Most firms are still struggling to turn AI pilots into measurable value, Cisco’s 2025 AI Readiness Index finds. Only 13% are ‘AI-ready’, having scaled deployments with results. The rest face gaps in data, security and governance.
Southeast Asia outperforms the global average at 16% ready. Indonesia reaches 23% and Thailand 21%, ahead of Europe at 11% and the Americas at 14%. Cisco says lower tech debt helps some emerging markets leapfrog.
Infrastructure debt is mounting: limited GPU capacity, fragmented data and constrained networks slow progress. Just 34% say their tech stack can adapt and scale for evolving compute needs. Most remain stuck in pilots.
Adoption plans are ambitious: 83% intend to deploy AI agents, with almost 40% expecting them to support staff within a year. Yet only one in three have change-management programmes, risking stalled workplace integration.
The leaders pair strong digital foundations with clear governance and cybersecurity embedded by design. Cisco urges broader collaboration among industry, government and tech firms, arguing that trust, regulation and investment will determine who monetises AI first.
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McAfee won ‘Best Use of AI in Cybersecurity’ at the 2025 A.I. Awards for its Scam Detector. The tool, which McAfee says is the first to automate deepfake, email, and text-scam detection, underscores a consumer-focused defence. The award recognises its bid to counter fast-evolving online fraud.
Scams are at record levels, with one in three US residents reporting victimisation and average losses of $1,500. Threats now range from fake job offers and text messages to AI-generated deepfakes, increasing the pressure on tools that can act in real time across channels.
McAfee’s Scam Detector uses advanced AI to analyse text, email, and video, blocking dangerous links and flagging deepfakes before they cause harm. It is included with core McAfee plans and available on PC, mobile, and web, positioning it as a default layer for everyday protection.
Adoption has been rapid, with the product crossing one million users in its first months, according to the company. Judges praised its proactive protection and emphasis on accuracy and trust, citing its potential to restore user confidence as AI-enabled deception becomes more sophisticated.
McAfee frames the award as validation of its responsible, consumer-first AI strategy. The company says it will expand Scam Detector’s capabilities while partnering with the wider ecosystem to keep users a step ahead of emerging threats, both online and offline.
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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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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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In a world once defined by borders and treaties, a new kind of diplomacy is emerging, one where cities, not nations, take the lead. Instead of traditional embassies, this new diplomacy unfolds in startup hubs and conference halls, where ‘tech ambassadors’ represent cities in negotiations with powerful technology companies.
These modern envoys focus not on trade tariffs but on data sharing, digital infrastructure, and the balance between innovation and public interest. The growing influence of global tech firms has shifted the map of power.
Apple’s 2024 revenue alone exceeded the GDP of several mid-sized nations, and algorithms designed in Silicon Valley now shape urban life worldwide. Recognising this shift, cities such as Amsterdam, Barcelona, and London have appointed tech ambassadors to engage directly with the digital giants.
Their role combines diplomacy, investment strategy, and public policy, ensuring that cities have a voice in how technologies, from ride-sharing platforms to AI systems, affect their citizens. But the rise of this new urban diplomacy comes with risks.
Tech firms wield enormous influence, spending tens of millions on lobbying while many municipalities struggle with limited resources. Cities eager for investment may compromise on key issues like data governance or workers’ rights.
There’s also a danger of ‘technological solutionism’, the belief that every problem can be solved by an app, overshadowing more democratic or social solutions.
Ultimately, the mission of the tech ambassador is to safeguard the public interest in a digital age where power often lies in code rather than constitutions. As cities negotiate with the world’s most powerful corporations, they must balance innovation with accountability, ensuring that the digital future serves citizens, not just shareholders.
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Windows 10 support ends on Tuesday, 14 October 2025, and routine security patches and fixes will no longer be provided. Devices will face increased cyber risk without updates. Microsoft urges upgrades to Windows 11 where possible.
Windows powers more than 1.4 billion devices, with Windows 10 still widely used. UK consumer group Which? estimates 21 million local users. Some plan to continue regardless, citing cost, waste, and working hardware.
Upgrade to Windows 11 is free for eligible PCs via the Settings app. Others can enrol in Extended Security Updates, which deliver security fixes only until October 2026. ESU offers no technical support or feature updates.
