The ambitious $500 billion Stargate AI infrastructure project is moving significantly slower than anticipated, with SoftBank Group CFO Yoshimitsu Goto publicly acknowledging the delays during the company’s Q1 2025 earnings call.

“It’s taking a little longer than our initial timeline,” Goto said during the call, describing the project as proceeding “slower than usual.”

Source:: Computer World

By Deepti Pathak Panasonic has expanded its TV line-up with the launch of the new P-Series range, featuring the…
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By Deepti Pathak Just started playing Verse Piece and not sure where to find updates or connect with others?…
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By Brandon Andrews You’ve spent a lifetime building skills, learning lessons, nurturing relationships, and developing a perspective as prescient and powerful as your personal drive. You’ve poured it all into your business. Now, you have five minutes (or less) to communicate an irresistible vision for the world and convince a panel of respected — and sometimes disrespectful — judges that you can make the vision real and make some money. How do you do it? A pitch competition is a unique moment: I have pitched in, judged, and hosted pitch competitions from Miami to Mongolia. I’m an entrepreneur and investor, and I’ve spent…This story continues at The Next Web

Source:: The Next Web

More than two years after GPT-4’s release, OpenAI has unveiled GPT-5, boasting sharper reasoning, multimodal input, better math skills, and cleaner task execution, according to the company.

The large language model (LLM) — now rolling out to ChatGPT users and available in the API — is “smarter, more stable, and more versatile” and built to handle real-world tasks more like a human expert, OpenAI said.

In anticipation of OpenAI’s new AI model, Anthropic released the latest version of its own chatbot, Claude, earlier in the week.

Claude Opus 4.1 came with improvements particularly in two key areas: it significantly improved its coding capabilities, solving up to 75% of real-world programming tasks based on SWE Verified benchmarks; and the model is capable in detailed research and analysis, especially in tasks that require tracking lots of information and intelligently finding answers, according to Anthropic.

For developers, OpenAI claims GPT-5 is its most powerful coding model to date, outperforming GPT-o3 in benchmarks and real-world tasks. The model is “fine-tuned for agentic tools” like Cursor, Windsurf, Copilot, and Codex CLI, and it set new records in testing, the company stated in a blog.

According to OpenAI, GPT-5 delivers sharper reasoning, handling complex problems and multi-step instructions with greater accuracy and focus. It stays on track, follows directions more precisely, and produces more useful, reliable output, the company said.

Users can also expect to see fewer hallucinations and will have better customization tools, making GPT-5 more dependable and easier to adapt to specific industries and needs, OpenAI said.

It also builds on GPT-4o’s multimodal abilities, offering smoother interactions across text, images, and audio, according to OpenAI.

GPT-5 will be OpenAI’s “most significant do or die moment yet,” according to Nathaniel Whittemore, CEO of Superintelligent, a New York-based AI education platform.

“Ever since the launch of ChatGPT, they’ve been the model state of the art. While competitors like Google and Meta can take advantage of hundreds of millions of existing users to put AI products in front of, OpenAI relies on winning new users by being far ahead of the other AI labs,” Whittemore said.

OpenAI chief operating officer Brad Lightcap said ChatGPT is now in use by more than five million business users — up from three million in June.

Biopharmaceutical company Amgen is one of the early adopters of GPT-5. Sean Bruich, senior vice president of AI & Data at Amgen, said AI only works in science if it meets the highest bar, and “GPT-5 clears it,” delivering sharper accuracy, better context, and faster results across Amgen’s workflows.

“GPT-5… is doing a better job navigating ambiguity where context matters. We are seeing promising early results from deploying GPT-5 across workflows,” he said. He also said the model was faster, more reliable, and had higher quality outputs than GPT-4 and other earlier models.

Ethan Mollick, an associate professor at The Wharton School, had early access to GPT-5. “It is a big deal,” he said in a blog post. He asked the model to do something dramatic to prove that point. The model thought for 24 seconds and then delivered a poetic manifesto of AI capability — specifically, a rhetorical, alliterative showcase of “a multifunctional intelligence system.”

“GPT-5 just does stuff, often extraordinary stuff, sometimes weird stuff, sometimes very AI stuff, on its own. And that is what makes it so interesting,” Mollick said.

