AM Edition: Top 10 Business Articles on LiveNews.co.nz for April 22, 2026 – Full Text

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Source: The Conversation – UK – By Ursula F Ott, Professor of International Business, Nottingham Trent University

It is now almost a decade since the UK voted for Brexit and since the tariffs of US president Donald Trump’s first term increased global trade frictions. Brexit removed the UK from the European single market for goods and services. Now though, the country is proposing a pivot back towards alignment with EU regulations.

What could have not been widely predicted back in 2016 was the COVID pandemic, nor a war on European soil. The UK has been exposed to these shocks without the EU support system. So what may once have been impossible to imagine is now on the cards: adopting EU single market rules under new UK legislation.

In May 2025, the UK and EU reached a new trade agreement, paving the way for both sides to move closer on their economies and business. This was hastened by unpredictable US trade tariffs and a weakening of the US-UK-EU relationship. In addition, it has been estimated in a comprehensive study that Brexit has reduced the size of the UK economy by 6-8%.

Politically, the approach announced by the UK prime minister, Keir Starmer, is a courageous step. UK legislation would allow the country to adopt new EU laws without the need for parliament to vote each time. But any plan is certain to provoke strong opposition from the Conservatives and Reform UK.

However, it is a signal of the seriousness of the UK’s intentions to move closer to the EU by adapting to its regulations and giving up independence from EU law. That is a costly move for the UK in terms of its credibility, but the U-turn should reinforce its commitment to the EU.

But beyond this, there are three clear benefits to the UK.

1. The EU is built on rules and regulations that guide the bloc’s labour market, trade and security systems. Alignment would clearly help UK businesses, consumers and individual workers to manoeuvre within these systems.

2. By breaking from the single market, the UK chose a costlier approach to trading and investing across the EU border. Aligning regulations would reduce cross-border bureaucracy.

3. The EU is looking for new trading partners after supply chain disruptions from COVID and the Ukraine war – not to mention the current impact on oil and gas supplies. The EU does not need to rely on the UK, but a new direction in the relationship could reduce the threat of supply chain disruption in future.

A better deal for consumers?

So what could this mean for UK businesses and consumers? Food producers trading within the UK-EU zone would have a quicker turnaround of their fresh produce. This would reach shop shelves in the UK and EU more quickly, giving shoppers better-quality fresh foods.

Reducing the amount of complex paperwork and export health certificates at borders would allow a free flow of fresh food even between Great Britain and Northern Ireland (which remained part of the single market). This trade has been disrupted since Brexit and affects both trade between food producers due to paperwork and border delays, and food security.

Border checks, paperwork and adapting to legal requirements are expensive and so increase food prices (and with that, inflation). Bringing trade between the EU and the UK closer could reduce these costs, and should also allow producers to benefit more from global value chains.

US tariffs are at their highest levels since the second world war, and the knock-on cost effects of supply chain disruption in the Middle East make a strong case for strengthening ties between neighbours.

Going forward, it will be resilience rather than efficiency in trade that will be important for both businesses and nations. Both will want to be able to reconfigure networks at speed. If inflation rises due to product shortages, governments have limited fiscal space to offer direct support to citizens (which would mean increased levels of spending), or to cut taxes.

Another benefit could come in the form of foreign direct investment into the UK from overseas. In 2025, this began shifting from low-cost developing countries towards capital-intensive and technologically-driven investments in developed countries – and especially in the EU (Germany, Italy and France).

Alignment with EU regulation could give investors more confidence to commit to the UK. Foreign direct investment in renewable energy and AI products, for example, would benefit both the UK’s workers and its consumers.

This is a time of new geopolitical alliances, cooperation and blocs. Trading and investment options could help secure economic, political and societal stability in a volatile world. So far, this is a relatively small step by the UK – but starting to align to EU regulations could ease a complex relationship.

Ursula F Ott does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. With talk of closer EU alignment, the UK is signalling to Europe that it’s a partner worthy of trust – https://theconversation.com/with-talk-of-closer-eu-alignment-the-uk-is-signalling-to-europe-that-its-a-partner-worthy-of-trust-280961

Source: MIL-OSI-Submissions-English

Source: The Conversation – France – By Christine Abdalla Mikhaeil, Assistant professor in information systems, IÉSEG School of Management

When Amazon Web Services (AWS) went down globally in October 2025, millions of users were abruptly reminded how invisible yet indispensable cloud technology has become.

From banks and hospitals to airlines and retail platforms, entire sectors slowed or came to a standstill. The disruption followed a separate catastrophe earlier in July 2024, when CrowdStrike’s software update grounded operations around the world.

Different companies. Different causes. Yet both events exposed the same uncomfortable truth: the world’s digital infrastructure, the networks, servers and software that underpin nearly every modern service, is far more fragile than we like to believe.

Technically, these were very different failures, but the similarity lies in how quickly they cascaded. A single error in a single company rippled across global systems that had no direct relationship to that company at all.

The illusion of resilience

For years, cloud providers have marketed themselves as the answer to such fragility. Distributed computing, automated backup, and redundant systems are supposed to keep data and services online even when local components fail. However, the cloud model depends heavily on network connectivity and can introduce latency and other vulnerabilities, that mitigates certain failures, but does not eliminate fragility entirely.

As both the AWS and CrowdStrike incidents show, redundancy on paper doesn’t always mean resilience in practice. Many organisations that rely on AWS for critical services also use AWS for their backup, monitoring or authentication. When a core network fails, so do the fail-over mechanisms designed to prevent downtime. In other words, “diversification” often exists only within the same provider’s ecosystem, a classic case of putting all eggs in one digital basket.

At the heart of the issue is cloud concentration. A small number of companies, primarily AWS, Microsoft and Google, now host the majority of the world’s digital infrastructure. Even more when, cloud computing has become the backbone of modern AI by relying on large, centralized data centers that offer substantial processing power and scalability.

Governments, universities, hospitals and even competitors run their critical services on these same platforms. The convenience and cost efficiency are undeniable. But this consolidation has created a structural vulnerability. A single misconfiguration or software flaw in one of these providers can have global consequences, similar to how a major bank failure can destabilise the financial system.

The situation is further complicated by opacity: cloud providers rarely disclose full details of their interdependencies or internal resilience practices. Customers often have no clear map of how their services are distributed, where their data resides, or which other systems they rely on indirectly. When outages happen, even identifying who’s responsible can be a challenge.

Europe’s dependence and ‘digital sovereignty’

What makes these incidents particularly concerning is that they involve private companies running public infrastructure. AWS and CrowdStrike aren’t just serving commercial clients, they underpin hospitals, airports, energy grids and government systems. When they fail, entire ecosystems fail, not just their direct customers. Yet oversight of these critical dependencies remains minimal.

For Europe, these outages turned an abstract “digital sovereignty” debate into a very concrete dependency problem.

Digital sovereignty is about the capacity to ensure that critical data, infrastructure, and AI systems operate under EU rules and remain controllable in crises. This sovereignty framing ties outages to broader issues of jurisdiction (US access to data), trade power, and strategic autonomy for critical sectors, like finance, health, and public administration.

Politically, it responds to dependence on a handful of US hyperscalers who hold over 70% of the European cloud market and are also subject to US laws like the CLOUD Act. On the CLOUD Act side, explanations by EU‑focused providers and analysts emphasise that US‑headquartered cloud firms (including AWS, Microsoft, Google) are subject to the Clarifying Lawful Overseas Use of Data Act, which can compel disclosure of data stored in European data centers.

