Digital business models Archives | Page 422 of 571

Microsoft pauses $1 billion data centre project in Ohio

Microsoft has announced it is ‘slowing or pausing’ some data centre construction projects, including a $1 billion plan in Ohio, amid shifting demand for AI infrastructure.

The company confirmed it would halt early-stage development on rural land in Licking County, near Columbus, and will repurpose two of the sites for farmland.

The decision follows Microsoft’s rapid scaling of infrastructure to meet the soaring demand for AI and cloud services, which has since softened. The company acknowledged that such large projects require continuous adaptation to align with customer needs.

While Microsoft did not specify other paused projects, it revealed the suspension of later stages of a Wisconsin data centre expansion.

The slowdown also coincides with changes in Microsoft’s partnership with OpenAI, with the two companies revising their agreement to allow OpenAI to build its own AI infrastructure. This move reflects broader trends in AI computing needs, which are expensive and energy-intensive.

Despite the pause in Ohio, Microsoft plans to invest over $80 billion in AI infrastructure this fiscal year, continuing its global expansion, though it will now strategically pace its growth to align with evolving business priorities.

Local officials in Licking County expressed their disappointment, as the area had been a hub for significant tech investments, including those from Google and Meta.

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IBM pushes towards quantum advantage in two years with breakthrough code

IBM’s Quantum CTO, Oliver Dial, predicts that quantum advantage, where quantum computers outperform classical ones on specific tasks, could be achieved within two years.

The milestone is seen as possible due to advances in error mitigation techniques, which enable quantum computers to provide reliable results despite their inherent noise. While full fault-tolerant quantum systems are still years away, IBM’s focus on error mitigation could bring real-world results soon.

A key part of IBM’s progress is the introduction of the ‘Gross code,’ a quantum error correction method that drastically reduces the number of physical qubits needed per logical qubit, making the engineering of quantum systems much more feasible.

Dial described this development as a game changer, improving both efficiency and practicality, making quantum systems easier to build and test. The Gross code reduces the need for large, cumbersome arrays of qubits, streamlining the path toward more powerful quantum computers.

Looking ahead, IBM’s roadmap outlines ambitious goals, including building a fully error-corrected system with 200 logical qubits by 2029. Dial stressed the importance of flexibility in the roadmap, acknowledging that the path to these goals could shift but would still lead to the achievement of quantum milestones.

The company’s commitment to these advancements reflects the dedication of the quantum team, many of whom have been working on the project for over a decade.

Despite the excitement and the challenges that remain, IBM’s vision for the future of quantum computing is clear: building the world’s first useful quantum computers.

The company’s ongoing work in quantum computing continues to capture imaginations, with significant steps being taken towards making these systems a reality in the near future.

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Google unveils new AI agent toolkit

This week at Google Cloud Next in Las Vegas, Google revealed its latest push into ‘agentic AI’. A software designed to act independently, perform tasks, and communicate with other digital systems.

Central to this effort is the Agent Development Kit (ADK), an open-source toolkit said to let developers build AI agents in under 100 lines of code.

Instead of requiring complex systems, the ADK includes pre-built connectors and a so-called ‘agent garden’ to streamline integration with data platforms like BigQuery and AlloyDB.

Google also introduced a new Agent2Agent (A2A) protocol, aimed at enabling cooperation between agents from different vendors. With over 50 partners, including Accenture, SAP and Salesforce, already involved, the company hopes to establish a shared standard for AI interaction.

Powering these tools is Google’s latest AI chip, Ironwood, a seventh-generation TPU promising tenfold performance gains over earlier models. These chips, designed for use with advanced models like Gemini 2.5, reflect Google’s ambition to dominate AI infrastructure.

Despite the buzz, analysts caution that the hype around AI agents may outpace their actual utility. While vendors like Microsoft, Salesforce and Workday push agentic AI to boost revenue, in some cases even replacing staff, experts argue that current models still fall short of real human-like intelligence.

Instead of widespread adoption, businesses are expected to focus more on managing costs and complexity, especially as economic uncertainty grows. Without strong oversight, these tools risk becoming costly, unpredictable, and difficult to scale.

