Insights

The Global South, historically bypassed by industrial revolutions, now has a unique opportunity to lead the emerging bioeconomy through biotechnology and deep tech innovation. While the Global North dominates AI with rapid development and regulatory challenges, the South faces risks from unregulated AI adoption without proportional market influence. Instead, by leveraging agricultural strengths and low-cost manufacturing, developing nations can cultivate high-value biotech industries, including biopharma, bioenergy, climate-resilient agriculture, and bioplastics. Policymakers must balance regulation and innovation to ensure equitable access and harness international cooperation. Addressing past missed opportunities in software and chip manufacturing, the Global South can reclaim leadership by focusing on biotech convergence amid geopolitical fragmentation and technological acceleration.
Africa, ASEAN | ICT, Health
Beyond the AI gold rush: How the Global South can lead the bioeconomy revolution
The rapid rise of generative AI has intensified the need for a science of questions, as AI emphasizes prompting—a skill focused on eliciting desired answers from systems based on existing knowledge. However, prompting typically relies on preconceived notions of answers and acts more like precise engineering, whereas genuine questioning starts from true curiosity and not-knowing. As AI makes information and answers abundant and inexpensive, the vital human skill diminishing is the ability to formulate valuable, meaningful questions. This distinction highlights why cultivating authentic human inquiry remains crucial in the AI era.
Education
Beyond the Prompt: Reclaiming Human Inquiry and the Science of Questions in the Age of AI
AI is driving resilient economic growth in the MENA region by enhancing productivity, optimizing resource use, and unlocking long-term value. Collaborations with key regional institutions led to five AI prototypes focused on sustainable finance, climate hazard prediction, ESG data analysis, SME financing, and cross-sector collaboration. These real-world applications offer replicable models for broader adoption. However, maximizing AI's economic impact relies on overcoming barriers such as implementation challenges, fragmented initiatives, and institutional capacity gaps.
Africa
Unlocking Value: AI for Resilient Growth in MENA
Stock markets demonstrate resilience to geopolitical risks as declines in valuations of exposed firms lead to higher expected returns, attracting investment funds to purchase these undervalued assets. Investment funds, especially those with greater risk tolerance and weaker past performance, actively acquire such stocks, helping to rapidly restore valuations. This strategy results in increased portfolio concentration, partially balanced by holding higher cash reserves. Conversely, ultimate investors tend to withdraw from funds exposed to geopolitical risk.
Teflon Capitalism: Geopolitical Risk and Investment Funds
Existing governance frameworks for secondary health data use are inadequate for AI model training, which produces persistent, deployable artifacts that encode local clinical patterns as generalizable knowledge. Building on two decades of stewardship framework development, a shift from data stewardship to model stewardship is proposed to extend governance and better address the challenges posed by AI-era health data use.
Health
From Data Stewardship to Model Stewardship: Extending Governance Frameworks for AI Era Health Data Use
AI is transforming financial decision-making by detecting fraud, assessing credit, and guiding investments. Autonomous AI tools enhance information processing and investment research, but their interaction may impact market competition and stability. Ensuring human oversight is crucial for accountability and reliability in these technologies.
When AI Makes Financial Decisions
Global approaches to educational technology (EdTech) in schools reveal a shift from EdTech 1.0, which prioritized engagement without solid learning evidence, to EdTech 2.0, emphasizing evidence-based use, and now toward EdTech 3.0, harnessing AI for personalized learning. Countries like France and Sweden restrict screen use, reacting against ineffective EdTech 1.0 models, while Malawi and Colombia invest in evidence-backed digital initiatives aimed at improving learning outcomes. Effective EdTech deployment depends on independent research, transparent procurement, and building local capacities to evaluate impact. However, the uneven development of evidence frameworks and market complexities challenge the consistent adoption of beneficial EdTech tools worldwide.
