Coreweave expands AI infrastructure with Google tie‑up

CoreWeave has secured a pivotal role in Google Cloud’s new infrastructure partnership with OpenAI. The specialist GPU cloud provider will supply Nvidia‑based compute resources to Google, which will allocate them to OpenAI to support the rising demand for services like ChatGPT.

Already under a $11.9 billion, five‑year contract with OpenAI and backed by a $350 million equity investment, CoreWeave recently expanded the deal by another. 

Adding Google Cloud as a customer helps the company diversify beyond Microsoft, its top client in 2024.

The arrangement positions Google as a neutral provider of AI computing power amid fierce competition with Amazon and Microsoft.

CoreWeave’s stock has surged over 270 percent since its March IPO, illustrating investor confidence in its expanding role in the AI infrastructure boom.

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Cisco to reinvent network security for the AI era

Cisco has introduced a major evolution in security policy management, aiming to help enterprises scale securely without increasing complexity. At the centre of this transformation is Cisco’s Security Cloud Control, a unified policy framework designed to simplify and centralise the enforcement of security policies across a wide range of environments and technologies.

With the introduction of the Mesh Policy Engine, organisations can now define a single, intent-based policy that applies seamlessly across Cisco and third-party firewalls. Cisco is also upgrading its network security infrastructure to support AI-ready environments.

The new Hybrid Mesh Firewall includes the high-performance 6100 Series for data centres and the cost-efficient 200 Series for branch deployments, offering advanced threat inspection and integrated SD-WAN. Enforcement is extended across SD-WAN, smart switches, and ACI fabric, ensuring consistent protection.

Additionally, Cisco has deepened its integration with Splunk to enhance threat detection, investigation, and response (TDIR). Firewall log data feeds into Splunk for advanced analytics, while new SOAR integrations automate key responses like host isolation and policy enforcement.

Combined with telemetry from Cisco’s broader ecosystem, these tools provide faster, more informed threat management. Together, these advancements position Cisco as a leader in AI-era cybersecurity, offering a unified and intelligent platform that reduces complexity, improves detection and response, and secures emerging technologies like agentic AI. By embedding policy-driven security into the core of enterprise networks, Cisco is enabling organisations to innovate with AI safely and securely.

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IBM sets 2029 target for quantum breakthrough

IBM has set out a detailed roadmap to deliver a practical quantum computer by 2029, marking a major milestone in its long-term strategy.

The company plans to build its ‘Starling’ quantum system at a new data centre in Poughkeepsie, New York, targeting around 200 logical qubits—enough to begin outperforming classical computers in specific tasks instead of lagging due to error correction limitations.

Quantum computers rely on qubits to perform complex calculations, but high error rates have held back their potential. IBM shifted its approach in 2019, designing error-correction algorithms based on real, manufacturable chips instead of theoretical models.

The change, as the company says, will significantly reduce the qubits needed to fix errors.

With confidence in its new method, IBM will build a series of quantum systems until 2027, each advancing toward a larger, more capable machine.

Vice President Jay Gambetta stated the key scientific questions have already been resolved, meaning what remains is primarily an engineering challenge instead of a scientific one.

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Amazon boosts AI infrastructure with $20B investment

Amazon has announced a massive $20 billion investment to build two new AI-focused data centres in Pennsylvania. The exact locations are yet to be finalised, but Salem Township and Falls Township are currently leading candidates.

The move signals Amazon’s ongoing commitment to expanding its AI infrastructure amid an increasingly competitive technology race.

Alongside the data centres, Amazon has pledged to support education and workforce development across the state. Collaborations with local institutions will bring programmes for data centre technicians, fibre optic workshops and STEM learning initiatives aimed at school-aged children.

These efforts are intended to prepare the future workforce for careers in AI and cloud computing infrastructure.

The investment is part of Amazon’s broader strategy to establish the US as a global AI leader. The company highlighted that its advanced computing infrastructure and AI hardware are designed to power the next generation of generative and agentic AI, defining digital innovation’s future.