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Tech stocks rally after Trump halts tariffs

Global stock markets experienced a significant surge following President Donald Trump’s announcement of a 90-day suspension on tariffs for several countries. The tech-heavy Nasdaq Composite Index soared over 12%, marking its second-best day ever and the most substantial gain since January 2001.

Leading technology firms saw remarkable recoveries. Apple’s shares jumped over 15%, achieving their best performance since January 1998, after enduring a severe four-day decline that erased nearly $800 million in market value.

Tesla and Nvidia also experienced substantial gains, rising 18% and 22% respectively, while Meta Platforms increased by 15%. Amazon, Microsoft, and Alphabet each posted gains of around 10%.

Asian markets mirrored this positive trend, with Japan’s benchmark index climbing more than 2,000 points shortly after the Tokyo exchange opened. Investors responded favourably to the tariff relief, anticipating reduced trade tensions and improved economic prospects.

Despite the optimism, concerns remain regarding ongoing trade disputes, particularly with China. While tariffs were paused for several nations, levies on Chinese imports were raised to 125%, potentially impacting companies with significant manufacturing operations in China, such as Apple.

Analysts caution that, despite the current market rally, the long-term implications of these trade policies warrant close monitoring.

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Google pushes AI limits with Ironwood

Google has announced Ironwood, its latest and most advanced AI processor, marking the seventh generation of its custom Tensor Processing Unit (TPU) architecture.

Designed specifically for the growing demands of its Gemini models, particularly those requiring complex simulated reasoning, which Google refers to as ‘thinking’, Ironwood represents a significant leap forward in performance.

Instead of relying solely on software updates, Google is highlighting how hardware like Ironwood plays a central role in boosting AI capabilities, ushering in what it calls the ‘age of inference.’

However, this TPU is not just faster but dramatically more scalable. Ironwood chips will operate in tightly connected clusters of up to 9,216 units, each cooled by liquid and linked through an enhanced Inter-Chip Interconnect.

These chips can also be deployed in smaller 256-chip servers, offering flexibility for cloud developers and researchers.

Instead of offering modest improvements, Ironwood delivers a peak throughput of 4,614 teraflops per chip, alongside 192GB of memory and 7.2 terabits per second of bandwidth, making it vastly superior to its predecessor, Trillium.

Google says this advancement is more than a performance boost, it’s a foundation for building AI agents that can act on a user’s behalf by gathering information and producing outputs proactively.

Rather than functioning as passive tools, AI systems powered by Ironwood are intended to behave more independently, reflecting a growing trend toward what Google calls ‘agentic AI.’

While Google’s comparison to supercomputers like El Capitan may be flawed due to differing hardware standards, there’s no doubt Ironwood is a substantial upgrade. The company claims it is twice as powerful per watt as the v5p TPU, even if the newer Trillium (v6) chip wasn’t included in the comparison.

Regardless, Ironwood is expected to power the next generation of AI breakthroughs, as the company prepares to move beyond its current Gemini 2.5 model.

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Virtual AI agents tested in social good experiment

Nonprofit organisation Sage Future has launched an unusual initiative that puts AI agents to work for philanthropy.

In a recent experiment backed by Open Philanthropy, four AI models, including OpenAI’s GPT-4o and two of Anthropic’s Claude Sonnet models, were tasked with raising money for a charity of their choice. Within a week, they collected $257 for Helen Keller International, which supports global health efforts.

The AI agents were given a virtual workspace where they could browse the internet, send emails, and create documents. They collaborated through group chats and even launched a social media account to promote their campaign.

Though most donations came from human spectators observing the experiment, the exercise revealed the surprising resourcefulness of these AI tools. one Claude model even generated profile pictures using ChatGPT and let viewers vote on their favourite.

Despite occasional missteps, including agents pausing for no reason or becoming distracted by online games, the experiment offered insights into the emerging capabilities of autonomous systems.

Sage’s director, Adam Binksmith, sees this as just the beginning, with future plans to introduce conflicting agent goals, saboteurs, and larger oversight systems to stress-test AI coordination and ethics.

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Brinc drones raises $75M to boost emergency drone tech

Brinc Drones, a Seattle-based startup founded by 25-year-old Blake Resnick, has secured $75 million in fresh funding led by Index Ventures.