Cloud and AI sovereignty frameworks address where and under which law sensitive data and workloads run, and how easily European users can exit, port, or reconfigure in the face of outages or geopolitical shocks.

Recent European initiatives explicitly treat hyperscalers and major Information and Communication Technology (ICT) providers as systemic infrastructure, not just vendors.

Under the Digital Operational Resilience Act (DORA), in force since 2025, EU financial regulators can designate “critical third party ICT service providers” and subject them to direct oversight to reduce systemic risk.

EU debates on cloud now emphasise exit, portability, and multi‑cloud architectures, arguing that resilience depends less on “more providers” and more on avoiding structural lock‑in that makes switching or redundancy impossible in practice. DORA addresses who runs critical digital infrastructure for finance and how the European Union can oversee and stress test them as systemic actors.

Guaranteeing cybersecurity across Europe

The Cyber Resilience Act (CRA), in force since December 2024, is the EU’s way of hard wiring “resilience by design” into the entire stack of connected hardware and software that underpins Europe’s digital infrastructure.

CRA addresses what characteristics all networked digital products must have so they do not import unmanageable cyber risk or opaque vulnerability handling into the EU.

The NIS2 (Directive (EU) 2022/2555 came into effect in January 2023 and required transposition into national law by October 2024, expanding from NIS1’s narrow scope to cover medium/large entities in energy, transport, health, finance, digital infrastructure (including cloud), public administration, manufacturing, and more. NIS2 operationalises sovereignty at the entity level: critical operators must align their practices with EU standards, even when relying on non-EU providers, creating a harmonised resilience baseline across the single market. It integrates with CRA, DORA, and cloud initiatives by requiring entities to demand equivalent resilience from suppliers, closing gaps in the dependency chain.

Beyond regulations, the Commission is building practical sovereignty tools around cloud and AI.

A “Cloud Sovereignty Framework” tender (up to €180 million for 6 years), launched in 2025 and awarded in April 2026 to Luxembourg’s Post Telecom, Germany’s StackIT, French Iliad’s data centre unit Scaleway and Belgium’s Proximus, sets concrete sovereignty criteria, strategic, legal, operational, environmental, supply chain transparency, openness, security, and EU law compliance, for cloud services procured by EU institutions.

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A weekly e-mail in English featuring expertise from scholars and researchers. It provides an introduction to the diversity of research coming out of the continent and considers some of the key issues facing European countries. Get the newsletter!

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Christine Abdalla Mikhaeil est membre de l’Association for Information Systems (AIS).

– ref. Cloud tech outages: how the EU plans to bolster its digital infrastructure – https://theconversation.com/cloud-tech-outages-how-the-eu-plans-to-bolster-its-digital-infrastructure-280928

Source: MIL-OSI-Submissions-English

Source: The Conversation – Africa – By Nicodemus Minde, Researcher, United States International University

The East African Community is one of Africa’s oldest regional economic organisations. Its birth in 1967 was the culmination of decades of economic ties forged in the colonial era between Kenya, Uganda and Tanzania. It’s no surprise that the EAC is also the most deeply integrated regional entity.

In its heyday between 1967 and 1977, the bloc shared a common currency, jointly operated a development bank and administered its transport infrastructure as one. There was a common education policy with a single syllabus and examining body as well as the University of East Africa with specialised colleges in the three countries.

Political friction and conflicting priorities, among other factors, led to its collapse in 1977 but it was revived in 1999. Citizens within the bloc currently benefit from free movement of goods, services, labour and capital, along with the rights of establishment and residence. Unmet objectives include the return of a common currency and a political federation.

Meanwhile, the bloc has grown from three to eight – Rwanda and Burundi joined in 2007; South Sudan in 2016, the DR Congo in 2022 and Somalia in 2023. The territory covers stretches from the Indian Ocean to the Atlantic and brings together over 331 million people and a combined GDP of US$313 billion as of 2025.

However, this rapid expansion has triggered financial difficulties, putting the economic integration agenda at risk. While partner states are expected to contribute to fund the bloc’s operations, only Kenya, Tanzania and Uganda regularly meet their quota. The budget shortfall has led to massive staff layoffs and a freeze on new recruitment.

So serious is the crisis that it was top of the agenda at the annual summit of the heads of state in March 2026. The leaders stepped up to reform the funding model and signalled that the bloc was ready to sanction or sideline countries that compromise funding.

I have studied regionalism and integration in eastern Africa, conducted research on the EAC and published on Tanzanian citizens’ sovereignty, popular participation, and the EAC integration and democratisation.

It is my view that the radical proposals will compel non-paying partner states to either shape up or ship out. These reforms will salvage the East African Community but could potentially trigger mistrust and perception of unequal benefits in the long run.

The cost of rapid expansion

Each of the eight partner states is expected to contribute approximately US$7 million to fund the bloc’s operations. In addition, the bloc relies on development partners to fund some activities.

In recent years, six of the eight member states have missed their budget contributions. This resulted in a US$90 million budget shortfall. Regional institutions affected by these include:

• the East African Legislative Assembly, the regional parliament

• the East African Court of Justice, responsible for the interpretation and application of the EAC Treaty.

The two have failed to perform their core functions due to resource constraints. The regional assembly, on occasion, has been forced to skip sittings. This has an effect on critical debates and enactment of new laws to foster economic integration. The regional court grapples with case backlogs.

In November 2023, the EAC Summit adopted a new financing model. It shared 65% of the budget equally among partner states and the rest based on each country’s financial capacity. This capacity is assessed using the World Bank’s average nominal GDP per capita metric for the previous five years.

But only Kenya, Tanzania, and Uganda – and occasionally Rwanda – have remitted their contributions on time. Domestic conflicts in South Sudan, the DRC and Somalia may have played a role in the slow contributions of these newer EAC members. In the 2024-2025 financial year, Burundi paid only 19% of its expected contribution, the DRC paid 14%, Somalia paid around half, and South Sudan paid a mere 7%.

Overall compliance stood at roughly 58%, leaving the bloc with arrears exceeding US$55 million. In the 2025-2026 cycle, the picture was even bleaker: compliance slipped to just 36.6%, while outstanding obligations climbed to about US$90 million.

The pattern also hints at something deeper: political ambivalence among non-paying members, and uneasiness among some partner states about the benefits of belonging to the bloc. Despite the funding challenges, inter-regional trade in the EAC has been on the rise due to increased trade facilitation under the customs union and common markets protocols. The EAC has also made advances in peace and security. In 2022 for example, through the Nairobi Process, the EAC facilitated peace talks and deployed the East African Community Regional Force in DRC.

Beyond funding, personal and political differences between the DRC’s President Felix Tshisekedi and Rwanda’s Paul Kagame have contributed to tensions within the bloc.

What did the leaders decide at the March summit?

Kenya, Uganda and Tanzania, in a rather surprising but decisive move, pushed for a new financing formula, replacing the model adopted in 2023.

The highlights of the new financing formula include:

• 50% of the budget will be shared equally among all partner states, while the remaining 50% will be based on each country’s economic strength. The formula will take effect from 1 July 2026. By factoring in differences in economic capacity, the reform aims to reduce the burden on smaller economies and make the bloc’s funding more sustainable.

• members of the legislative assembly should be paid by their respective national assemblies with effect from December 2027

• the council of ministers should finalise the schedule of sanctions considering the new financing formula. The EAC aims to deal with mounting arrears and non-payment through a sanction regime.

