Future of work Archives | Page 6 of 210 | Digital Watch Observatory

Romania’s job market faces structural change as AI and automation rise

A Think by ING analysis finds that Romania’s recent macroeconomic slowdown reflects more profound structural change than cyclical weakness.

After years of robust consumption-led expansion, fiscal tightening and weak domestic demand have curbed growth, while firms increasingly invest in automation and AI to boost productivity rather than expand headcount.

Industrial employment has declined; for example, manufacturing jobs fell by around 25,000 in late 2025, and labour market hiring has shifted toward defensive, replacement-only patterns.

Firms are integrating robotics, automated assembly lines and intelligent logistics systems, and service-sector work is also being reshaped by AI tools, even where formal adoption is still emerging.

A recent survey suggests that 68% of people in Romania have used AI tools, and 44% rely on them for work tasks such as administrative support and analysis, signalling rising informal use ahead of widespread enterprise deployment.

While automation and AI can raise productivity and output without proportional employment growth, they also tilt the labour market: high-skill specialised roles (e.g. AI, engineering, advanced management) are expected to remain resilient or grow, while routine roles, including some entry-level tech positions, call-centre jobs and administrative tasks, face stagnation or decline.

However, this can create a ‘barbell’ labour market with growth chiefly at the high and low ends, and limited opportunities in mid-skill roles.

Real wage erosion, tight hiring and demographic trends (including a shrinking workforce) add to short-term challenges. In the near term, employment may remain subdued even as economic output recovers modestly by 2027.

Over the longer term, the economy’s shift toward capital-intensive, productivity-driven growth could support stronger output without generating broad employment, underscoring the need for education, reskilling and policy strategies that help workers adapt to AI-driven labour demand.

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AI-generated film removed from cinemas after public backlash

A prize-winning AI-generated short film has been pulled from cinemas following criticism from audiences. Thanksgiving Day, created by filmmaker Igor Alferov, was due to screen in selected theatres before feature presentations.

Concerns emerged after news of the screening spread online, prompting complaints directed at AMC Theatres. The chain stated it had not programmed the film and that pre-show advertising partner Screenvision Media had arranged the placement.

AMC confirmed it would not participate in the initiative, meaning the AI film will no longer appear in its locations. The animated short, produced using Google’s Gemini 3.1 and Nano Banana Pro tools, had recently won an AI film festival award.

The episode comes amid broader debate about artificial intelligence in Hollywood. Industry insiders suggest studios are quietly increasing AI use in production, even as concerns grow over job losses and economic uncertainty within Los Angeles’ entertainment sector.

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Commission delays high risk AI guidance

The European Commission has confirmed it will again delay publishing guidance on high-risk AI systems under the EU AI Act. The guidelines were due by 2 February 2026, but will now follow a revised timeline.

According to Euractiv, the document is intended to clarify which AI systems fall into the high-risk category and therefore face stricter obligations. Officials said more time is needed to incorporate significant stakeholder feedback.

The delay marks the second missed deadline and adds to broader implementation setbacks surrounding the EU AI Act. Several member states have yet to designate national enforcement bodies, complicating oversight preparations.

Brussels is also considering postponing the application of high-risk rules through a digital simplification package. Parliament and Council appear supportive of moving the August deadline back by more than a year, easing pressure on companies awaiting guidance.

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IQM puts Finland on Europe’s quantum computing map

Finland is emerging as a key hub in Europe’s quantum computing landscape as startup IQM prepares to become one of the continent’s first publicly listed quantum firms.

The company is developing full-stack, open-architecture quantum systems designed for on-premise deployment or cloud access. It aims to advance the practical use of quantum computing across research and industry.

Founded in 2018, IQM has already delivered 21 quantum systems to 13 customers, highlighting growing European interest in commercial quantum technologies.

Analysts note that while challenges remain, meaningful breakthroughs are now occurring, signalling that quantum computing is shifting from purely experimental science to an operational industry.

IQM’s technology could support advancements in medicine, science, and computational research, enabling the solution to complex problems far beyond the reach of classical computers.

The firm exemplifies Europe’s ambition to build quantum capabilities independently of larger players in the US and China, positioning Finland as a strategic hub for next-generation computing.

The company’s work aligns with broader European efforts to foster innovation in quantum technologies.

By combining domestic expertise with open-access systems, IQM demonstrates how Finland is contributing to the continent’s emerging quantum ecosystem, bridging academic research and industrial application.

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AI-driven physics speeds up industrial innovation

PhysicsX, a London-based startup founded by former F1 engineers and AI experts, is redefining engineering with its AI-driven physics platform.

Design and testing cycles are reduced from weeks or months to seconds. Engineers can now iterate rapidly and optimise systems across multiple industries, including aerospace, automotive, semiconductors, energy, and materials.

The technology enables teams to evaluate thousands of design variations simultaneously. Semiconductor firms speed up prototype development, electronics improve thermal performance, and mining boosts copper recovery for renewable energy and AI data centres.

PhysicsX achieves this using Large Physics Models and Large Geometry Models that base design evaluation on real-world physics rather than assumptions.

Predictive reasoning lets engineers simulate multiple parameter changes before acting. The approach shifts control from reactive adjustments to proactive optimisation, helping teams make faster, better-informed decisions.