After “many AI conversations,” Mollick said he has found two big issues that limit most people’s success in using AI models: First, most people don’t know which model to use — so they get fast, weak results instead of more complete answers from the powerful reasoning models.

“The longer [the models] think, the better the answer, but thinking costs money and takes time. So OpenAI previously made the default ChatGPT use fast, dumb models, hiding the good stuff from most users,” Mollick said. “A surprising number of people have never seen what AI can actually do because they’re stuck on GPT-4o, and don’t know which of the confusingly named models are better.”

Second, most people also don’t know what AI can do or how to ask — especially with newer agentic AIs. GPT-5 fixes both problems by choosing models well and suggesting actions, he said. “It is very proactive, always suggesting things to do.”

GPT-5 is beginning to roll out to ChatGPT Plus, Pro, Team, and Free users, with access for Enterprise and Edu customers coming next week. “Once free users reach their GPT‑5 usage limits, they will transition to GPT‑5 mini,” OpenAI said.

Source:: Computer World

Administrators with hybrid Exchange Server environments are urged by Microsoft and the US Cybersecurity and Infrastructure Security Agency (CISA) to quickly plug a high-severity vulnerability or risk system compromise.

Hybrid Exchange deployments offer organizations the ability to extend the user features and admin controls of the on-prem version of Exchange within Microsoft 365. Hybrid deployment can serve as an intermediate step to moving completely to an Exchange Online organization, Microsoft said.

The benefits include secure mail routing between on-premises and Exchange Online organizations, mail routing with a shared domain namespace (for example, both on-premises and Exchange Online organizations use the @contoso.com SMTP domain) and calendar sharing between on-premises and Exchange Online organizations.

Source:: Computer World

By Thomas Macaulay It’s harvest day at the Karolinska Institute in Stockholm. As sunshine bathes the leafy university campus, scientists inside the labs work under cool fluorescent light. Clad in green protective gear, they tend meticulously to test tubes within hermetically sealed cleanrooms. The containers hold the fruits of today’s labour: mesenchymal stem cells (MSCs). Each cell is barely a quarter the width of a human hair but wields remarkable power. MSCs reduce inflammation, repair damaged tissue, and modulate the immune system. They can treat chronic diseases and delay ageing. They may even prevent illness before it begins. But to become a mainstay…This story continues at The Next Web

Source:: The Next Web

By Hisan Kidwai Free Fire Max is one of the most popular games on the planet, and for good…
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Once upon a time there was an amazing little voice assistant that ended up being exclusively available in Apple products. It was called Siri and it was ahead of its time.

Because Siri seemed pretty magical when it first hit the iPhone; it would answer requests, find out information, and even do useful things such as taking photographs or naming songs you heard on the radio.

Available in numerous languages and with a range of male and female speaking voices, Siri remains the most widely distributed on-device chatbot in terms of language support. The assistant also scaled well, eventually appearing across Apple’s product lines. But critics and competitors now agree, Siri was too ambitious and failed to keep up with the times.

Back to the future

Cast your mind back to 2010 when Siri first appeared for iPhone. At that time, research into artificial intelligence (AI) and autonomous technologies, ongoing since the launch of the Stanford Artificial Intelligence Lab (SAIL) in the 60’s, really accelerated. Apple led the charge, at least in terms of media profile, and Siri (which the company acquired soon after its introduction) was a leading-edge challenger in the nascent field

What was great about Siri was its fluffy, friendly image. 

Being an Apple product gave the solution access to a huge market of engaged and happy consumers willing to overcome their general concern at the dystopian application of AI to give the friendly little assistant a try. 

Building acceptance one error at a time

Arguably, Apple’s little assistant helped drive acceptance of technologies that have become critical to today’s cutting edge generative AI (genAI) systems, including:

Speech recognition

The idea of intelligent machines

Devices equipped to listen for your commands 24/7

Fast and real-time access to information on spoken request

Andr even real-time transcription.

Siri’s well-publicized errors actually helped build acceptance. After all, if you think about it, the fact that Siri sometimes made mistakes somehow helped humanize it. It is better to think that AI is stupid than to see it as threateningly smart.

This helped a skeptical public come to terms with AI, even while Apple’s assistant embodied a range of concepts people resisted. The logic was that it couldn’t be too bad if machines had this kind of intelligence built inside, right? It’s not as if they are smart enough to fully understand. Did it really matter if the tech listened to you when you thought it was switched off? 

