The European Investment Fund (EIF) will manage a €210 million financing initiative to support high-tech businesses in Bulgaria, focusing on sectors such as AI, microelectronics and advanced technologies.
The programme operates within the JEREMIE Bulgaria framework, which aims to improve access to capital for small and medium-sized enterprises.
An initiative that reflects a broader EU strategy to strengthen innovation capacity and support sustainable economic growth through targeted investment mechanisms.
The EIF, a subsidiary of the EIB Group, will prioritise equity financing and scale-up support to address structural gaps that often limit the expansion of high-growth companies within national markets.
A programme that also aligns with wider efforts to retain technological talent and reduce reliance on external capital by reinforcing domestic innovation ecosystems.
By supporting dual-use technologies and strategic sectors, the measure contributes to both economic competitiveness and technological resilience.
Through its revolving funding model, reinvested capital is expected to sustain long-term financing capacity, reinforcing the position of Bulgaria within regional venture capital networks and supporting the development of a more mature innovation economy.
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Every technological leap forces society to renegotiate its relationship with power. Intelligence, once a uniquely human advantage, is now being abstracted, scaled, and embedded into machines. As AI evolves from a tool into an autonomous force shaping economies and institutions, the question is no longer what AI can do, but who it will ultimately serve.
A new framework published by OpenAI sets out a vision for managing the transition towards advanced AI systems, often described as superintelligence. Framed as a policy agenda for governments and institutions, it attempts to define how societies should respond to rapid advances in AI governance, economic transformation, and workforce disruption.
At its core, the document is not a regulation but influence: an attempt to shape how policymakers think about industrial policy for AI, productivity gains, and the redistribution of technological power.
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AI industrial policy and the next economic transformation
The central argument is that AI will act as a general-purpose technology comparable to electricity or the combustion engine. It promises higher productivity, lower costs, and accelerated innovation across industries. In policy terms, this aligns with broader discussions around AI-driven productivity growth and economic restructuring.
However, historical precedent suggests that such transitions are rarely evenly distributed. Industrial revolutions typically begin with labour displacement, rising inequality, and capital concentration, before broader gains are realised. AI may intensify this dynamic due to its dependence on compute infrastructure, proprietary models, and large-scale data ecosystems.
Economic power may become increasingly concentrated among a small number of AI developers and infrastructure providers, posing a structural risk of reinforcing existing inequalities rather than reducing them.
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The return of industrial policy in the AI economy
A key feature of the document is its explicit endorsement of AI industrial policy as a necessary response to market limitations. Governments, it argues, must play a more active role in shaping outcomes through regulation, investment, and public-private coordination.
A broader global shift in economic thinking is reflected in this approach. Strategic sectors such as semiconductors, energy, and digital infrastructure are already experiencing increased state intervention. AI now joins that category as a critical technology.
Yet this approach introduces a significant tension. When leading AI firms contribute directly to the design of AI regulation and governance frameworks, the risk of regulatory capture increases. Policies intended to ensure fairness and safety may inadvertently reinforce the dominance of incumbent companies by raising compliance costs and technical barriers for smaller competitors.
In this sense, AI industrial policy may not only guide innovation but also determine market entry, competition, and the long-term economic structure.
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Redistribution, taxation, and the question of AI wealth
The document places strong emphasis on economic inclusion in the AI economy, proposing mechanisms such as a public wealth fund, AI taxation, and expanded access to capital markets. These ideas are designed to address one of the central challenges of AI-driven growth: the potential for extreme wealth concentration.
As AI systems increase productivity while reducing reliance on human labour, traditional tax bases such as wages and payroll contributions may weaken. The proposal to tax AI-generated profits or automated labour reflects an attempt to stabilise public finances in an increasingly automated economy.
Equally significant is the idea of a ‘right to AI’, which frames access to AI as a foundational requirement for participation in modern economic life. This positions AI not merely as a tool, but as a form of digital infrastructure essential to economic agency and inclusion.
