Bilal Abu-Ghazaleh has launched 1001 AI, a London–Dubai startup building an AI-native operating system for critical MENA industries. The two-month-old firm raised $9m seed from CIV, General Catalyst and Lux Capital, with angels including Chris Ré, Amjad Masad and Amira Sajwani.
Target sectors include airports, ports, construction, and oil and gas, where 1001 AI sees billions in avoidable inefficiencies. Its engine ingests live operational data, models workflows and issues real-time directives, rerouting vehicles, reassigning crews and adjusting plans autonomously.
Abu-Ghazaleh brings scale-up experience from Hive AI and Scale AI, where he led GenAI operations and contributor networks. 1001 borrows a consulting-style rollout: embed with clients, co-develop the model, then standardise reusable patterns across similar operational flows.
Investors argue the Gulf is an ideal test bed given sovereign-backed AI ambitions and under-digitised, mission-critical infrastructure. Deena Shakir of Lux says the region is ripe for AI that optimises physical operations at scale, from flight turnarounds to cargo moves.
First deployments are slated for construction by year-end, with aviation and logistics to follow. The funding supports early pilots and hiring across engineering, operations and go-to-market, as 1001 aims to become the Gulf’s orchestration layer before expanding globally.
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Canada’s cyber insurance market is stabilising, with stronger underwriting, steadier loss trends, and more product choice, the Insurance Bureau of Canada says. But the threat landscape is accelerating as attackers weaponise AI, leaving many small and medium-sized enterprises exposed and underinsured.
Rapid market growth brought painful losses during the ransomware surge: from 2019 to 2023, combined loss ratios averaged about 155%, forcing tighter pricing and coverage. Insurers have recalibrated, yet rising AI-enabled phishing and deepfake impersonations are lifting complexity and potential severity.
Policy is catching up unevenly. Bill C-8 in Canada would revive critical-infrastructure cybersecurity standards, stronger oversight, and baseline rules for risk management and incident reporting. Public–private programmes signal progress but need sustained execution.
SMEs remain the pressure point. Low uptake means minor breaches can cost tens or hundreds of thousands, while severe incidents can be fatal. Underinsurance shifts shock to the wider economy, challenging insurers to balance affordability with long-term viability.
The Bureau urges practical resilience: clearer governance, employee training, incident playbooks, and fit-for-purpose cover. Education campaigns and free guidance aim to demystify coverage, boost readiness, and help SMEs recover faster when attacks hit, supporting a more durable digital economy.
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As the European Commission’s public consultation on the Digital Fairness Act enters its final phase, growing criticism points to flaws in how citizen feedback is collected.
Critics say the survey’s structure favours those who support additional regulation while restricting opportunities for dissenting voices to explain their reasoning. The issue raises concerns over how such results may influence the forthcoming impact assessment.
The Call for Evidence and Public Consultation, hosted on the Have Your Say portal, allows only supporters of the Commission’s initiative to provide detailed responses. Those who oppose new regulation are reportedly limited to choosing a single option with no open field for justification.
Such an approach risks producing a partial view of European opinion rather than a balanced reflection of stakeholders’ perspectives.
Experts argue that this design contradicts the EU’s Better Regulation principles, which emphasise inclusivity and objectivity.
They urge the Commission to raise its methodological standards, ensuring surveys are neutral, questions are not loaded, and all respondents can present argument-based reasoning. Without these safeguards, consultations may become instruments of validation instead of genuine democratic participation.
Advocates for reform believe the Commission’s influence could set a positive precedent for the entire policy ecosystem. By promoting fairer consultation practices, the EU could encourage both public and private bodies to engage more transparently with Europe’s diverse digital community.
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Regulators continue to face hurdles in overseeing global crypto markets as privacy laws block effective cross-border data sharing, the Financial Stability Board warned. Sixteen years after Bitcoin’s launch, regulation remains inconsistent, with differing national approaches causing data gaps and fragmented oversight.
The FSB, under the Bank for International Settlements, said secrecy laws hinder authorities from monitoring risks and sharing information. Some jurisdictions block data sharing with foreign regulators, while others delay cooperation over privacy and reciprocity concerns.
According to the report, addressing these legal and institutional barriers is essential to improving cross-border collaboration and ensuring more effective global oversight of crypto markets.
However, the FSB noted that reliable data on digital assets remain scarce, as regulators rely heavily on incomplete or inconsistent sources from commercial data providers.
Despite the growing urgency to monitor financial stability risks, little progress has been made since similar concerns were raised nearly four years ago. The FSB has yet to outline concrete solutions for bridging the gap between data privacy protection and effective crypto regulation.
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OpenAI signalled a break with Australia’s tech lobby on copyright, with global affairs chief Chris Lehane telling SXSW Sydney the company’s models are ‘going to be in Australia, one way or the other’, regardless of reforms or data-mining exemptions.