What use would the information picked up be? (The answer: around 81% of UK consumers now claim to have experienced targeted consumer advertising generated by AI.)

Trust in me

We’ve had many debates on these topics since then — debates that show Apple’s commitment to user privacy in AI to be unique, and under attack from competitors and authoritarians alike. It’s almost as if, when some leaders heard Apple CEO Tim Cook warn this is surveillance, they chose to exploit it as an opportunity, rather than protect against it.

All the same, Siri helped people become more capable of placing trust in AI. 

Years later, OpenAI was introduced to a public already more accepting of such tech. That acceptance was to some extent built on the back of Siri adoption, and the appearance of other big name search assistants across the industry.

That acceptance means around 77% of devices in use today have some form of AI, and roughly 90% of organizations are using AI. Investment in the sector is booming, with the tech giants (Apple, Amazon, Google, Microsoft and Meta) spending $92.17 billion on capital expenditures in Q2 2025 alone. That’s up a whopping 66.67% on the year ago quarter, mainly on the strength of big investments in data centers, servers, and AI infrastructure.

Hope, hype, and history

The hype around AI is growing as fast as the investments.

Firms in the space are signing massive multi-billion dollar deals, governments are investing vast quantities of cash and resources to support AI industry development, and consumers are preparing to pay for all this investment come the inevitable industry collapse. 

Consumers will pay? Just look at history. We know this because that’s what happened following the dotcom boom, the South Sea Bubble collapse, and the financial crisis, when unsustainable investments came before a fall. We know this because by the time the highly probable AI industry collapse happens, the tech will be so deeply intertwined in our daily lives we will be told these companies are strategically important, making them “too big to fail.”

So we will bail them out.

What follows Siri? 

I come to bury Siri, not to praise it, because competitors say it was ambitious and failed to keep up with them. But if you’d never experienced Siri, would you have trusted ChatGPT? Perhaps a little, but not as much.

For the future of Siri, ask whether privacy continue to be baked in, or will governments get their way when it comes to data encryption, in which case no one will be private unless they can afford to be.

If governments do get their way and Apple is made to take privacy out of its algorithms, just how much of a threat will Siri become to other AI services, which already seem to respect privacy less? Siri doesn’t seem to know the answer (yet).

Though it probably has quite a lot of data to help it work one out.

You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

Source:: Computer World

OpenAI has released its first open-weight language models since GPT-2, marking a significant strategic shift as the company seeks to expand enterprise adoption through more flexible deployment options and reduced operational costs.

The two new models — gpt-oss-120b and gpt-oss-20b — deliver what OpenAI describes as competitive performance while running efficiently on consumer-grade hardware. The larger model reportedly achieves near-parity with OpenAI’s o4-mini on reasoning benchmarks while running on a single 80 GB GPU, while the smaller variant matches o3-mini performance and can operate on edge devices with just 16 GB of memory.

“This is a bold go-to-market move by OpenAI and is now really open,” said Neil Shah, VP for research and partner at Counterpoint Research. “This move nicely challenges rivals such as Meta, DeepSeek, and other proprietary vendors both for cloud and more specifically edge.”

Open-weight models provide access to the trained model parameters, allowing organizations to run and customize the AI locally, but differ from traditional open-source software by not necessarily including the original training code or datasets.

Architecture designed for enterprise efficiency

The models leverage a mixture-of-experts (MoE) architecture to optimize computational efficiency. The gpt-oss-120b activates 5.1 billion parameters per token from its 117 billion total parameters, while gpt-oss-20b activates 3.6 billion from its 21 billion parameter base. Both support 128,000-token context windows and are released under the Apache 2.0 license, enabling unrestricted commercial use and customization.

The models are available for download on Hugging Face and come natively quantized in MXFP4 format, according to the statement. The company has partnered with deployment platforms, including Azure, AWS, Hugging Face, vLLM, Ollama, Fireworks, Together AI, Databricks, and Vercel to ensure broad accessibility.

For enterprise IT teams, this architecture could translate to more predictable resource requirements and potentially significant cost savings compared to proprietary model deployments. According to the statement, the models include instruction following, web search integration, Python code execution, and reasoning capabilities that can be adjusted based on task complexity.

“This will accelerate adoption of OpenAI models for research as well as commercial use under Apache 2.0 license,” Shah noted, highlighting the strategic value of the licensing approach.

