A new Oxford University Press (OUP) report has found that most teenagers are using AI for schoolwork but many cannot tell when information is false. Over 2,000 students aged 13 to 18 took part, with many finding it hard to verify AI content.
Around eight in ten pupils admitted using AI for homework or revision, often treating it as a digital tutor. However, many are simply copying material without being able to check its accuracy.
Despite concerns about misinformation, most pupils view AI positively. Nine in ten said they had benefited from using it, particularly in improving creative writing, problem-solving and critical thinking.
To support schools, OUP has launched an AI and Education Hub to help teachers develop confidence with the technology, while the Department for Education has released guidance on using AI safely in classrooms.
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A new Microsoft survey has revealed that nearly three in four employees in the UK use AI tools at work without company approval.
A practice, referred to as ‘shadow AI’, that involves workers relying on unapproved systems such as ChatGPT to complete routine tasks. Microsoft warned that unauthorised AI use could expose businesses to data leaks, non-compliance risks, and cyber attacks.
The survey, carried out by Censuswide, questioned over 2,000 employees across different sectors. Seventy-one per cent admitted to using AI tools outside official policies, often because they were already familiar with them in their personal lives.
Many reported using such tools to respond to emails, prepare presentations, and perform financial or administrative tasks, saving almost eight hours of work each week.
Microsoft said only enterprise-grade AI systems can provide the privacy and security organisations require. Darren Hardman, Microsoft’s UK and Ireland chief executive, urged companies to ensure workplace AI tools are designed for professional use rather than consumer convenience.
He emphasised that secure integration can allow firms to benefit from AI’s productivity gains while protecting sensitive data.
The study estimated that AI technology saves 12.1 billion working hours annually across the UK, equivalent to about £208 billion in employee time. Workers reported using the time gained through AI to improve work-life balance, learn new skills, and focus on higher-value projects.
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In the UK, an AI system developed at the University of Surrey can predict what a patient’s knee X-ray will look like a year in the future, offering a visual forecast alongside a risk score for osteoarthritis progression.
The technology is designed to help both patients and doctors better understand how the condition may develop, allowing earlier and more informed treatment decisions.
Trained on nearly 50,000 knee X-rays from almost 5,000 patients, the system delivers faster and more accurate predictions than existing AI tools.
It uses a generative diffusion model to produce a future X-ray and highlights 16 key points in the joint, giving clinicians transparency and confidence in the areas monitored. Patients can compare their current and predicted X-rays, which can encourage adherence to treatment plans and lifestyle changes.
Researchers hope the technology could be adapted for other chronic conditions, including lung disease in smokers or heart disease progression, providing similar visual insights.
The team is seeking partnerships to integrate the system into real-world clinical settings, potentially transforming how millions of people manage long-term health conditions.
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Oracle and NVIDIA have joined forces to advance sovereign AI, supporting Abu Dhabi’s vision of becoming an AI-native government by 2027.
The partnership combines the computing platforms of NVIDIA with Oracle Cloud Infrastructure to create secure, high-performance systems that deliver next-generation citizen services, including multilingual AI assistants, automatic notifications, and intelligent compliance solutions.
The Government Digital Strategy 2025-2027 of Abu Dhabi, backed by a 13-billion AED investment, follows a phased ‘crawl, walk, run’ approach. The initiative has already gone live across 25 government entities, enabling over 15,000 daily users to access AI-accelerated services.
Generative AI applications are now integrated into human resources, procurement, and financial reporting, while advanced agentic AI and autonomous workflows will further enhance government-wide operations.
The strategy ensures full data sovereignty while driving innovation and efficiency across the public sector.
Partnerships with Deloitte and Core42 provide infrastructure and compliance support, while over 200 AI-powered capabilities are deployed to boost digital skills, economic growth, and employment opportunities.
By 2027, the initiative is expected to contribute more than 24 billion AED to Abu Dhabi’s GDP and create over 5,000 jobs, demonstrating a global blueprint for AI-native government transformation.
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The rapid rise of AI has drawn a wave of ambitious investors eager to tap into what many consider the next major economic engine. Capital has flowed into AI companies at an unprecedented pace, fuelled by expectations of substantial future returns.