Lehane framed two global approaches: US-style fair use that enables ‘frontier’ AI, versus a tighter, historical copyright that narrows scope, saying OpenAI will work under either regime. Asked if Australia risked losing datacentres without loser laws, he replied ‘No’.
Pressed on launching and monetising Sora 2 before copyright issues are settled, Lehane argued innovation precedes adaptation and said OpenAI aims to ‘benefit everyone’. The company paused videos featuring Martin Luther King Jr.’s likeness after family complaints.
Lehane described the US-China AI rivalry as a ‘very real competition’ over values, predicting that one ecosystem will become the default. He said US-led frontier models would reflect democratic norms, while China’s would ‘probably’ align with autocratic ones.
To sustain a ‘democratic lead’, Lehane said allies must add gigawatt-scale power capacity each week to build AI infrastructure. He called Australia uniquely positioned, citing high AI usage, a 30,000-strong developer base, fibre links to Asia, Five Eyes membership, and fast-growing renewables.
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The UK outsourcing firm Capita has been fined £14 million after a cyber-attack exposed the personal data of 6.6 million people. Sensitive information, including financial details, home addresses, passport images, and criminal records, was compromised.
Initially, the fine was £45 million, but it was reduced after Capita improved its cybersecurity, supported affected individuals, and engaged with regulators.
A breach that affected 325 of the 600 pension schemes Capita manages, highlighting risks for organisations handling large-scale sensitive data.
The Information Commissioner’s Office (ICO) criticised Capita for failing to secure personal information, emphasising that proper security measures could have prevented the incident.
Experts note that holding companies financially accountable reinforces the importance of data protection and sends a message to the market.
Capita’s CEO said the company has strengthened its cyber defences and remains vigilant to prevent future breaches.
The UK government has advised companies like Capita to prepare contingency plans following a rise in nationally significant cyberattacks, a trend also seen at Co-op, M&S, Harrods, and Jaguar Land Rover earlier in the year.
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The UK and US governments have jointly sanctioned a transnational network operating illegal scam centres across Southeast Asia. These centres use sophisticated methods, including fake romantic relationships, to defraud victims worldwide.
Many of the individuals forced to conduct these scams are trafficked foreign nationals, coerced under threat of torture. Authorities have frozen a £12 million North London mansion, along with a £100 million City office and several London flats.
Network leader Chen Zhi and his associates used corporate proxies and overseas companies to launder proceeds from their scams through London’s property market.
The sanctioned entities include the Prince Group, Jin Bei Group, Golden Fortune Resorts World Ltd., and Byex Exchange. Scam operations trap foreign nationals with fake job adverts, forcing them to commit online fraud, often through fake cryptocurrency schemes.
Proceeds are then laundered through a complex system of front businesses and gambling platforms.
Foreign Secretary Yvette Cooper and Fraud Minister Lord Hanson said the action protects human rights, UK citizens, and blocks criminals from storing illicit funds. Coordination with the US ensures these sanctions disrupt the network’s international operations and financial access.
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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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EU countries have agreed to step up efforts to improve child protection online by supporting Denmark’s Jutland Declaration. The initiative, signed by 25 member states, focuses on strengthening existing EU rules that safeguard minors from harmful and illegal online content.
However, Denmark’s proposal to ban social media for children under 15 did not gain full backing, with several governments preferring other approaches.
The declaration highlights growing concern about young people’s exposure to inappropriate material and the addictive nature of online platforms.
It stresses the need for more reliable age verification tools and refers to the upcoming Digital Fairness Act as an opportunity to introduce such safeguards. Ministers argued that the same protections applied offline should exist online, where risks for minors remain significant.
Danish officials believe stronger measures are essential to address declining well-being among young users. Some EU countries, including Germany, Spain and Greece, expressed support for tighter protections but rejected outright bans, calling instead for balanced regulation.
Meanwhile, the European Commission has asked major platforms such as Snapchat, YouTube, Apple and Google to provide details about their age verification systems under the Digital Services Act.
These efforts form part of a broader EU drive to ensure a safer digital environment for children, as investigations into online platforms continue across Europe.
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A federal jury in Marshall, Texas, USA, has ordered Samsung Electronics to pay $445.5 million to Collision Communications, a New Hampshire-based Company, after finding that Samsung infringed on multiple wireless communication patents.
The lawsuit, filed in 2023, alleged that Samsung’s Galaxy smartphones, laptops, and other wireless products incorporated patented technologies without authorisation. These patents cover innovations in how devices manage and transmit data over 4G, 5G, and Wi-Fi network technologies.
Collision Communications argued that the inventions were originally developed by defense contractor BAE Systems and later licensed to Collision for commercial use. While BAE Systems was not directly involved in the case, its research formed the basis of the patented technologies.
Samsung denied wrongdoing, asserting that the patents were either invalid or not used in the ways described. The company says it plans to appeal the decision.
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