Total cost calculations favor high-volume users

The economics of open-weight deployment versus AI-as-a-service present complex calculations for enterprise decision-makers. Organizations face initial infrastructure investments and ongoing operational costs for self-hosting, but can eliminate per-token API fees that accumulate with high-volume usage.

“The TCO calculation will break even for enterprises with high-volume usage or mission-critical needs where the per-token savings of self-hosting and open weights will eventually outweigh the high initial and operational costs,” Shah explained. “For low usage, AI-as-a-Service will benefit better.”

Early enterprise partners, including AI Sweden, Orange, and Snowflake, have begun testing real-world applications, from on-premises hosting for data security to fine-tuning on specialized datasets, the statement added. The timing aligns with enterprise technology spending expected to reach $4.9 trillion in 2025, with AI investments driving much of that growth.

OpenAI said that it subjected the models to comprehensive safety training and evaluations, including testing an adversarially fine-tuned version of gpt-oss-120b under the company’s Preparedness Framework. Also, its methodology was reviewed by external experts, addressing enterprise concerns about open-source AI deployments.

According to OpenAI’s benchmarks, the models showed competitive performance: gpt-oss-120b achieved 79.8% Pass@1 on AIME 2024 and 97.3% on MATH-500, while demonstrating coding capabilities with a 2,029 Elo rating on Codeforces. The company reported that both models performed well on tool use and few-shot function calling — capabilities relevant for business automation.

Strategic decoupling from Microsoft

The release has significant implications for OpenAI’s relationship with Microsoft, its primary investor and cloud partner. Despite the open-weight approach, Microsoft is bringing GPU-optimized versions of the gpt-oss-20b model to Windows devices through ONNX Runtime, supporting local inference via Foundry Local and the AI Toolkit for VS Code, the statement added.

Shah noted that “OpenAI with this move smartly decouples itself from Microsoft Azure and developers can now attach the open-weights models they have been working on and host it if they want to in the future on other rival clouds such as AWS or Google or even OpenAI-Oracle cloud.”

This strategic flexibility could pressure Microsoft to diversify beyond OpenAI partnerships while providing enterprises with greater vendor negotiating power. “This also now offers higher bargaining power for the enterprise against other AI vendors and even AI-as-a-Service models,” Shah observed.

Enterprise deployment considerations

The shift represents OpenAI’s recognition that enterprise AI adoption increasingly requires deployment flexibility. Organizations in regulated industries particularly value data sovereignty options, while others seek to escape vendor lock-in concerns associated with cloud-dependent AI services.

However, enterprises must weigh operational complexity against cost savings. While hardware requirements may be more accessible than previous generations, organizations need expertise in model deployment, fine-tuning, and ongoing maintenance—capabilities that vary significantly across enterprises.

The company is working with hardware providers, including Nvidia, AMD, Cerebras, and Groq, to ensure optimized performance across different systems, potentially easing deployment concerns for enterprise IT teams.

For IT decision-makers, the release expands strategic options in AI deployment models and vendor relationships. The Apache 2.0 licensing removes traditional barriers to customization while enabling organizations to develop proprietary AI applications without ongoing licensing fees.

“In the end it’s a win for enterprises,” Shah concluded, summarizing the broader market impact of OpenAI’s strategic pivot toward openness in the increasingly competitive enterprise AI landscape.

Source:: Computer World

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By Jano Costard Imagine drones that map disaster zones today and scout military targets tomorrow. Or seismic activity sensors built for construction that go on to detect submarines underwater. These ideas represent the promise of dual-use technologies that serve both civilian and military purposes. For the first time, the European Commission is explicitly proposing to fund them through programmes such as Horizon Europe. But as we race to embrace dual-use technologies, we face a pivotal choice: continue the old model where military applications drive innovation that civilians later adopt, or turn this paradigm on its head? Technological innovation has long followed a well-trodden…This story continues at The Next Web

Source:: The Next Web

Apple continues to grow in the enterprise, supporting this push with regular enterprise-focused enhancements for its platforms. We can see the results as Mac adoption sets new records. But the company can still do more for enterprise IT. To get a sense of some improvements it could make, I caught up with Apu Pavithran, the CEO of device management vendor Hexnode.