A quorum for the meeting of all organs and institutions of the community will be two-thirds of all partner states. Previously, all states had to participate in passing crucial resolutions, and this was frustrated by absenteeism, especially by non-paying countries.

Nominations for the key institutional positions will depend on the sponsor state’s ratification of all community legal instruments, domestication of the treaty, and full implementation of the roadmap for the partner state’s integration.

What’s next

These are radical proposals, with consequences. Take the example of the decision to appoint Stephen Mbundi of Tanzania as the new secretary general. Based on the rotational principles of the EAC, South Sudan was poised to take over the position from Kenya’s Veronica Nduva. But South Sudan is a defaulter.

This decision signalled the bloc’s commitment to financial compliance and commitment to the spirit of regional integration. Uganda’s president, Yoweri Museveni, also took over the chairman’s position, bypassing Somalia and the DRC, which were poised to lead the community for a year. Somalia and the DRC have been behind in their annual payments.

The proposals, which appear to have been orchestrated by the founding members, suggest a pragmatic move to salvage the EAC.

Nicodemus Minde does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. East African Community’s expansion has triggered financial troubles: why solutions come with risks – https://theconversation.com/east-african-communitys-expansion-has-triggered-financial-troubles-why-solutions-come-with-risks-280632

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Source: The Conversation – France – By Frédéric Fréry, Professeur de stratégie, CentraleSupélec, ESCP Business School

OpenAI, the creator of ChatGPT, is gearing up to launch its Initial Public Offerings (IPO) this year. This financial manoeuvre would represent a pivotal shift for a project originally designed for the “common good” towards a market-driven logic. Established in 2015, OpenAI started out amidst growing anxiety regarding artificial intelligence (AI). Founded by Sam Altman and Elon Musk, the tech company adopted a non-profit structure and made no secret of its goal to develop AI that is “beneficial to humanity” and prevent it from remaining in the hands of a few dominant players.

This ambition distinguished it from tech giants like Google, Microsoft, Meta, and Amazon, which were built on proprietary models and rent-seeking effects.

In contrast, OpenAI intended to champion general public interest by emphasising open research and sharing knowledge. However, this orientation – symbolised by its name – quickly collided with a structural constraint: the astronomical cost of generative AI.

Massive costs

Unlike traditional software, where marginal costs tend towards zero (for example, the millionth copy of Windows costs Microsoft nothing), generative AI requires massive infrastructure.

Every interaction mobilises computing resources, energy, and specialised equipment. A standard ChatGPT query, consisting of one question and one answer, costs between $0.01 and $0.10. Similarly, generating a high-definition image can cost between $0.10 and $0.20. While these amounts seem negligible in isolation, they become staggering when scaled to the billions of daily queries seen in 2026.

This is explained by the underlying infrastructure, particularly the Graphics Processing Units (GPUs) supplied by players like Nvidia. These chips can cost tens of thousands of dollars to purchase and several dollars per hour via cloud access.

OpenAI, like its competitors, depends on tens of thousands of these GPUs running continuously in massive data centers. According to some estimates,the necessary investments will reach hundreds of billions by the end of this decade.

As early as the late 2010s, it became clear that a purely non-profit model could not meet such capital intensity. This is why OpenAI adopted a hybrid status in 2019, allowing it to raise funds while maintaining control through a foundation. It was a first foray into the market economy, albeit one tempered by the ambition to resist investor demands.

Brutal acceleration with ChatGPT

However, at the end of 2022, the chatbot ChatGPT radically changed the game, attracting 100 million users in just two months, before surpassing 900 million weekly users by early 2026.

OpenAI’s revenue surged from approximately $200 million (€173.15 million) in 2022 to over $10 billion (€8.65 billion) in 2025 – a sixty-fold increase in three years.

This exponential growth was accompanied by the implementation of a business model with multiple revenue streams. For individuals, OpenAI offers paid subscriptions (ranging from $20 to $200 per month). However, the bulk of the revenue comes from enterprises, via subscriptions priced between $25 and $60 per user per month. A company with 10,000 employees thus represents several million dollars in annual revenue.

Corporate money

OpenAI additionally bills for the use of its models by companies that integrate them directly into their own solutions. Every use is metered, often on a massive scale. An application processing a million queries a day can generate tens of thousands of dollars in monthly billing.

Finally, a growing portion of revenue comes from strategic agreements, notably with Microsoft, which integrates OpenAI technologies into its products under the Copilot brand.

It is the sum of these flows – subscriptions, licences, third-party usage, and partnerships – that allowed OpenAI to reach approximately $1 billion in monthly revenue in 2025. Yet, this commercial rise masks an intrinsic economic fragility.

A gigantic cash-burning machine

Despite sharply rising revenues, OpenAI remains structurally loss-making. In the first half of 2025, the company reportedly generated approximately $4.3 billion in revenue while recording losses between $7 billion and $13 billion – more than $2 billion in losses every month. In total, cumulative losses could exceed $140 billion (€121.19 billion) between 2024 and 2029.

This drift is explained by the very nature of OpenAI’s business model, where every interaction incurs a cost alongside gargantuan necessary investments. Beyond infrastructure, Research and Development (R&D) is a major expense. To stay in the technological race against an increasingly competitive environment, OpenAI reportedly invested nearly $16 billion in R&D in 2025 alone.

To this is added the cost of human resources, which is sometimes extraordinary. While base salaries for the most in-demand AI experts range from $250,000 – $700,000 per year, their total compensation – including stock and bonuses – frequently exceeds $1 million. In some cases, annual compensation even exceeds $10 million. Here again, bidding wars from competitors like Meta force OpenAI to match these offers for fear of seeing its key talent vanish.

Nearing bankruptcy?

In short, OpenAI’s business is not enough to cover its costs, to the point that some analysts suggest that at this rate, it could be forced to file for bankruptcy as early as 2027. Recourse to external financing is therefore indispensable to cover these losses.

To sustain its growth, OpenAI has already raised approximately $58 billion since its inception, including more than $13 billion from Microsoft. In 2025, an exceptional funding round reportedly raised up to $40 billion more, pushing its valuation to several hundred billion dollars.

At the end of March 2026, a new $122 billion funding round – notably involving Amazon ($50 billion), Nvidia, and SoftBank ($30 billion each) – brought the valuation to $852 billion (€737.6 billion). Yet, these amounts remain insufficient given the requirements.

Industrial Dependency

Dependency on industrial partners appears particularly problematic. Microsoft provides OpenAI with its cloud infrastructure via Azure, while Nvidia plays a key role upstream by providing GPUs. Much like the Gold Rush era, when shovel sellers grew rich at the expense of prospectors, it is the infrastructure providers in the AI sector making a fortune, not the model designers.

In practice, every AI query generates revenue for infrastructure providers, amounting to a form of “invisible tax” captured upstream.

In 2025, Nvidia generated nearly $73 billion in net profit on approximately $130 billion in revenue, and its stock market valuation is 1.5 times higher than the entire Paris stock exchange!

Governance missteps

OpenAI’s economic tensions have spilled over into its corporate governance. The hybridisation of a public interest mission with private financing mechanisms resulted in a complex structure. A non-profit foundation controls a for-profit “public benefit corporation”, which is funded by investors and tasked with raising capital and developing activities – all while theoretically remaining subordinate to the foundation’s public interest mission. This construction, designed to avoid purely financial logic, quickly fuelled tensions between different stakeholders.

