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South Korea prioritises AI and semiconductor investment in the 2027 budget

South Korea plans to introduce a record national budget exceeding KRW 800 trillion (around €500 billion) in 2027, with semiconductors, AI and youth employment at the centre of its investment strategy.

Announced during the National Fiscal Strategy Meeting, the proposed budget would increase by more than 10% compared with 2026, reflecting the government’s focus on strengthening industrial competitiveness, technological leadership and long-term economic growth.

A significant share of the funding will support three flagship initiatives focused on semiconductors, AI data centres and physical AI technologies.

The government also plans to accelerate the development of Yongin and the Honam region as major semiconductor manufacturing hubs through administrative measures including fast-track licensing and exemptions from preliminary feasibility studies for strategic projects.

Beyond industrial policy, the budget includes measures aimed at supporting citizens directly. A new Future Response Fund will finance the training of 200,000 young professionals, help create around 300,000 jobs and improve housing stability.

South Korea also plans to expand employment insurance and workers’ compensation coverage for platform workers while establishing a new K-Labour Council to strengthen labour protections.

To address fiscal sustainability, the government announced a comprehensive review of public spending aimed at generating around KRW 50 trillion in efficiency savings, described as the largest restructuring of government expenditure in the country’s history.

According to the government, the combination of strategic investment and spending reforms is intended to promote innovation while maintaining long-term fiscal sustainability.

Why does it matter?

The budget demonstrates how industrial policy is becoming a central tool for strengthening technological competitiveness. By prioritising semiconductors, AI infrastructure and advanced manufacturing, South Korea is seeking to reinforce its position in sectors that are increasingly viewed as critical to economic growth and national security.

The package also shows that governments are increasingly pairing technology investment with workforce development and labour reforms. Building AI and semiconductor capacity will require not only infrastructure and capital but also a skilled workforce capable of supporting long-term innovation.

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Nobel laureates call for urgent AI economic planning

Sixteen Nobel laureates have joined leading economists and AI researchers in calling for urgent preparation for the economic changes that more powerful AI systems could trigger.

The statement, titled We Must Act Now: A Statement on AI’s Transformation of the Economy, was released by the Stanford Digital Economy Lab.

It was organised by economists Erik Brynjolfsson, Ajay Agrawal, Anton Korinek and Tom Cunningham, and has been signed by more than 200 experts.

The statement warns that AI may become radically more powerful over the next decade, potentially driving an economic transformation larger than the Industrial Revolution but unfolding over a much shorter period.

The signatories say AI could bring major gains in living standards, but also risks, including large-scale job displacement.

They argue that economists, policymakers and technology leaders must act now to understand these impacts and prepare society for the transition.

The statement calls for incentives, guardrails and institutions that steer AI towards complementing human labour and benefiting society.

Its authors stress that the economic outcome of AI is not predetermined and will depend on choices made by governments, companies and researchers.

Why does it matter?

The statement adds weight to the debate over AI’s economic impact because it brings together Nobel laureates, economists, AI researchers and technology leaders around a common warning: societies may have far less time to adapt to AI than they had during earlier technological shifts. Its central message is not that job displacement is inevitable, but that policy choices made now will shape whether AI raises living standards broadly or concentrates economic power and leaves many workers behind.

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Google open-sources k8s-aibom to detect shadow AI

Google has open-sourced k8s-aibom, a lightweight Kubernetes controller designed to detect unregistered AI workloads and generate standardised inventories of the AI models, runtimes and frameworks operating inside a cluster.

The tool targets shadow AI: workloads deployed by developers without formal registration or integration with an organisation’s security and governance systems. Such deployments can evade conventional security scanners, particularly where organisations avoid privileged agents, kernel-level access or manual changes to Kubernetes workloads.

Google says k8s-aibom addresses that gap by continuously monitoring Kubernetes APIs and container environments. It detects running AI components and generates CycloneDX 1.6 Machine Learning Bills of Materials (ML-BOMs) based on what is actually executing, rather than what was intended during the build process.

The controller runs as a single unprivileged deployment in the k8s-aibom-system namespace. It does not require sidecars, eBPF modules, privileged DaemonSets or modifications to developers’ continuous integration and deployment pipelines.

The controller monitors KServe resources, deployments, StatefulSets, DaemonSets and jobs across a cluster. It then analyses container images, environment variables and command-line arguments to identify different categories of AI workloads.

Supported systems include inference runtimes such as vLLM, Triton Inference Server, TGI, and Ollama; agent frameworks including LangChain, AutoGen, and CrewAI; retrieval and vector database tools such as Milvus, Qdrant, and pgvector; and distributed training and evaluation workloads.