Personal users in the European Economic Area can register for ESU at no charge. Elsewhere, eligibility may unlock ESU for free, or it costs $30 or 1,000 Microsoft Rewards points. Businesses pay $61 per device for year one.
Unsupported systems become easier targets for malware and scams, and some software may degrade over time. Organisations risk compliance issues running out-of-support platforms. Privacy-minded users may also dislike Windows 11’s tighter Microsoft account requirements.
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Jim Lee rejects generative AI for DC storytelling, pledging no AI writing, art, or audio under his leadership. He framed AI alongside other overhyped threats, arguing that predictions falter while human craft endures. DC, he said, will keep its focus on creator-led work.
Lee rooted the stance in the value of imperfection and intent. Smudges, rough lines, and hesitation signal authorship, not flaws. Fans, he argued, sense authenticity and recoil from outputs that feel synthetic or aggregated.
Concerns ranged from shrinking attention spans to characters nearing the public domain. The response, Lee said, is better storytelling and world-building. Owning a character differs from understanding one, and DC’s universe supplies the meaning that endures.
Policy meets practice in DCs recent moves against suspected AI art. In 2024, variant covers were pulled after high-profile allegations of AI-generated content. The episode illustrated a willingness to enforce standards rather than just announce them.
Lee positioned 2035 and DC’s centenary as a waypoint, not a finish line. Creative evolution remains essential, but without yielding authorship to algorithms. The pledge: human-made stories, guided by editors and artists, for the next century of DC.
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Asia’s creative future takes centre stage at Singapore’s All That Matters, a September forum for sports, tech, marketing, gaming, and music. AI dominated the music track, spanning creation, distribution, and copyright. Session notes signal rapid structural change across the industry.
The web is shifting again as AI reshapes search and discovery. AI-first browsers and assistants challenge incumbents, while Google’s Gemini and Microsoft’s Copilot race on integration. Early builds feel rough, yet momentum points to a new media discovery order.
Consumption defined the last 25 years, moving from CDs to MP3s, piracy, streaming, and even vinyl’s comeback. Creation looks set to define the next decade as generative tools become ubiquitous. Betting against that shift may be comfortable, yet market forces indicate it is inevitable.
Music generators like Suno are advancing fast amid lawsuits and talks with rights holders. Expected label licensing will widen training data and scale models. Outputs should grow more realistic and, crucially, more emotionally engaging.
Simpler interfaces will accelerate adoption. The prevailing design thesis is ‘less UI’: creators state intent and the system orchestrates cloud tools. Some services already turn a hummed idea into an arranged track, foreshadowing release-ready music from plain descriptions.
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The Dutch Minister of Economic Affairs has invoked the Goods Availability Act in response to serious governance issues at semiconductor manufacturer Nexperia.
The measure, announced on 30 September 2025, seeks to ensure the continued availability of the company’s products in the event of an emergency. Nexperia, headquartered in Nijmegen, will be allowed to maintain its normal production activities.
A decision that follows recent indications of significant management deficiencies and actions within Nexperia that could affect the safeguarding of vital technological knowledge and capacity in the Netherlands and across Europe.
Authorities view these capabilities as essential for economic security, as Nexperia supplies chips for the automotive sector and consumer electronics industries.
Under the order, the Minister of Economic Affairs may block or reverse company decisions considered harmful to Nexperia’s long-term stability or to the preservation of Europe’s semiconductor value chain.
The Netherlands government described the use of the Goods Availability Act as exceptional, citing the urgency and scale of the governance concerns.
Officials emphasised that the action applies only to Nexperia and does not target other companies, sectors, or countries. The decision may be contested through the courts.
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Google faces new regulatory scrutiny in the UK after the competition watchdog designated it with strategic market status under a new digital markets law. The ruling could change how users select search engines and how Google ranks online content.
The Competition and Markets Authority said Google controls more than 90 percent of UK searches, giving it a position of unmatched influence. The designation enables the regulator to propose targeted measures to ensure fair competition, with consultations expected later in 2025.
Google argued that tighter restrictions could slow innovation, claiming its search tools contributed £118 billion to the UK economy in 2023. The company warned that new rules might hinder product development during rapid AI advancement.
The move adds to global scrutiny of the tech giant, which faces significant fines and court cases in the US and EU over advertising and app store practices. The CMA’s decision marks the first important use of its new powers to regulate digital platforms with strategic control.
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