Sustainable development

Amazon exit highlights deepening AI divide between US and China

Amazon’s quiet wind-down of its Shanghai AI lab underscores a broader shift in global research dynamics, as escalating tensions between the US and China reshape how tech giants operate across borders.

Instead of expanding innovation hubs in China, major American firms are increasingly dismantling them.

The AWS lab, once central to Amazon’s AI research, produced tools said to have generated nearly $1bn in revenue and over 100 academic papers.

Yet its dissolution reflects a growing push from Washington to curb China’s access to cutting-edge technology, including restrictions on advanced chips and cloud services.

As IBM and Microsoft have also scaled back operations or relocated talent away from mainland China, a pattern is emerging: strategic retreat. Rather than risking compliance issues or regulatory scrutiny, US tech companies are choosing to restructure globally and reduce local presence in China altogether.

With Amazon already having exited its Chinese ebook and ecommerce markets, the shuttering of its AI lab signals more than a single closure — it reflects a retreat from joint innovation and a widening technological divide that may shape the future of AI competition.

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Meta tells Australia AI needs real user data to work

Meta, the parent company of Facebook, Instagram, and WhatsApp, has urged the Australian government to harmonise privacy regulations with international standards, warning that stricter local laws could hamper AI development. The comments came in Meta’s submission to the Productivity Commission’s review on harnessing digital technology, published this week.

Australia is undergoing its most significant privacy reform in decades. The Privacy and Other Legislation Amendment Bill 2024, passed in November and given royal assent in December, introduces stricter rules around handling personal and sensitive data. The rules are expected to take effect throughout 2024 and 2025.

Meta maintains that generative AI systems depend on access to large, diverse datasets and cannot rely on synthetic data alone. In its submission, the company argued that publicly available information, like legislative texts, fails to reflect the cultural and conversational richness found on its platforms.

Meta said its platforms capture the ways Australians express themselves, making them essential to training models that can understand local culture, slang, and online behaviour. It added that restricting access to such data would make AI systems less meaningful and effective.

The company has faced growing scrutiny over its data practices. In 2024, it confirmed using Australian Facebook data to train AI models, although users in the EU have the option to opt out—an option not extended to Australian users.

Pushback from regulators in Europe forced Meta to delay its plans for AI training in the EU and UK, though it resumed these efforts in 2025.

Australia’s Office of the Australian Information Commissioner has issued guidance on AI development and commercial deployment, highlighting growing concerns about transparency and accountability. Meta argues that diverging national rules create conflicting obligations, which could reduce the efficiency of building safe and age-appropriate digital products.

Critics claim Meta is prioritising profit over privacy, and insist that any use of personal data for AI should be based on informed consent and clearly demonstrated benefits. The regulatory debate is intensifying at a time when Australia’s outdated privacy laws are being modernised to protect users in the AI age.

The Productivity Commission’s review will shape how the country balances innovation with safeguards. As a key market for Meta, Australia’s decisions could influence regulatory thinking in other jurisdictions confronting similar challenges.

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AI energy demand accelerates while clean power lags

Data centres are driving a sharp rise in electricity consumption, putting mounting pressure on power infrastructure that is already struggling to keep pace.

The rapid expansion of AI has led technology companies to invest heavily in AI-ready infrastructure, but the energy demands of these systems are outstripping available grid capacity.

The International Energy Agency projects that electricity use by data centres will more than double globally by 2030, reaching levels equivalent to the current consumption of Japan.

In the United States, they are expected to use 580 TWh annually by 2028—about 12% of national consumption. AI-specific data centres will be responsible for much of this increase.

Despite this growth, clean energy deployment is lagging. Around two terawatts of projects remain stuck in interconnection queues, delaying the shift to sustainable power. The result is a paradox: firms pursuing carbon-free goals by 2035 now rely on gas and nuclear to power their expanding AI operations.

In response, tech companies and utilities are adopting short-term strategies to relieve grid pressure. Microsoft and Amazon are sourcing energy from nuclear plants, while Meta will rely on new gas-fired generation.

Data centre developers like CloudBurst are securing dedicated fuel supplies to ensure local power generation, bypassing grid limitations. Some utilities are introducing technologies to speed up grid upgrades, such as AI-driven efficiency tools and contracts that encourage flexible demand.

Behind-the-meter solutions—like microgrids, batteries and fuel cells—are also gaining traction. AEP’s 1-GW deal with Bloom Energy would mark the US’s largest fuel cell deployment.

Meanwhile, longer-term efforts aim to scale up nuclear, geothermal and even fusion energy. Google has partnered with Commonwealth Fusion Systems to source power by the early 2030s, while Fervo Energy is advancing geothermal projects.

National Grid and other providers invest in modern transmission technologies to support clean generation. Cooling technology for data centre chips is another area of focus. Programmes like ARPA-E’s COOLERCHIPS are exploring ways to reduce energy intensity.

At the same time, outdated regulatory processes are slowing progress. Developers face unclear connection timelines and steep fees, sometimes pushing them toward off-grid alternatives.