Education, ICT, Health
What the screen time debate in schools gets wrong about EdTech
AI planning technologies enable autonomous trading agents to execute multistep strategies, potentially facilitating collusive behavior and increasing financial market fragility. Such AI-driven trading bots can generate large price bubbles and crashes by coordinating trades using market prices as signals, harming retail investors and risking broader financial instability. This shift from traditional machine learning tools to agentic AI, which independently identifies and exploits complex patterns, raises concerns among regulators and academics. The challenge lies in detecting and measuring these risks due to the opacity and proprietary nature of AI-informed trading data.
Financial services
How AI Planning Could Create Financial Market Fragility
Generative AI offers significant opportunities for learning by quickly explaining concepts, solving problems, and providing feedback. However, effective learning requires deliberate strategies, such as attempting problems before seeking AI help, retrieval practice, and reflection. Studies show that AI tools designed as tutors, encouraging active engagement rather than just providing answers, help preserve and enhance learning skills. Metacognitive awareness—the ability to regulate one's own learning—is key to using AI effectively. AI literacy combined with learning literacy is crucial to use AI as a tool for self-regulated, durable learning, emphasizing thoughtful integration by educators and designers to maximize positive outcomes.
Education
Here’s how a learning strategy can make AI work for you
Generative AI offers vast learning opportunities by quickly explaining concepts and solving problems, but effective learning requires deliberate strategies beyond just obtaining answers. Successful use of AI involves metacognition—monitoring and regulating one’s learning process—and choosing how to engage with AI tools, such as attempting problems first or seeking feedback instead of passively accepting answers. Studies show AI designed as a tutor promoting active learning prevents skill decline, unlike AI that simply supplies answers. Enhancing AI literacy alongside learning literacy—understanding how to learn effectively—is crucial. Educators and developers must design learning experiences and AI systems that encourage reflection, retrieval practice, and active problem-solving to maximize AI’s educational benefits.
Education
Here’s how to find the learning strategy that makes AI work for you
The 2026 AI Adoption Report reveals that widespread AI adoption is now embedded in enterprise workflows, shifting focus from usage to value realization through workflow redesign, organizational alignment, and clear governance. Companies with strong AI returns emphasize growth-oriented metrics rather than just efficiency, standardize platforms, appoint Chief AI Officers, and adapt management structures. Leadership and employee perspectives often differ on AI strategy alignment, impacting performance. Departments integrating AI deeply and redesigning workflows around AI report significantly higher ROI, confirming that success depends on comprehensive, measured transformation beyond mere adoption.
2026 AI Adoption Report
AI-assisted software development, initially expected to liberate programmers, has instead become a tool for Big Tech control. Ian Bogost shares his experience using generative AI to create an iPhone app called Beanslaw, designed to track barbecue sides preferences. What once would have taken weeks to develop was accomplished in two days, demonstrating AI's ability to rapidly produce sophisticated, professional-grade software. Despite this efficiency, the broader impact suggests confinement rather than freedom in programming.
The Tragedy of AI Coding
States must ensure new weapons and military technologies comply with international law before use, particularly as emerging technologies like AI, autonomy, and cyber capabilities complicate assessments. Article 36 of Additional Protocol I to the Geneva Conventions mandates legal reviews of new weapons to identify prohibitions under international law. Effective implementation requires legal expertise, clear institutional roles, access to technical data, and integrated procedures. DCAF’s Guide for Operationalizing Article 36 offers practical tools, templates, and training to help states establish or improve review mechanisms, addressing challenges from evolving technologies. This guidance links legal compliance to responsible procurement, accountability, and oversight, supporting states in meeting their humanitarian and governance obligations.
Guide for operationalizing Article 36: Implementing legal review mechanisms for new weapons, means and methods of warfare
Children today have fewer opportunities for independent, adventurous outdoor play, with outdoor playtime declining 50% in a generation. Research with 3- to 5-year-olds showed that children often avoid risky play not due to lack of confidence but because of the subtle cautious messages they receive from adults about safety and mess. When schools adjusted their approach—reorganized outdoor equipment, changed adult language to express trust, and allowed messiness—children became more willing to take risks, explore, and engage in sensory-rich play. Supporting risk-taking involves reframing risk as learning, pausing before intervening, permitting mess, using trusting language, and offering meaningful choices, helping children build confidence, problem-solving skills, and resilience.