“Apple has already shown that it’s listening closely to IT needs, and the path ahead is promising,” he said, arguing that as more Apple devices embed themselves across the enterprise, it’s “a win for everyone: users, IT, and the company itself.”

Pavithran does have three suggestions that should help IT manage Apple’s platforms better, including granular permissions within apps, more advanced support for shared devices, and new APIs to help manage apps acquired outside of Apple’s App Store.

Granular permissions for Apple Intelligence in apps

“Take, for example, Apple intelligence,” he said. “Apple’s on-device AI and its focus on privacy is aligned with the expectations of security-conscious organizations. But as these tools make their way into enterprise and education, IT needs ways to fine-tune how and where they show up.  He argued that one improvement Apple could make would be to give IT admins the power to allocate Apple Intelligence support on a per-app basis. 

“Not all apps should have access to summarization or generative suggestions, particularly when working with sensitive or regulated data,” he said. “Adding an easier process to toggle Apple Intelligence features on a per-app basis would help organizations maintain control without compromising the user experience.”

Pavithran also looked at personalization and AI. He argued that by tying personalization and context to the authenticated user, not just the device, Apple would be able to better ensure that AI-generated insights are truly relevant. 

That’s particularly important on shared devices, as by making the connection between user and context, Apple would drastically reduce the chance AI responses might carry over between users in environments like hospitals, classrooms, or retail floors. 

Making shared devices even more secure

Apple has built a foundation to support shared devices with tools such as Shared iPad, Return to Service (RTS), and Authenticated Guest Mode, but the Hexnode CEO thinks Apple could go further. “RTS has the potential to offer more granular control over wipe behavior or session persistence,” Pavithran told me.

“Apple could take this even further by unifying shared device behavior across iOS, macOS, and visionOS. The ability to pre-stage apps and configurations based on the next user’s role, define what gets retained post-session, or automate the return-to-service flow based on schedules or events would simplify management and reduce friction.”

Better yet, of course, “for privacy-conscious deployments, session isolation and user sandboxing would round out the experience. This allows IT teams to streamline management for devices that change hands multiple times, such as in healthcare, logistics, or field operations.”

What about securing the apps?

Apple’s App Store might be under attack from regulators eager to open these platforms for all the wrong reasons, but it remains the most stable, secure, and trusted app ecosystem on any platform. That means many enterprises rely on the Apple App Store for the distribution of their employee apps.

Announced at WWDC 2025, one of the big changes coming up in the ’26 series of operating systems will be something called version-pinning for App Store apps. This is a great feature that gives IT precise control over when and how updates roll out across their managed fleets.

The problem — and this is one that will likely worsen as third-party App Stores appear — is that many enterprise apps are still not made available via Apple’s store. “These apps often power essential workflows in healthcare, logistics, field service, and finance, places where reliability and predictability aren’t optional,” Pavithran said.

The snag is that as these apps come from beyond the App Store, it means they can’t be managed by the new version-pinning feature introduced at WWDC. 

There is one way Apple might be able to enable App Pinning on non-App Store Apps, suggests Pavithran. “Apple would likely need to introduce a dedicated framework for enterprises to roll out their apps through the App Store. With the right tooling in place, Apple could give IT teams the same level of granular control for every app,” he said.

What’s interesting about these three pain points is the extent to which they represent how Apple use in the enterprise has become so commonplace that the improvements the company can make represent quite unique use cases – shared devices, proprietary app support, and so on. 

At the risk of being a bit of a broken record, if these are the problems IT now needs Apple to solve, you can read that to mean its platforms are already fit for deployment in your business.

You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

Source:: Computer World

A public war of words has erupted between cloud infrastructure leader Cloudflare and AI search company Perplexity, with both sides making serious allegations about each other’s technical competence in a dispute that industry analysts say exposes fundamental flaws in how enterprises protect content from AI data collection.

The controversy began when Cloudflare published a scathing technical report accusing Perplexity of “stealth crawling” — using disguised web browsers to sneak past website blocks and scrape content that site owners explicitly wanted to keep away from AI training. Perplexity quickly fired back, accusing Cloudflare of creating a “publicity stunt” by misattributing millions of web requests from unrelated services to boost its own marketing efforts.

Industry experts warn that the heated exchange reveals that current bot detection tools are failing to distinguish between legitimate AI services and problematic crawlers, leaving enterprises without reliable protection strategies.