Other tech giants are making similar moves. Amazon announced a $10 billion data centre expansion in North Carolina in June.

Meanwhile, Meta, OpenAI, and Microsoft are also scaling AI operations, and crypto mining firms like Riot Platforms and Hive Digital are shifting part of their infrastructure towards high-performance computing for AI, reflecting a wider industry transformation.

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Nvidia and FCA open AI sandbox for UK fintechs

Financial firms across the UK will soon be able to experiment with AI in a new regulatory sandbox, launched by the Financial Conduct Authority (FCA) in partnership with Nvidia.

Known as the Supercharged Sandbox, it offers a secure testing ground for firms wanting to explore AI tools without needing their advanced computing resources.

Set to begin in October, the initiative is open to any financial services company testing AI-driven ideas. Firms will have access to Nvidia’s accelerated computing platform and tailored AI software, helping them work with complex data, improve automation, and enhance risk management in a controlled setting.

The FCA said the sandbox is designed to support firms lacking the in-house capacity to test new technology.

It aims to provide not only computing power but also regulatory guidance and access to better datasets, creating an environment where innovation can flourish while remaining compliant with rules.

The move forms part of a wider push by the UK government to foster economic growth through innovation. Finance minister Rachel Reeves has urged regulators to clear away obstacles to growth and praised the FCA and Bank of England for acting on her call to cut red tape.

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ChatGPT adds meeting recording and cloud access

OpenAI has launched new features for ChatGPT that allow it to record meetings, transcribe conversations, and pull information directly from cloud platforms like Google Drive and SharePoint.

Instead of relying on typed input alone, users can now speak to ChatGPT, which records audio, creates editable summaries, and helps generate follow-up content such as emails or project outlines.

‘Record’ is currently available to Team users via the macOS app and will soon expand to Enterprise and Edu accounts.

The recording tool automatically deletes the audio after transcription and applies existing workspace data rules, ensuring recordings are not used for training.

Instead of leaving notes scattered across different platforms, users gain a structured and searchable history of conversations, voice notes, or brainstorming sessions, which ChatGPT can recall and apply during future interactions.

At the same time, OpenAI has introduced new connectors for business users that let ChatGPT access files from cloud services like Dropbox, OneDrive, Box, and others.

These connectors allow ChatGPT to search and summarise information from internal documents, rather than depending only on web search or user uploads. The update also includes beta support for Deep Research agents that can work with tools like GitHub and HubSpot.

OpenAI has embraced the Model Context Protocol, an open standard allowing organisations to build their own custom connectors for proprietary tools.

Rather than serving purely as a general-purpose chatbot, ChatGPT is evolving into a workplace assistant capable of tapping into and understanding a company’s complete knowledge base.

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Salt Typhoon and Silk Typhoon reveal weaknesses

Recent revelations about Salt Typhoon and Silk Typhoon have exposed severe weaknesses in how organisations secure their networks.

These state-affiliated hacking groups have demonstrated that modern cyber threats come from well-resourced and coordinated actors instead of isolated individuals.

Salt Typhoon, responsible for one of the largest cyber intrusions into US infrastructure, exploited cloud network vulnerabilities targeting telecom giants like AT&T and Verizon, forcing companies to reassess their reliance on traditional private circuits.

Many firms continue to believe private circuits offer better protection simply because they are off the public internet. Some even add MACsec encryption for extra defence. However, MACsec’s ‘hop-by-hop’ design introduces new risks—data is repeatedly decrypted and re-encrypted at each routing point.

Every one of these hops becomes a possible target for attackers, who can intercept, manipulate, or exfiltrate data without detection, especially when third-party infrastructure is involved.

Beyond its security limitations, MACsec presents high operational complexity and cost, making it unsuitable for today’s cloud-first environments. In contrast, solutions like Internet Protocol Security (IPSec) offer simpler, end-to-end encryption.