Messy, risky outdoor play is important for children, but adults can stop them enjoying it
Europe's automotive industry faces a pivotal transformation as electric, software-defined vehicles (ESDVs) replace internal combustion engine vehicles, with potentially profound implications for the continent's economy. Despite Europe's strong automotive sector, production has declined while global output, especially in China, has surged. Key technologies like batteries, semiconductors, AI, and robotics are central to this shift, where Europe currently lags behind Chinese and Korean competitors. However, the move from domain to zonal vehicle architectures offers opportunities for innovation in high-performance computing and software updates. Europe's success depends on coordinated yet decentralized public policies that support domestic demand and leverage regional experimentation, enabling the scaling of effective technologies and preserving Europe's industrial competitiveness.
Europe can still win the race to build the cars of the future
Young people will live longer in a world with aging populations and declining birthrates, facing challenges like financial insecurity, healthcare access, and social isolation. Current systems, designed for younger demographics, must adapt to support non-linear life paths involving lifelong learning, caregiving, and phased retirement. The World Economic Forum’s Longevity Economy Playbook empowers youth to identify local gaps and create solutions for longevity preparedness, emphasizing prevention, intergenerational solidarity, and social connection. Employers and policymakers play key roles by promoting flexible careers, preventive health, and coordinated policies to build inclusive, resilient societies suited to longer, more diverse lifespans.
Africa | Education, Health
Why an ageing world is a question for young people
The new U.S. banking model risk regulation, SR 26-2, replaces the 2011 SR 11-7 by shifting from prescriptive rules to a principles-based, proportional approach requiring banks to defend their chosen model risk frameworks. While offering flexibility, this change transfers the supervisory burden onto banks rather than reducing oversight. Notably, the updated rule excludes generative and agentic AI models pending future guidance, leaving banks accountable but without specific regulatory playbooks for AI systems. In contrast, regulators in Singapore, the UK, and Canada have integrated AI governance into their frameworks, emphasizing the need for oversight of adaptive AI. Early AI-related compliance failures, such as a $2.5 million settlement for biased AI lending practices, highlight the urgency for clear AI risk management in banking.
Financial services
America’s new banking risk regulation overlooks AI and shifts the burden of defence
Tajikistan can achieve sustainable and inclusive growth by shifting from its current reliance on public investment, low-productivity industries, and limited commodity exports to a diversified, private sector-led economy. With a growing youth population, the country should invest in modern skills development, improve the business environment, encourage private investment, and reform the pension system. These measures will drive productivity, broaden exports, and foster innovation, supporting an inclusive future and stronger economic transformation.
Africa
Economic Transformation for Sustainable and Inclusive Growth in Tajikistan: A Country Diagnostic Study
Frontier AI labs and nonprofit organizations are increasingly collaborating to apply advanced AI technologies in addressing global health, climate, humanitarian, and development challenges. Significant funding from individual and institutional donors has supported these AI-for-good initiatives, though understanding of partnership dynamics remains limited. Based on interviews with 36 stakeholders from 24 organizations and four detailed case studies, key insights reveal how these partnerships form, what factors drive their success or failure, and offer recommendations for structuring effective collaboration and funding to maximize social impact.
Health
Beyond Pilots and PR: What Makes AI-for-Good Partnerships Work
Benefit recipients acknowledge potential advantages of sharing administrative social security data across public bodies but express strong distrust toward government agencies managing their information. Their concerns stem from experiences of vulnerability and negative interactions with data systems, revealing a gap between system design and users’ realities. The study highlights risks of “automated paternalism,” where policy automation may inadvertently reinforce control over recipients. These findings emphasize the need to align data-sharing initiatives with recipients’ lived experiences to improve trust and outcomes.
Health
Data benefits? Attitudes and aspirations of recipients towardssocial security administrative data sharing

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