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Dva nové špičkové modely od Roborocku, dva odlišné přístupy k mopování • . • Infrakamera ukázala světelnou show – rozdíl mezi konvenčním lineárním lidarem a zdvojeným 3D lidarem. • Nenápadný trumf je podvozek – překonává vysoké prahy a zvyšuje přítlak na mopy.

Microsoft has reportedly had a hard time convincing corporate customers to pay premium prices for its enterprise AI products, particularly as their employees prefer ChatGPT or Gemini. The Information reported in December that Microsoft quietly slashed sales targets for some of those products — a report that Microsoft denied. Nevertheless, by Microsoft’s own admission, just 3.3% of its vast Microsoft 365 user base has a paid Microsoft 365 Copilot license.

Customers’ reluctance to pay top dollar for Microsoft’s AI tools likely reflects the pace at which new AI alternatives have raced to market. With so many services to choose between and the industry at such a febrile and inventive stage, customers want to dance between the options as they seek those they prefer, rather than being in thrall to one provider — while recent Microsoft 365 price increases show the extent to which sector dominance can leave customers exposed to price hikes. Now bitten, customers are shy to commit too much to one provider.

Spoiled for choice

Why would they want to, given that there are so many alternatives to choose from? AI services are like streaming services, except you don’t need to subscribe to them all: they pretty much all offer the same thing, though some are better for some tasks.

Apple understands this. By its actions, it is showing us that that AI models are destined to become commodities, which is why the company is resolutely focused on making sure its systems become the best platforms on which to run the models.

This recognition means Apple Intelligence is likely to only ever become a selection of hand-picked on-device assistants most of us will use some of the time, with additional tasks supported by third-party providers. We know that Apple plans to use Google Gemini to help it fast-track development of additional Apple Intelligence services, but we also know it intends to support multiple AI services. 

Apple’s App Store for AI

I think the best way to look at this is as an App Store for AI. You’ll be able to do a certain amount using on-device AI and Siri, and you’ll be able to choose between third-party AI services to handle other tasks. That plan means the existing exclusive arrangement with OpenAI’s ChatGPT will be abandoned as the company opens Siri up so its customers can choose which third-party AI services to use. It isn’t clear yet if this will extend to use of on-prem AI systems, which will be a particularly attractive proposition to regulated industries and privacy advocates using Mac minis to run independent LLMs. 

An App Store model also gives Apple a chance to offer up APIs to AI developers to enable sophisticated AI applications that do not devour personal privacy. That seems a very Apple-like approach to these things.

What is clear is the extent to which Apple the hardware company now understands that AI doesn’t replace platforms, but depends on them. Apple as a hardware and operating systems provider just needs to focus on providing the best available ecosystem on which to build and run AI systems, with a user experience to match.

Pop goes the weasel

Asymco’s Horace Dediu notes the significance of such a shift: “If foundation models are heading toward commodity status, then the strategic value shifts to whoever controls the integration layer and the user relationship,” he wrote. 

Apple’s 2-billion-plus ecosystem gives it the edge in distribution, while its tried and tested App Store approach helps validate and optimize the user relationship. The idea that AI services become apps to be bought and sold on Apple’s platforms isn’t far-fetched. Bloomberg suggests that Apple is building tools to let chatbot apps installed via the App Store work with Siri and other Apple Intelligence features. 

Reflecting that some AI services are better at some tasks than they are at others, Apple will also make it possible for customers to choose which AI service handles each request. Apple will probably take a slice from any AI services subscription sales made via its platform as part of this plan, just as it makes bank from every other fee-based app.

I do wonder if this could end up with a weird Catch 22-like situation for Microsoft Office users, in which everything they do on their Apple device is handled by their chosen AI service, except when using an Office app when they may find themselves trapped with Copilot. 

More to come at WWDC?

Summing up, it looks very much as if after the filth and the fury of the first stage of AI evolution, the song remains the same — you still need solid platforms to run this stuff on. Which is, of course, where Apple’s powerful Apple silicon-powered devices have so much to bring.

The company is expected to introduce this with its 27-series of OS updates, the first glance of which we will gain at WWDC in early June. Once we see what kind of system Apple is putting together, we’ll have a much better understanding of what the future of the AI industry is going to be. From where I sit, it seems obvious: while AI may change the world, it’s likely to do so while running on an Apple product.