Although not perfect in cloud settings, IPSec can be enhanced through parallel connections or expert guidance. The Cybersecurity and Infrastructure Security Agency (CISA) urges organisations to prioritise complete encryption of all data in transit, regardless of the underlying network.

Silk Typhoon has further amplified concerns by exploiting privileged credentials and cloud APIs to infiltrate both on-premise and cloud systems. These actors use covert networks to maintain long-term access while remaining hidden.

As threats evolve, companies must adopt Zero Trust principles, strengthen identity controls, and closely monitor their cloud environments instead of relying on outdated security models.

Collaborating with cloud security experts can help shut down exposure risks and protect sensitive data from sophisticated and persistent threats.

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Oracle and OpenAI target AI leadership with massive chip project

Oracle has reportedly acquired around 400,000 Nvidia GB200 AI chips valued at approximately $40 billion for deployment at a data centre in Abilene, Texas.

The location will be the first site of the Stargate project—a $500 billion AI infrastructure initiative backed by OpenAI, Oracle, SoftBank, and Abu Dhabi’s MGX fund, which President Trump announced earlier this year.

Once completed, the Abilene facility is expected to provide up to 1.2 gigawatts of computing power, rivalling Elon Musk’s Colossus project in Memphis.

Although Oracle will operate from the site, the land is owned by AI infrastructure firm Cruso and US investment company Blue Owl Capital, which have collectively invested more than $15 billion through financing.

Oracle will lease the campus for 15 years, using the chips to offer computing power to OpenAI for training its next-generation AI models.

Previously dependent solely on Microsoft’s data centres, OpenAI faced bottlenecks due to limited capacity, prompting it to end the exclusivity agreement and look elsewhere.

While individual investors have committed funds, the Stargate project has not officially financed any facility yet. In parallel, OpenAI has announced Stargate UAE—a 5-gigawatt site in Abu Dhabi using over 2 million Nvidia chips, built in partnership with G42.

A surging demand for AI infrastructure has significantly boosted Nvidia’s market value, with the company reclaiming its top global ranking in late 2024.

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Quandela presents Belenos, a powerful 12-qubit quantum computer

French quantum computing startup Quandela has unveiled Belenos, a 12-qubit photonic quantum computer that it claims delivers 4,000 times more computing power than its predecessor.

The first integrated version is set to be delivered to EuroHPC/GENCI and operated at the CEA’s Très Grand Centre de Calcul (TGCC) before the end of 2025.

Currently, Belenos is accessible via the cloud to over 1,200 researchers across 30 countries in Europe, North America, and Asia.

Instead of relying solely on local deployments, this cloud availability enables researchers to explore hybrid HPC-quantum use cases in fields such as structural mechanics, meteorology, and materials science.

Quandela has ambitious plans to double the qubit count by 2026 with the launch of Canopus. Within three years, the company aims to develop a photonic quantum computer with more than 40 qubits, continuing its focus on systems that avoid cryogenic cooling by using photonics-based methods instead.

‘Our cloud-accessible Belenos system lets partners work on tasks where computing speed and operations per data point are crucial — areas where competitors fall short,’ said co-founder and CEO Niccolo Somaschi.

The platform is designed for practical applications in machine learning and at the AI-quantum interface, which Quandela views as strategically vital sectors for the future.

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The rise of tech giants in healthcare: How AI is reshaping life sciences

Silicon Valley targets health

The intersection of technology and healthcare is rapidly evolving, fuelled by advancements in ΑΙ and driven by major tech companies that are expanding their reach into the life sciences sector.

Once primarily known for consumer electronics or search engines, companies like Google, Amazon, Microsoft, Apple, and IBM are now playing an increasingly central role in transforming the medical field.

These companies, often referred to as ‘Big Tech’, are pushing the boundaries of what was once considered science fiction, using AI to innovate across multiple aspects of healthcare, including diagnostics, treatment, drug development, clinical trials, and patient care.

silicon valley tech companies

AI becomes doctors’ new tool

At the core of this revolution is AI. Over the past decade, AI has evolved from a theoretical tool to a practical and transformative force within healthcare.