Known for its police and public safety drones, Brinc is scaling its presence across emergency services, with the new funds bringing total investment to over $157 million. The round also includes participation from Motorola Solutions, a major player in US security infrastructure.

The company, founded in 2017, is part of a growing wave of American drone startups benefiting from tightened restrictions on Chinese drone manufacturers.

Brinc’s drones are designed for rapid response in hard-to-reach areas and boast unique features, such as the ability to break windows or deliver emergency supplies.

The new partnership with Motorola will enable tighter integration into 911 call centres, allowing AI systems to dispatch drones directly to emergency scenes.

Despite growing competition from other US startups like Flock Safety and Skydio, Brinc remains confident in the market’s potential.

With its enhanced funding and Motorola collaboration, the company is aiming to position itself as a leader in AI-integrated public safety technology while helping shift drone manufacturing back to the US.

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ChatGPT accused of enabling fake document creation

Concerns over digital security have intensified after reports revealed that OpenAI’s ChatGPT has been used to generate fake identification cards.

The incident follows the recent introduction of a popular Ghibli-style feature, which led to a sharp rise in usage and viral image generation across social platforms.

Among the fakes circulating online were forged versions of India’s Aadhaar ID, created with fabricated names, photos, and even QR codes.

While the Ghibli release helped push ChatGPT past 150 million active users, the tool’s advanced capabilities have now drawn criticism.

Some users demonstrated how the AI could replicate Aadhaar and PAN cards with surprising accuracy, even using images of well-known figures like OpenAI CEO Sam Altman and Tesla’s Elon Musk. The ease with which these near-perfect replicas were produced has raised alarms about identity theft and fraud.

The emergence of AI-generated IDs has reignited calls for clearer AI regulation and transparency. Critics are questioning how AI systems have access to the formatting of official documents, with accusations that sensitive datasets may be feeding model development.

As generative AI continues to evolve, pressure is mounting on both developers and regulators to address the growing risk of misuse.

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Gemini 2.5 Pro boosts Deep Research tool with smarter AI

Google has upgraded its Deep Research tool with the experimental Gemini 2.5 Pro model, promising major improvements in how users access and process complex information.

Deep Research acts as an AI research assistant capable of scanning hundreds of websites, evaluating content, and producing multi-page reports complete with citations and even podcast-style summaries.

Previously powered by Gemini 2.0 Flash, the new iteration significantly enhances reasoning, planning, and reporting capabilities. Human evaluators in Google’s testing preferred Deep Research’s outputs over those generated by OpenAI’s equivalent by a ratio greater than 2 to 1.

Users also noted clearer analytical thinking and better synthesis of information across sources.

The Gemini 2.5 Pro upgrade is available now to Gemini Advanced subscribers across web, Android, and iOS platforms.

For those using the free version, the Gemini 2.0 Flash model remains accessible in over 150 countries, continuing Google’s push to offer powerful research tools to a wide user base.

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DeepSeek highlights the risk of data misuse

The launch of DeepSeek, a Chinese-developed LLM, has reignited long-standing concerns about AI, national security, and industrial espionage.

While issues like data usage and bias remain central to AI discourse, DeepSeek’s origins in China have introduced deeper geopolitical anxieties. Echoing the scrutiny faced by TikTok, the model has raised fears of potential links to the Chinese state and its history of alleged cyber espionage.

With China and the US locked in a high-stakes AI race, every new model is now a strategic asset. DeepSeek’s emergence underscores the need for heightened vigilance around data protection, especially regarding sensitive business information and intellectual property.

Security experts warn that AI models may increasingly be trained using data acquired through dubious or illicit means, such as large-scale scraping or state-sponsored hacks.

The practice of data hoarding further complicates matters, as encrypted data today could be exploited in the future as decryption methods evolve.

Cybersecurity leaders are being urged to adapt to this evolving threat landscape. Beyond basic data visibility and access controls, there is growing emphasis on adopting privacy-enhancing technologies and encryption standards that can withstand future quantum threats.

Businesses must also recognise the strategic value of their data in an era where the lines between innovation, competition, and geopolitics have become dangerously blurred.

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