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Secrets sprawl isn't slowing down: in 2025, it accelerated faster than most security teams anticipated. GitGuardian's State of Secrets Sprawl 2026 report analyzed billions of commits across public GitHub and uncovered 29 million new hardcoded secrets in 2025 alone, a 34% increase year over year and the largest single-year jump ever recorded. This year's findings reveal three core trends: AI has

Secrets sprawl isn't slowing down: in 2025, it accelerated faster than most security teams anticipated. GitGuardian's State of Secrets Sprawl 2026 report analyzed billions of commits across public GitHub and uncovered 29 million new hardcoded secrets in 2025 alone, a 34% increase year over year and the largest single-year jump ever recorded. This year's findings reveal three core trends: AI has [email protected]

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The European Commission has admitted that attackers broke into its public-facing web infrastructure and siphoned off data in a bare-bones disclosure that answers the what but ducks most of the how.…

Enterprise spending on generative AI has surged over the past year, but for many CIOs, the hardest conversations are only now beginning. Boards and CFOs are no longer asking whether the organization is investing in AI. They are asking what it’s getting back — in measurable financial terms.

According to analysts at Forrester Research, genAI budgets have increased substantially year over year, yet a majority of organizations still struggle to demonstrate sustained return on investment. Early pilots often look promising, but value becomes harder to explain as systems scale, costs fluctuate, and governance expectations rise.

Interviews with analysts, CIOs, and AI platform and governance leaders point to a consistent pattern. The problem is not that AI fails technically. It’s that enterprises are applying legacy budgeting, operating, and accountability models to a technology whose economics behave very differently. As a result, ROI erodes not because AI stops working, but because organizations lose the ability to explain, defend, and prioritize it.

Analyst framing: from cost control to value co-creation

From an analyst perspective, the AI ROI debate is best understood as part of a broader convergence between IT and finance. Greg Zorella, lead principal analyst at Forrester covering IT financial management, argues that high-performing IT organizations no longer treat finance as a gatekeeper focused on cost containment. Instead, IT finance becomes a capability for strategic value delivery — connecting technology investment directly to business growth and competitive advantage.

“IT finance isn’t there because IT spends a lot of money,” Zorella said. “It’s there because IT spend can really drive strategic outcomes for the enterprise.” That distinction matters for AI. Traditional IT investments — ERP systems, infrastructure refreshes, SaaS licenses — fit relatively well into established financial models. Generative AI does not. Costs are consumption-based, usage patterns are unpredictable, and benefits are often indirect or risk-adjusted rather than transactional.

Zorella notes that many enterprises intellectually recognize this shift but underestimate the organizational lift required to act on it. Mature cost transparency depends on shared attribution models, reliable data, and agreement across IT, product, sales, and marketing about how value is defined.

“Trying to do that all at once is just too much,” he said. The organizations making progress tend to start with narrow proof points that demonstrate how better financial visibility improves decision-making.

Importantly, Zorella challenges the assumption that exceeding IT budgets is inherently negative. Overspending may be rational — if it reflects deliberate investment in higher-value initiatives. The real failure, he argues, is overspending without a prioritization mechanism that allows leaders to deprioritize lower-impact work when new opportunities emerge.

CIO decision reality: budgets don’t expand forever

That analytical framing meets a far more constrained reality inside the enterprise. Sumit Johar, CIO of BlackLine, which makes finance automation and management software, describes AI investment moving through a familiar cycle. In recent years, initial skepticism gave way to peer pressure as boards and executives demanded visible AI initiatives. Today, that phase is ending. Finance leaders are asking harder questions, and AI is no longer treated as a special category exempt from scrutiny.

“If I tell my CFO that 95% of employees are using AI, that doesn’t mean anything,” Johar said. “It’s like saying 100% of employees use email. Finance cares about impact on profitability, revenue, or risk — everything else falls flat.”

Johar draws a sharp distinction between two classes of AI investment. The first is broad productivity platforms — what he calls “everyday AI” — that help employees write, search, summarize, or analyze information. These tools can be transformative culturally, but they are notoriously difficult to quantify. Engagement metrics and self-reported productivity gains rarely survive financial scrutiny.

The second class consists of outcome-driven AI initiatives tied explicitly to business priorities: accelerating customer onboarding, reducing deployment time, lowering operating costs, or increasing the revenue pipeline. These initiatives compete directly with other enterprise investments and are evaluated accordingly.

What has changed most, Johar says, is that AI spending is no longer additive. CIOs are not receiving incremental budget increases “because AI.” Any additional investment must be funded by reallocating existing budgets. “Nobody is blindly throwing money at AI anymore,” he said. “If we want to spend more, we have to move things around.”

At BlackLine, AI governance reflects that reality. Proposed initiatives are reviewed jointly by IT, finance, and business leaders, with explicit expectations for outcomes and accountability. The goal is not to slow experimentation, but to ensure that responsibility for value creation does not sit solely with the CIO.

“This is a business transformation problem, not a technology problem,” Johar said. “If ownership stays only with IT, you’ll never get the value you’re expecting.”

Operational failure modes: why ROI collapses at scale

Even when AI initiatives clear budget hurdles, many fail to deliver sustained ROI once they move beyond pilots. According to Jim Olsen, CTO of AI lifecycle management and governance platform maker ModelOp, the breakdown is rarely caused by a single flaw. It is structural. Early AI projects are typically developed in controlled environments with limited data and predictable usage. Costs appear manageable, and performance looks strong. Production environments behave very differently.

