Scientists in the US have shown how easy it is to intercept private messages and military information from satellites using equipment costing less than €500.
Researchers from the University of California, San Diego and the University of Maryland scanned internet traffic from 39 geostationary satellites and 411 transponders over seven months.
They discovered unencrypted data, including phone numbers, text messages, and browsing history from networks such as T-Mobile, TelMex, and AT&T, as well as sensitive military communications from the US and Mexico.
The researchers used everyday tools such as TV satellite dishes to collect and decode the signals, proving that anyone with a basic setup and a clear view of the sky could potentially access unprotected data.
They said there is a ‘clear mismatch’ between how satellite users assume their data is secured and how it is handled in reality. Despite the industry’s standard practice of encrypting communications, many transmissions were left exposed.
Companies often avoid stronger encryption because it increases costs and reduces bandwidth efficiency. The researchers noted that firms such as Panasonic could lose up to 30 per cent in revenue if all data were encrypted.
While intercepting satellite data still requires technical skill and precise equipment alignment, the study highlights how affordable tools can reveal serious weaknesses in global satellite security.
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The UK’s Financial Conduct Authority (FCA) has unveiled new plans to advance tokenisation in the asset management sector, aiming to drive innovation and long-term growth. With 2,600 firms managing £14 trillion in assets, the regulator aims to give firms clarity and confidence in adopting blockchain solutions.
Tokenisation, which represents assets digitally using distributed ledger technology, is expected to increase competition, enhance investor choice, and open access to private markets. It could also make investing more cost-effective and tailored, particularly for new investors.
The FCA’s plans include guidance for tokenised fund registers, a simpler dealing model, and a roadmap to tackle blockchain settlement barriers. The regulator’s approach aligns with its broader digital assets strategy, aiming to make the UK a global leader in asset management innovation.
Simon Walls, executive director of markets at the FCA, said tokenisation could bring ‘fundamental changes’ to the industry, highlighting that the UK now has a real opportunity to lead globally in this emerging space.
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YouTube is introducing a new shelf of mental health and wellbeing content designed specifically for teenagers. The feature will provide age-appropriate, evidence-based videos covering topics such as depression, anxiety, ADHD, and eating disorders.
Content is created in collaboration with trusted organisations and creators, including Black Dog Institute, ReachOut Australia, and Dr Syl, to ensure it is both reliable and engaging.
The initiative will initially launch in Australia, with plans to expand to the US, the UK, and Canada. Videos are tailored to teens’ developmental stage, offering practical advice, coping strategies, and medically-informed guidance.
By providing credible information on a familiar platform, YouTube hopes to improve mental health literacy and reduce stigma among young users.
YouTube has implemented teen-specific safeguards for recommendations, content visibility, and advertising eligibility, making it easier for adolescents to explore their interests safely.
The company emphasises that the platform is committed to helping teens access trustworthy resources, while supporting their wellbeing in a digital environment increasingly filled with misinformation.
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Nscale has signed an expanded deal with Microsoft to deliver about 200,000 NVIDIA GB300 GPUs across Europe and the US, with Dell collaborating. The company calls it one of the largest AI infrastructure contracts to date. The build-out targets surging enterprise demand for GPU capacity.
A ~240MW hyperscale AI campus in Texas, US, will host roughly 104,000 GB300s from Q3 2026, leased from Ionic Digital. Nscale plans to scale the site to 1.2GW, with Microsoft holding an option on a second 700MW phase from late 2027. The campus is optimised for air-cooled, power-efficient deployments.
In Europe, Nscale will deploy about 12,600 GB300s from Q1 2026 at Start Campus in Sines, Portugal, supporting sovereign AI needs within the EU. A separate UK facility at Loughton will house around 23,000 GB300s from Q1 2027. The 50MW site is scalable to 90MW to support Azure services.
A Norway programme also advances Aker-Nscale’s joint venture plans for about 52,000 GB300s at Narvik, along with Nscale’s GW+ greenfield sites and orchestration for target training, fine-tuning, and inference at scale. Microsoft emphasises sustainability and global availability.
Both firms cast the pact as deepening transatlantic tech ties and accelerating the rollout of next-gen AI services. Nscale says few providers can deploy GPU fleets at this pace. The roadmap points to sovereign-grade, multi-region capacity with lower-latency platforms.
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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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Google is launching two new AI-powered features in its Search and Discover tools to help people connect with more recent content on the web. The first feature upgrades Discover. It shows brief previews of trending stories and topics you care about, which you can expand to view more.
Each preview includes links so you can explore the full content on the web. This aims to make catching up on stories from both known and new publishers easier. The feature is now live in the US, South Korea and India.
The second is a sports-oriented update in Search: when looking up players or teams on your phone, you’ll soon see a ‘What’s new’ button. That will surface a feed of the latest updates and articles so you can follow recent action more directly. Rolling out in the US in the coming weeks.
These features are part of Google’s effort to use AI to help people stay better informed about topics they care about, trending news, sports, etc. At the same time, Google emphasises that web links remain a core part of the experience, helping users explore sources and dive deeper.
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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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Vodafone says its nationwide network outage that left thousands across the UK without broadband and mobile data has been fully resolved. The disruption, which began on Monday afternoon, triggered over 130,000 complaints to Downdetector as customers reported losing internet access.
The company confirmed that a software error from one of its vendors had caused the problem but stressed it was not the result of a cyberattack. Vodafone apologised and said the network had fully recovered after engineers implemented fixes late on Monday night.
Industry experts warned that the outage highlighted the need for stronger digital resilience. Analysts said businesses relying on Vodafone likely suffered missed deadlines and financial losses, with many expected to seek compensation.
The fault also impacted UK customers of Voxi, Lebara, and Talkmobile, which operate on Vodafone’s infrastructure. Cloudflare data showed Vodafone traffic temporarily dropped to zero, effectively cutting the network off from the internet for over an hour.
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