Elon Musk’s departure in 2018 was the first signal of a strategic disagreement. In 2020, several researchers left OpenAI to found Anthropic, citing differences over safety and governance. However, it was primarily the crisis of November 2023 that fully revealed the system’s fragilities, when the board of directors suddenly announced the firing of Sam Altman, citing a lack of transparency in his communications.

Within hours, the situation spiralled into an open crisis. Nearly all employees threatened to leave the company if Altman was not reinstated. Microsoft, the main partner and investor, publicly supported Altman and even discussed the possibility of hiring him and his teams. Faced with this pressure, the board was forced to reverse its decision within days. Sam Altman was reinstated, and the board’s composition was profoundly overhauled. This episode highlighted internal tensions, specifically the difficulty of making divergent logics coexist within the same company: ethical posturing, industrial imperatives, and investor demands.

Intensifying Competition

In addition to these internal constraints, competitive intensity is particularly fierce.

Google, the inventor of generative AI, is making rapid progress with Gemini. Anthropic, with Claude, has established itself in certain segments, particularly programming, while emphasising safety.

China’s DeepSeek has claimed to use less expensive processors. France’s Mistral AI advocates for a frugal approach and European digital sovereignty. In a sign of this shifting landscape, Apple which initially partnered with OpenAI to include ChatGPT for certain Siri features – has chosen to replace it with Gemini.

In this context of ecosystem reorganisation, OpenAI’s position, while still central, is being challenged. Intensifying competition reinforces the need for ever-greater financial resources.

The stock market: lifeline or mirage?

OpenAI’s Initial Public Offering (IPO) is presented as a response to these constraints: a way to fund massive investments and consolidate a weakened competitive position. An IPO could raise between $50 billion and $100 billion by selling 10% to 20% of the capital. Such an operation would constitute one of the largest in the history of financial markets.

However, this transformation involves delicate trade-offs. A listed company is subject to profitability and transparency requirements that may clash with the experimental nature of artificial intelligence. Added to this is the persistent dependence on Microsoft and Nvidia, which limits the company’s strategic autonomy.

Most importantly, there is no indication that an IPO would suffice to resolve OpenAI’s structural problems. At best, without a significant shift in the business model, it would only delay its bankruptcy by a few years. The economic model of generative AI remains fundamentally unstable today.

A Question Beyond OpenAI

Beyond the case of OpenAI, one can legitimately question the current functioning of an economy dominated by tech giants. Artificial intelligence is establishing itself as an essential infrastructure whose effects far exceed the economic sphere. For some analysts, control over AI now carries the same geostrategic importance link please as the possession of nuclear weapons.

Consequently, a civilisational question arises: can we entrust the development and direction of such a technology solely to financial markets? Can we imagine Elon Musk or Mark Zuckerberg personally owning the equivalent of one or more atomic bombs? OpenAI’s IPO will not provide the answer alone. However, it will constitute one of the first large-scale tests.

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A weekly e-mail in English featuring expertise from scholars and researchers. It provides an introduction to the diversity of research coming out of the continent and considers some of the key issues facing European countries. Get the newsletter!

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Frédéric Fréry ne travaille pas, ne conseille pas, ne possède pas de parts, ne reçoit pas de fonds d’une organisation qui pourrait tirer profit de cet article, et n’a déclaré aucune autre affiliation que son organisme de recherche.

– ref. OpenAI gets set to go public: can we entrust the financial markets with ChatGPT and AI? – https://theconversation.com/openai-gets-set-to-go-public-can-we-entrust-the-financial-markets-with-chatgpt-and-ai-280943

Source: MIL-OSI-Submissions-English

Source: The Conversation – Global Perspectives – By Rajat Roy, Associate Professor, Bond Business School, Bond University

Today, Apple announced the tech company’s longtime chief executive Tim Cook will step down and transition to the role of executive chairman of Apple’s board of directors.

This change will take effect from September 1 2026. John Ternus, currently Apple’s senior vice president of hardware engineering, will take over as chief executive.

However, Cook will remain in place until then to ensure a “smooth takeover”. As chairman, he’ll then take on a more strategic role of engaging with policy makers and corporate governance.

Ternus is a 25-year-veteran at Apple. He is widely seen as an internal replacement shaped by long-term succession planning. His appointment marks Apple’s first leadership transition since Cook took over from Steve Jobs in 2011.

Rather than signalling a dramatic shift, this transition is likely going to be more subtle, without a major strategic reset.

A rich legacy for Cook

Cook was only the second chief executive in Apple’s history, after cofounder Steve Jobs resigned in 2011 and died six weeks later.

Cook is widely recognised for his strengths in operations, scale and business model innovation. Under his leadership, Apple became one of the most successful global supply chain organisations reaching more than 200 markets worldwide. The company’s value grew from about US$350 billion in 2011 to US$4 trillion today.

Importantly, Cook drove a decisive shift towards service monetisation – charging users fees for Apple’s digital services and subscriptions, rather than just making money from selling devices such as iPhones, iPads and laptops. Cook’s strategy capitalised on Apple’s already massive base of 2.5 billion active devices.

Service monetisation led to high-margin revenues from Apple’s offerings such as iCloud, Apple Music and the Apple store. Consequently, Apple made more than US$100 billion in 2025 from this business, providing a stable and predictable income beyond cyclical hardware sales.

Who is John Ternus?

In contrast to Cook, Ternus has a deeply technical, product-oriented background shaped by more than two decades in hardware engineering.

At Apple, he has overseen the development of key product lines that include many iterations of the iPhone, iPad, AirPods and the Apple Watch, among others. He’s been closely associated with advances in materials, durability and performance.

Ternus spearheaded the recent introduction of the relatively affordable MacBook Neo and the radically thin yet durable iPhone Air. He also led the way on incorporating an unprecedented active noise cancellation feature into AirPods, which the company described as “world’s best”.

The difference in background between Cook and Ternus suggests a subtle but important shift in emphasis for the technology giant.

While Cook focused on transforming Apple into a highly monetised ecosystem anchored in services and global scale, Ternus is likely to reassert the importance of product-led innovation. In his current role, he’s been focusing on engineering excellence and integrating fresh technologies into Apple devices.

With Ternus at the helm, it’s likely the company will try to balance an optimised ecosystem of revenue (that is, service monetisation) with reinvigorating the hardware products that sustain it. That would make a lot of sense.

Apple faces numerous pressures

A stronger product focus under Ternus may also become the company’s response to multiple structural pressures facing Apple.

In the Cook era, Apple was often criticised for incremental innovation, in contrast to Jobs’ visionary leadership that was credited with changing modern consumer tech.

Major competitors Google and Microsoft are making rapid advances in cloud-based artificial intelligence (AI), with Apple seemingly lagging behind (although some experts say not investing as heavily in AI could be a worthwhile response to the AI hype bubble).

Apple’s device-centric approach will ensure products are meaningfully distinct from competitors through partnerships. For example, the company will be using Google’s Gemini AI as the basis for an enhanced Siri assistant. At the same time, consumers are upgrading their devices more slowly, so Apple will need more compelling product innovation to drive demand.

The company is also vulnerable to global supply chain disruption due to geopolitical tensions. This can negatively impact Apple’s timelines of product delivery, and even lead to lower-quality products if suppliers can’t fulfil Apple’s expectations. In recent years, Apple has already been addressing this by moving some of its manufacturing from China to Vietnam.