Companies are developing advanced machine learning algorithms, cognitive computing models, and AI-powered systems capable of matching—and sometimes surpassing—human capabilities in diagnosing and treating diseases.

AI is also reshaping many aspects of healthcare, from early disease detection to personalised treatments and even drug discovery. This shift is creating a future where AI plays a significant role in diagnosing diseases, developing treatment plans, and improving patient outcomes at scale.

One of the most significant contributions of AI is in diagnostics. Google Health and its subsidiary DeepMind are prime examples of how AI can be used to outperform human experts in certain medical tasks.

For instance, DeepMind’s AI tools have demonstrated the ability to diagnose conditions like breast cancer and lung disease with remarkable accuracy, surpassing the abilities of human radiologists in some cases.

google deepmind AI progress Demis Hassabis

Similarly, Philips has filed patents for AI systems capable of detecting neurodegenerative diseases and tracking disease progression using heart activity and motion sensors.

From diagnosis to documentation

These breakthroughs represent only a small part of how AI is revolutionising diagnostics by improving accuracy, reducing time to diagnosis, and potentially saving lives.

In addition to AI’s diagnostic capabilities, its impact extends to medical documentation, an often-overlooked area that affects clinician efficiency.

Traditionally, doctors spend a significant amount of time on paperwork, reducing the time they can spend with patients.

However, AI companies like Augmedix, DeepScribe, and Nabla are addressing this problem by offering solutions that generate clinical notes directly from doctor-patient conversations.

AI doctor

These platforms integrate with electronic health record (EHR) systems and automate the note-taking process, which reduces administrative workload and frees up clinicians to focus on patient care.

Augmedix, for example, claims to save up to an hour per day for clinicians, while DeepScribe’s AI technology is reportedly more accurate than even GPT-4 for clinical documentation.

Nabla takes this further by offering AI-driven chatbots and decision support tools that enhance clinical workflows and reduce physician burnout.

Portable ultrasounds powered by AI

AI is also transforming medical imaging, a field traditionally dependent on expensive, bulky equipment that requires specialised training.

Innovators like Butterfly Network are developing portable, AI-powered ultrasound devices that can provide diagnostic capabilities at a fraction of the cost of traditional equipment. These devices offer greater accessibility, particularly in regions with limited access to medical imaging technology.

The ability to perform ultrasounds and MRIs in remote areas, using portable devices powered by AI, is democratising healthcare and enabling better diagnostic capabilities in underserved regions.

An advanced drug discovery

In the realm of drug discovery and treatment personalisation, AI is making significant strides. Companies like IBM Watson are at the forefront of using AI to personalise treatment plans by analysing vast amounts of patient data, including medical histories, genetic information, and lifestyle factors.

IBM Watson has been particularly instrumental in the field of oncology, where it assists physicians by recommending tailored cancer treatment protocols.

treatment costs.

A capability like this is made possible by the vast amounts of medical data Watson processes to identify the best treatment options for individual patients, ensuring that therapies are more effective by considering each patient’s unique characteristics.

Smart automation in healthcare

Furthermore, AI is streamlining administrative tasks within healthcare systems, which often burden healthcare providers with repetitive, time-consuming tasks like appointment scheduling, records management, and insurance verification.

By automating these tasks, AI allows healthcare providers to focus more on delivering high-quality care to patients.

Amazon Web Services (AWS), for example, is leveraging its cloud platform to develop machine learning tools that assist healthcare providers in making more effective clinical decisions while improving operational efficiency.

It includes using AI to enhance clinical decision-making, predict patient outcomes, and manage the growing volume of patient data that healthcare systems must process.

Startups and giants drive the healthcare race

Alongside the tech giants, AI-driven startups are also playing a pivotal role in healthcare innovation. Tempus, for example, is integrating genomic sequencing with AI to provide physicians with actionable insights that improve patient outcomes, particularly in cancer treatment.