Yet despite these bloated investments, none of the leading players have managed to break even, let alone deliver a net-positive financial year. Even so, funding shows no signs of slowing, driven by the belief that profitability is only a matter of time. Is this optimism justified, or is the AI boom, for now, little more than smoke and mirrors?
Where the AI money flows
Understanding the question of AI profitability starts with following the money. Capital flows through the ecosystem from top to bottom, beginning with investors and culminating in massive infrastructure spending. Tracing this flow makes it easier to see where profits might eventually emerge.
The United States is the clearest focal point. The country has become the main hub for AI investment, where the technology is presented as the next major economic catalyst and treated by many investors as a potential cash cow.
The US market fuels AI through a mix of venture capital, strategic funding from Big Tech, and public investment. By late August 2025, at least 33 US AI startups had each raised 100 million dollars or more, showing the depth of available capital and investor appetite.
OpenAI stands apart from the rest of the field. Multiple reports point to a primary round of roughly USD 40 billion at a USD 300 billion post-money valuation, followed by secondary transactions that pushed the implied valuation even higher. No other AI company has matched this scale.
Much of the capital is not aimed at quick profits. Large sums support research, model development, and heavy infrastructure spending on chips, data centres, and power. Plans to deploy up to 6 gigawatts of AMD accelerators in 2026 show how funding moves into capacity rather than near-term earnings.
Strategic partners and financiers supply some of the largest investments. Microsoft has a multiyear, multibillion-dollar deal with OpenAI. Amazon has invested USD 4 billion in Anthropic, Google has pledged up to USD 2 billion, and infrastructure players like Oracle and CoreWeave are backed by major Wall Street banks.
AI makes money – it’s just not enough (yet)
Winning over deep-pocketed investors has become essential for both scrappy startups and established AI giants. Tech leaders have poured money into ambitious AI ventures for many reasons, from strategic bets to genuine belief in the technology’s potential to reshape industries.
No matter their motives, investors eventually expect a return. Few are counting on quick profits, but sooner or later, they want to see results, and the pressure to deliver is mounting. Hype alone cannot sustain a company forever.
To survive, AI companies need more than large fundraising rounds. Real users and reliable revenue streams are what keep a business afloat once investor patience runs thin. Building a loyal customer base separates long-term players from temporary hype machines.
OpenAI provides the clearest example of a company that has scaled. In the first half of 2025, it generated around 4.3 billion dollars in revenue, and by October, its CEO reported that roughly 800 million people were using ChatGPT weekly. The scale of its user base sets it apart from most other AI firms, but the company’s massive infrastructure and development costs keep it far from breaking even.
Microsoft has also benefited from the surge in AI adoption. Azure grew 39 percent year-over-year in Q4 FY2025, reaching 29.9 billion dollars. AI services drive a significant share of this growth, but data-centre expansion and heavy infrastructure costs continue to weigh on margins.
NVIDIA remains the biggest financial winner. Its chips power much of today’s AI infrastructure, and demand has pushed data-centre revenue to record highs. In Q2 FY2026, the company reported total revenue of 46.7 billion dollars, yet overall industry profits still lag behind massive investment levels due to maintenance costs and a mismatch between investment and earnings.
Why AI projects crash and burn
Besides the major AI players earning enough to offset some of their costs, more than two-fifths of AI initiatives end up on the virtual scrapheap for a range of reasons. Many companies jumped on the AI wave without a clear plan, copying what others were doing and overlooking the huge upfront investments needed to get projects off the ground.
GPU prices have soared in recent years, and new tariffs introduced by the current US administration have added even more pressure. Running an advanced model requires top-tier chips like NVIDIA’s H100, which costs around 30,000 dollars per unit. Once power consumption, facility costs, and security are added, the total bill becomes daunting for all but the largest players.
Another common issue is the lack of a scalable business model. Many companies adopt AI simply for the label, without a clear strategy for turning interest into revenue. In some industries, these efforts raise questions with customers and employees, exposing persistent trust gaps between human workers and AI systems.