Time will tell, but so far everything suggests Ternus succeeding Cook as Apple chief executive will represent a logical and necessary calibration of strategy, rather than a radical shakeup.

Rajat Roy does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Apple chief executive Tim Cook resigns after 15 years. What’s next for the tech giant? – https://theconversation.com/apple-chief-executive-tim-cook-resigns-after-15-years-whats-next-for-the-tech-giant-281122

Source: MIL-OSI-Submissions-English

Source: The Conversation – UK – By Anja Shortland, Professor in Political Economy, King’s College London

When evolutionary biologist Joseph Popp coded the first documented piece of ransomware in 1989, he had little idea it would become a major criminal business model capable of bringing economies to their knees.

Popp, who worked for the World Health Organization at the time, wanted to warn people about the dangers of ignoring health warnings, poor sexual hygiene and (human) virus transmission.

He sent out 20,0000 floppy discs that, when loaded, flashed up a demand for money to regain files that had supposedly been encrypted (in fact, it was just their file names). He was later arrested and charged with 11 counts of blackmail, but declared mentally unfit to stand trial.

In 1996, two Columbia University computer scientists published a paper explaining how criminals could use more sophisticated versions of Popp’s scheme to mount large-scale extortion operations. At the heart of this was malicious software that could be used to encrypt, block access to or steal a person or organisation’s files and data.

However, two preconditions still had to be met for ransomware to become a feasible criminal business: communication channels that were difficult to monitor, and a payments process outside financial regulation.

The Tor protocol, released by US intelligence services to protect their covert communications, solved the first problem in 2004. Cryptocurrencies solved the second – in particular, when bitcoin cash machines started appearing in North American cities from 2013.

Today, artifical intelligence makes malware coding and crafting convincing phishing-emails in any language simple. And the latest model in Anthropic’s AI system, Claude Mythos, recently proved more effective at hacking into computer systems than humans.

As an expert in extortive crime, I am increasingly concerned about public and political apathy to the threats posed by ransomware. To better understand these, it’s worth tracing its evolution over the past two decades – and how improvements in computer security and law enforcement, plus changes in data regulation, have led to new criminal strategies each time.

Cut out the middlemen

The first generation, which came to global attention in the mid-2010s, was known as “commodity ransomware”. A pioneering example, Cryptolocker, was developed by Russia-based hackers who infiltrated hundreds of thousands of computers, seeking to cut out the middlemen previously needed to commit financial fraud. They proved that a large majority of their victims would happily pay a small ransom to restore data that had been locked by their malware.

As both competent and incompetent hackers piled into this new market, victims shared information about rogue operators and put them out of business. This led to the second generation of ransomware such as Ryuk, which emerged in 2018.

In this phase, criminals abandoned the indiscriminate “spray-and-pray” approach in favour of targeting individual cash-rich businesses. They would set an individual ransom, negotiate with the company, and even offer to help with decryption if paid. Fast-rising ransoms more than compensated for this increased administrative effort.

In response, many companies began investing in multi-factor authentication, better threat monitoring, advance warning systems and software patches for known vulnerabilities.

However, these security benefits were soon offset by the impact of COVID on work practices across the world. The pandemic led to widespread remote working, with many people using unsecured devices and connections that were vulnerable to cyber-attack.

A multibillion-dollar industry

The next ransomware innovation was driven by the emergence of back-up systems that enabled companies to restore encrypted files without the criminals’ help. This was coupled with the emergence of tighter data privacy regulation such as GDPR in Europe and the UK.

Invented in 2019, third-generation ransomware weaponised these regulations, which threatened firms with massive fines if confidential data about clients or staff was revealed. The criminal gangs now sought out and exfiltrated an organisation’s most sensitive files, then threatened to publicise them through dedicated dark web leak sites.

This so-called double-extortion model – encrypting an organisation’s data while threatening to make it public – brought many businesses back to the negotiation table.

Ransomware had become a multibillion-dollar industry – with the Conti gang, sheltered by Russia and employing hundreds of people, among the key players setting new records for ransomware demands. Its attacks on critical infrastructure and hospitals saw it sanctioned by the UK government in 2023.

This new approach forced many governments to row back on imposing hefty fines for data breaches, since many were the result of criminal attacks. Meanwhile, new initiatives by law enforcement – supported by the private sector – targeted and broke up the largest and most egregious ransomware gangs.

Today’s fourth generation of ransomware, building on the latest AI technology, looks nimbler and slimmed-down in comparison. Anyone who gains access to a network can lease weapons-grade malware on the dark web without forming long-term ties with a particular gang.

Advanced AI-based hacking tools make ransomware accessible to many more criminals and politically motivated hacktivists. And around one-quarter of breaches still result in ransom payments. For criminals sheltered by their governments, only the digital infrastructure is at risk of being taken down by western law enforcement.

Lessons not learned

While coverage of Claude Mythos suggests even the most sophisticated cyber defences could now be vulnerable, the troubling reality is that many individuals and organisations are still using out-of date, unpatched or only partially upgraded software. This means even early-generation ransomware techniques are still lucrative.

While Popp sent out his floppy discs to promote better sexual hygiene, today’s poor cyberhygiene is leaving many public and private networks open to malware attacks. The intended lesson of his original ransomware caper – be vigilant and properly heed health warnings – has still only been partially learnt in the digital world.

Many western societies appear to have grown accepting of criminals leaching on business conducted on the internet. Not even a steady stream of human fatalities, caused by attacks on hospitals and medical providers, has generated the level of response required to stamp out this dangerous threat.

The hope that governments sheltering cybercriminals can be encouraged (or forced) to stop them targeting critical national infrastructure appears increasingly fragile amid current geopolitical tensions. At all levels of society, we need to get smarter about cyber defence.

Anja Shortland does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. Anja’s latest book, We Know You Can Pay a Million: Inside the Dark Economy of Hacking and Ransomware, is published by Profile Books.

– ref. From floppy discs to Claude Mythos, how ransomware grew into a multibillion-dollar industry – https://theconversation.com/from-floppy-discs-to-claude-mythos-how-ransomware-grew-into-a-multibillion-dollar-industry-281000

Source: French to English Tester   Published on: 2026-04-21

Source: The Conversation – France (in French)– By Hugo Spring-Ragain, Doctoral student in economics / mathematical economics, Center for Diplomatic and Strategic Studies (CEDS)

Artificial intelligence does not so much destroy jobs as it profoundly changes the skills required to perform them. From this confusion between jobs and skills, errors may arise in policies supporting the ongoing transformations.

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Each major technological wave has produced its share of contradictory predictions about employment. Artificial intelligence (AI) is no exception. But before knowing how many jobs AI will create or destroy, it is necessary to agree on what it actually automates. The answer requires distinguishing three concepts that public debate regularly confuses: employment, skill, and task.

The major waves of automation have followed a remarkably stable logic over two centuries: steam, electricity, industrial robotics have displaced repetitive physical tasks and spared non-routine cognitive work. This empirical regularity has beenformalized by Autor, Levy, and Murnanesince 2003 under the name “task polarization hypothesis”.

A persistent illusion

Automation is eroding intermediate jobs, those of skilled blue-collar workers and office employees performing routine tasks, but spares the two extremes. On one side, non-routine manual tasks, such as plumbing or caregiving; on the other, non-routine cognitive tasks, such as analysis, consulting, or expert writing. The latter constituted the core of skilled tertiary professions, and there was a firmly established belief that they would remain out of reach.