PhysicsX also bridges disciplinary divides, enabling aerodynamics, structural, and thermal considerations to be optimised together rather than in isolation.

By combining speed, system-level insight, and predictive control, PhysicsX is shrinking the gap between cutting-edge research and practical industrial impact. The platform uses physics-based AI to improve efficiency, drive innovation, and support sustainable growth.

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AI drives faster modernisation of legacy COBOL systems

Critical to finance, airlines, and government, COBOL handles about 95% of US ATM transactions. Despite its ubiquity, the pool of developers able to read and maintain COBOL is shrinking as seasoned engineers retire and universities offer limited instruction.

Institutional knowledge is now embedded in decades-old code, and documentation often lags.

Modernising COBOL differs from typical software updates. It requires untangling intricate dependencies and reverse-engineering business logic that has evolved over decades.

Traditional modernisation efforts involved large teams of consultants over the years, resulting in high costs and lengthy timelines. AI tools are changing that paradigm by automating the most labour-intensive tasks.

AI-driven solutions like Claude Code map code dependencies, trace execution paths, document workflows, and identify risks. They provide teams with actionable insights for prioritisation, risk management, and refactoring, dramatically shortening modernisation timelines from years to months.

Human experts remain essential to reviewing AI recommendations, ensuring regulatory compliance, and making strategic decisions about which components to modernise first.

Implementation follows an incremental approach. AI translates COBOL logic into modern languages, creates integration scaffolding, and supports side-by-side operation with legacy components.

Continuous validation at each step reduces risk, allowing teams to build confidence as complex parts of the system are modernised. AI automation combined with expert oversight makes large-scale COBOL modernisation feasible.

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AI data centre surge pushes electricity demand in the UK to new heights

The UK faces rising pressure on its electricity system as about 140 new data centre projects could demand more power than the country’s current peak consumption, according to Ofgem.

The regulator said developers are seeking about 50 gigawatts of capacity, a level driven by rapid growth in AI and far beyond earlier forecasts.

Connection requests have surged since late 2024, placing strain on a grid already struggling to support vital renewable projects that are key to national climate targets.

Work needed to connect expanding data centre capacity could delay schemes considered essential for decarbonisation and economic growth, instead of supporting the transition at the required pace.

The growing electricity footprint of AI infrastructure also threatens the aim of creating a virtually carbon-free power system by 2030, particularly as high costs and slow grid integration continue to hinder progress.

A proposed data centre in Lincolnshire has already raised concerns by projecting emissions greater than those of several international airports combined.

Ofgem now warns that speculative grid applications are blocking more viable projects, including those tied to government AI growth zones.

The regulator is considering more stringent financial requirements and new fees for access to grid connections, arguing that developers may need to build their own routes to the network rather than rely entirely on existing infrastructure.

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AWS warns of AI powered cybercrime

Amazon Web Services has revealed that a Russian-speaking threat actor used commercial AI tools to compromise more than 600 FortiGate firewalls across 55 countries. AWS described the campaign as an AI-powered assembly line for cybercrime.

According to AWS, the attacker relied on exposed management ports and weak single-factor credentials rather than exploiting software vulnerabilities. The campaign targeted FortiGate devices globally and focused on harvesting credentials and configuration data.

AWS said the potentially Russian group appeared unsophisticated but achieved scale through AI-assisted mass scanning and automation. When encountering stronger defences, the attackers reportedly shifted to easier targets rather than persist.

The company advised organisations using FortiGate appliances to secure management interfaces, change default credentials and enforce complex passwords. Amazon said it was not compromised during the campaign.

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Wikipedia removes Archive.today links

Wikipedia editors have voted to remove all links to Archive.today, citing allegations that the web archive was involved in a distributed denial of service attack.

Editors said Archive.today, which also operates under domains such as archive.is and archive.ph, should not be linked because it allegedly used visitors’ browsers to target blogger Jani Patokallio. The site has also been accused of altering archived pages, raising concerns about reliability.

Archive.today had previously been blacklisted in 2013 before being reinstated in 2016. Wikipedia’s latest guidance calls for replacing Archive.today links with original sources or alternative archives such as the Wayback Machine.

The apparent owner of Archive.today denied wrongdoing in posts linked from the site and suggested the controversy had been exaggerated. Wikipedia editors nevertheless concluded that readers should not be directed to a service facing such allegations.

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OpenAI model revises proof claim

OpenAI has published its attempts to solve all 10 problems in the First Proof challenge, a research-level maths test designed to assess whether AI can produce checkable, domain-specific proofs. Leading experts created the issues and require extended reasoning rather than short answers.

The company said at least five of its proof attempts are likely correct following expert feedback, although one previously confident submission has now been judged incorrect. Several other attempts remain under review as specialists continue to assess the arguments.

According to OpenAI, the evaluation involved limited human supervision, with researchers sometimes prompting the model to refine or clarify reasoning. The process included exchanges between an internal model and ChatGPT for verification, formatting and style adjustments.

OpenAI described frontier research challenges, such as First Proof, as crucial for testing next-generation AI systems. The company said it plans to deepen its engagement with academics to develop more rigorous evaluation frameworks for research-grade reasoning.

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