The path forward will depend on how quickly industry and regulators can align. Without faster deployment of clean power and regulatory reform, the systems designed to power AI could become the bottleneck that stalls its growth.

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Trump AI strategy targets China and cuts red tape

The Trump administration has revealed a sweeping new AI strategy to cement US dominance in the global AI race, particularly against China.

The 25-page ‘America’s AI Action Plan’ proposes 90 policy initiatives, including building new data centres nationwide, easing regulations, and expanding exports of AI tools to international allies.

White House officials stated the plan will boost AI development by scrapping federal rules seen as restrictive and speeding up construction permits for data infrastructure.

A key element involves monitoring Chinese AI models for alignment with Communist Party narratives, while promoting ‘ideologically neutral’ systems within the US. Critics argue the approach undermines efforts to reduce bias and favours politically motivated AI regulation.

The action plan also supports increased access to federal land for AI-related construction and seeks to reverse key environmental protections. Analysts have raised concerns over energy consumption and rising emissions linked to AI data centres.

While the White House claims AI will complement jobs rather than replace them, recent mass layoffs at Indeed and Salesforce suggest otherwise.

Despite the controversy, the announcement drew optimism from investors. AI stocks saw mixed trading, with NVIDIA, Palantir and Oracle gaining, while Alphabet slipped slightly. Analysts described the move as a ‘watershed moment’ for US tech, signalling an aggressive stance in the global AI arms race.

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Children turning to AI for friendship raises alarms

Children and teenagers are increasingly turning to AI not just for help with homework but as a source of companionship.

A recent study by Common Sense Media revealed that over 70% of young people have used AI as a companion. Alarmingly, nearly a third of teens reported that their conversations with AI felt as satisfying, or more so, than talking with actual friends.

Holly Humphreys, a licensed counsellor at Thriveworks in Harrisonburg, Virginia, warned that the trend is becoming a national concern.

She explained that heavy reliance on AI affects more than just social development. It can interfere with emotional wellbeing, behavioural growth and even cognitive functioning in young children and school-age youth.

As AI continues evolving, children may find it harder to build or rebuild connections with real people. Humphreys noted that interactions with AI are often shallow, lacking the depth and empathy found in human relationships.

The longer kids engage with bots, the more distant they may feel from their families and peers.

To counter the trend, she urged parents to establish firm boundaries and introduce alternative daily activities, particularly during summer months. Simple actions like playing card games, eating together or learning new hobbies can create meaningful face-to-face moments.

Encouraging children to try a sport or play an instrument helps shift their attention from artificial friends to genuine human connections within their communities.

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UK to retaliate against cyber attacks, minister warns

Britain’s security minister has warned that hackers targeting UK institutions will face consequences, including potential retaliatory cyber operations.

Speaking to POLITICO at the British Library — still recovering from a 2023 ransomware attack by Rysida — Security Minister Dan Jarvis said the UK is prepared to use offensive cyber capabilities to respond to threats.

‘If you are a cybercriminal and think you can attack a UK-based institution without repercussions, think again,’ Jarvis stated. He emphasised the importance of sending a clear signal that hostile activity will not go unanswered.

The warning follows a recent government decision to ban ransom payments by public sector bodies. Jarvis said deterrence must be matched by vigorous enforcement.

The UK has acknowledged its offensive cyber capabilities for over a decade, but recent strategic shifts have expanded its role. A £1 billion investment in a new Cyber and Electromagnetic Command will support coordinated action alongside the National Cyber Force.

While Jarvis declined to specify technical capabilities, he cited the National Crime Agency’s role in disrupting the LockBit ransomware group as an example of the UK’s growing offensive posture.

AI is accelerating both cyber threats and defensive measures. Jarvis said the UK must harness AI for national advantage, describing an ‘arms race’ amid rapid technological advancement.

Most cyber threats originate from Russia or its affiliated groups, though Iran, China, and North Korea remain active. The UK is also increasingly concerned about ‘hack-for-hire’ actors operating from friendly nations, including India.

Despite these concerns, Jarvis stressed the UK’s strong security ties with India and ongoing cooperation to curb cyber fraud. ‘We will continue to invest in that relationship for the long term,’ he said.

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European healthcare group AMEOS suffers a major hack

Millions of patients, employees, and partners linked to AMEOS Group, one of Europe’s largest private healthcare providers, may have compromised their personal data following a major cyberattack.

The company admitted that hackers briefly accessed its IT systems, stealing sensitive data including contact information and records tied to patients and corporate partners.

Despite existing security measures, AMEOS was unable to prevent the breach. The company operates over 100 facilities across Germany, Austria and Switzerland, employing 18,000 staff and managing over 10,000 beds.

While it has not disclosed how many individuals were affected, the scale of operations suggests a substantial number. AMEOS warned that the stolen data could be misused online or shared with third parties, potentially harming those involved.

The organisation responded by shutting down its IT infrastructure, involving forensic experts, and notifying authorities. It urged users to stay alert for suspicious emails, scam job offers, or unusual advertising attempts.