However, these proposals face major implementation challenges. Measuring AI-generated value is complex, particularly in hybrid systems where human and machine inputs are deeply integrated. Without clear definitions, AI taxation frameworks and redistribution mechanisms could prove difficult to enforce at scale.
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Workforce disruption and the future of work
The document recognises that AI will significantly reshape labour markets. Many tasks that currently require hours of human effort are already being automated, with future systems expected to handle more complex, multi-step workflows.
To manage this transition, the proposal highlights reskilling programmes, portable benefits systems, and adaptive social safety nets, alongside experimental ideas such as a reduced working week. These measures aim to mitigate the impact of automation and workforce disruption while maintaining economic stability.
However, the pace of change introduces uncertainty. Historically, labour markets have adjusted over decades, allowing new roles to emerge gradually. AI-driven disruption may occur much faster, compressing adjustment periods and increasing transitional risk.
While the document highlights expansion in sectors such as healthcare, education, and care services, these ‘human-centred jobs’ require substantial investment in training, wages, and institutional support to absorb displaced workers effectively.
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AI safety, governance, and systemic control
Beyond economic considerations, the proposal places a strong emphasis on AI safety, auditing frameworks, and risk mitigation systems. The proposed measures include model evaluation standards, incident reporting mechanisms, and international coordination structures.
These safeguards respond to growing concerns around cybersecurity risks, biosecurity threats, and systemic model misalignment. As AI systems become more autonomous and embedded in critical infrastructure, governance mechanisms must evolve accordingly.
However, safety frameworks also introduce questions of control. Determining which systems are classified as high-risk inevitably centralises authority within regulatory and institutional bodies. In practice, this may restrict access to advanced AI systems to organisations capable of meeting stringent compliance requirements.
A structural trade-off between security and openness is emerging in the AI economy, raising questions about how innovation and oversight can coexist without reinforcing centralisation.
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Strategic influence and the future of AI governance
The proposal from OpenAI is both policy-oriented and strategically positioned. It acknowledges legitimate risks- inequality, labour disruption, and systemic instability, while offering a roadmap for managing them through structured intervention.
At the same time, it reflects the perspective of a leading actor in the AI industry. As a result, its recommendations exist at the intersection of public interest and commercial strategy. The dual role raises important questions about who defines AI governance frameworks and how economic power is distributed in the intelligence age.
The broader challenge is not only technological but also institutional: ensuring that AI industrial policy, regulation, ethics and economic design are shaped through transparent and democratic processes, rather than through concentrated private influence.
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AI industrial policy will define economic power
AI is no longer solely a technological development- it is a structural force reshaping global economic systems. The emergence of AI industrial policy frameworks reflects an attempt to manage this transformation proactively rather than reactively.
The success or failure of these approaches will determine whether AI-driven growth leads to broader prosperity or deeper concentration of wealth and power. Without effective governance, the risks of inequality and centralisation are significant. With carefully designed policies, there is real potential to expand access, improve productivity, and distribute benefits more widely.
Digital diplomacy may increasingly come to the fore as a mechanism for arbitrating competing approaches to AI policy and governance across jurisdictions. As regulatory frameworks diverge, diplomatic channels could serve to bridge gaps, negotiate standards, and balance strategic interests, positioning digital diplomacy as a practical tool for managing fragmentation in the evolving AI economy.
Ultimately, the intelligence age will not be defined by technology alone, but by the AI governance systems, economic frameworks, and industrial policy decisions that guide its development. The outcome will depend on the extent to which global stakeholders succeed in building a shared and coordinated vision for its future.
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Argentina’s securities regulator has officially recognised cryptocurrencies as part of an individual’s net worth when determining qualified investor status. The change is set out in CNV Resolution 1125/2026, which allows digital assets to be included in the financial threshold of roughly $479,000.