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Also to read:
Why AI forces companies to rethink the value of work

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This conviction was based on a conceptual confusion that must be cleared up above all. It was not the job of lawyer or financial analyst that was protected, but a set of specific tasks that made up this job and that have so far resisted automation. The distinction between these three levels is fundamental.

A job refers to a position held within an organization, with a contract, a salary, and a job description. A skill is a cognitive or technical ability that can be applied in various professional contexts. A task is a specific, definable action, for which it is possible to assess whether it can be automated at a given cost. It is at this third level that the ongoing transformation truly takes place, and it is precisely this level that the public debate ignores.

Break in the long history of industrial capitalism

Generative AI represents a break in this long history. For the first time since industrialization, qualified cognitive tasks—writing, document analysis, synthesis, production of first drafts—are directly exposed.Eloundou, Manning, Mishkin and Rockestimate that about 80% of the active U.S. workforce could see at least 10% of their tasks affected by large language models, and that this exposure increases with salary level. This is the exact opposite of the pattern observed in all previous waves.

The analytical framework developed byAcemoglu and Restrepoallows to go further. Their model distinguishes two opposing effects produced by any wave of automation:

• The displacement effect, first: workers lose tasks to the advantage of the machine, which mechanically reduces the demand for labor and puts pressure on the wages of the affected groups;

• The reintegration effect, then: automation produces new tasks where human value is decisive, generating compensatory demand.

The long history of industrial capitalism can be read as a succession of these two effects, the second generally ending up compensating for the first.

The case of translation allows us to see very concretely how displacement and reintegration combine; generative AI can produce a first draft in another language in a few seconds, which shifts part of the work previously done by human translators to the machine. But this automation simultaneously reintegrates other tasks or strengthens their importance, such as checking for misinterpretations, adapting to cultural context, harmonizing terminology, quality control, and final validation.

Potential imbalance

What is worrisome with generative AI is the potential imbalance between these two dynamics. The displacement occurs at a speed that labor markets and training institutions struggle to absorb, while reintegration still remains largely to be built.

However, the most important phenomenon is not sectoral, but internal to the professions themselves. In its“Employment Outlook,” the OECDhighlights that the professions most exposed to generative AI are precisely those with high cognitive density: finance, law, consulting, higher education. Unlike previous waves that impacted rural areas and industrial regions, the exposure is now stronger in large metropolitan areas and among highly skilled workers, an unprecedented geographical and social reversal.

Redistribute tasks

This reversal takes place concretely at the task level.

In the same position of financial analyst or legal advisor, some tasks move towards AI (producing an executive summary, generating a first contract analysis, synthesizing a literature review), while others mechanically gain value: defining the relevant analytical framework, evaluating the quality of automated reasoning, detecting a factual error in an output, assuming the legal or ethical responsibility of a decision. These are not jobs that disappear. These are bundles of tasks that are redistributed between humans and machines, transforming from within what an employer expects of a qualified employee.

This redistribution of tasks has a direct implication on the skills that will truly be valued in the years to come, and it overturns some of the usual assumptions about professional training.

Train workers to use AI in an instrumental sense, master a tool, writepromptsEffective, mastering an interface is useful in the short term, but it is insufficient if the skill truly required tomorrow is not to produce with AI, but to supervise and critique what it produces.

A training challenge

However, effectively supervising an AI output requires exactly what short technical training programs struggle to develop: a solid general culture that allows detecting a fundamental error, argumentative skills to evaluate the coherence of a reasoning, knowledge of cognitive biases to identify the blind spots of an automated analysis. These are skills thateducation sciences group under the term of meta-skillsTo learn to learn, to exercise critical judgment, to mobilize knowledge in unprecedented situations.

The paradox then becomes the following. As AI automates routine knowledge tasks, it precisely values what generalist training and humanities curricula have long cultivated and what debates on employability have tended to discredit in favor of more immediately measurable technical skills.

Not out of nostalgia for the humanities, but out of pure economic logic. If the machine produces the text, the analysis, and the synthesis, the marginal value of the human lies in their ability to judge whether this text tells the truth, whether this analysis is relevant in light of the real context, whether this synthesis serves the pursued objective.

Hugo Spring-Ragain does not work for, advise, own shares in, or receive funds from any organization that could benefit from this article, and has declared no other affiliation than his research organization.

–ref. Generative AI will not destroy your job but it will profoundly change your profession –https://theconversation.com/lia-generative-will-not-destroy-your-job-but-it-will-profoundly-change-your-profession-279911

Source: French to English Tester   Published on: 2026-04-21

Source: The Conversation – France in French (3)– By Ivette Caldelas, Senior Researcher, UDIT – University of Design, Innovation and Technology

Recent data suggest that the baby receives, through breast milk, a microbial ecosystem, including certain beneficial bacteria that prove essential for its development, particularly in terms of the immune system. Rather than placing the responsibility solely on mothers, this research should inspire public policies to support them, for the benefit of infants.

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We know that thebreast milkis theideal foodfor thenewbornsthanks to the subtle balance of its components: it contains micro and macronutrients, immune and growth factors, as well as hormones essential for the proper development of infants at every stage of their growth.

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Also to read:
Colostrum, first breast milk: benefits, ignorance, and beliefs

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However, recent studies reveal something much deeper: milk does not merely nourish, it also transmits aliving ecosystemto the baby. It contains bacteria, metabolites, and bioactive compounds that can shape the newborn’s health from its very first days of life. These discoveries could transform our understanding of modern pediatrics.

Milk is not sterile: it is biologically active

Just a little over a decade ago, according to the prevailing idea, breast milk was considered a sterile food; any bacterial presence was considered contamination. However, large-scale sequencing studies conducted on milk samples from various species have shown that milk contains complex microbial communities. Among these are bacteria belonging to the generaBifidobacterium sp.,Lactobacillus sp.andStreptococcus sp., which are closely linked to the healthy colonization of the neonatal intestine.

This bacterial transfer occurs at a critical moment, when the development of the newborn’s immune system largely depends on the immune modulation provided by breast milk.This microbiotaprovided by the mother plays an important role in the maturation of the intestinal barrier, the regulation of inflammation, and the metabolic programming of the newborn.

In other words, breast milk does not only provide calories: it also contributes to the development of the immune system.

A biological dialogue between the intestine and the breast

Recent data highlight a fascinating phenomenon, which scientists have called “entero-mammary pathway.” Thanks to this mechanism, certain bacteria present in the maternal intestine would be able to migrate to the mammary gland, where immune cells would act as carriers.

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Also to read:
Dialogue around the microbiota: how the mother and baby communicate through breast milk

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If this were to be fully confirmed – the results obtained in animal models and studies in humansincreasingly support this hypothesis– this would mean that the maternal gut microbiome would be able to directly influence the one present in breast milk. And this raises an unavoidable question: what role does maternal diet really play?

Feeding, modulator of the baby’s first ecosystem

There is no doubt that the composition of the gut microbiome is closely linked to diet.Several studieshave demonstrated that adiet rich in fiber, fruits, vegetables, and legumespromotes greater microbial diversity and the production ofshort-chain fatty acids. These promote intestinal permeability and have anti-inflammatory effects.

Conversely, diets rich in refined sugars or fats are associatedAt a lower bacterial diversity, a reduced presence of beneficial bacteria or an increase in pathogenic bacteria. This leads to an imbalance in the production of metabolites, which promotes the development of inflammation and metabolic complications.