The fusion of data from multiple sources is enhancing the precision and effectiveness of medical decisions. Zebra Medical Vision, another AI-driven company, is using AI to analyse medical imaging data and detect a wide range of conditions, from liver disease to breast cancer.

Zebra’s AI algorithms are designed to identify conditions often before symptoms even appear, which greatly improves the chances of successful treatment through early detection.

Tech giants are deeply embedded in the healthcare ecosystem, using their advanced capabilities in cloud computing, AI, and data analytics to reshape the industry.

partners handshake ai companies

Microsoft, for example, has made significant strides in AI for accessibility, focusing on creating healthcare solutions that empower individuals with disabilities. Their work is helping to make healthcare more inclusive and accessible for a broader population.

Amazon’s AWS cloud platform is another example of how Big Tech is leveraging its infrastructure to develop machine learning tools that support healthcare providers in delivering more effective care.

M&A meets medicine

In addition to developing their own AI tools, these tech giants have made several high-profile acquisitions to accelerate their healthcare strategies.

Google’s acquisition of Fitbit, Amazon’s purchase of PillPack and One Medical, and Microsoft’s $19.7 billion acquisition of Nuance are all clear examples of how Big Tech is seeking to integrate AI into every aspect of the healthcare value chain, from drug discovery to clinical delivery.

These acquisitions and partnerships also enable tech giants to tap into new areas of the healthcare market and provide more comprehensive, end-to-end solutions to healthcare providers and patients alike.

Smart devices empower health

Consumer health technologies have also surged in popularity, thanks to the broader trend of digital health and wellness tools. Fitness trackers, smartwatches, and mobile health apps allow users to monitor everything from heart rates to sleep quality.

Devices like the Apple Watch and Google’s Fitbit collect health data continuously, providing users with personalised insights into their well-being.

seoul 05 02 2022 male hand with two apple watches with pink and gray strap on white background

Instead of being isolated within individual devices, the data is increasingly being integrated into broader healthcare systems, enabling doctors and other healthcare providers to have a more complete view of a patient’s health.

This integration has also supported the growth of telehealth services, with millions of people now opting for virtual consultations powered by Big Tech infrastructure and AI-powered triage tools.

Chinese hospitals embrace generative AI

The rise of generative AI is also transforming healthcare, particularly in countries like China, where technology is advancing rapidly. Once considered a distant ambition, the use of generative AI in healthcare is now being implemented at scale.

The technology is being used to manage massive drug libraries, assist with complex diagnoses, and replicate expert reasoning processes, which helps doctors make more informed decisions.

At Beijing Hospital of Traditional Chinese Medicine, Ant Group’s medical model has impressed staff by offering diagnostic suggestions and replicating expert reasoning, streamlining consultations without replacing human doctors.

Our choice in a tech-driven world

As AI continues to evolve, tech giants are likely to continue disrupting the healthcare industry while also collaborating with traditional healthcare providers.

While some traditional life sciences companies may feel threatened by the rise of Big Tech in healthcare, those that embrace AI and form partnerships with tech companies will likely be better positioned for success.

The convergence of AI and healthcare is already reshaping the future of medicine, and traditional healthcare players must adapt or risk being left behind.

generate an image of an artificial intelligence head in front of a human head and digital codes in the background reproducing all the human heads inputs and psychological reactions

Despite the tremendous momentum, there are challenges that need to be addressed. Data privacy, regulatory concerns, and the growing dominance of Big Tech in healthcare remain significant hurdles.

If these challenges are addressed responsibly, however, the integration of AI into healthcare could modernise care delivery on a global scale.

Rather than replacing doctors, the goal is to empower them with better tools, insights, and outcomes. The future of healthcare is one where technology and human expertise work in tandem, enhancing the patient experience and improving overall health outcomes.

As human beings, we must understand that the integration of technology across multiple sectors is a double-edged sword. It can either benefit us and help build better future societies, or mark the beginning of our downfall— but in the end, the choice will always be ours.

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