“You develop something locally and it looks very doable,” Olsen said. “But once it hits production, usage patterns change, contexts explode, and suddenly the true cost shows up.”

Generative AI amplifies this problem. Free-form user interaction increases token consumption unpredictably. Models are embedded across workflows and reused by multiple teams, making it difficult to attribute cost or value to specific outcomes. Without clear inventory and lifecycle tracking, enterprises end up managing AI spend in aggregate — while value is created or lost at the margins.

Olsen says many organizations lack even a basic understanding of what AI systems they have in production. “If you don’t know what’s out there, you can’t measure it, govern it, or tie it back to ROI,” he said.

The result is a familiar pattern: promising pilots followed by cost overruns, followed by skepticism. In some cases, high-profile missteps make organizations risk-averse, slowing future adoption even where AI could deliver real advantage.

The remedy, Olsen argues, is to treat AI as industrial infrastructure rather than experimental tooling. Lifecycle management — covering development, deployment, monitoring, and retirement — is not bureaucratic overhead. It is the only way to maintain accountability as models evolve and usage grows.

Governance and defensibility: when value must be proven

Operational discipline alone, however, is not enough. As AI investments face regulatory and board-level scrutiny, governance increasingly determines whether ROI can be defended at all. Anthony Habayeb, CEO and co-founder of AI governance software vendor Monitaur, argues that many AI initiatives fail under review not because they perform poorly, but because success was never clearly defined.

“We’re running around with a hammer looking for a nail,” he said. “If you don’t know what success looks like at inception, you can’t defend ROI later.”

Governance failures often surface only after deployment, when organizations attempt to retroactively justify spend. At that point, gaps in documentation, monitoring, and accountability become liabilities. Projects that lack clearly articulated objectives or outcomes are easy targets when budgets tighten.

Habayeb challenges the idea that governance is primarily about compliance. In practice, he says, governance improves ROI by exposing unknown risks and optimization opportunities. As organizations introduce structured validation and monitoring, they often identify ways to improve accuracy, robustness, and efficiency — directly enhancing business impact.

Regulatory pressure is accelerating this shift. Frameworks such as the EU AI Act are pushing organizations to formalize oversight, but Habayeb says the smartest enterprises are using regulation as a forcing function to build broader governance capabilities.

“Governance shouldn’t be a separate compliance line item,” he said. “It should be part of how you make AI work for the business.”

From enthusiasm to endurance

Taken together, these perspectives point to a maturing phase of enterprise AI adoption. The question is no longer whether AI can deliver value, but whether organizations can prove that it does — consistently, transparently, and under scrutiny.

The enterprises making progress share several traits. They align AI investment with business strategy rather than treating it as a standalone category. They build financial models that accommodate consumption-based costs and indirect value. They enforce operational discipline across the AI lifecycle. And they embed governance early — not as a brake on innovation, but as a foundation for trust and sustainability.

For CIOs planning 2026 budgets, the message is sobering but constructive. AI will not justify itself. Value must be designed, measured, and defended, using tools and practices that many organizations are only now beginning to develop.

The era of AI as an experiment is ending. The era of AI as an accountable enterprise asset has begun.

Experiment potvrdil existenci dlouho hledaného velmi neobvyklého těžkého baryonu • Tento objev konečně vyřešil více než dvacet let starou záhadu • Experimentální data nyní překvapivě předbíhají dosavadní teoretické modely

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Career-limiting stupidity and rudeness exposed, with terminal consequences

Who, Me?  The week before Easter may be a short one for many in the Reg-reading world, but that won't stop us from opening it with a fresh installment of Who, Me? It's the reader-contributed column in which you share stories of things you did at work that had interesting consequences.…

Three threat activity clusters aligned with China have targeted a government organization in Southeast Asia as part of what has been described as a "complex and well-resourced operation." The campaigns have led to the deployment of various malware families, including HIUPAN (aka USBFect, MISTCLOAK, or U2DiskWatch), PUBLOAD, EggStremeFuel (aka RawCookie), EggStremeLoader (aka Gorem RAT), MASOL

Three threat activity clusters aligned with China have targeted a government organization in Southeast Asia as part of what has been described as a "complex and well-resourced operation." The campaigns have led to the deployment of various malware families, including HIUPAN (aka USBFect, MISTCLOAK, or U2DiskWatch), PUBLOAD, EggStremeFuel (aka RawCookie), EggStremeLoader (aka Gorem RAT), MASOL Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Odolný telefon nemusí vypadat jako tank • I s velkou baterií zůstává hmotnost v rozumné hladině • V těžké konkurenci má potřebná esa v rukávu

The European Commission has confirmed a data breach after its Europa.eu web platform was hacked in a cyberattack claimed by the ShinyHunters extortion gang. [...]

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