Cloudflare’s technical allegations

Cloudflare’s investigation started after customers complained that Perplexity was still accessing their content despite blocking its known crawlers through robots.txt files and firewall rules. To test this, Cloudflare created brand-new domains, blocked all AI crawlers, and then asked Perplexity questions about those sites.

“We discovered Perplexity was still providing detailed information regarding the exact content hosted on each of these restricted domains,” Cloudflare reported in a blog post. “This response was unexpected, as we had taken all necessary precautions to prevent this data from being retrievable by their crawlers.”

The company found that when Perplexity’s declared crawler was blocked, it allegedly switched to a generic browser user agent designed to look like Chrome on macOS. This alleged stealth crawler generated 3-6 million daily requests across tens of thousands of websites, while Perplexity’s declared crawler handled 20-25 million daily requests.

Cloudflare emphasized that this behavior violated basic web principles: “The Internet as we have known it for the past three decades is rapidly changing, but one thing remains constant: it is built on trust. There are clear preferences that crawlers should be transparent, serve a clear purpose, perform a specific activity, and, most importantly, follow website directives and preferences.”

By contrast, when Cloudflare tested OpenAI’s ChatGPT with the same blocked domains, “we found that ChatGPT-User fetched the robots file and stopped crawling when it was disallowed. We did not observe follow-up crawls from any other user agents or third-party bots.”

Perplexity’s ‘publicity stunt’ accusation

Perplexity wasn’t having any of it. In a LinkedIn post that pulled no punches, the company accused Cloudflare of deliberately targeting its own customer for marketing advantage.

The AI company suggested two possible explanations for Cloudflare’s report: “Cloudflare needed a clever publicity moment and we – their own customer – happened to be a useful name to get them one” or “Cloudflare fundamentally misattributed 3-6M daily requests from BrowserBase’s automated browser service to Perplexity.”

Perplexity claimed the disputed traffic actually came from BrowserBase, a third-party cloud browser service that Perplexity uses sparingly, accounting for fewer than 45,000 of their daily requests versus the 3-6 million Cloudflare attributed to stealth crawling.

“Cloudflare fundamentally misattributed 3-6M daily requests from BrowserBase’s automated browser service to Perplexity, a basic traffic analysis failure that’s particularly embarrassing for a company whose core business is understanding and categorizing web traffic,” Perplexity shot back.

The company also argued that Cloudflare misunderstands how modern AI assistants work: “When you ask Perplexity a question that requires current information — say, ‘What are the latest reviews for that new restaurant?’ — the AI doesn’t already have that information sitting in a database somewhere. Instead, it goes to the relevant websites, reads the content, and brings back a summary tailored to your specific question.”

Perplexity took direct aim at Cloudflare’s competence: “If you can’t tell a helpful digital assistant from a malicious scraper, then you probably shouldn’t be making decisions about what constitutes legitimate web traffic.”

Expert analysis reveals deeper problems

Industry analysts say the dispute exposes broader vulnerabilities in enterprise content protection strategies that go beyond this single controversy.

“Some bot detection tools exhibit significant reliability issues, including high false positives and susceptibility to evasion tactics, as evidenced by inconsistent performance in distinguishing legitimate AI services from malicious crawlers,” said Charlie Dai, VP and principal analyst at Forrester.

Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, argued that the dispute “signals an urgent inflection point for enterprise security teams: traditional bot detection tools — built for static web crawlers and volumetric automation — are no longer equipped to handle the subtlety of AI-powered agents operating on behalf of users.”

The technical challenge is nuanced, Gogia explained, “While advanced AI assistants often fetch content in real-time for a user’s query — without storing or training on that data — they do so using automation frameworks like Puppeteer or Playwright that bear a striking resemblance to scraping tools. This leaves bot detection systems guessing between help and harm.”

The path to new standards

This fight isn’t just about technical details — it’s about establishing rules for AI-web interaction. Perplexity warned of broader consequences: “The result is a two-tiered internet where your access depends not on your needs, but on whether your chosen tools have been blessed by infrastructure controllers.”

Industry frameworks are emerging, but slowly. “Mature standards are unlikely before 2026. Enterprises might still have to rely on custom contracts, robots.txt, and evolving legal precedents in the interim,” Dai noted. Meanwhile, some companies are developing solutions: OpenAI is piloting identity verification through Web Bot Auth, allowing websites to cryptographically confirm agent requests.