The measure defines virtual assets as transferable digital value, covering cryptocurrencies, tokenised assets, and stablecoins. Authorities stated that incorporating these assets reflects a broader view of financial capacity and aims to expand participation in investment markets.
A 2022 central bank ban still prevents banks from offering crypto services, though some institutions are testing blockchain-based settlement systems internally. The restriction is expected to ease as the government signals a more open stance towards digital assets.
The policy shift positions Argentina as gradually integrating crypto into its formal financial framework, with the potential to widen investor access and align regulation with evolving digital markets.
Financial systems are gradually adapting to digital assets, even in jurisdictions with strict restrictions, signalling a slow convergence between traditional banking infrastructure and blockchain-based settlement technologies.
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The report analyses 2,972 companies across 11 sectors and multiple regions using publicly available disclosures and company survey responses collected through the AI Company Data Initiative.
The report says AI is being embedded across companies’ products, services, and internal operations faster than governance and disclosure are developing. It states that 43.7% of companies publicly communicate having an AI strategy or guidelines, but only 13% publicly claim adherence to a formal AI governance framework.
Among those that do cite a framework, 53% refer to the EUAI Act, while the report says 43.6% cite ‘other’ frameworks, which it presents as weakening comparability across the wider AI governance ecosystem.
The publication also says many companies describe AI governance in conceptual terms while providing less evidence on operational controls, accountability pathways, monitoring, and remediation. It states that 40% report board- or committee-level oversight on AI, and 12.4% report having a policy to ensure a human oversees AI systems.
At the same time, the publication says 72% of companies do not report conducting any AI-related impact assessment. Of those that do, 11% report environmental impact assessments and 7% report human rights impact assessments. The key statistics on page 10 visually present these findings.
Regarding labour impacts, the report says companies do not provide adequate protection for workers as AI reshapes jobs. It states that while 31% of companies claim to have AI training programmes, only 12% offered structured training with comprehensive coverage. It also argues that effective worker protection requires stronger evidence of reskilling, retraining, redeployment, transition support, and access to remedy where AI affects workers’ rights.
Why does it matter?
The report further states that ethical issues, including human rights and environmental impacts, are being sidelined in AI governance and risk management, while transparency regarding training data, third-party systems, and user rights remains uneven. It presents the AI Company Data Initiative as a tool to help companies assess their governance practices against UNESCO’s Recommendation on the Ethics of AI and to give investors more comparable information on how AI is governed in practice.
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The European Commission has approved a €211 million Italian State aid measure to support the development of photonic chips based on graphene technology.
A funding will be provided to the Italian SME CamGraPhIC, with project activities taking place in Pisa and Bergamo.
Such an initiative focuses on optical transceivers that transmit data using light rather than electrons. The use of graphene instead of silicon is expected to enhance performance and energy efficiency across sectors such as telecommunications, automotive, aerospace and defence.
The Commission assessed the measure under the EU State aid rules and concluded that the funding is necessary, proportionate and aligned with research and innovation objectives. It also found that the project would not proceed without public support, demonstrating an incentive effect.
A decision that reflects broader EU efforts to strengthen semiconductor capabilities and support advanced digital technologies through targeted public investment and regulatory oversight.
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A shift from early AI adoption towards what it terms ‘frontier transformation’ has been described by Microsoft, where AI is integrated into core organisational processes.
Such an approach reflects how AI is increasingly embedded within everyday workflows rather than used in isolated pilots.
According to Microsoft, scaling AI requires moving beyond experimentation and establishing structured operating models. It includes addressing practical challenges such as data integration, system reliability, and alignment with organisational objectives.
A framework that also highlights the importance of governance and execution, with AI systems expected to operate under defined standards similar to other critical infrastructure. Something that involves coordination across platforms, internal processes, and external partners.
Why does it matter?
Frontier transformation illustrates a broader transition in how organisations approach AI deployment, focusing on long-term integration, operational consistency, and scalable implementation across different sectors.