Once identified, the components are compiled into CycloneDX ML-BOM documents. These records can be stored as Kubernetes custom resources or exported to destinations including Google Cloud Storage and webhook endpoints.

Google also designed the tool to produce identical ML-BOM documents when given identical cluster inputs. This deterministic behaviour is intended to support GitOps workflows, allowing security and reliability teams to compare records and identify changes when AI dependencies drift.

Unlike build-time scanners, which document what organisations intended to deploy, k8s-aibom observes live clusters to identify which AI systems are actually running, how they are connected and how those findings were established.

A confidence model separates detected components into three categories. Declared assets are explicitly specified in workload configurations, inferred assets are identified through runtime patterns, and unresolved assets indicate that an AI presence was detected but the precise model, version, or weights could not be established.

Unresolved findings can therefore be prioritised for further security review, while declared and inferred classifications help auditors distinguish documented engineering intent from conclusions reached by the controller.

Google says the controller follows least-privilege principles and can export records using a dedicated identity with permission to create objects in Cloud Storage. Creation preconditions can prevent existing ML-BOM records from being silently overwritten, strengthening the historical evidence available to security and compliance teams.

Google also positions k8s-aibom as a tool for regulatory and standards compliance. Runtime inventories could help organisations gather evidence relevant to the EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001 requirements for AI asset management.

Why does it matter?

Shadow AI has become a growing governance challenge as developers deploy AI tools outside formal security and compliance processes. Without visibility into what is actually running in production, organisations may struggle to assess risk, investigate incidents or demonstrate regulatory compliance.

By generating inventories of live AI workloads rather than relying solely on build-time records, k8s-aibom could help organisations improve AI governance while supporting audits, security operations and compliance with emerging AI standards and regulations.

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Meta expands Louisiana AI data centre with $50 billion investment

Meta has announced plans to expand its data centre in Richland Parish, Louisiana, increasing the site’s AI computing capacity to 5 GW as part of the company’s broader investment in AI infrastructure. Meta said the expansion represents an investment of more than $50 billion and will support more than 1,000 permanent jobs once the facility becomes operational.

According to Meta, the project includes more than $1 billion in local infrastructure improvements, covering roads, water and wastewater systems. The company also said it has awarded more than $1.6 billion in contracts to Louisiana businesses since construction began in 2024.

The project also includes workforce development initiatives. Meta has committed $5 million to Louisiana Delta Community College for scholarships and training programmes related to data centre employment, while continuing to support local schools, businesses and community initiatives through grants and skills programmes.

Meta said the expanded campus forms part of its wider investment in AI infrastructure across the United States as demand for computing capacity continues to grow.

Why does it matter?

The expansion reflects the rapid growth in demand for AI computing infrastructure as technology companies invest heavily in data centres capable of training and running increasingly advanced AI models. Access to large-scale computing power is becoming a key competitive advantage in the AI industry.

The project also illustrates the broader economic impact of AI infrastructure investments. Beyond computing capacity, hyperscale data centres require significant spending on energy, water, transport and workforce development, making them increasingly important drivers of regional economic development.

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Europe expands tech scaleup funding with ETCI 2.0

The European Investment Bank (EIB) Group, EU governments and private institutional investors have launched the second phase of the European Tech Champions Initiative (ETCI), aiming to mobilise up to €80 billion for Europe’s technology scaleups.

Known as ETCI 2.0, the initiative aims to strengthen Europe’s late-stage investment ecosystem by creating a pan-European platform that helps highly innovative companies scale into global technology leaders. The announcement was made in Brussels on the sidelines of the ECOFIN meeting of EU finance ministers.

The first phase, launched in 2023, backed 15 mega-funds investing in European startups and contributed to the emergence of 12 EU-based unicorns. ETCI 2.0 will expand both the size and scope of the initiative with continued backing from EU governments and new participation from private institutional investors.

The new initiative aims to raise up to €15 billion, around four times the size of the original fund of funds, and is expected to mobilise as much as €80 billion in investment for more than 1,500 European scaleups.

The EIB Group plans to invest up to €1.25 billion into the fund. Final contributions from participating governments and investors are expected to be determined in the second half of 2026 during the initiative’s first closing.

For the first time, ETCI 2.0 will support both European mega-funds and mid-sized growth funds managing more than €300 million in assets. The initiative is expected to anchor more than 100 funds, including up to 45 mega-funds making average investments of around €200 million per company.

Private investors joining the initiative include AltamarCAM, Azimut Holding, Banco Santander, BBVA, Compagnia di San Paolo, Danske Bank and Green Arrow Capital, with more investors expected to join later.