Gogia warned of broader implications: “The risk is a balkanised web, where only vendors deemed compliant by major infrastructure providers are allowed access, thus favouring incumbents and freezing out open innovation.”

Source:: Computer World

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By Megan Carnegie Roei Samuel, founder of networking platform Connectd, has been hiring at speed — 14 roles in six months. But he’s begun to wonder if candidates’ answers are genuine, even on video calls. “I can see their eyes shifting across the screen,” he says. “Then they come back with the perfect answer to a question.” The trust gap between employer and jobseeker is widening, and it’s fast becoming one of the trickiest knots in modern hiring. From ChatGPT-polished CVs to full-blown applications submitted by bots, GenAI has hit the job market hard and gone fully mainstream. For a sizable generation of…This story continues at The Next Web

Source:: The Next Web

Autotrader has evolved from its roots as an automative magazine to become the UK’s largest automotive marketplace, a tech business that is now 100% using Apple products. 

Autotrader isn’t the first to move to Mac; the opportunity to move from a Windows ecosystem to Macs is being seized by a rapidly growing number of enterprises in response to Microsoft’s push to drive customers to Windows 11 following the CrowdStrike disaster.

Accelerating Apple adoption

The company has shifted to Apple products over the last couple of years, after becoming a 60% Mac environment in 2023-24. “User experience is everything to us and is one of the reasons we choose Apple technology,” Lee Skade, enterprise IT engineer at the company, said in a short video clip explaining its embrace of Apple technology. (The video is available here.)

At that time, Autotrader’s own internal data showed that 72% of all business employees would choose a Mac above all other devices, and 78% of millennial employees believe that access to the tech they like makes them more effective. We know data like that reflects findings at companies worldwide, from Cisco to IBM to Rituals, SAP, and elsewhere. With digital experiences becoming the primary employee experiences, these lights show the road.

Autotrader gets this and as a title focused on methods of transport seems to understand that the direction is accelerating to become the norm, which is why since publishing that clip, the company has gone all in and now operates a 100% Mac fleet. 

The transformation of IT

To help manage and support its Mac fleet, Autotrader is now looking for a Senior Mac Engineer, a role in which it intends to help optimize the user and employee experiences Apple tech brings to any company making the migration. This isn’t seen as a reactive role for troubleshooting tech problems, but as a leadership position intended to nurture positive deployments of technology for real business challenges.

This reflects one of the big trends in IT right now, particularly within Apple-based ecosystems in which the culture of IT moves away from support toward enablement. At this stage of the tech evolution, support should be seen as a business investment rather than a cost, Autotrader believes. “Tech’s not an afterthought anymore. Our people are driving what they want out of tech,” says Holly Redman, technology experience partner at Sync, which assisted Autotrader’s deployment.

“We believe in consistent, high-performing, collaborative experiences (and let’s be honest, we love a good trackpad gesture),” said Autotrader Technology Resource Partner Claire Isherwood.

It’s more than trackpad gestures, of course. Autotrader is a solid illustration of a company that understands that well-applied digital technologies can generate major business transformation. In this case, it has helped the title transition from a print business to become an all-points technology service. 

How Mac shapes up for business

The move to Mac implicitly empowers this. Apple Silicon, for example, supports the company’s development teams when building apps and services for its millions of users. These M-series processors easily handle the demands when building and supporting apps for multiple platforms. The video also explains who the adoption of Apple products benefits, how it helps the company in terms of sustainability, and how it enables a better corporate focus. 

Autotrader joins a growing number of companies to have successfully moved from Windows hardware to Macs, from high street brands like Rituals to accelerating deployments at PayPal and Roche. Banks, most recently including Siam Commercial Bank in Thailand, are also migrating to Apple’s products.

When they do so, they benefit from significantly lower software licensing costs and significantly more secure computing experiences. They also get to offer incoming and existing employees’ access to the tech they use at home, boosting productivity and engagement. None of these claims should surprise anyone, of course — they are all reflected in last year’s Apple-sponsored Forrester Research report which identified that the adoption of its products in business boost digital employee experiences, decrease support costs, improve security and bolster productivity.

This is the reality that continues to drive change in enterprise IT.

Long may it continue.

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Source:: Computer World

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Source:: Fossbytes