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The State Administration of Taxation and the National Financial Regulatory Administration of China have called on banks to integrate blockchain and privacy computing into lending systems, aiming to improve transparency and expand access to financing for small businesses.
The initiative focuses on upgrading the ‘bank-tax interaction’ model by strengthening data sharing between financial institutions, tax authorities, and enterprises.
Authorities emphasise the need to standardise data exchange and reduce information asymmetry, which has long limited credit access for smaller firms. Improved credit models and faster approvals aim to support compliant businesses while boosting financial efficiency.
The directive aligns with China’s broader strategy to build a national data infrastructure supported by blockchain technology. A roadmap led by the National Development and Reform Commission targets nationwide implementation by 2029, with projected annual investment reaching 400 billion yuan.
Despite strict restrictions on cryptocurrency trading, China continues to promote blockchain as a core technology for economic development. Earlier initiatives, including blockchain invoicing, show a steady push to integrate the technology into real-world finance and administration.
Strengthening data sharing and transparency in lending could improve access to finance for small businesses, which remain a key driver of economic growth.
Wider blockchain integration may also support more efficient financial systems, reinforce trust in institutional processes, and advance China’s long-term digital infrastructure strategy.
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United Nations Conference on Trade and Development reports that global trade expanded by $2.5 trillion in 2025, reaching a total value of $35 trillion, driven by continued growth in goods and services.
Despite this expansion, the outlook has become more uncertain due to rising geopolitical tensions and disruptions to key shipping routes.
Conflicts in the Middle East and instability in critical maritime corridors are increasing energy and transport costs, placing additional pressure on developing economies. Higher import expenses and tighter financial conditions are limiting fiscal flexibility and constraining growth prospects in vulnerable regions.
While trade growth remains broad-based, services expansion has slowed, and much of the recent increase is linked to higher prices rather than volume gains. Emerging markets in East Asia and Africa remain central, supported by strong South–South trade and shifting supply chains.
The report notes that ongoing fragmentation in global trade, including US–China decoupling, is reshaping commercial flows and creating new ‘connector economies’. Although offering some value chain opportunities, inflation, debt pressures, and protectionism are expected to weigh on global trade growth in 2026.
Rising fragmentation and uneven growth highlight widening gaps in how countries benefit from globalisation, with developing economies most exposed to cost shocks and financial constraints.
Shifting global trade will shape investment flows, development prospects, and economic resilience, increasing the need for coordinated policy responses.
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Representatives from Samruk-Energy JSC took part in the 13th Kazakhstan Machinery Forum, according to the company. The event brought together government and business figures to discuss the future of the machinery and manufacturing sectors.
During a sector session, Managing Director Galymbek Autalipov outlined plans to adopt and localise clean coal technologies. These are described as a strategic priority to balance energy security with environmental commitments.
Company representatives also joined discussions on procurement and industrial policy under Samruk-Kazyna JSC. Talks focused on import substitution, technological modernisation and increasing domestic value in supply chains.
The forum serves as a platform for shaping long-term industrial strategy through cooperation between state bodies and businesses, including the development of manufacturing capacity and modern technologies in Kazakhstan.
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Researchers from the University of California and Iowa have warned that structural weaknesses in the digital advertising ecosystem continue to expose advertisers to hidden risks and fraud. The study highlights how complexity and limited transparency enable manipulation across the supply chain.
A key issue identified is ‘dark pooling’, in which lower-quality advertising inventory is bundled with premium placements, obscuring their true value. This practice can mislead buyers and distort pricing across the market.
The authors argue that current safeguards fail to address these vulnerabilities effectively, as responsibilities are fragmented among multiple stakeholders. This lack of coordination allows systemic issues to persist.
To address the problem, the researchers propose a shared vulnerability notification framework involving advertisers, publishers and intermediaries. The study suggests such collaboration could strengthen accountability and improve trust in digital advertising markets in the US.
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