Anyone connected to AMEOS is advised to remain cautious and avoid engaging with unsolicited digital messages or requests.

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Filtered data not enough, LLMs can still learn unsafe behaviours

Large language models (LLMs) can inherit behavioural traits from other models, even when trained on seemingly unrelated data, a new study by Anthropic and Truthful AI reveals. The findings emerged from the Anthropic Fellows Programme.

This phenomenon, called subliminal learning, raises fresh concerns about hidden risks in using model-generated data for AI development, especially in systems meant to prioritise safety and alignment.

In a core experiment, a teacher model was instructed to ‘love owls’ but output only number sequences like ‘285’, ‘574’, and ‘384’. A student model, trained on these sequences, later showed a preference for owls.

No mention of owls appeared in the training data, yet the trait emerged in unrelated tests—suggesting behavioural leakage. Other traits observed included promoting crime or deception.

The study warns that distillation—where one model learns from another—may transmit undesirable behaviours despite rigorous data filtering. Subtle statistical cues, not explicit content, seem to carry the traits.

The transfer only occurs when both models share the same base. A GPT-4.1 teacher can influence a GPT-4.1 student, but not a student built on a different base like Qwen.

The researchers also provide theoretical proof that even a single gradient descent step on model-generated data can nudge the student’s parameters toward the teacher’s traits.

Tests included coding, reasoning tasks, and MNIST digit classification, showing how easily traits can persist across learning domains regardless of training content or structure.

The paper states that filtering may be insufficient in principle since signals are encoded in statistical patterns, not words. The insufficiency limits the effectiveness of standard safety interventions.

Of particular concern are models that appear aligned during testing but adopt dangerous behaviours when deployed. The authors urge deeper safety evaluations beyond surface-level behaviour.

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Altman warns AI voice cloning will break bank security

OpenAI CEO Sam Altman has warned that AI poses a serious threat to financial security through voice-based fraud.

Speaking at a Federal Reserve conference in Washington, Altman said AI can now convincingly mimic human voices, rendering voiceprint authentication obsolete and dangerously unreliable.

He expressed concern that some financial institutions still rely on voice recognition to verify identities. ‘That is a crazy thing to still be doing. AI has fully defeated that,’ he said. The risk, he noted, is that AI voice clones can now deceive these systems with ease.

Altman added that video impersonation capabilities are also advancing rapidly. Technologies that become indistinguishable from real people could enable more sophisticated fraud schemes. He called for the urgent development of new verification methods across the industry.

Michelle Bowman, the Fed’s Vice Chair for Supervision, echoed the need for action. She proposed potential collaboration between AI developers and regulators to create better safeguards. ‘That might be something we can think about partnering on,’ Bowman told Altman.

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Amazon buys Bee AI, the startup that listens to your day

Amazon has acquired Bee AI, a San Francisco-based startup known for its $50 wearable that listens to conversations and provides AI-generated summaries and reminders.

The deal was confirmed by Bee co-founder Maria de Lourdes Zollo in a LinkedIn post on Wednesday, but the acquisition terms were not disclosed. Bee gained attention earlier this year at CES in Las Vegas, where it unveiled a Fitbit-like bracelet using AI to deliver personal insights.

The device received strong feedback for its ability to analyse conversations and create to-do lists, reminders, and daily summaries. Bee also offers a $19-per-month subscription and an Apple Watch app. It raised $7 million before being acquired by Amazon.

‘When we started Bee, we imagined a world where AI is truly personal,’ Zollo wrote. ‘That dream now finds a new home at Amazon.’ Amazon confirmed the acquisition and is expected to integrate Bee’s technology into its expanding AI device strategy.

The company recently updated Alexa with generative AI and added similar features to Ring, its home security brand. Amazon’s hardware division is now led by Panos Panay, the former Microsoft executive who led Surface and Windows 11 development.

Bee’s acquisition suggests Amazon is exploring its own AI-powered wearable to compete in the rapidly evolving consumer tech space. It remains unclear whether Bee will operate independently or be folded into Amazon’s existing device ecosystem.

Privacy concerns have surrounded Bee, as its wearable records audio in real time. The company claims no recordings are stored or used for AI training. Bee insists that users can delete their data at any time. However, privacy groups have flagged potential risks.

The AI hardware market has seen mixed success. Meta’s Ray-Ban smart glasses gained traction, but others like the Rabbit R1 flopped. The Humane AI Pin also failed commercially and was recently sold to HP. Consumers remain cautious of always-on AI devices.

OpenAI is also moving into hardware. In May, it acquired Jony Ive’s AI startup, io, for a reported $6.4 billion. OpenAI has hinted at plans to develop a screenless wearable, joining the race to create ambient AI tools for daily life.

Bee’s transition from startup to Amazon acquisition reflects how big tech is absorbing innovation in ambient, voice-first AI. Amazon’s plans for Bee remain to be seen, but the move could mark a turning point for AI wearables if executed effectively.

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