Somescientific studiesindicate a correlation between the quality of the mother’s diet and the bacterial composition of the milk, as well as with the presence of certain metaboliteslipid and immunomodulatory. It was also established that the consumption of fatty acidsomega-3can influence the inflammatory profile and, possibly, the microbial community transmitted to the infant.

Long-term effects

At present, there are no clinical recommendations based on the milk microbiome. However, the scientific consensus tends to indicate that maternal diet may have effects that go beyond nutritional aspects, as it could also modulate the baby’s first intestinal ecosystem and influence its development and health throughout life.

More precisely, theintestinal colonizationearly can have an impact on the later risk of allergies, obesity, metabolic diseases and even disordersneurobehavioral.

Longitudinal studies (conducted over time) suggest that thefirst months of lifeconstitute a critical period of biological programming. This does not mean that breastfeeding is the only determining factor: other factors such as the type of delivery, the use of antibiotics, the family environment, and the social determinants of health also have a decisive influence. Nevertheless, this implies that we are dealing with an aspect of breastfeeding that, until now, has been clearly underestimated.

From scientific data to public policies

Modern pediatrics, which traditionally focused solely on aspects such as nutrition and growth, is beginning to incorporate an ecological perspective. According to this approach, the baby is no longer an isolated organism: it must now be considered as a metaorganism coexisting with billions of microorganisms. There is a bidirectional dialogue between the newborn and the microbiota through the production of specific molecules that impact its development and whether the baby remains healthy or develops a disease. And this mechanism is only beginning to be elucidated.

These new data on the composition of the microbiota in breast milk should not become a new source of pressure on mothers: not all can breastfeed and not all have access to a balanced diet. If science confirms that the mother’s nutritional quality directly influences the microbial colonization of the newborn, the response cannot be an individual responsibility. It must instead translate into policies that facilitate access for women of childbearing age to healthy food, as well as support for breastfeeding and work environments compatible with maternity.

Without a doubt, investing in maternal health also means investing in child health. And now, we know that this investment must also take into account the mother’s nutrition. The invisible — that is to say the bacteria, metabolites, and the interaction between nutrition and the microbiome — could well redefine the medicine of tomorrow.

Juan Pablo Ochoa Romo received funding from the secretariat dedicated to sciences, humanities, technology, and innovation (SECITHI/Mexico).

Ana María Salazar Martínez, Erika Navarrete Monroy, and Ivette Caldelas do not work for, advise, own shares in, or receive funds from any organization that could benefit from this article, and have declared no other affiliations besides their university positions.

–ref. The mother’s diet during breastfeeding could play a key role in her baby’s future health –https://theconversation.com/the-mother’s-diet-during-breastfeeding-could-play-a-key-role-in-her-baby’s-future-health-280878

Source: MIL-OSI-Submissions-English

Source: The Conversation – UK – By Anna Linder, Researcher in Health Economics, Lund University

Schools increasingly rely on testing, grading and performance accountability. In England, Ofsted inspections and school league tables sharpen the focus on measurable performance. Similar developments have taken place in Sweden, where repeated reforms have introduced earlier and more detailed assessments.

Performance-driven school environments shape young people’s wellbeing. Yet despite frequent reforms to evaluation systems, their psychological consequences rarely take centre stage in policy debates.

Our new study connects these trends with rising youth mental health issues. Our research shows that earlier and more formal grading can increase clinically diagnosed mental health problems, particularly among girls.

Our research examined a Swedish reform introduced in 2012 that moved the start of formal grading from grade eight (around age 14) to grade six (around age 12). This meant official grades and clearer signals of relative performance arrived two years earlier than before.

To estimate the effects, we compared children born just before and just after the reform cut-off. Because exposure depended strictly on date of birth, students on either side were similar in background but differed in whether they received earlier grades. We also accounted for certain underlying trends across this time period, such as an overall increase in mental health diagnoses over time. Comparing cohorts in this way allows us to isolate whether earlier grading itself led to changes in mental health diagnoses.

Our analysis draws on nationwide linked education and health registers covering more than 520,000 children born between July 1992 and June 2000. We examined psychiatric diagnoses recorded in outpatient and inpatient care during the year students entered grade nine (the end of lower-secondary school).

Earlier grades affect girls’ mental health

Earlier grading increased diagnoses of depression and anxiety among girls, with the largest effects among girls whose academic achievement ranged from low to average. Effects for boys were smaller and less consistent.

Among girls, the share diagnosed with depression or anxiety increased from 1.4% to 2.0%. While the absolute change (0.6 percentage points) may appear modest, psychiatric diagnoses at this age are relatively uncommon. The change represents roughly a two-fifths increase compared with before the reform.

Our findings point to academic pressure and social comparison as likely reasons for this increase in mental health problems. Formal grades make performance more visible at a younger age, clearly signalling how a child ranks among their peers. At a stage when young people’s understanding of themselves is still developing, this may heighten their sensitivity to comparison and perceived failure.

One plausible explanation is greater sensitivity to performance feedback among girls. In earlier research, we found that when girls received grades more favourable than their measured performance would predict, their mental health improved. This suggests they may be particularly responsive to evaluative feedback, and therefore more vulnerable when grading intensifies.

Wider consequences

Our findings indicate that academic pressure may contribute to gender gaps in adolescent mental health. If girls are more likely to internalise the pressure and stress of academic evaluation, earlier grading may unintentionally widen the well-documented existing gender disparities in mental health.

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Read more:
Making sense of the widening gender mental health gap: what teenage girls told us

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We do not argue that grading is inherently harmful. Grades can motivate, guide learning and inform parents and teachers. But timing and design matter. When evaluation becomes more formal earlier in schooling, unintended psychological costs can emerge alongside academic goals.

As grading systems continue to evolve, questions of timing and intensity deserve careful thought. Schools are not only institutions for measuring performance, but environments where young people form their identities. Designing education systems that support both learning and healthy development requires taking both aims seriously.

Education policy inevitably involves trade-offs. Systems designed to measure and raise standards also shape students’ daily experience. Our findings suggest that when policymakers move formal evaluation to younger ages, they should weigh mental health impacts alongside academic benefits.

Accountability policies should consider psychological effects. This does not mean abandoning grading, but evaluation systems should be sensitive to the development stage of students and accompanied by relevant support that helps students interpret feedback constructively.

Students respond differently to evaluation. Reforms that work well for some may create strain for others, particularly those already vulnerable to performance pressure. Monitoring wellbeing alongside academic outcomes can help identify unintended consequences early.

Anna Linder receives funding from the Swedish Research Council for Health, Working Life and Welfare and the Public Health Agency of Sweden.

Gawain Heckley currently receives funding from Swedish Research Council , Swedish Research Council for Health, Working Life and Welfare and Jan Wallanders och Tom Hedelius stiftelse.

Ulf Gerdtham receives funding from Swedish Council for Working Life and Social Research.

– ref. How school grades can affect mental health – particularly for girls – https://theconversation.com/how-school-grades-can-affect-mental-health-particularly-for-girls-277907

Source: MIL-OSI-Submissions-English

Source: The Conversation – USA – By Anne Toomey McKenna, Affiliated Faculty Member, Institute for Computational and Data Sciences, Penn State

On a Saturday morning, you head to the hardware store. Your neighbors’ Ring cameras film your walk to the car. Your car’s sensors, cameras and microphones record your speed, how you drive, where you’re going, who’s with you, what you say, and biological metrics such as facial expression, weight and heart rate. Your car may also collect text messages and contacts from your connected smartphone.