The talent shortage creates further challenges. A young AI startup needs skilled engineers, data scientists, and operations teams to keep everything running smoothly. Building and managing a capable team requires both money and expertise. Unrealistic goals often add extra strain, causing many projects to falter before reaching the finish line.
Legal and ethical hurdles can also derail projects early on. Privacy laws, intellectual property disputes, and unresolved ethical questions create a difficult environment for companies trying to innovate. Lawsuits and legal fees have become routine, prompting some entrepreneurs to shut down rather than risk deeper financial trouble.
All of these obstacles together have proven too much for many ventures, leaving behind a discouraging trail of disbanded companies and abandoned ambitions. Sailing the AI seas offers a great opportunity, but storms can form quickly and overturn even the most confident voyages.
How AI can become profitable
While the situation may seem challenging now, there is still light at the end of the AI tunnel. The key to building a profitable and sustainable AI venture lies in careful planning and scaling only when the numbers add up. Companies that focus on fundamentals rather than hype stand the best chance of long-term success.
Lowering operational costs is one of the most important steps. Techniques such as model compression, caching, and routing queries to smaller models can dramatically reduce the cost of running AI systems. Improvements in chip efficiency and better infrastructure management can also help stretch every dollar further.
Shifting the revenue mix is another crucial factor. Many companies currently rely on cheap consumer products that attract large user bases but offer thin margins. A stronger focus on enterprise clients, who pay for reliability, customisation, and security, can provide a steadier and more profitable income stream.
Building real platforms rather than standalone products can unlock new revenue sources. Offering APIs, marketplaces, and developer tools allows companies to collect a share of the value created by others. The approach mirrors the strategies used by major cloud providers and app ecosystems.
Improving unit economics will determine which companies endure. Serving more users at lower per-request costs, increasing cache hit rates, and maximising infrastructure utilisation are essential to moving from growth at any cost to sustainable profit. Careful optimisation can turn large user bases into reliable sources of income.
Stronger financial discipline and clearer regulation can also play a role. Companies that set realistic growth targets and operate within stable policy frameworks are more likely to survive in the long run. Profitability will depend not only on innovation but also on smart execution and strategic focus.
Charting the future of AI profitability
The AI bubble appears stretched thin, and a constant stream of investments can do little more than artificially extend the lifespan of an AI venture doomed to fail. AI companies must find a way to create viable, realistic roadmaps to justify the sizeable cash injections, or they risk permanently compromising investors’ trust.
That said, the industry is still in its early and formative years, and there is plenty of room to grow and adapt to current and future landscapes. AI has the potential to become a stable economic force, but only if companies can find a compromise between innovation and financial pragmatism. Profitability will not come overnight, but it is within reach for those willing to build patiently and strategically.
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AI is rapidly changing the podcast scene. Virtual hosts, no microphones or studios, are now producing content at a scale and cost that traditional podcasters find hard to match.
One of the pioneers in this trend is Inception Point AI, founded in 2023. With just eight people, the company produces around 3,000 podcast episodes per week, each costing about one dollar to make. With as few as twenty listens, an episode can be profitable.
Startups like ElevenLabs and Wondercraft have also entered the field, alongside companies leveraging Google’s Audio Overview. Many episodes are generated from documents, lectures, local data, anything that can be turned into a voice-narrated script. The tools are getting good at sounding natural.
Yet there is concern among indie podcasters and audio creators. The flood of inexpensive AI podcasts could saturate platforms, making it harder for smaller creators to attract listeners without big marketing budgets.
Another issue is disclosure: many AI-podcast platforms do note that content is AI-generated, but there is no universal requirement for clear labelling. Some believe listener expectations and trust may erode if distinction between human vs. synthetic voices becomes blurred.
As the output volume rises, so do questions about content quality, artistic originality, and how advertising revenues will be shared. The shift is real, but whether it will stifle creative diversity is still up for debate.
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Scientists at the University of Oxford have achieved quantum teleportation between two quantum computers, marking a major step toward distributed quantum computing. The experiment successfully transmitted a quantum algorithm wirelessly between processors using quantum entanglement.
