest effects will likely show up in the services sector long before they show up on an assembly line in Georgia.

As AI accelerates, what uncertainties or unknowns stand out to you when you think about the future of economic analysis? “This is an emerging story. The technology is changing month to month. I’m using it professionally, and it’s making me more efficient.

“I don’t know what this looks like in five or ten years, or whether the economist profession will face the same fate as others, with a reduced need for bean counters like me. It’s a wild future. I can’t predict it with any certainty.”

Arm has created a new Physical AI unit focused on robotics and automotive systems, a sign that enterprise AI is increasingly moving out of the data center and into machines operating in the physical world.

As part of the reorganization, Arm has split its operations into three core groups, separating cloud and AI technologies, edge products such as smartphones and PCs, and a newly formed Physical AI division that brings automotive and robotics under one roof, according to Reuters.

Arm’s decision comes as enterprises experiment with robotics beyond pilots, deploying autonomous systems in factories, warehouses, and logistics operations where real-time decision-making matters more than raw compute power.

This shifts AI workloads toward the edge, forcing CIOs to prioritize device reliability over cloud scale.

Enterprise implications

Arm’s move marks a structural shift in how computing is being aligned for robotics and automotive systems.

“The industry has moved through three distinct phases in the three years since the ‘ChatGPT moment’, from generative AI to agentic AI and now Physical AI,” said Neil Shah, vice president for research at Counterpoint Research. “Bridging digital agents to physical robots requires a massive investment in synthetic data. Unlike agentic AI, which can be trained on text or code, Physical AI requires ‘world models’ trained on high-fidelity video and physics simulations.”

For enterprises, this means planning infrastructure capable of supporting heavy, simulation-driven workloads needed to train robots across a wide range of real-world scenarios, Shah added.

Physical AI is also changing where AI workloads are executed. Arm’s approach shifts more inference and control functions toward edge and on-device environments, particularly for robotics and other real-time systems.

“These workloads require ultra-low latency, energy efficiency, and resilience, which centralized cloud cannot always deliver,” said Biswajeet Mahapatra, principal analyst at Forrester. “CIOs should adopt hybrid architectures: inference and control tasks at the edge or on-device using Arm-based accelerators, while training and large-scale analytics remain in the cloud.”

Networking also becomes a critical factor. Physical AI systems depend on predictable, low-latency connectivity to coordinate sensors and controllers in real time, particularly in factories and warehouses. This can push enterprises to revisit industrial networking designs, with greater emphasis on deterministic performance using technologies such as private 5G, Wi-Fi 7, and time-sensitive networking.

“The result is not cloud displacement, but a rebalance: the cloud serves as the system of learning and coordination, while Arm-based edge and device environments handle real-time perception, decisions, and physical action,” said Manish Rawat, semiconductor analyst at TechInsights.

Steps for CIOs

Preparing for Physical AI requires changes across the technology stack. “IT leaders need to optimize operating systems, AI frameworks, and container platforms for Arm architectures,” Mahapatra said. “Security and lifecycle management for distributed robotics systems must be strengthened. Running pilot projects with Arm-based robotic applications will help validate performance and integration before scaling.”

Rawat noted that enterprises should start by treating robotics and Physical AI as an extension of their core IT stack, not a niche OT experiment.

“This means designing applications with clear separations between training, orchestration, and real-time execution, so components can move cleanly between cloud and Arm-based edge or device platforms,” Rawat said.

The guidance reflects a shift toward treating robotics and Physical AI as long-term infrastructure investments, rather than standalone automation projects.

Arm’s enterprise strategy

With its increased focus on Physical AI, Arm is aiming to design highly optimized architectures as the AI economy shifts from paying for tokens generated to paying for precision in real-time decision-making in physical environments.

“Arm is designing end-to-end architecture to support decision making at the edge,” Shah said. “By standardizing on Arm across both the server and the robot, enterprises can create a ‘seamless compute fabric’ that allows these AI models to move from the cloud to the edge without rebuilding the underlying software stack.”

Standardizing on Arm can reduce fragmentation across device classes, streamline developer skills, and improve portability of workloads from data center to edge to machine.

“However, the risk lies less in vendor lock-in and more in dependency on Arm’s licensing and roadmap decisions as it moves closer to full chip designs,” Rawat said.

For most enterprises, adoption is expected to be gradual. CIOs are likely to begin with targeted deployments in controlled settings, such as factories or warehouses, before scaling robotics and autonomous systems more broadly across their operations.

Cisco has released updates to address a medium-severity security flaw in Identity Services Engine (ISE) and ISE Passive Identity Connector (ISE-PIC) with a public proof-of-concept (PoC) exploit. The vulnerability, tracked as CVE-2026-20029 (CVSS score: 4.9), resides in the licensing feature and could allow an authenticated, remote attacker with administrative privileges to gain access to

Cisco has released updates to address a medium-severity security flaw in Identity Services Engine (ISE) and ISE Passive Identity Connector (ISE-PIC) with a public proof-of-concept (PoC) exploit. The vulnerability, tracked as CVE-2026-20029 (CVSS score: 4.9), resides in the licensing feature and could allow an authenticated, remote attacker with administrative privileges to gain access to Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Cybersecurity researchers have discovered three malicious npm packages that are designed to deliver a previously undocumented malware called NodeCordRAT. The names of the packages, all of which were taken down as of November 2025, are listed below. They were uploaded by a user named "wenmoonx." bitcoin-main-lib (2,300 Downloads) bitcoin-lib-js (193 Downloads) bip40 (970 Downloads) "The

Cybersecurity researchers have discovered three malicious npm packages that are designed to deliver a previously undocumented malware called NodeCordRAT. The names of the packages, all of which were taken down as of November 2025, are listed below. They were uploaded by a user named "wenmoonx." bitcoin-main-lib (2,300 Downloads) bitcoin-lib-js (193 Downloads) bip40 (970 Downloads) "The Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]