The initiative will also establish a pan-European investment platform providing investors with access to European technology funds, market intelligence and ecosystem insights, supported by a dedicated digital engagement tool.

ETCI 2.0 is designed to complement national and European initiatives, including France’s Tibi initiative, Germany’s WIN initiative and the Scaleup Europe Fund. The EIB said the aim is to build a more federated European investment ecosystem and help European tech companies remain anchored in Europe.

Why does it matter?

Europe has long struggled to provide sufficient late-stage financing for high-growth technology companies, with many scaleups seeking capital from US investors or relocating abroad as they expand. ETCI 2.0 is designed to address that gap by combining public and private investment to strengthen Europe’s own growth capital ecosystem.

The initiative also reflects the EU’s broader push for technological sovereignty and economic competitiveness. By helping innovative companies remain headquartered and financed in Europe, ETCI 2.0 aims to retain strategic technologies, talent and intellectual property while building globally competitive European firms.

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Philippines uses AI and satellites to strengthen food security

The Philippine Department of Agriculture and the Philippine Statistics Authority are partnering to use AI and satellite technology to improve agricultural data collection, strengthen national food security and support more informed policymaking. Agriculture Secretary Francisco Tiu Laurel Jr. stated that enhanced data sharing between the two agencies would enable policymakers to make more informed decisions on food production, logistics and supply.

The Philippine Statistics Authority has begun piloting AI and satellite imagery to estimate crop production, building on approaches already used in several countries. National Statistician Claire Dennis Mapa said the technology would become more accurate as the Department of Agriculture expands field verification to validate satellite-generated data. The agencies also agreed to broaden the use of digital technologies in agricultural statistics and strengthen the capacity of local government units.

Agriculture Secretary Tiu Laurel also renewed calls to rebuild the department’s network of agricultural extension workers, describing them as its missing ‘boots on the ground’. Expanding the field workforce would support near real-time data collection, improve production forecasts and enable faster responses to challenges affecting farmers and fisherfolk. He also welcomed this year’s national census, saying updated population data would improve food demand forecasting.

The partnership aims to shift the Department of Agriculture from reactive to proactive food security management. Updated agricultural and population data will help the government better estimate future food demand, refine production targets and improve budget planning. According to Tiu Laurel, data should help policymakers anticipate future challenges rather than simply document past events in the Philippines.

Why does it matter?

The initiative illustrates how AI and Earth observation technologies are becoming practical tools for agricultural governance. More timely and accurate data can help governments improve production planning, respond more quickly to climate-related disruptions and strengthen long-term food security.

The partnership also highlights that digital transformation depends on both technology and institutional capacity. By combining AI, satellite imagery and field verification through agricultural extension workers, the Philippines is seeking to build a more reliable and responsive agricultural information system that could serve as a model for other countries facing similar food security challenges.

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ILO and EU deepen cooperation on AI and jobs

The International Labour Organization (ILO) and the European Commission have reaffirmed their strategic partnership, agreeing to strengthen cooperation on social justice, quality jobs and the human-centred governance of AI and digital transformation.

Against a backdrop of geopolitical uncertainty, climate change, demographic shifts and rapid technological change, the two organisations committed to ensuring that global transitions create inclusive labour markets and resilient economies.

Co-chaired by ILO Director-General Gilbert F. Houngbo and European Commission Executive Vice-President Roxana Mînzatu, the meeting agreed on a renewed cooperation agenda building on the 2021 Exchange of Letters between the two institutions.

Both organisations stressed that multilateral cooperation, international labour standards and social dialogue remain essential for addressing the challenges and opportunities created by AI, digitalisation and broader economic transformation.

AI and its impact on the future of work featured prominently in the discussions. Participants agreed that AI governance should remain human-centred, supporting the creation and preservation of decent jobs while strengthening labour market institutions.

The partners also highlighted the importance of international cooperation on AI governance, workforce skills and policies that help workers adapt to technological change rather than be displaced by it.

The meeting also covered trade, international partnerships and sustainable development. The ILO and the European Commission reaffirmed that trade policies should uphold international labour standards and improve working conditions throughout global supply chains.

They also agreed to deepen cooperation by combining the EU’s financial and policy instruments with the ILO’s expertise on labour standards, social protection, skills development and just transitions, reinforcing their shared objective of building more inclusive, resilient and sustainable economies.

Why does it matter?

The renewed partnership highlights how AI governance is becoming closely linked with employment and social policy. Rather than treating AI solely as a technology issue, the EU and ILO are framing it as a labour market challenge that requires international cooperation, workforce development and strong social protections.

The agreement also reinforces the growing role of international organisations in shaping a human-centred approach to AI. As governments seek to harness AI for economic growth, ensuring that technological change supports decent work, skills development and social inclusion is becoming an increasingly important part of global AI governance.