Rather than moving physical matter, the process transferred data instantaneously by linking qubits, the basic units of quantum information. The two computers, though separated by two metres, shared data as if operating as one, greatly enhancing their combined computing power.
The British breakthrough demonstrates how multiple quantum systems could one day work together as a single global supercomputer. Researchers say the approach could enable quantum networks and lay the groundwork for a future quantum internet capable of unprecedented speeds and security.
Quantum teleportation works by using pairs of entangled particles that remain connected across any distance. While humans and objects cannot yet teleport, the technology could soon allow scientists to connect remote machines into unified, ultra-powerful computing systems.
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California has become the first US state to regulate AI companion chatbots after Governor Gavin Newsom signed landmark legislation designed to protect children and vulnerable users. The new law, SB 243, holds companies legally accountable if their chatbots fail to meet new safety and transparency standards.
The US legislation follows several tragic cases, including the death of a teenager who reportedly engaged in suicidal conversations with an AI chatbot. It also comes after leaked documents revealed that some AI systems allowed inappropriate exchanges with minors.
Under the new rules, firms must introduce age verification, self-harm prevention protocols, and warnings for users engaging with companion chatbots. Platforms must clearly state that conversations are AI-generated and are barred from presenting chatbots as healthcare professionals.
Major developers including OpenAI, Replika, and Character.AI say they are introducing stronger parental controls, content filters, and crisis support systems to comply. Lawmakers hope the move will inspire other states to adopt similar protections as AI companionship tools become increasingly popular.
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The US cloud-based software company, Salesforce, has announced a $15 billion investment in San Francisco over the next five years, strengthening the city’s position as the world’s AI capital.
The funding will support a new AI Incubator Hub on the company’s campus, workforce training programmes, and initiatives to help businesses transform into ‘Agentic Enterprises’.
A move that coincides with the company’s annual Dreamforce conference, expected to generate $130 million in local revenue and create 35,000 jobs.
Chief Executive Marc Benioff said the investment demonstrates Salesforce’s deep commitment to San Francisco, aiming to boost AI innovation and job creation.
Dreamforce, now in its 23rd year, is the world’s largest AI event, attracting nearly 50,000 participants and millions more online. Benioff described the company’s goal as leading a new technological era where humans and AI collaborate to drive progress and productivity.
Founded in 1999 as an online CRM service, Salesforce has evolved into a global leader in enterprise AI and cloud computing. It is now San Francisco’s largest private employer and continues to expand through acquisitions of local AI firms such as Bluebirds, Waii, and Regrello.
The company’s new AI Incubator Hub will support early-stage startups, while its Trailhead learning platform has already trained more than five million people for the AI-driven workplace.
Salesforce remains one of the city’s most active corporate philanthropists. Its 1-1-1 model has inspired thousands of companies worldwide to dedicate a share of equity, product, and employee time to social causes.
With an additional $39 million pledged to education and healthcare, Salesforce and the Benioffs have now donated over $1 billion to the Bay Area.
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Google has announced a $15 billion commitment for 2026–2030 to build its first Indian AI hub in Visakhapatnam, positioning itself as a foundational partner in India’s AI-first push and strengthening US–India tech ties.
The hub will centre on a purpose-built, gigawatt-scale data-centre campus engineered to Google’s global standards for performance, reliability, and low latency. Partners AdaniConnex and Airtel will help deliver enterprise-grade capacity, enabling large companies and startups to build and scale AI-powered services.
Beyond compute, Google will anchor an international subsea gateway in Visakhapatnam, landing multiple cables to complement those in Mumbai and Chennai, adding route diversity, lowering latency across India’s east coast, and strengthening national connectivity for users, developers, and enterprises.
Clean growth is a core pillar of the plan, with work on transmission lines, new clean-energy generation, and storage in Andhra Pradesh. Google will apply its energy-efficient data centre design to expand India’s diverse clean power portfolio while supporting grid reliability and long-term sustainability goals.
The initiative aligns with the Viksit Bharat 2047 vision, targeting high-value jobs in India and spillover benefits to US research and development. By combining compute, connectivity, and clean energy at scale, Google aims to accelerate AI adoption across sectors and broaden digital inclusion nationwide.
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