Meanwhile, your phone continuously senses and records your communications, info about your health, what apps you’re using, and tracks your location via cell towers, GPS satellites and Wi-Fi and Bluetooth.

As you enter the store, its surveillance cameras identify your face and track your movements through the aisles. If you then use Apple or Google Pay to make your purchase, your phone tracks what you bought and how much you paid.

All this data quickly becomes commercially available, bought and sold by data brokers. Aggregated and analyzed by artificial intelligence, the data reveals detailed, sensitive information about you that can be used to predict and manipulate your behavior, including what you buy, feel, think and do.

Companies unilaterally collect data from most of your activities. This “surveillance capitalism” is often unrelated to the services device manufacturers, apps and stores are providing you. For example, Tinder is planning to use AI to scan your entire camera roll. And despite their promises, “opting out” doesn’t actually stop companies’ data collection.

While companies can manipulate you, they cannot put you in jail. But the U.S. government can, and it now purchases massive quantities of your information from commercial data brokers. The government is able to purchase Americans’ sensitive data because the information it buys is not subject to the same restrictions as information it collects directly.

The federal government is also ramping up its abilities to directly collect data through partnerships with private tech companies. These surveillance tech partnerships are becoming entrenched, domestically and abroad, as advances in AI take surveillance to unprecedented levels.

As a privacy, electronic surveillance and tech law attorney, author and legal educator, I have spent years researching, writing and advising about privacy and legal issues related to surveillance and data use. To understand the issues, it is critical to know how these technologies function, who collects what data about you, how that data can be used against you, and why the laws you might think are protecting your data do not apply or are ignored.

Big money for AI-driven tech and more data

Congressional funding is supercharging huge government investments in surveillance tech and data analytics driven by AI, which automates analysis of very large amounts of data. The massive 2025 tax-and-spending law netted the Department of Homeland Security an unprecedented US$165 billion in yearly funding. Immigration and Customs Enforcement, part of DHS, got about $86 billion.

Disclosure of documents allegedly hacked from Homeland Security reveal a massive surveillance web that has all Americans in its scope.

DHS is expanding its AI surveillance capabilities with a surge in contracts to private companies. It is reportedly funding companies that provide more AI-automated surveillance in airports; adapters to convert agents’ phones into biometric scanners; and an AI platform that acquires all 911 call center data to build geospatial heat maps to predict incident trends. Predicting incident trends can be a form of predictive policing, which uses data to anticipate where, when and how crime may occur.

DHS has also spent millions on AI-driven software used to detect sentiment and emotion in users’ online posts. Have you been complaining about Immigration and Customs Enforcement policies online? If so, social media companies including Google, Reddit, Discord, and Facebook and Instagram owner Meta may have sent identifying data, such as your name, email address, phone number and activity, to DHS in response to hundreds of DHS subpoenas served on the companies.

Meanwhile, the Trump administration’s national policy framework for artificial intelligence, released on March 20, 2026, urges Congress to use grants and tax incentives to fund “wider deployment of AI tools across American industry” and to allow industry and academia to use federal datasets to train AI.

Using federal datasets this way raises privacy law concerns because they contain a lifetime of sensitive details about you, including biographical, employment and tax information.

Blurring lines and little oversight

In foreign intelligence work, the funding, development and controlled use of certain AI-driven gathering of data makes sense. The CIA’s new acquisition framework to turbocharge collaboration with the private sector may be legal with proper oversight. But the line between collaborating for lawful national security purposes versus unlawful domestic spying is becoming dangerously blurred or ignored.

For example, the Pentagon has declared a contractor, Anthropic, a national security risk because Anthropic insisted that its powerful agentic AI model, Claude, not be used for mass domestic surveillance of Americans or fully autonomous weapons.

On March 18, 2026, FBI Director Kash Patel confirmed to Congress that the FBI is buying Americans’ data from data brokers, including location histories, to track American citizens.

As the federal government accelerates the use of and investment in AI-driven spy tech, it is mandating less oversight around AI technology. In addition to the national AI policy framework, which discourages state regulation of AI, the president has issued executive orders to accelerate federal government adoption of AI systems, remove state law AI regulation barriers and require that the federal government not procure the use of AI models that attempt to adjust for bias. But using advanced AI systems is risky, given reports of AI agents going rogue, exposing sensitive data and becoming a threat, even during routine tasks.

Your data

The surveillance capitalism system requires people to unwittingly participate in a manipulative cycle of group- and self-surveillance. Neighborhood doorbell cameras, Flock license plate readers and hyperlocal social media sites like Nextdoor create a crowdsourced record of all people’s movements in public spaces.

Sensors in phones and wearable devices, such as earbuds and rings, collect ever more sensitive details. These include health data, including your heart rate and heart rate variability, blood oxygen, sweat and stress levels, behavioral patterns, neurological changes and even brain waves. Smartphones can be used to diagnose, assess and treat Parkinson’s disease. Earbuds could be used to monitor brain health.

This data is not protected under HIPAA, which prohibits health care providers and those working with them from disclosing your health information without your permission, because the law does not consider tech companies to be health care providers nor these wearables to be medical devices.

Legal protections

People have little choice when buying devices, using apps or opening accounts but to agree to lengthy terms that include consent for companies to collect and sell their personal data. This “consent” allows their data to end up in the largely unregulated commercial data market.

The government claims it can lawfully purchase this data from data brokers. But in buying your data in bulk on the commercial market, the government is circumventing the Constitution, Supreme Court decisions and federal laws designed to protect your privacy from unwarranted government overreach.

The Fourth Amendment prohibits unreasonable search and seizure by the government. Supreme Court cases require police to get a warrant to search a phone or use cellular or GPS location information to track someone. The Electronic Communications Privacy Act’s Wiretap Act prohibits unauthorized interception of wire, oral and electronic communications.

Despite some efforts, Congress has failed to enact legislation to protect data privacy, the use of sensitive data by AI systems or to restore the intent of the Electronic Communications Privacy Act. Courts have allowed the broad electronic privacy protections in the federal Wiretap Act to be eviscerated by companies claiming consent.

In my opinion, the way to begin to address these problems is to restore the Wiretap Act and related laws to their intended purposes of protecting Americans’ privacy in communications, and for Congress to follow through on its promises and efforts by passing legislation that secures Americans’ data privacy and protects them from AI harms.

This article is part of a series on data privacy that explores who collects your data, what and how they collect, who sells and buys your data, what they all do with it, and what you can do about it.

Anne Toomey McKenna serves on the Advisory Board to the Institute for Electrical and Electronics Engineers (IEEE)-USA’s Artificial Intelligence Policy Committee (AIPC) and Chairs multiple AIPC subcommittees. The AIPC work involves subject matter and education-related interaction with U.S. Senate and House congressional staffers and the Congressional AI Caucus. McKenna has received funding from the National Security Agency for the development of legal educational materials about cyberlaw (a course which the government still makes available online for the public) and funding from The National Police Foundation together with the U.S. Department of Justice-COPS division for legal analysis regarding the use of drones in domestic policing.

– ref. US government ramps up mass surveillance with help of AI tech, data brokers – and your apps and devices – https://theconversation.com/us-government-ramps-up-mass-surveillance-with-help-of-ai-tech-data-brokers-and-your-apps-and-devices-277440