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Dutch government unveils plan to strengthen future workforce

The Dutch government has launched a national Talent Strategy to strengthen the country’s workforce and support future prosperity.

The strategy aims to attract, educate, and retain talent in areas considered crucial to the Netherlands’ economic growth, public services, and response to social challenges.

The government identifies four priority domains for investment: digitalisation and AI; security and resilience; energy and climate technology; and life sciences and biotechnology.

It says these are areas where the Netherlands has strengths in innovation and research, and where it wants to strengthen strategic autonomy.

The strategy responds to demographic pressure, skills mismatches and the need to make better use of scarce talent.

Planned measures include closer cooperation between the government, employers, workers, education institutions and other stakeholders.

The government also wants to improve training for priority sectors, expand lifelong learning and support smarter, more productive ways of working.

International recruitment will focus on targeted knowledge and expertise, while the government aims to reduce dependence on low-productivity labour migration.

The strategy forms part of the Ministerial Taskforce on Future Prosperity and Business Climate, with further policy updates expected in the coming months and a progress update by the end of 2026.

Why does it matter?

The strategy shows how workforce planning is becoming part of digital and industrial policy. By naming digitalisation and AI as one of four strategic domains, the Netherlands is linking talent development to competitiveness, strategic autonomy and long-term public-service capacity. The approach also reflects a wider European challenge: countries need enough specialised workers for AI and emerging technologies, while also expanding lifelong learning so existing workers are not left behind.

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IMF sees AI supporting global economic growth

The International Monetary Fund (IMF) has identified geopolitical tensions and rapid technological development as two of the main forces shaping the global economy. According to its latest World Economic Outlook Update, global growth is projected to reach 3.0% in 2026 before rising to 3.4% in 2027, with investment in AI and digital technologies supporting economic activity despite continued geopolitical uncertainty.

The report identifies AI as an increasingly important source of productivity growth and investment, particularly for economies integrated into technology supply chains. Countries involved in AI hardware, digital infrastructure and advanced technology exports are expected to benefit from rising demand.

At the same time, conflict in the Middle East continues to create uncertainty through higher energy prices, supply chain disruptions and inflationary pressures. The IMF expects global inflation to rise temporarily in 2026 before easing, although the pace of recovery is likely to vary across regions depending on their exposure to energy markets and technological capacity.

The IMF says governments should strengthen economic resilience by maintaining price stability, rebuilding fiscal buffers and supporting investment in digital infrastructure, energy security and AI adoption.

Why does it matter?

The outlook highlights how economic growth is increasingly being shaped by two competing forces: technological innovation and geopolitical instability. While AI investment is emerging as a driver of productivity and competitiveness, conflict and supply chain disruptions continue to create significant risks for the global economy.

The report also suggests that countries able to invest in AI, digital infrastructure and resilient supply chains may be better positioned to benefit from future growth. At the same time, uneven technological capacity and continued geopolitical uncertainty could widen economic disparities between regions.

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OpenAI launches GPT-Live-1 for ChatGPT Voice

OpenAI has launched GPT-Live-1, introducing a new voice experience in ChatGPT designed to make conversations feel more natural and responsive. The company is rolling out GPT-Live-1 for paid users and GPT-Live-1 mini for Free users.

The new models can listen and speak simultaneously, allowing users to interrupt, pause or continue speaking while ChatGPT responds. OpenAI says this improves turn-taking and makes voice interactions feel closer to a natural conversation.

GPT-Live-1 works within a standard ChatGPT conversation, with spoken responses appearing alongside streamed text. The model can also use web search and memory, display visual results through supported widgets, and work with text and images where those features are available.

OpenAI says GPT-Live is rolling out globally on ChatGPT.com and the ChatGPT iOS and Android apps. GPT-Live-1 will become the default voice model for Go, Plus and Pro users, while GPT-Live-1 mini will serve as the default for Free users.

At launch, GPT-Live is not available in ChatGPT Business, Enterprise or Edu workspaces. It also does not currently support video or screen sharing, although eligible users can continue using those features through Advanced Voice Mode where available.

OpenAI says GPT-Live-1 can hand more complex tasks to other models, such as GPT-5.5, when they require search, advanced reasoning or more agentic capabilities. The company also plans to make GPT-Live available through its API in the future.

Why does it matter?

GPT-Live-1 reflects OpenAI’s broader effort to make voice a core interface for interacting with AI rather than a separate feature. By combining real-time speech, streamed text, search, memory and visual results within a single conversation, the company is moving towards more seamless multimodal assistants capable of supporting everyday tasks, research and longer, more natural interactions.

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